diff --git a/TensorFlow/contrib/cv/InsightFace_TF/LICENSE b/TensorFlow/contrib/cv/InsightFace_TF/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..1562c52dff26c5d846489922bd15eeb8c16f1a80
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2018 Jiankang Deng and Jia Guo
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/README.md b/TensorFlow/contrib/cv/InsightFace_TF/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..d1a40c91b1a9ff2dd5c6da9e290ffb237d830f4a
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/README.md
@@ -0,0 +1,197 @@
+- [基本信息](#基本信息.md)
+- [概述](#概述.md)
+- [训练环境准备](#训练环境准备.md)
+- [快速上手](#快速上手.md)
+- [高级参考](#高级参考.md)
+<h2 id="基本信息.md">基本信息</h2>
+
+**发布者（Publisher）：Huawei**
+
+**应用领域（Application Domain）：Object Detection**
+
+**版本（Version）：1.1**
+
+**修改时间（Modified） ：2022.11.29**
+
+**大小（Size）：648KB**
+
+**框架（Framework）：TensorFlow-gpu_1.14.0**
+
+**模型格式（Model Format）：ckpt**
+
+**精度（Precision）：Mixed**
+
+**处理器（Processor）：昇腾910**
+
+**应用级别（Categories）：Official**
+
+**描述（Description）：人脸识别算法**
+
+<h2 id="概述.md">概述</h2>
+
+## 简述<a name="section194554031510"></a>
+
+InsightFace 是基于 MXNet 框架实现的业界主流人脸识别解决方案。
+
+- 参考论文：
+  
+  [https://arxiv.org/abs/1801.07698](ArcFace: Additive Angular Margin Loss for Deep Face Recognition)
+
+- 参考实现：
+
+  https://github.com/auroua/InsightFace_TF
+
+- 适配昇腾 AI 处理器的实现：
+  
+  https://gitee.com/ascend/ModelZoo-TensorFlow/tree/master/TensorFlow/contrib/cv/InsightFace_TF
+
+- 通过Git获取对应commit\_id的代码方法如下：
+  
+        git clone {repository_url}    # 克隆仓库的代码
+        cd {repository_name}    # 切换到模型的代码仓目录
+        git checkout  {branch}    # 切换到对应分支
+        git reset --hard ｛commit_id｝     # 代码设置到对应的commit_id
+        cd ｛code_path｝    # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换
+    
+
+## 默认配置<a name="section91661242121611"></a>
+
+-   训练超参（单卡）：
+    - Batch size: 32
+    - net_depth：100
+    - epochs:200
+    - momentum:0.9
+    - weight_deacy:5e-4
+    - eval_datasets:lfw
+    - eval_db_path:/datasets/faces_ms1m_112x112
+    - image_size:112,112
+    - tfrecords_file_path: ./datasets/tfrecords_webface
+    - summary_path:./output/summary
+    - ckpt_path:./output/ckpt
+    - log_file_path:./output/logs
+    - saver_maxkeep: 100
+    - buffer_size: 10000
+    - log_device_mapping:False
+    - summary_interval:300
+    - ckpt_interval:10000
+    - validate_interval:2000
+    - show_info_interval:20
+
+## 精度<a name="section91661242121611"></a>
+
+-   GPU上运行精度：0.9880000000000001
+-   NPU上运行精度：0.9876666666666665
+## 性能<a name="section91661242121611"></a>
+
+-   GPU：80.461 samples/sec
+-   NPU：205.789 samples/sec
+    
+
+<h2 id="训练环境准备.md">训练环境准备</h2>
+
+-  硬件环境和运行环境准备请参见《[CANN软件安装指南](https://support.huawei.com/enterprise/zh/ascend-computing/cann-pid-251168373?category=installation-update)》
+-  运行以下命令安装依赖。
+```
+pip3 install requirements.txt
+```
+说明：依赖配置文件requirements.txt文件位于模型的根目录
+
+<h2 id="快速上手.md">快速上手</h2>
+
+## 数据集准备<a name="section361114841316"></a>
+
+训练数据集路径
+  datasets/tfrecords_webface
+测试数据集路径
+  datasets/faces_ms1m_112x112
+
+
+## 模型训练<a name="section715881518135"></a>
+
+- 运行train_nets.py文件
+- 开始训练。
+
+    - 启动训练之前，首先要配置程序运行相关环境变量。
+
+      环境变量配置信息参见：
+
+      [Ascend 910训练平台环境变量设置](https://gitee.com/ascend/ModelZoo-TensorFlow/wikis/01.%E8%AE%AD%E7%BB%83%E8%84%9A%E6%9C%AC%E8%BF%81%E7%A7%BB%E6%A1%88%E4%BE%8B/Ascend%20910%E8%AE%AD%E7%BB%83%E5%B9%B3%E5%8F%B0%E7%8E%AF%E5%A2%83%E5%8F%98%E9%87%8F%E8%AE%BE%E7%BD%AE)
+
+    - 单卡训练
+
+        1.首先在脚本train_nets.py中，配置训练数据集和测试数据集参数如下所示：
+
+             ```
+
+             --tfrecords_file_path=./datasets/tfrecords_webface  --eval_db_path=./datasets/faces_ms1m_112x112 
+
+             ```
+
+        2.启动训练
+        
+             启动单卡训练 （脚本为train_nets.py） 
+        
+             ```
+             python3 train_nets.py
+
+             ``` 
+
+
+<h2 id="高级参考.md">高级参考</h2>
+
+## 脚本和示例代码
+
+```
+|--README.md                                                      #说明文档									
+|--train_nets.py                                                       #训练代码
+|--train_nets_mgpu.py
+|--train_nets_mgpu_new.py
+|--train_nets_mgpu_new.py
+|--verification.py
+|--eval_ckpt_file.py						
+|--requirements.txt                                               #所需依赖
+|--datasets                                                       #训练需要的数据集
+|--data                                                           #训练需要的数据集
+|       |--eval_data_reader.py
+|       |--mx2tfrecords.py                                        #数据集转tfrecords格式脚本
+|--figures
+|--losses
+|       |--face_losses.py                                         #损失函数
+|--nets			           	                         
+|	|--imagenet_classes.py
+|	|--L_Resnet_E_IR.py
+|	|--L_Resnet_E_IR_fix_issue9.py
+|	|--L_Resnet_E_IR_GBN.py
+|	|--L_Resnet_E_IR_MGPU.py
+|	|--L_Resnet_E_IR_RBN.py
+|	|--nets_utils.py
+|	|--networks.py
+|	|--resnet.py
+|	|--tl_layers_modify.py
+|	|--vgg16.py
+|	|--vgg19.py
+|--output         
+|--test
+|	|--memory_usage_test.py
+|	|--resnet_test_static.py
+|	|--test_losses.py
+|	|--multiple_gpu_test
+|	|	|--test_mgpu_mnist.py  
+|	|	|--test_tensorlayer.py
+|	|--benchmark
+|	|	|--gluon_batchsize_test.py  
+|	|	|--mxnet_batchsize_test.py
+|	|	|--resnet_slim_benchmark.py 
+|	|	|--resnet_tl_benchmark.py 
+|	|	|--tensorlayer_batchsize_test.py  
+|	|	|--utils_final.py
+|	|	|--vgg19_slim_benchmark.py
+|	|	|--vgg19_tl_benchmark.py                                            
+```
+
+
+
+## 训练过程<a name="section1589455252218"></a>
+
+通过“模型训练”中的训练指令启动单卡或者多卡训练。
+
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/data/__init__.py b/TensorFlow/contrib/cv/InsightFace_TF/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/data/eval_data_reader.py b/TensorFlow/contrib/cv/InsightFace_TF/data/eval_data_reader.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2ef5b4df481b719400c986928b4d8cd5e97190f
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/data/eval_data_reader.py
@@ -0,0 +1,145 @@
+from npu_bridge.npu_init import *
+import tensorflow as tf
+import numpy as np
+import pickle
+import argparse
+import os
+import mxnet as mx
+import cv2
+import io
+import PIL.Image
+import mxnet.ndarray as nd
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(description='evluation data parser')
+    parser.add_argument('--eval_datasets', default=['lfw', 'cfp_ff', 'cfp_fp', 'agedb_30'], help='evluation datasets')
+    # parser.add_argument('--eval_datasets', default=['cfp_fp'], help='evluation datasets')
+    parser.add_argument('--eval_db_path', default='../datasets/faces_ms1m_112x112', help='evluate datasets base path')
+    parser.add_argument('--image_size', default=[112, 112], help='the image size')
+    parser.add_argument('--tfrecords_file_path', default='../datasets/tfrecords/eval', help='the image size')
+    parser.add_argument('--db_base_path', default='../datasets/faces_ms1m_112x112', help='the image size')
+    args = parser.parse_args()
+    return args
+
+
+def load_bin(path, image_size):
+    '''
+    :param path: the input file path
+    :param image_size: the input image size
+    :return: the returned datasets is opencv format BGR  [112, 112, 3]
+    '''
+    bins, issame_list = pickle.load(open(path, 'rb'), encoding='bytes')
+    issame_list_int = list(map(int, issame_list))
+    data_list = []
+    for _ in [0, 1]:
+        data = np.zeros(shape=[len(issame_list)*2, *image_size, 3])
+        data_list.append(data)
+    for i in range(len(issame_list)*2):
+        _bin = bins[i]
+        tf_images = tf.image.decode_jpeg(_bin)
+        tf_images = tf.reshape(tf_images, shape=(112, 112, 3))
+        sess = tf.Session(config=npu_config_proto())
+        images = sess.run(tf_images)
+        img_cv = cv2.cvtColor(images, cv2.COLOR_RGB2BGR)
+        print(np.min(img_cv), np.max(img_cv), img_cv.dtype)
+        cv2.imshow('test', img_cv)
+        cv2.waitKey(0)
+        for flip in [0,1]:
+            if flip == 1:
+                # print(i, flip)
+                img_cv = np.fliplr(img_cv)
+                # cv2.imshow('test', img_cv)
+                # cv2.waitKey(0)
+            data_list[flip][i][:] = img_cv
+        i += 1
+        if i % 1000 == 0:
+            print('loading bin', i)
+    print(data_list[0].shape)
+    return data_list, issame_list
+
+
+def mx2tfrecords(imgidx, imgrec, args):
+    output_path = os.path.join(args.tfrecords_file_path, 'tran.tfrecords')
+    writer = tf.python_io.TFRecordWriter(output_path)
+    for i in imgidx:
+        img_info = imgrec.read_idx(i)
+        header, img = mx.recordio.unpack(img_info)
+        encoded_jpg_io = io.BytesIO(img)
+        image = PIL.Image.open(encoded_jpg_io)
+        np_img = np.array(image)
+        img = cv2.cvtColor(np_img, cv2.COLOR_RGB2BGR)
+        img_raw = img.tobytes()
+        label = int(header.label)
+        example = tf.train.Example(features=tf.train.Features(feature={
+            'image_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw])),
+            "label": tf.train.Feature(int64_list=tf.train.Int64List(value=[label]))
+        }))
+        writer.write(example.SerializeToString())  # Serialize To String
+        if i % 10000 == 0:
+            print('%d num image processed' % i)
+    writer.close()
+
+
+def mx2tfrecords_eval_data(args, db_name):
+    '''
+    Change evaluation data to tfrecords
+    :param args:
+    :param type: lfw, ......
+    :return:
+    '''
+    bins, issame_list = pickle.load(open(os.path.join(args.db_base_path, db_name+'.bin'), 'rb'), encoding='bytes')
+    output_image_path = os.path.join(args.tfrecords_file_path, db_name+'_eval_data.tfrecords')
+    writer_img = tf.python_io.TFRecordWriter(output_image_path)
+    for i in range(len(bins)):
+        img_info = bins[i]
+        img = mx.image.imdecode(img_info).asnumpy()
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+        img_b = img.tobytes()
+        # # decode test
+        # sess = tf.Session()
+        # img_2 = tf.decode_raw(img_b, out_type=tf.uint8)
+        # img_2 = tf.reshape(img_2, shape=(112, 112, 3))
+        # img_2 = tf.image.flip_left_right(img_2)
+        # img_2_np = sess.run(img_2)
+        # print(img_2_np.shape)
+        # cv2.imshow('test', img_2_np)
+        # cv2.waitKey(0)
+        example = tf.train.Example(features=tf.train.Features(feature={
+            'image_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_b]))
+        }))
+        writer_img.write(example.SerializeToString())  # Serialize To String
+        if i % 1000 == 0:
+            print('%d num image processed' % i)
+    writer_img.close()
+
+
+def load_bin(db_name, image_size, args):
+    bins, issame_list = pickle.load(open(os.path.join(args.eval_db_path, db_name+'.bin'), 'rb'), encoding='bytes')
+    data_list = []
+    for _ in [0,1]:
+        data = np.empty((len(issame_list)*2, image_size[0], image_size[1], 3))
+        data_list.append(data)
+    for i in range(len(issame_list)*2):
+        _bin = bins[i]
+        img = mx.image.imdecode(_bin).asnumpy()
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+        for flip in [0,1]:
+            if flip == 1:
+                img = np.fliplr(img)
+            data_list[flip][i, ...] = img
+        i += 1
+        if i % 1000 == 0:
+            print('loading bin', i)
+    print(data_list[0].shape)
+    return data_list, issame_list
+
+
+if __name__ == '__main__':
+    args = get_parser()
+    ver_list = []
+    ver_name_list = []
+    for db in args.eval_datasets:
+        print('begin db %s convert.' % db)
+        # mx2tfrecords_eval_data(args, db)
+        data_set = load_bin(db, args.image_size)
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/data/mx2tfrecords.py b/TensorFlow/contrib/cv/InsightFace_TF/data/mx2tfrecords.py
new file mode 100644
index 0000000000000000000000000000000000000000..3050cc6fa92de5d486cbb8eded4454f9b1c30245
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/data/mx2tfrecords.py
@@ -0,0 +1,128 @@
+from npu_bridge.npu_init import *
+import mxnet as mx
+import argparse
+import PIL.Image
+import io
+import numpy as np
+import cv2
+import tensorflow as tf
+import os
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+        description='data path information'
+    )
+    parser.add_argument('--bin_path', default='../datasets/faces_ms1m_112x112/train.rec', type=str,
+                        help='path to the binary image file')
+    parser.add_argument('--idx_path', default='../datasets/faces_ms1m_112x112/train.idx', type=str,
+                        help='path to the image index path')
+    parser.add_argument('--tfrecords_file_path', default='../datasets/tfrecords', type=str,
+                        help='path to the output of tfrecords file path')
+    args = parser.parse_args()
+    return args
+
+
+def mx2tfrecords_old(imgidx, imgrec, args):
+    output_path = os.path.join(args.tfrecords_file_path, 'tran.tfrecords')
+    writer = tf.python_io.TFRecordWriter(output_path)
+    for i in imgidx:
+        img_info = imgrec.read_idx(i)
+        header, img = mx.recordio.unpack(img_info)
+        encoded_jpg_io = io.BytesIO(img)
+        image = PIL.Image.open(encoded_jpg_io)
+        np_img = np.array(image)
+        img = cv2.cvtColor(np_img, cv2.COLOR_RGB2BGR)
+        img_raw = img.tobytes()
+        label = int(header.label)
+        example = tf.train.Example(features=tf.train.Features(feature={
+            'image_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw])),
+            "label": tf.train.Feature(int64_list=tf.train.Int64List(value=[label]))
+        }))
+        writer.write(example.SerializeToString())  # Serialize To String
+        if i % 10000 == 0:
+            print('%d num image processed' % i)
+    writer.close()
+
+
+def mx2tfrecords(imgidx, imgrec, args):
+    output_path = os.path.join(args.tfrecords_file_path, 'tran.tfrecords')
+    writer = tf.python_io.TFRecordWriter(output_path)
+    for i in imgidx:
+        img_info = imgrec.read_idx(i)
+        header, img = mx.recordio.unpack(img_info)
+        label = int(header.label)
+        example = tf.train.Example(features=tf.train.Features(feature={
+            'image_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img])),
+            "label": tf.train.Feature(int64_list=tf.train.Int64List(value=[label]))
+        }))
+        writer.write(example.SerializeToString())  # Serialize To String
+        if i % 10000 == 0:
+            print('%d num image processed' % i)
+    writer.close()
+
+
+def parse_function(example_proto):
+    features = {'image_raw': tf.FixedLenFeature([], tf.string),
+                'label': tf.FixedLenFeature([], tf.int64)}
+    features = tf.parse_single_example(example_proto, features)
+    # You can do more image distortion here for training data
+    img = tf.image.decode_jpeg(features['image_raw'])
+    img = tf.reshape(img, shape=(112, 112, 3))
+    r, g, b = tf.split(img, num_or_size_splits=3, axis=-1)
+    img = tf.concat([b, g, r], axis=-1)
+    img = tf.cast(img, dtype=tf.float32)
+    img = tf.subtract(img, 127.5)
+    img = tf.multiply(img,  0.0078125)
+    img = tf.image.random_flip_left_right(img)
+    label = tf.cast(features['label'], tf.int64)
+    return img, label
+
+
+if __name__ == '__main__':
+    # define parameters
+    id2range = {}
+    data_shape = (3, 112, 112)
+    args = parse_args()
+    imgrec = mx.recordio.MXIndexedRecordIO(args.idx_path, args.bin_path, 'r')
+    s = imgrec.read_idx(0)
+    header, _ = mx.recordio.unpack(s)
+    print(header.label)
+    imgidx = list(range(1, int(header.label[0])))
+    seq_identity = range(int(header.label[0]), int(header.label[1]))
+    for identity in seq_identity:
+        s = imgrec.read_idx(identity)
+        header, _ = mx.recordio.unpack(s)
+        a, b = int(header.label[0]), int(header.label[1])
+        id2range[identity] = (a, b)
+    print('id2range', len(id2range))
+
+    # generate tfrecords
+    mx2tfrecords(imgidx, imgrec, args)
+
+    config = tf.ConfigProto(allow_soft_placement=True)
+    sess = tf.Session(config=npu_config_proto(config_proto=config))
+    # training datasets api config
+    tfrecords_f = os.path.join(args.tfrecords_file_path, 'tran.tfrecords')
+    dataset = tf.data.TFRecordDataset(tfrecords_f)
+    dataset = dataset.map(parse_function)
+    dataset = dataset.shuffle(buffer_size=30000)
+    dataset = dataset.batch(32, drop_remainder=True)
+    iterator = dataset.make_initializable_iterator()
+    next_element = iterator.get_next()
+    # begin iteration
+    for i in range(1000):
+        sess.run(iterator.initializer)
+        while True:
+            try:
+                images, labels = sess.run(next_element)
+                cv2.imshow('test', images[1, ...])
+                cv2.waitKey(0)
+            except tf.errors.OutOfRangeError:
+                print("End of dataset")
+
+
+
+
+
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/eval_ckpt_file.py b/TensorFlow/contrib/cv/InsightFace_TF/eval_ckpt_file.py
new file mode 100644
index 0000000000000000000000000000000000000000..88a09c75ff144f3f3889fd612408095574059e97
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/eval_ckpt_file.py
@@ -0,0 +1,66 @@
+from npu_bridge.npu_init import *
+import tensorflow as tf
+import argparse
+from data.eval_data_reader import load_bin
+from losses.face_losses import arcface_loss
+from nets.L_Resnet_E_IR import get_resnet
+import tensorlayer as tl
+from verification import ver_test
+
+
+def get_args():
+    parser = argparse.ArgumentParser(description='input information')
+    parser.add_argument('--ckpt_file', default='/home/aurora/workspaces2018/InsightFace_TF/output/ckpt_model_c/InsightFace_iter_best_',
+                       type=str, help='the ckpt file path')
+    # parser.add_argument('--eval_datasets', default=['lfw', 'cfp_ff', 'cfp_fp', 'agedb_30'], help='evluation datasets')
+    parser.add_argument('--eval_datasets', default=['agedb_30'], help='evluation datasets')
+    parser.add_argument('--eval_db_path', default='./datasets/faces_ms1m_112x112', help='evluate datasets base path')
+    parser.add_argument('--image_size', default=[112, 112], help='the image size')
+    parser.add_argument('--net_depth', default=50, help='resnet depth, default is 50')
+    parser.add_argument('--num_output', default=85164, help='the image size')
+    parser.add_argument('--batch_size', default=32, help='batch size to train network')
+    parser.add_argument('--ckpt_index_list',
+                        default=['1950000.ckpt'], help='ckpt file indexes')
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+    args = get_args()
+    ver_list = []
+    ver_name_list = []
+    for db in args.eval_datasets:
+        print('begin db %s convert.' % db)
+        data_set = load_bin(db, args.image_size, args)
+        ver_list.append(data_set)
+        ver_name_list.append(db)
+
+    images = tf.placeholder(name='img_inputs', shape=[None, *args.image_size, 3], dtype=tf.float32)
+    labels = tf.placeholder(name='img_labels', shape=[None, ], dtype=tf.int64)
+    dropout_rate = tf.placeholder(name='dropout_rate', dtype=tf.float32)
+
+    w_init_method = tf.contrib.layers.xavier_initializer(uniform=False)
+    net = get_resnet(images, args.net_depth, type='ir', w_init=w_init_method, trainable=False, keep_rate=dropout_rate)
+    embedding_tensor = net.outputs
+    # mv_mean = tl.layers.get_variables_with_name('resnet_v1_50/bn0/moving_mean', False, True)[0]
+    # 3.2 get arcface loss
+    logit = arcface_loss(embedding=net.outputs, labels=labels, w_init=w_init_method, out_num=args.num_output)
+
+    sess = tf.Session(config=npu_config_proto())
+    saver = tf.train.Saver()
+
+    result_index = []
+    for file_index in args.ckpt_index_list:
+        feed_dict_test = {}
+        path = args.ckpt_file + file_index
+        saver.restore(sess, path)
+        print('ckpt file %s restored!' % file_index)
+        feed_dict_test.update(tl.utils.dict_to_one(net.all_drop))
+        feed_dict_test[dropout_rate] = 1.0
+        results = ver_test(ver_list=ver_list, ver_name_list=ver_name_list, nbatch=0, sess=sess,
+                           embedding_tensor=embedding_tensor, batch_size=args.batch_size, feed_dict=feed_dict_test,
+                           input_placeholder=images)
+        result_index.append(results)
+    print(result_index)
+
+
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/figures/if_gpu.log b/TensorFlow/contrib/cv/InsightFace_TF/figures/if_gpu.log
new file mode 100644
index 0000000000000000000000000000000000000000..9ee5e1253da28e03360c39ee8e3523a8bff52e08
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/figures/if_gpu.log
@@ -0,0 +1,17005 @@
+nohup: ignoring input
+/home/wangdawei/.conda/envs/insightface/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
+  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
+/home/wangdawei/.conda/envs/insightface/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
+  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
+/home/wangdawei/.conda/envs/insightface/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
+  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
+/home/wangdawei/.conda/envs/insightface/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
+  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
+/home/wangdawei/.conda/envs/insightface/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
+  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
+/home/wangdawei/.conda/envs/insightface/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
+  np_resource = np.dtype([("resource", np.ubyte, 1)])
+/home/wangdawei/.conda/envs/insightface/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
+  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
+/home/wangdawei/.conda/envs/insightface/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
+  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
+/home/wangdawei/.conda/envs/insightface/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
+  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
+/home/wangdawei/.conda/envs/insightface/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
+  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
+/home/wangdawei/.conda/envs/insightface/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
+  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
+/home/wangdawei/.conda/envs/insightface/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
+  np_resource = np.dtype([("resource", np.ubyte, 1)])
+WARNING:tensorflow:From /home/wangdawei/.conda/envs/insightface/lib/python3.7/site-packages/tensorlayer/layers.py:28: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.
+
+WARNING:tensorflow:From /home/wangdawei/.conda/envs/insightface/lib/python3.7/site-packages/tensorlayer/layers.py:4030: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.
+
+WARNING:tensorflow:From /home/wangdawei/InsightFace_TF/train_nets.py:50: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.
+
+WARNING:tensorflow:From /home/wangdawei/InsightFace_TF/train_nets.py:51: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.
+
+WARNING:tensorflow:From /home/wangdawei/InsightFace_TF/data/mx2tfrecords.py:63: The name tf.FixedLenFeature is deprecated. Please use tf.io.FixedLenFeature instead.
+
+WARNING:tensorflow:From /home/wangdawei/InsightFace_TF/data/mx2tfrecords.py:65: The name tf.parse_single_example is deprecated. Please use tf.io.parse_single_example instead.
+
+2022-11-30 21:16:48.665426: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1
+2022-11-30 21:16:48.807033: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: 
+name: NVIDIA TITAN Xp major: 6 minor: 1 memoryClockRate(GHz): 1.582
+pciBusID: 0000:04:00.0
+2022-11-30 21:16:48.807238: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.1
+2022-11-30 21:16:48.808938: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10
+2022-11-30 21:16:48.810485: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10
+2022-11-30 21:16:48.810740: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10
+2022-11-30 21:16:48.812521: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10
+2022-11-30 21:16:48.813586: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10
+2022-11-30 21:16:48.817749: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
+2022-11-30 21:16:48.824898: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
+WARNING:tensorflow:From /home/wangdawei/InsightFace_TF/train_nets.py:64: DatasetV1.make_initializable_iterator (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
+Instructions for updating:
+Use `for ... in dataset:` to iterate over a dataset. If using `tf.estimator`, return the `Dataset` object directly from your input function. As a last resort, you can use `tf.compat.v1.data.make_initializable_iterator(dataset)`.
+begin db lfw convert.
+loading bin 1000
+loading bin 2000
+loading bin 3000
+loading bin 4000
+loading bin 5000
+loading bin 6000
+loading bin 7000
+loading bin 8000
+loading bin 9000
+loading bin 10000
+loading bin 11000
+loading bin 12000
+(12000, 112, 112, 3)
+begin db cfp_ff convert.
+loading bin 1000
+loading bin 2000
+loading bin 3000
+loading bin 4000
+loading bin 5000
+loading bin 6000
+loading bin 7000
+loading bin 8000
+loading bin 9000
+loading bin 10000
+loading bin 11000
+loading bin 12000
+loading bin 13000
+loading bin 14000
+(14000, 112, 112, 3)
+begin db cfp_fp convert.
+loading bin 1000
+loading bin 2000
+loading bin 3000
+loading bin 4000
+loading bin 5000
+loading bin 6000
+loading bin 7000
+loading bin 8000
+loading bin 9000
+loading bin 10000
+loading bin 11000
+loading bin 12000
+loading bin 13000
+loading bin 14000
+(14000, 112, 112, 3)
+begin db agedb_30 convert.
+loading bin 1000
+loading bin 2000
+loading bin 3000
+loading bin 4000
+loading bin 5000
+loading bin 6000
+loading bin 7000
+loading bin 8000
+loading bin 9000
+loading bin 10000
+loading bin 11000
+loading bin 12000
+(12000, 112, 112, 3)
+  [TL] InputLayer  resnet_v1_100/input_layer: (?, 112, 112, 3)
+  [TL] Conv2dLayer resnet_v1_100/conv1: shape:[3, 3, 3, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/bn0: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/bn0/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/bn0/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/prelu0: channel_shared:False
+  [TL] PoolLayer   resnet_v1_100/block1/unit_1/bottleneck_v1/shortcut: ksize:[1, 1, 1, 1] strides:[1, 2, 2, 1] padding:SAME pool:max_pool
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn1/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv1: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn2/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block1/unit_1/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] PadLayer   resnet_v1_100/block1/unit_1/bottleneck_v1/padding_conv2: paddings:[[0, 0], [1, 1], [1, 1], [0, 0]] mode:CONSTANT
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv2: shape:[3, 3, 64, 64] strides:[1, 2, 2, 1] pad:VALID act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block1/unit_1/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block1/unit_1/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block1/unit_1/bottleneck_v1/combine_layer: size:(?, 56, 56, 64) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn1/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv1: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn2/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block1/unit_2/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv2: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block1/unit_2/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block1/unit_2/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block1/unit_2/bottleneck_v1/combine_layer: size:(?, 56, 56, 64) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn1/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv1: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn2/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block1/unit_3/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv2: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block1/unit_3/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block1/unit_3/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block1/unit_3/bottleneck_v1/combine_layer: size:(?, 56, 56, 64) fn:add
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_1/bottleneck_v1/shortcut_conv: shape:[1, 1, 64, 128] strides:[1, 2, 2, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_1/bottleneck_v1/shortcut_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_1/bottleneck_v1/shortcut_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_1/bottleneck_v1/shortcut_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn1/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv1: shape:[3, 3, 64, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_1/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] PadLayer   resnet_v1_100/block2/unit_1/bottleneck_v1/padding_conv2: paddings:[[0, 0], [1, 1], [1, 1], [0, 0]] mode:CONSTANT
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 2, 2, 1] pad:VALID act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_1/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_1/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_1/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_2/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_2/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_2/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_2/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_3/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_3/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_3/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_3/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_4/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_4/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_4/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_4/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_5/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_5/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_5/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_5/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_6/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_6/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_6/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_6/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_7/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_7/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_7/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_7/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_8/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_8/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_8/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_8/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_9/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_9/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_9/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_9/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_10/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_10/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_10/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_10/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_11/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_11/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_11/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_11/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_12/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_12/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_12/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_12/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_13/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_13/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_13/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_13/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_1/bottleneck_v1/shortcut_conv: shape:[1, 1, 128, 256] strides:[1, 2, 2, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_1/bottleneck_v1/shortcut_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_1/bottleneck_v1/shortcut_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_1/bottleneck_v1/shortcut_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv1: shape:[3, 3, 128, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_1/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] PadLayer   resnet_v1_100/block3/unit_1/bottleneck_v1/padding_conv2: paddings:[[0, 0], [1, 1], [1, 1], [0, 0]] mode:CONSTANT
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 2, 2, 1] pad:VALID act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_1/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_1/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_1/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_2/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_2/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_2/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_2/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_3/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_3/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_3/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_3/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_4/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_4/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_4/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_4/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_5/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_5/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_5/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_5/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_6/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_6/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_6/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_6/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_7/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_7/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_7/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_7/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_8/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_8/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_8/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_8/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_9/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_9/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_9/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_9/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_10/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_10/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_10/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_10/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_11/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_11/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_11/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_11/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_12/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_12/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_12/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_12/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_13/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_13/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_13/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_13/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_14/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_14/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_14/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_14/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_15/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_15/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_15/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_15/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_16/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_16/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_16/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_16/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_17/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_17/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_17/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_17/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_18/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_18/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_18/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_18/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_19/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_19/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_19/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_19/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_20/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_20/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_20/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_20/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_21/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_21/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_21/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_21/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_22/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_22/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_22/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_22/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_23/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_23/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_23/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_23/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_24/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_24/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_24/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_24/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_25/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_25/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_25/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_25/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_26/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_26/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_26/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_26/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_27/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_27/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_27/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_27/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_28/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_28/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_28/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_28/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_29/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_29/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_29/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_29/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_30/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_30/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_30/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_30/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_1/bottleneck_v1/shortcut_conv: shape:[1, 1, 256, 512] strides:[1, 2, 2, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_1/bottleneck_v1/shortcut_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block4/unit_1/bottleneck_v1/shortcut_bn/BatchNorm/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_1/bottleneck_v1/shortcut_bn/BatchNorm/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv1: shape:[3, 3, 256, 512] strides:[1, 1, 1, 1] pad:SAME act:identityWARNING:tensorflow:From /home/wangdawei/InsightFace_TF/nets/L_Resnet_E_IR_fix_issue9.py:314: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
+Instructions for updating:
+Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
+WARNING:tensorflow:From /home/wangdawei/InsightFace_TF/losses/face_losses.py:20: calling norm (from tensorflow.python.ops.linalg_ops) with keep_dims is deprecated and will be removed in a future version.
+Instructions for updating:
+keep_dims is deprecated, use keepdims instead
+WARNING:tensorflow:From /home/wangdawei/InsightFace_TF/losses/face_losses.py:21: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
+Instructions for updating:
+Deprecated in favor of operator or tf.math.divide.
+WARNING:tensorflow:From /home/wangdawei/InsightFace_TF/losses/face_losses.py:40: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
+Instructions for updating:
+Use tf.where in 2.0, which has the same broadcast rule as np.where
+
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn2/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block4/unit_1/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] PadLayer   resnet_v1_100/block4/unit_1/bottleneck_v1/padding_conv2: paddings:[[0, 0], [1, 1], [1, 1], [0, 0]] mode:CONSTANT
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv2: shape:[3, 3, 512, 512] strides:[1, 2, 2, 1] pad:VALID act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block4/unit_1/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_1/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block4/unit_1/bottleneck_v1/combine_layer: size:(?, 7, 7, 512) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn1/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv1: shape:[3, 3, 512, 512] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn2/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block4/unit_2/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv2: shape:[3, 3, 512, 512] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block4/unit_2/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_2/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block4/unit_2/bottleneck_v1/combine_layer: size:(?, 7, 7, 512) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn1/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv1: shape:[3, 3, 512, 512] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn2/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block4/unit_3/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv2: shape:[3, 3, 512, 512] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block4/unit_3/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_3/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block4/unit_3/bottleneck_v1/combine_layer: size:(?, 7, 7, 512) fn:add
+  [TL] BatchNormLayer resnet_v1_100/E_BN1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/E_BN1/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/E_BN1/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] ReshapeLayer resnet_v1_100/E_Reshapelayer: (?, 25088)
+  [TL] DenseLayer  resnet_v1_100/E_DenseLayer: 512 identity
+  [TL] BatchNormLayer resnet_v1_100/E_BN2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/E_BN2/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/E_BN2/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] InputLayer  resnet_v1_100/input_layer: (?, 112, 112, 3)
+  [TL] Conv2dLayer resnet_v1_100/conv1: shape:[3, 3, 3, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/bn0: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/prelu0: channel_shared:False
+  [TL] PoolLayer   resnet_v1_100/block1/unit_1/bottleneck_v1/shortcut: ksize:[1, 1, 1, 1] strides:[1, 2, 2, 1] padding:SAME pool:max_pool
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv1: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block1/unit_1/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] PadLayer   resnet_v1_100/block1/unit_1/bottleneck_v1/padding_conv2: paddings:[[0, 0], [1, 1], [1, 1], [0, 0]] mode:CONSTANT
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv2: shape:[3, 3, 64, 64] strides:[1, 2, 2, 1] pad:VALID act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block1/unit_1/bottleneck_v1/combine_layer: size:(?, 56, 56, 64) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv1: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block1/unit_2/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv2: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block1/unit_2/bottleneck_v1/combine_layer: size:(?, 56, 56, 64) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv1: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block1/unit_3/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv2: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block1/unit_3/bottleneck_v1/combine_layer: size:(?, 56, 56, 64) fn:add
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_1/bottleneck_v1/shortcut_conv: shape:[1, 1, 64, 128] strides:[1, 2, 2, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_1/bottleneck_v1/shortcut_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv1: shape:[3, 3, 64, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_1/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] PadLayer   resnet_v1_100/block2/unit_1/bottleneck_v1/padding_conv2: paddings:[[0, 0], [1, 1], [1, 1], [0, 0]] mode:CONSTANT
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 2, 2, 1] pad:VALID act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_1/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_2/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_2/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_3/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_3/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_4/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_4/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_5/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_5/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_6/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_6/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_7/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_7/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_8/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_8/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_9/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_9/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_10/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_10/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_11/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_11/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_12/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_12/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_13/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_13/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_1/bottleneck_v1/shortcut_conv: shape:[1, 1, 128, 256] strides:[1, 2, 2, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_1/bottleneck_v1/shortcut_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv1: shape:[3, 3, 128, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_1/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] PadLayer   resnet_v1_100/block3/unit_1/bottleneck_v1/padding_conv2: paddings:[[0, 0], [1, 1], [1, 1], [0, 0]] mode:CONSTANT
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 2, 2, 1] pad:VALID act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_1/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_2/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_2/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_3/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_3/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_4/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_4/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_5/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_5/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_6/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_6/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_7/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_7/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_8/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_8/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_9/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_9/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_10/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_10/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_11/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_11/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_12/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_12/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_13/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_13/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_14/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_14/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_15/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_15/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_16/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_16/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_17/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_17/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_18/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_18/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_19/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_19/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_20/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_20/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_21/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_21/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_22/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_22/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_23/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_23/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_24/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_24/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_25/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_25/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_26/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_26/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_27/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_27/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_28/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_28/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_29/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_29/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_30/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_30/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_1/bottleneck_v1/shortcut_conv: shape:[1, 1, 256, 512] strides:[1, 2, 2, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_1/bottleneck_v1/shortcut_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv1: shape:[3, 3, 256, 512] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block4/unit_1/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] PadLayer   resnet_v1_100/block4/unit_1/bottleneck_v1/padding_conv2: paddings:[[0, 0], [1, 1], [1, 1], [0, 0]] mode:CONSTANT
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv2: shape:[3, 3, 512, 512] strides:[1, 2, 2, 1] pad:VALID act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block4/unit_1/bottleneck_v1/combine_layer: size:(?, 7, 7, 512) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv1: shape:[3, 3, 512, 512] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block4/unit_2/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv2: shape:[3, 3, 512, 512] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block4/unit_2/bottleneck_v1/combine_layer: size:(?, 7, 7, 512) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv1: shape:[3, 3, 512, 512] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block4/unit_3/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv2: shape:[3, 3, 512, 512] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block4/unit_3/bottleneck_v1/combine_layer: size:(?, 7, 7, 512) fn:add
+  [TL] BatchNormLayer resnet_v1_100/E_BN1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ReshapeLayer resnet_v1_100/E_Reshapelayer: (?, 25088)
+  [TL] DenseLayer  resnet_v1_100/E_DenseLayer: 512 identity
+  [TL] BatchNormLayer resnet_v1_100/E_BN2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [*] geting variables with W_conv2d
+  got   0: resnet_v1_100/conv1/W_conv2d:0   (3, 3, 3, 64)
+  got   1: resnet_v1_100/block1/unit_1/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 64, 64)
+  got   2: resnet_v1_100/block1/unit_1/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 64, 64)
+  got   3: resnet_v1_100/block1/unit_2/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 64, 64)
+  got   4: resnet_v1_100/block1/unit_2/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 64, 64)
+  got   5: resnet_v1_100/block1/unit_3/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 64, 64)
+  got   6: resnet_v1_100/block1/unit_3/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 64, 64)
+  got   7: resnet_v1_100/block2/unit_1/bottleneck_v1/shortcut_conv/W_conv2d:0   (1, 1, 64, 128)
+  got   8: resnet_v1_100/block2/unit_1/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 64, 128)
+  got   9: resnet_v1_100/block2/unit_1/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  10: resnet_v1_100/block2/unit_2/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  11: resnet_v1_100/block2/unit_2/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  12: resnet_v1_100/block2/unit_3/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  13: resnet_v1_100/block2/unit_3/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  14: resnet_v1_100/block2/unit_4/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  15: resnet_v1_100/block2/unit_4/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  16: resnet_v1_100/block2/unit_5/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  17: resnet_v1_100/block2/unit_5/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  18: resnet_v1_100/block2/unit_6/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  19: resnet_v1_100/block2/unit_6/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  20: resnet_v1_100/block2/unit_7/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  21: resnet_v1_100/block2/unit_7/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  22: resnet_v1_100/block2/unit_8/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  23: resnet_v1_100/block2/unit_8/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  24: resnet_v1_100/block2/unit_9/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  25: resnet_v1_100/block2/unit_9/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  26: resnet_v1_100/block2/unit_10/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  27: resnet_v1_100/block2/unit_10/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  28: resnet_v1_100/block2/unit_11/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  29: resnet_v1_100/block2/unit_11/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  30: resnet_v1_100/block2/unit_12/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  31: resnet_v1_100/block2/unit_12/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  32: resnet_v1_100/block2/unit_13/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  33: resnet_v1_100/block2/unit_13/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  34: resnet_v1_100/block3/unit_1/bottleneck_v1/shortcut_conv/W_conv2d:0   (1, 1, 128, 256)
+  got  35: resnet_v1_100/block3/unit_1/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 256)
+  got  36: resnet_v1_100/block3/unit_1/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  37: resnet_v1_100/block3/unit_2/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  38: resnet_v1_100/block3/unit_2/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  39: resnet_v1_100/block3/unit_3/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  40: resnet_v1_100/block3/unit_3/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  41: resnet_v1_100/block3/unit_4/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  42: resnet_v1_100/block3/unit_4/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  43: resnet_v1_100/block3/unit_5/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  44: resnet_v1_100/block3/unit_5/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  45: resnet_v1_100/block3/unit_6/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  46: resnet_v1_100/block3/unit_6/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  47: resnet_v1_100/block3/unit_7/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  48: resnet_v1_100/block3/unit_7/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  49: resnet_v1_100/block3/unit_8/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  50: resnet_v1_100/block3/unit_8/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  51: resnet_v1_100/block3/unit_9/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  52: resnet_v1_100/block3/unit_9/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  53: resnet_v1_100/block3/unit_10/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  54: resnet_v1_100/block3/unit_10/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  55: resnet_v1_100/block3/unit_11/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  56: resnet_v1_100/block3/unit_11/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  57: resnet_v1_100/block3/unit_12/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  58: resnet_v1_100/block3/unit_12/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  59: resnet_v1_100/block3/unit_13/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  60: resnet_v1_100/block3/unit_13/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  61: resnet_v1_100/block3/unit_14/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  62: resnet_v1_100/block3/unit_14/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  63: resnet_v1_100/block3/unit_15/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  64: resnet_v1_100/block3/unit_15/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  65: resnet_v1_100/block3/unit_16/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  66: resnet_v1_100/block3/unit_16/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  67: resnet_v1_100/block3/unit_17/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  68: resnet_v1_100/block3/unit_17/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  69: resnet_v1_100/block3/unit_18/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  70: resnet_v1_100/block3/unit_18/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  71: resnet_v1_100/block3/unit_19/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  72: resnet_v1_100/block3/unit_19/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  73: resnet_v1_100/block3/unit_20/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  74: resnet_v1_100/block3/unit_20/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  75: resnet_v1_100/block3/unit_21/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  76: resnet_v1_100/block3/unit_21/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  77: resnet_v1_100/block3/unit_22/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  78: resnet_v1_100/block3/unit_22/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  79: resnet_v1_100/block3/unit_23/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  80: resnet_v1_100/block3/unit_23/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  81: resnet_v1_100/block3/unit_24/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  82: resnet_v1_100/block3/unit_24/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  83: resnet_v1_100/block3/unit_25/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  84: resnet_v1_100/block3/unit_25/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  85: resnet_v1_100/block3/unit_26/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  86: resnet_v1_100/block3/unit_26/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  87: resnet_v1_100/block3/unit_27/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  88: resnet_v1_100/block3/unit_27/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  89: resnet_v1_100/block3/unit_28/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  90: resnet_v1_100/block3/unit_28/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  91: resnet_v1_100/block3/unit_29/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  92: resnet_v1_100/block3/unit_29/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  93: resnet_v1_100/block3/unit_30/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  94: resnet_v1_100/block3/unit_30/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  95: resnet_v1_100/block4/unit_1/bottleneck_v1/shortcut_conv/W_conv2d:0   (1, 1, 256, 512)
+  got  96: resnet_v1_100/block4/unit_1/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 512)
+  got  97: resnet_v1_100/block4/unit_1/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 512, 512)
+  got  98: resnet_v1_100/block4/unit_2/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 512, 512)
+  got  99: resnet_v1_100/block4/unit_2/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 512, 512)
+  got 100: resnet_v1_100/block4/unit_3/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 512, 512)
+  got 101: resnet_v1_100/block4/unit_3/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 512, 512)
+  [*] geting variables with resnet_v1_50/E_DenseLayer/W
+  [*] geting variables with embedding_weights
+  got   0: arcface_loss/embedding_weights:0   (512, 85164)
+  [*] geting variables with gamma
+  got   0: resnet_v1_100/bn0/gamma:0   (64,)
+  got   1: resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn1/gamma:0   (64,)
+  got   2: resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn2/gamma:0   (64,)
+  got   3: resnet_v1_100/block1/unit_1/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (64,)
+  got   4: resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn1/gamma:0   (64,)
+  got   5: resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn2/gamma:0   (64,)
+  got   6: resnet_v1_100/block1/unit_2/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (64,)
+  got   7: resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn1/gamma:0   (64,)
+  got   8: resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn2/gamma:0   (64,)
+  got   9: resnet_v1_100/block1/unit_3/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (64,)
+  got  10: resnet_v1_100/block2/unit_1/bottleneck_v1/shortcut_bn/BatchNorm/gamma:0   (128,)
+  got  11: resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn1/gamma:0   (64,)
+  got  12: resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  13: resnet_v1_100/block2/unit_1/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  14: resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  15: resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  16: resnet_v1_100/block2/unit_2/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  17: resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  18: resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  19: resnet_v1_100/block2/unit_3/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  20: resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  21: resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  22: resnet_v1_100/block2/unit_4/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  23: resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  24: resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  25: resnet_v1_100/block2/unit_5/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  26: resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  27: resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  28: resnet_v1_100/block2/unit_6/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  29: resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  30: resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  31: resnet_v1_100/block2/unit_7/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  32: resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  33: resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  34: resnet_v1_100/block2/unit_8/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  35: resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  36: resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  37: resnet_v1_100/block2/unit_9/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  38: resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  39: resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  40: resnet_v1_100/block2/unit_10/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  41: resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  42: resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  43: resnet_v1_100/block2/unit_11/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  44: resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  45: resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  46: resnet_v1_100/block2/unit_12/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  47: resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  48: resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  49: resnet_v1_100/block2/unit_13/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  50: resnet_v1_100/block3/unit_1/bottleneck_v1/shortcut_bn/BatchNorm/gamma:0   (256,)
+  got  51: resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  52: resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  53: resnet_v1_100/block3/unit_1/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  54: resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  55: resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  56: resnet_v1_100/block3/unit_2/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  57: resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  58: resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  59: resnet_v1_100/block3/unit_3/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  60: resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  61: resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  62: resnet_v1_100/block3/unit_4/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  63: resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  64: resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  65: resnet_v1_100/block3/unit_5/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  66: resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  67: resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  68: resnet_v1_100/block3/unit_6/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  69: resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  70: resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  71: resnet_v1_100/block3/unit_7/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  72: resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  73: resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  74: resnet_v1_100/block3/unit_8/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  75: resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  76: resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  77: resnet_v1_100/block3/unit_9/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  78: resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  79: resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  80: resnet_v1_100/block3/unit_10/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  81: resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  82: resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  83: resnet_v1_100/block3/unit_11/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  84: resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  85: resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  86: resnet_v1_100/block3/unit_12/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  87: resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  88: resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  89: resnet_v1_100/block3/unit_13/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  90: resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  91: resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  92: resnet_v1_100/block3/unit_14/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  93: resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  94: resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  95: resnet_v1_100/block3/unit_15/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  96: resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  97: resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  98: resnet_v1_100/block3/unit_16/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  99: resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 100: resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 101: resnet_v1_100/block3/unit_17/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 102: resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 103: resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 104: resnet_v1_100/block3/unit_18/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 105: resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 106: resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 107: resnet_v1_100/block3/unit_19/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 108: resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 109: resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 110: resnet_v1_100/block3/unit_20/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 111: resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 112: resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 113: resnet_v1_100/block3/unit_21/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 114: resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 115: resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 116: resnet_v1_100/block3/unit_22/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 117: resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 118: resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 119: resnet_v1_100/block3/unit_23/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 120: resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 121: resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 122: resnet_v1_100/block3/unit_24/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 123: resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 124: resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 125: resnet_v1_100/block3/unit_25/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 126: resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 127: resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 128: resnet_v1_100/block3/unit_26/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 129: resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 130: resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 131: resnet_v1_100/block3/unit_27/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 132: resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 133: resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 134: resnet_v1_100/block3/unit_28/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 135: resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 136: resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 137: resnet_v1_100/block3/unit_29/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 138: resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 139: resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 140: resnet_v1_100/block3/unit_30/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 141: resnet_v1_100/block4/unit_1/bottleneck_v1/shortcut_bn/BatchNorm/gamma:0   (512,)
+  got 142: resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 143: resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn2/gamma:0   (512,)
+  got 144: resnet_v1_100/block4/unit_1/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (512,)
+  got 145: resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn1/gamma:0   (512,)
+  got 146: resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn2/gamma:0   (512,)
+  got 147: resnet_v1_100/block4/unit_2/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (512,)
+  got 148: resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn1/gamma:0   (512,)
+  got 149: resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn2/gamma:0   (512,)
+  got 150: resnet_v1_100/block4/unit_3/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (512,)
+  got 151: resnet_v1_100/E_BN1/gamma:0   (512,)
+  [*] geting variables with alphas
+  got   0: resnet_v1_100/prelu0/alphas:0   (64,)
+  got   1: resnet_v1_100/block1/unit_1/bottleneck_v1/prelu_layer/alphas:0   (64,)
+  got   2: resnet_v1_100/block1/unit_2/bottleneck_v1/prelu_layer/alphas:0   (64,)
+  got   3: resnet_v1_100/block1/unit_3/bottleneck_v1/prelu_layer/alphas:0   (64,)
+  got   4: resnet_v1_100/block2/unit_1/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got   5: resnet_v1_100/block2/unit_2/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got   6: resnet_v1_100/block2/unit_3/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got   7: resnet_v1_100/block2/unit_4/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got   8: resnet_v1_100/block2/unit_5/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got   9: resnet_v1_100/block2/unit_6/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got  10: resnet_v1_100/block2/unit_7/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got  11: resnet_v1_100/block2/unit_8/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got  12: resnet_v1_100/block2/unit_9/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got  13: resnet_v1_100/block2/unit_10/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got  14: resnet_v1_100/block2/unit_11/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got  15: resnet_v1_100/block2/unit_12/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got  16: resnet_v1_100/block2/unit_13/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got  17: resnet_v1_100/block3/unit_1/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  18: resnet_v1_100/block3/unit_2/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  19: resnet_v1_100/block3/unit_3/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  20: resnet_v1_100/block3/unit_4/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  21: resnet_v1_100/block3/unit_5/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  22: resnet_v1_100/block3/unit_6/bottleneck_v1/prelu_layer/alphas:0   (256,)WARNING:tensorflow:From /home/wangdawei/InsightFace_TF/train_nets.py:113: The name tf.train.piecewise_constant is deprecated. Please use tf.compat.v1.train.piecewise_constant instead.
+
+WARNING:tensorflow:From /home/wangdawei/InsightFace_TF/train_nets.py:115: The name tf.train.MomentumOptimizer is deprecated. Please use tf.compat.v1.train.MomentumOptimizer instead.
+
+2022-11-30 21:18:28.811504: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
+2022-11-30 21:18:28.845049: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2999970000 Hz
+2022-11-30 21:18:28.848269: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x9c70f70 executing computations on platform Host. Devices:
+2022-11-30 21:18:28.848328: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>
+2022-11-30 21:18:29.175044: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2829c90 executing computations on platform CUDA. Devices:
+2022-11-30 21:18:29.175112: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): NVIDIA TITAN Xp, Compute Capability 6.1
+2022-11-30 21:18:29.179947: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: 
+name: NVIDIA TITAN Xp major: 6 minor: 1 memoryClockRate(GHz): 1.582
+pciBusID: 0000:04:00.0
+2022-11-30 21:18:29.180246: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.1
+2022-11-30 21:18:29.180312: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10
+2022-11-30 21:18:29.180397: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10
+2022-11-30 21:18:29.180456: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10
+2022-11-30 21:18:29.180510: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10
+2022-11-30 21:18:29.180576: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10
+2022-11-30 21:18:29.180633: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
+2022-11-30 21:18:29.191273: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
+2022-11-30 21:18:29.191323: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.1
+2022-11-30 21:18:29.194778: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
+2022-11-30 21:18:29.194796: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 
+2022-11-30 21:18:29.194803: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N 
+2022-11-30 21:18:29.201064: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10829 MB memory) -> physical GPU (device: 0, name: NVIDIA TITAN Xp, pci bus id: 0000:04:00.0, compute capability: 6.1)
+WARNING:tensorflow:From /home/wangdawei/InsightFace_TF/train_nets.py:132: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.
+
+WARNING:tensorflow:From /home/wangdawei/InsightFace_TF/train_nets.py:137: The name tf.summary.histogram is deprecated. Please use tf.compat.v1.summary.histogram instead.
+
+WARNING:tensorflow:From /home/wangdawei/InsightFace_TF/train_nets.py:142: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.
+
+WARNING:tensorflow:From /home/wangdawei/InsightFace_TF/train_nets.py:147: The name tf.summary.merge is deprecated. Please use tf.compat.v1.summary.merge instead.
+
+WARNING:tensorflow:From /home/wangdawei/InsightFace_TF/train_nets.py:149: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.
+
+2022-11-30 21:18:38.110777: W tensorflow/compiler/jit/mark_for_compilation_pass.cc:1412] (One-time warning): Not using XLA:CPU for cluster because envvar TF_XLA_FLAGS=--tf_xla_cpu_global_jit was not set.  If you want XLA:CPU, either set that envvar, or use experimental_jit_scope to enable XLA:CPU.  To confirm that XLA is active, pass --vmodule=xla_compilation_cache=1 (as a proper command-line flag, not via TF_XLA_FLAGS) or set the envvar XLA_FLAGS=--xla_hlo_profile.
+2022-11-30 21:19:14.281975: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10
+2022-11-30 21:19:14.561981: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
+
+  got  23: resnet_v1_100/block3/unit_7/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  24: resnet_v1_100/block3/unit_8/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  25: resnet_v1_100/block3/unit_9/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  26: resnet_v1_100/block3/unit_10/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  27: resnet_v1_100/block3/unit_11/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  28: resnet_v1_100/block3/unit_12/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  29: resnet_v1_100/block3/unit_13/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  30: resnet_v1_100/block3/unit_14/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  31: resnet_v1_100/block3/unit_15/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  32: resnet_v1_100/block3/unit_16/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  33: resnet_v1_100/block3/unit_17/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  34: resnet_v1_100/block3/unit_18/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  35: resnet_v1_100/block3/unit_19/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  36: resnet_v1_100/block3/unit_20/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  37: resnet_v1_100/block3/unit_21/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  38: resnet_v1_100/block3/unit_22/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  39: resnet_v1_100/block3/unit_23/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  40: resnet_v1_100/block3/unit_24/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  41: resnet_v1_100/block3/unit_25/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  42: resnet_v1_100/block3/unit_26/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  43: resnet_v1_100/block3/unit_27/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  44: resnet_v1_100/block3/unit_28/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  45: resnet_v1_100/block3/unit_29/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  46: resnet_v1_100/block3/unit_30/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  47: resnet_v1_100/block4/unit_1/bottleneck_v1/prelu_layer/alphas:0   (512,)
+  got  48: resnet_v1_100/block4/unit_2/bottleneck_v1/prelu_layer/alphas:0   (512,)
+  got  49: resnet_v1_100/block4/unit_3/bottleneck_v1/prelu_layer/alphas:0   (512,)
+[640000, 960000, 1280000]
+epoch 0, total_step 20, total loss is 58.21 , inference loss is 43.74, weight deacy loss is 14.47, training accuracy is 0.000000, time 56.410 samples/sec
+epoch 0, total_step 40, total loss is 57.38 , inference loss is 42.91, weight deacy loss is 14.47, training accuracy is 0.000000, time 57.553 samples/sec
+epoch 0, total_step 60, total loss is 55.19 , inference loss is 40.72, weight deacy loss is 14.47, training accuracy is 0.000000, time 55.945 samples/sec
+epoch 0, total_step 80, total loss is 54.33 , inference loss is 39.86, weight deacy loss is 14.47, training accuracy is 0.000000, time 56.522 samples/sec
+epoch 0, total_step 100, total loss is 51.04 , inference loss is 36.57, weight deacy loss is 14.47, training accuracy is 0.000000, time 56.807 samples/sec
+epoch 0, total_step 120, total loss is 53.72 , inference loss is 39.25, weight deacy loss is 14.47, training accuracy is 0.000000, time 55.201 samples/sec
+epoch 0, total_step 140, total loss is 52.19 , inference loss is 37.72, weight deacy loss is 14.47, training accuracy is 0.000000, time 56.912 samples/sec
+epoch 0, total_step 160, total loss is 48.87 , inference loss is 34.40, weight deacy loss is 14.47, training accuracy is 0.000000, time 57.123 samples/sec
+epoch 0, total_step 180, total loss is 49.36 , inference loss is 34.89, weight deacy loss is 14.47, training accuracy is 0.000000, time 56.634 samples/sec
+epoch 0, total_step 200, total loss is 50.67 , inference loss is 36.21, weight deacy loss is 14.47, training accuracy is 0.000000, time 55.820 samples/sec
+epoch 0, total_step 220, total loss is 49.16 , inference loss is 34.69, weight deacy loss is 14.46, training accuracy is 0.000000, time 56.435 samples/sec
+epoch 0, total_step 240, total loss is 50.29 , inference loss is 35.83, weight deacy loss is 14.46, training accuracy is 0.000000, time 55.984 samples/sec
+epoch 0, total_step 260, total loss is 50.15 , inference loss is 35.69, weight deacy loss is 14.46, training accuracy is 0.000000, time 56.288 samples/sec
+epoch 0, total_step 280, total loss is 49.38 , inference loss is 34.92, weight deacy loss is 14.46, training accuracy is 0.000000, time 57.451 samples/sec
+epoch 0, total_step 300, total loss is 46.74 , inference loss is 32.28, weight deacy loss is 14.46, training accuracy is 0.000000, time 55.443 samples/sec
+epoch 0, total_step 320, total loss is 49.28 , inference loss is 34.83, weight deacy loss is 14.46, training accuracy is 0.000000, time 56.080 samples/sec
+epoch 0, total_step 340, total loss is 51.66 , inference loss is 37.21, weight deacy loss is 14.46, training accuracy is 0.031250, time 56.472 samples/sec
+epoch 0, total_step 360, total loss is 49.01 , inference loss is 34.56, weight deacy loss is 14.45, training accuracy is 0.000000, time 55.941 samples/sec
+epoch 0, total_step 380, total loss is 47.85 , inference loss is 33.40, weight deacy loss is 14.45, training accuracy is 0.000000, time 56.119 samples/sec
+epoch 0, total_step 400, total loss is 45.20 , inference loss is 30.74, weight deacy loss is 14.45, training accuracy is 0.031250, time 56.649 samples/sec
+epoch 0, total_step 420, total loss is 51.02 , inference loss is 36.57, weight deacy loss is 14.45, training accuracy is 0.000000, time 56.551 samples/sec
+epoch 0, total_step 440, total loss is 47.74 , inference loss is 33.30, weight deacy loss is 14.45, training accuracy is 0.000000, time 56.638 samples/sec
+epoch 0, total_step 460, total loss is 48.47 , inference loss is 34.02, weight deacy loss is 14.45, training accuracy is 0.031250, time 55.755 samples/sec
+epoch 0, total_step 480, total loss is 46.88 , inference loss is 32.44, weight deacy loss is 14.44, training accuracy is 0.062500, time 56.403 samples/sec
+epoch 0, total_step 500, total loss is 46.05 , inference loss is 31.61, weight deacy loss is 14.44, training accuracy is 0.031250, time 56.126 samples/sec
+epoch 0, total_step 520, total loss is 50.54 , inference loss is 36.10, weight deacy loss is 14.44, training accuracy is 0.000000, time 56.829 samples/sec
+epoch 0, total_step 540, total loss is 48.20 , inference loss is 33.76, weight deacy loss is 14.44, training accuracy is 0.000000, time 53.533 samples/sec
+epoch 0, total_step 560, total loss is 49.94 , inference loss is 35.50, weight deacy loss is 14.44, training accuracy is 0.000000, time 56.925 samples/sec
+epoch 0, total_step 580, total loss is 51.03 , inference loss is 36.59, weight deacy loss is 14.44, training accuracy is 0.000000, time 55.968 samples/sec
+epoch 0, total_step 600, total loss is 47.38 , inference loss is 32.94, weight deacy loss is 14.43, training accuracy is 0.062500, time 53.567 samples/sec
+epoch 0, total_step 620, total loss is 46.22 , inference loss is 31.78, weight deacy loss is 14.43, training accuracy is 0.000000, time 54.024 samples/sec
+epoch 0, total_step 640, total loss is 43.97 , inference loss is 29.54, weight deacy loss is 14.43, training accuracy is 0.000000, time 56.529 samples/sec
+epoch 0, total_step 660, total loss is 47.43 , inference loss is 33.00, weight deacy loss is 14.43, training accuracy is 0.000000, time 56.209 samples/sec
+epoch 0, total_step 680, total loss is 52.52 , inference loss is 38.09, weight deacy loss is 14.43, training accuracy is 0.000000, time 56.710 samples/sec
+epoch 0, total_step 700, total loss is 50.66 , inference loss is 36.23, weight deacy loss is 14.43, training accuracy is 0.000000, time 56.823 samples/sec
+epoch 0, total_step 720, total loss is 49.54 , inference loss is 35.12, weight deacy loss is 14.42, training accuracy is 0.000000, time 57.090 samples/sec
+epoch 0, total_step 740, total loss is 47.01 , inference loss is 32.59, weight deacy loss is 14.42, training accuracy is 0.000000, time 55.788 samples/sec
+epoch 0, total_step 760, total loss is 48.82 , inference loss is 34.39, weight deacy loss is 14.42, training accuracy is 0.000000, time 56.488 samples/sec
+epoch 0, total_step 780, total loss is 48.11 , inference loss is 33.69, weight deacy loss is 14.42, training accuracy is 0.000000, time 57.274 samples/sec
+epoch 0, total_step 800, total loss is 47.04 , inference loss is 32.63, weight deacy loss is 14.42, training accuracy is 0.000000, time 55.574 samples/sec
+epoch 0, total_step 820, total loss is 46.96 , inference loss is 32.54, weight deacy loss is 14.42, training accuracy is 0.000000, time 56.380 samples/sec
+epoch 0, total_step 840, total loss is 46.60 , inference loss is 32.18, weight deacy loss is 14.42, training accuracy is 0.000000, time 55.713 samples/sec
+epoch 0, total_step 860, total loss is 48.05 , inference loss is 33.63, weight deacy loss is 14.41, training accuracy is 0.031250, time 56.100 samples/sec
+epoch 0, total_step 880, total loss is 47.77 , inference loss is 33.36, weight deacy loss is 14.41, training accuracy is 0.000000, time 56.159 samples/sec
+epoch 0, total_step 900, total loss is 45.44 , inference loss is 31.03, weight deacy loss is 14.41, training accuracy is 0.000000, time 56.687 samples/sec
+epoch 0, total_step 920, total loss is 50.99 , inference loss is 36.58, weight deacy loss is 14.41, training accuracy is 0.000000, time 54.686 samples/sec
+epoch 0, total_step 940, total loss is 45.35 , inference loss is 30.94, weight deacy loss is 14.41, training accuracy is 0.000000, time 56.457 samples/sec
+epoch 0, total_step 960, total loss is 44.71 , inference loss is 30.30, weight deacy loss is 14.41, training accuracy is 0.000000, time 55.984 samples/sec
+epoch 0, total_step 980, total loss is 45.67 , inference loss is 31.27, weight deacy loss is 14.40, training accuracy is 0.031250, time 56.513 samples/sec
+epoch 0, total_step 1000, total loss is 46.44 , inference loss is 32.04, weight deacy loss is 14.40, training accuracy is 0.000000, time 56.570 samples/sec
+epoch 0, total_step 1020, total loss is 46.85 , inference loss is 32.45, weight deacy loss is 14.40, training accuracy is 0.031250, time 55.947 samples/sec
+epoch 0, total_step 1040, total loss is 46.83 , inference loss is 32.43, weight deacy loss is 14.40, training accuracy is 0.000000, time 57.063 samples/sec
+epoch 0, total_step 1060, total loss is 49.62 , inference loss is 35.23, weight deacy loss is 14.40, training accuracy is 0.031250, time 56.729 samples/sec
+epoch 0, total_step 1080, total loss is 44.60 , inference loss is 30.20, weight deacy loss is 14.40, training accuracy is 0.031250, time 56.032 samples/sec
+epoch 0, total_step 1100, total loss is 46.83 , inference loss is 32.43, weight deacy loss is 14.39, training accuracy is 0.000000, time 55.926 samples/sec
+epoch 0, total_step 1120, total loss is 46.29 , inference loss is 31.90, weight deacy loss is 14.39, training accuracy is 0.000000, time 55.506 samples/sec
+epoch 0, total_step 1140, total loss is 46.27 , inference loss is 31.88, weight deacy loss is 14.39, training accuracy is 0.000000, time 56.381 samples/sec
+epoch 0, total_step 1160, total loss is 48.53 , inference loss is 34.14, weight deacy loss is 14.39, training accuracy is 0.000000, time 56.856 samples/sec
+epoch 0, total_step 1180, total loss is 46.41 , inference loss is 32.02, weight deacy loss is 14.39, training accuracy is 0.000000, time 57.186 samples/sec
+epoch 0, total_step 1200, total loss is 43.89 , inference loss is 29.50, weight deacy loss is 14.39, training accuracy is 0.000000, time 56.548 samples/sec
+epoch 0, total_step 1220, total loss is 51.58 , inference loss is 37.20, weight deacy loss is 14.38, training accuracy is 0.000000, time 56.559 samples/sec
+epoch 0, total_step 1240, total loss is 46.62 , inference loss is 32.24, weight deacy loss is 14.38, training accuracy is 0.000000, time 56.403 samples/sec
+epoch 0, total_step 1260, total loss is 49.93 , inference loss is 35.55, weight deacy loss is 14.38, training accuracy is 0.000000, time 56.704 samples/sec
+epoch 0, total_step 1280, total loss is 43.50 , inference loss is 29.12, weight deacy loss is 14.38, training accuracy is 0.093750, time 55.614 samples/sec
+epoch 0, total_step 1300, total loss is 42.16 , inference loss is 27.78, weight deacy loss is 14.38, training accuracy is 0.000000, time 56.365 samples/sec
+epoch 0, total_step 1320, total loss is 45.78 , inference loss is 31.40, weight deacy loss is 14.38, training accuracy is 0.000000, time 56.670 samples/sec
+epoch 0, total_step 1340, total loss is 44.76 , inference loss is 30.38, weight deacy loss is 14.37, training accuracy is 0.000000, time 56.124 samples/sec
+epoch 0, total_step 1360, total loss is 44.90 , inference loss is 30.53, weight deacy loss is 14.37, training accuracy is 0.031250, time 56.140 samples/sec
+epoch 0, total_step 1380, total loss is 45.29 , inference loss is 30.92, weight deacy loss is 14.37, training accuracy is 0.031250, time 57.506 samples/sec
+epoch 0, total_step 1400, total loss is 43.49 , inference loss is 29.12, weight deacy loss is 14.37, training accuracy is 0.125000, time 56.094 samples/sec
+epoch 0, total_step 1420, total loss is 47.50 , inference loss is 33.13, weight deacy loss is 14.37, training accuracy is 0.000000, time 56.965 samples/sec
+epoch 0, total_step 1440, total loss is 48.81 , inference loss is 34.45, weight deacy loss is 14.37, training accuracy is 0.000000, time 56.227 samples/sec
+epoch 0, total_step 1460, total loss is 43.86 , inference loss is 29.49, weight deacy loss is 14.36, training accuracy is 0.062500, time 54.591 samples/sec
+epoch 0, total_step 1480, total loss is 43.90 , inference loss is 29.54, weight deacy loss is 14.36, training accuracy is 0.031250, time 56.506 samples/sec
+epoch 0, total_step 1500, total loss is 48.40 , inference loss is 34.04, weight deacy loss is 14.36, training accuracy is 0.000000, time 56.474 samples/sec
+epoch 0, total_step 1520, total loss is 51.01 , inference loss is 36.65, weight deacy loss is 14.36, training accuracy is 0.000000, time 56.398 samples/sec
+epoch 0, total_step 1540, total loss is 46.51 , inference loss is 32.15, weight deacy loss is 14.36, training accuracy is 0.000000, time 55.367 samples/sec
+epoch 0, total_step 1560, total loss is 46.69 , inference loss is 32.34, weight deacy loss is 14.36, training accuracy is 0.000000, time 57.218 samples/sec
+epoch 0, total_step 1580, total loss is 46.58 , inference loss is 32.23, weight deacy loss is 14.35, training accuracy is 0.000000, time 55.824 samples/sec
+epoch 0, total_step 1600, total loss is 47.70 , inference loss is 33.35, weight deacy loss is 14.35, training accuracy is 0.000000, time 56.118 samples/sec
+epoch 0, total_step 1620, total loss is 45.72 , inference loss is 31.37, weight deacy loss is 14.35, training accuracy is 0.000000, time 56.702 samples/sec
+epoch 0, total_step 1640, total loss is 42.48 , inference loss is 28.13, weight deacy loss is 14.35, training accuracy is 0.093750, time 57.345 samples/sec
+epoch 0, total_step 1660, total loss is 41.84 , inference loss is 27.49, weight deacy loss is 14.35, training accuracy is 0.000000, time 57.415 samples/sec
+epoch 0, total_step 1680, total loss is 45.08 , inference loss is 30.73, weight deacy loss is 14.35, training accuracy is 0.031250, time 54.068 samples/sec
+epoch 0, total_step 1700, total loss is 47.54 , inference loss is 33.20, weight deacy loss is 14.34, training accuracy is 0.000000, time 55.844 samples/sec
+epoch 0, total_step 1720, total loss is 45.82 , inference loss is 31.47, weight deacy loss is 14.34, training accuracy is 0.000000, time 55.514 samples/sec
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
+(12000, 512)
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+[lfw][8000]XNorm: 22.766812
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+2022-12-01 04:06:33.480690: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:162] Shuffle buffer filled.
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+<property object at 0x7f2d84d04710>
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+End of epoch 1
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+testing verification..
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+[lfw][32000]XNorm: 24.832845
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+testing verification..
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+testing verification..
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+testing verification..
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+[agedb_30][32000]XNorm: 23.959389
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+testing verification..
+(12000, 512)
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+[agedb_30][40000]XNorm: 23.883560
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+testing verification..
+(12000, 512)
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+testing verification..
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+testing verification..
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+testing verification..
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+[agedb_30][42000]XNorm: 22.615868
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
+(12000, 512)
+infer time 74.91141499999999
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+testing verification..
+(14000, 512)
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+[cfp_ff][60000]XNorm: 22.127951
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+testing verification..
+(14000, 512)
+infer time 85.82091900000003
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+[cfp_fp][60000]XNorm: 22.114211
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+testing verification..
+(12000, 512)
+infer time 73.59422199999999
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+[agedb_30][60000]XNorm: 22.924247
+[agedb_30][60000]Accuracy-Flip: 0.88017+-0.02468
+test accuracy is:  0.9798333333333333
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+<property object at 0x7f2d84d04710>
+<property object at 0x7f2d84cfcd10>
+<property object at 0x7f2d84d04650>
+<property object at 0x7f2d84d046b0>
+End of epoch 3
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+testing verification..
+(12000, 512)
+infer time 73.47836600000004
+best_threshold_index 110 0.9827777777777778
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+best_threshold_index 110 0.9816666666666667
+[lfw][62000]XNorm: 21.860537
+[lfw][62000]Accuracy-Flip: 0.98200+-0.00609
+testing verification..
+(14000, 512)
+infer time 85.53581399999992
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+epoch 4, total_step 75980, total loss is 20.68 , inference loss is 11.58, weight deacy loss is 9.10, training accuracy is 0.156250, time 55.383 samples/sec
+testing verification..
+(12000, 512)
+infer time 74.562921
+best_threshold_index 105 0.9835185185185186
+best_threshold_index 106 0.982037037037037
+best_threshold_index 106 0.9827777777777778
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+best_threshold_index 105 0.9814814814814815
+[lfw][76000]XNorm: 23.064518
+[lfw][76000]Accuracy-Flip: 0.98133+-0.01043
+testing verification..
+(14000, 512)
+infer time 85.82724200000011
+best_threshold_index 115 0.9838095238095238
+best_threshold_index 113 0.9836507936507937
+best_threshold_index 118 0.9834920634920635
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+[cfp_ff][76000]XNorm: 22.718067
+[cfp_ff][76000]Accuracy-Flip: 0.98129+-0.00396
+testing verification..
+(14000, 512)
+infer time 85.61406200000002
+best_threshold_index 132 0.8990476190476191
+best_threshold_index 132 0.8992063492063492
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+[cfp_fp][76000]XNorm: 21.879688
+[cfp_fp][76000]Accuracy-Flip: 0.89743+-0.01877
+testing verification..
+(12000, 512)
+infer time 73.73037099999993
+best_threshold_index 135 0.8955555555555555
+best_threshold_index 133 0.8927777777777778
+best_threshold_index 134 0.8968518518518519
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+[agedb_30][76000]XNorm: 23.105808
+[agedb_30][76000]Accuracy-Flip: 0.89483+-0.02282
+test accuracy is:  0.9813333333333333
+epoch 4, total_step 76000, total loss is 21.66 , inference loss is 12.57, weight deacy loss is 9.10, training accuracy is 0.156250, time 53.067 samples/sec
+epoch 4, total_step 76020, total loss is 31.04 , inference loss is 21.95, weight deacy loss is 9.09, training accuracy is 0.031250, time 56.104 samples/sec
+epoch 4, total_step 76040, total loss is 20.47 , inference loss is 11.38, weight deacy loss is 9.09, training accuracy is 0.218750, time 56.102 samples/sec
+epoch 4, total_step 76060, total loss is 20.50 , inference loss is 11.41, weight deacy loss is 9.09, training accuracy is 0.218750, time 56.314 samples/sec
+epoch 4, total_step 76080, total loss is 24.56 , inference loss is 15.47, weight deacy loss is 9.09, training accuracy is 0.125000, time 55.562 samples/sec2022-12-01 14:18:50.602643: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:111] Filling up shuffle buffer (this may take a while): 8478 of 10000
+2022-12-01 14:18:52.267078: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:162] Shuffle buffer filled.
+
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+<property object at 0x7f2d84d04710>
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+End of epoch 4
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
+(12000, 512)
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+testing verification..
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+testing verification..
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+testing verification..
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+[agedb_30][86000]XNorm: 21.608579
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+End of epoch 5
+testing verification..
+(12000, 512)
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+[lfw][92000]XNorm: 22.143844
+[lfw][92000]Accuracy-Flip: 0.98683+-0.00660
+testing verification..
+(14000, 512)
+infer time 85.14384800000002
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+testing verification..
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+testing verification..
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+[agedb_30][92000]XNorm: 22.115537
+[agedb_30][92000]Accuracy-Flip: 0.88967+-0.02122
+test accuracy is:  0.9868333333333335
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+[agedb_30][104000]XNorm: 22.167424
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+test accuracy is:  0.9828333333333333
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+testing verification..
+(12000, 512)
+infer time 73.89231399999997
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+[lfw][106000]XNorm: 23.306064
+[lfw][106000]Accuracy-Flip: 0.98183+-0.00880
+testing verification..
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+testing verification..
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+testing verification..
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+test accuracy is:  0.9818333333333333
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+epoch 6, total_step 106500, total loss is 20.83 , inference loss is 12.97, weight deacy loss is 7.86, training accuracy is 0.250000, time 56.402 samples/sec2022-12-01 21:02:29.972881: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:111] Filling up shuffle buffer (this may take a while): 7423 of 10000
+2022-12-01 21:02:33.237230: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:162] Shuffle buffer filled.
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
+(12000, 512)
+infer time 73.12867199999995
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+[lfw][112000]XNorm: 22.286510
+[lfw][112000]Accuracy-Flip: 0.98717+-0.00615
+testing verification..
+(14000, 512)
+infer time 85.48390799999989
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+[cfp_ff][112000]XNorm: 22.304243
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+testing verification..
+(14000, 512)
+infer time 85.99702499999995
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+[cfp_fp][112000]XNorm: 23.125312
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+testing verification..
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+[agedb_30][112000]XNorm: 23.399000
+[agedb_30][112000]Accuracy-Flip: 0.89150+-0.02760
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+testing verification..
+(12000, 512)
+infer time 73.23511900000003
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+[lfw][118000]XNorm: 21.312240
+[lfw][118000]Accuracy-Flip: 0.98200+-0.00510
+testing verification..
+(14000, 512)
+infer time 85.76414499999998
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+[cfp_ff][118000]XNorm: 21.673582
+[cfp_ff][118000]Accuracy-Flip: 0.98629+-0.00565
+testing verification..
+(14000, 512)
+infer time 85.330546
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+[cfp_fp][118000]XNorm: 21.193070
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+testing verification..
+(12000, 512)
+infer time 73.15120399999994
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+[agedb_30][118000]XNorm: 22.072490
+[agedb_30][118000]Accuracy-Flip: 0.90583+-0.02872
+test accuracy is:  0.982
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
+(12000, 512)
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+testing verification..
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+testing verification..
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+testing verification..
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+test accuracy is:  0.9858333333333332
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+testing verification..
+(12000, 512)
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
+(12000, 512)
+infer time 73.89879600000009
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+[lfw][132000]XNorm: 23.446644
+[lfw][132000]Accuracy-Flip: 0.98583+-0.00484
+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+[agedb_30][136000]XNorm: 23.050744
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+epoch 8, total_step 137840, total loss is 14.27 , inference loss is 7.20, weight deacy loss is 7.07, training accuracy is 0.312500, time 56.182 samples/sec
+epoch 8, total_step 137860, total loss is 17.66 , inference loss is 10.59, weight deacy loss is 7.07, training accuracy is 0.406250, time 55.817 samples/sec
+epoch 8, total_step 137880, total loss is 19.08 , inference loss is 12.01, weight deacy loss is 7.06, training accuracy is 0.156250, time 55.178 samples/sec
+epoch 8, total_step 137900, total loss is 17.38 , inference loss is 10.31, weight deacy loss is 7.06, training accuracy is 0.281250, time 57.168 samples/sec
+epoch 8, total_step 137920, total loss is 14.89 , inference loss is 7.82, weight deacy loss is 7.06, training accuracy is 0.187500, time 57.396 samples/sec
+epoch 8, total_step 137940, total loss is 16.90 , inference loss is 9.84, weight deacy loss is 7.06, training accuracy is 0.312500, time 56.463 samples/sec2022-12-02 03:47:26.304768: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:111] Filling up shuffle buffer (this may take a while): 7256 of 10000
+2022-12-02 03:47:29.888176: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:162] Shuffle buffer filled.
+
+epoch 8, total_step 137960, total loss is 16.73 , inference loss is 9.67, weight deacy loss is 7.06, training accuracy is 0.343750, time 56.232 samples/sec
+epoch 8, total_step 137980, total loss is 17.19 , inference loss is 10.13, weight deacy loss is 7.06, training accuracy is 0.375000, time 56.165 samples/sec
+<property object at 0x7f2d84d04710>
+<property object at 0x7f2d84cfcd10>
+<property object at 0x7f2d84d04650>
+<property object at 0x7f2d84d046b0>
+End of epoch 8
+testing verification..
+(12000, 512)
+infer time 73.97039299999999
+best_threshold_index 120 0.9874074074074074
+best_threshold_index 120 0.9864814814814815
+best_threshold_index 120 0.987037037037037
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+[lfw][138000]XNorm: 21.574000
+[lfw][138000]Accuracy-Flip: 0.98633+-0.00521
+testing verification..
+(14000, 512)
+infer time 86.19998700000006
+best_threshold_index 115 0.9882539682539683
+best_threshold_index 115 0.9877777777777778
+best_threshold_index 115 0.9871428571428571
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+[cfp_ff][138000]XNorm: 21.594550
+[cfp_ff][138000]Accuracy-Flip: 0.98771+-0.00368
+testing verification..
+(14000, 512)
+infer time 86.08775099999993
+best_threshold_index 135 0.9241269841269841
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+[cfp_fp][138000]XNorm: 21.313305
+[cfp_fp][138000]Accuracy-Flip: 0.92014+-0.01405
+testing verification..
+(12000, 512)
+infer time 74.00460100000006
+best_threshold_index 141 0.9033333333333333
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+[agedb_30][138000]XNorm: 21.952532
+[agedb_30][138000]Accuracy-Flip: 0.90283+-0.01985
+test accuracy is:  0.9863333333333333
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+<property object at 0x7f2d84d04710>
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+testing verification..
+(12000, 512)
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+<property object at 0x7f2d84d04710>
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+End of epoch 10
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+testing verification..
+(12000, 512)
+infer time 73.84193700000004
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+[lfw][170000]XNorm: 19.137640
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+testing verification..
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+testing verification..
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+testing verification..
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+[agedb_30][170000]XNorm: 20.287898
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+testing verification..
+(12000, 512)
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+testing verification..
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+testing verification..
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+testing verification..
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+[agedb_30][172000]XNorm: 21.736171
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
+(12000, 512)
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+testing verification..
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+testing verification..
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+testing verification..
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+<property object at 0x7f2d84d04710>
+<property object at 0x7f2d84cfcd10>
+<property object at 0x7f2d84d04650>
+<property object at 0x7f2d84d046b0>
+End of epoch 11
+testing verification..
+(12000, 512)
+infer time 73.262775
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+testing verification..
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+epoch 12, total_step 191740, total loss is 13.98 , inference loss is 7.49, weight deacy loss is 6.49, training accuracy is 0.531250, time 56.075 samples/sec
+epoch 12, total_step 191760, total loss is 10.90 , inference loss is 4.41, weight deacy loss is 6.49, training accuracy is 0.656250, time 57.040 samples/sec
+epoch 12, total_step 191780, total loss is 16.05 , inference loss is 9.56, weight deacy loss is 6.49, training accuracy is 0.531250, time 56.540 samples/sec
+epoch 12, total_step 191800, total loss is 15.91 , inference loss is 9.43, weight deacy loss is 6.49, training accuracy is 0.468750, time 56.470 samples/sec
+epoch 12, total_step 191820, total loss is 12.04 , inference loss is 5.56, weight deacy loss is 6.49, training accuracy is 0.625000, time 55.666 samples/sec
+epoch 12, total_step 191840, total loss is 15.06 , inference loss is 8.58, weight deacy loss is 6.49, training accuracy is 0.468750, time 56.244 samples/sec
+epoch 12, total_step 191860, total loss is 14.79 , inference loss is 8.30, weight deacy loss is 6.49, training accuracy is 0.406250, time 56.294 samples/sec
+epoch 12, total_step 191880, total loss is 16.02 , inference loss is 9.54, weight deacy loss is 6.49, training accuracy is 0.437500, time 57.027 samples/sec
+epoch 12, total_step 191900, total loss is 13.89 , inference loss is 7.40, weight deacy loss is 6.49, training accuracy is 0.406250, time 56.736 samples/sec
+epoch 12, total_step 191920, total loss is 13.69 , inference loss is 7.20, weight deacy loss is 6.49, training accuracy is 0.500000, time 56.500 samples/sec
+epoch 12, total_step 191940, total loss is 11.80 , inference loss is 5.31, weight deacy loss is 6.49, training accuracy is 0.531250, time 56.502 samples/sec
+epoch 12, total_step 191960, total loss is 15.82 , inference loss is 9.33, weight deacy loss is 6.49, training accuracy is 0.468750, time 55.253 samples/sec
+epoch 12, total_step 191980, total loss is 12.06 , inference loss is 5.58, weight deacy loss is 6.49, training accuracy is 0.500000, time 57.480 samples/sec
+testing verification..
+(12000, 512)
+infer time 73.42206199999993
+best_threshold_index 113 0.9864814814814815
+best_threshold_index 113 0.9853703703703703
+best_threshold_index 112 0.9859259259259259
+best_threshold_index 113 0.9872222222222222
+best_threshold_index 114 0.9859259259259259
+best_threshold_index 113 0.985
+best_threshold_index 112 0.9857407407407407
+best_threshold_index 113 0.9848148148148148
+best_threshold_index 113 0.9844444444444445
+best_threshold_index 113 0.9844444444444445
+[lfw][192000]XNorm: 21.589511
+[lfw][192000]Accuracy-Flip: 0.98467+-0.00792
+testing verification..
+(14000, 512)
+infer time 85.227685
+best_threshold_index 125 0.9853968253968254
+best_threshold_index 125 0.9846031746031746
+best_threshold_index 125 0.9855555555555555
+best_threshold_index 124 0.9844444444444445
+best_threshold_index 122 0.9850793650793651
+best_threshold_index 123 0.9846031746031746
+best_threshold_index 125 0.9852380952380952
+best_threshold_index 125 0.9844444444444445
+best_threshold_index 125 0.9850793650793651
+best_threshold_index 125 0.9855555555555555
+[cfp_ff][192000]XNorm: 20.944467
+[cfp_ff][192000]Accuracy-Flip: 0.98400+-0.00388
+testing verification..
+(14000, 512)
+infer time 85.28094899999998
+best_threshold_index 143 0.9276190476190476
+best_threshold_index 143 0.9266666666666666
+best_threshold_index 143 0.9273015873015873
+best_threshold_index 143 0.9295238095238095
+best_threshold_index 143 0.9306349206349206
+best_threshold_index
\ No newline at end of file
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/figures/if_npu.log b/TensorFlow/contrib/cv/InsightFace_TF/figures/if_npu.log
new file mode 100644
index 0000000000000000000000000000000000000000..1da22546aa58ca438175db51bf107bcb564995b1
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/figures/if_npu.log
@@ -0,0 +1,18141 @@
+nohup: ignoring input
+WARNING:tensorflow:From /usr/local/Ascend/tfplugin/latest/python/site-packages/npu_bridge/estimator/npu/npu_optimizer.py:128: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.
+
+WARNING:tensorflow:From /usr/local/python3.7.5/lib/python3.7/site-packages/tensorlayer/layers.py:28: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.
+
+WARNING:tensorflow:From /usr/local/python3.7.5/lib/python3.7/site-packages/tensorlayer/layers.py:4030: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.
+
+WARNING:tensorflow:From train_nets.py:51: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.
+
+WARNING:tensorflow:From train_nets.py:52: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.
+
+WARNING:tensorflow:From /usr/local/python3.7.5/lib/python3.7/site-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.FixedLenFeature is deprecated. Please use tf.io.FixedLenFeature instead.
+
+WARNING:tensorflow:From /usr/local/python3.7.5/lib/python3.7/site-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.parse_single_example is deprecated. Please use tf.io.parse_single_example instead.
+
+2022-11-30 23:57:30.309709: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/python3.7.5/lib/python3.7/site-packages/cv2/../../lib64:/usr/local/Ascend/ascend-toolkit/latest/lib64:/usr/local/Ascend/ascend-toolkit/latest/lib64/plugin/opskernel:/usr/local/Ascend/ascend-toolkit/latest/lib64/plugin/nnengine:/usr/local/Ascend/driver/lib64:/usr/local/Ascend/driver/lib64/common:/usr/local/Ascend/driver/lib64/driver:/usr/local/python3.7.5/lib:/usr/local/Ascend/ascend-toolkit/latest/lib64:/usr/local/Ascend/ascend-toolkit/latest/lib64/plugin/opskernel:/usr/local/Ascend/ascend-toolkit/latest/lib64/plugin/nnengine:/usr/local/Ascend/driver/lib64:/usr/local/Ascend/driver/lib64/common:/usr/local/Ascend/driver/lib64/driver:/usr/local/python3.7.5/lib:
+2022-11-30 23:57:30.309768: E tensorflow/stream_executor/cuda/cuda_driver.cc:318] failed call to cuInit: UNKNOWN ERROR (303)
+2022-11-30 23:57:30.309799: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (ubuntu): /proc/driver/nvidia/version does not exist
+WARNING:tensorflow:From train_nets.py:67: DatasetV1.make_initializable_iterator (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
+Instructions for updating:
+Use `for ... in dataset:` to iterate over a dataset. If using `tf.estimator`, return the `Dataset` object directly from your input function. As a last resort, you can use `tf.compat.v1.data.make_initializable_iterator(dataset)`.
+WARNING:tensorflow:
+The TensorFlow contrib module will not be included in TensorFlow 2.0.
+For more information, please see:
+  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
+  * https://github.com/tensorflow/addons
+  * https://github.com/tensorflow/io (for I/O related ops)
+If you depend on functionality not listed there, please file an issue.
+
+WARNING:tensorflow:From /home/test_user06/dave/transfer/InsightFace_TF_npu_20221130111637/nets/L_Resnet_E_IR_fix_issue9.py:295: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.
+
+WARNING:tensorflow:From /usr/local/python3.7.5/lib/python3.7/site-packages/tensorlayer/layers.py:288: The name tf.get_variable_scope is deprecated. Please use tf.compat.v1.get_variable_scope instead.
+
+WARNING:tensorflow:From /usr/local/python3.7.5/lib/python3.7/site-packages/tensorlayer/layers.py:1385: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.
+
+tfrecords_f path : ./datasets/tfrecords_webface/tran.tfrecords
+parse_function : <function parse_function at 0x7fa670db6dd0>
+let look at the next_element:
+32
+begin db lfw convert.
+loading bin 1000
+loading bin 2000
+loading bin 3000
+loading bin 4000
+loading bin 5000
+loading bin 6000
+loading bin 7000
+loading bin 8000
+loading bin 9000
+loading bin 10000
+loading bin 11000
+loading bin 12000
+(12000, 112, 112, 3)
+  [TL] InputLayer  resnet_v1_100/input_layer: (?, 112, 112, 3)
+  [TL] Conv2dLayer resnet_v1_100/conv1: shape:[3, 3, 3, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/bn0: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/bn0/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/bn0/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/prelu0: channel_shared:False
+  [TL] PoolLayer   resnet_v1_100/block1/unit_1/bottleneck_v1/shortcut: ksize:[1, 1, 1, 1] strides:[1, 2, 2, 1] padding:SAME pool:max_pool
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn1/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv1: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn2/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block1/unit_1/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] PadLayer   resnet_v1_100/block1/unit_1/bottleneck_v1/padding_conv2: paddings:[[0, 0], [1, 1], [1, 1], [0, 0]] mode:CONSTANT
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv2: shape:[3, 3, 64, 64] strides:[1, 2, 2, 1] pad:VALID act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block1/unit_1/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block1/unit_1/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block1/unit_1/bottleneck_v1/combine_layer: size:(?, 56, 56, 64) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn1/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv1: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn2/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block1/unit_2/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv2: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block1/unit_2/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block1/unit_2/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block1/unit_2/bottleneck_v1/combine_layer: size:(?, 56, 56, 64) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn1/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv1: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn2/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block1/unit_3/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv2: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block1/unit_3/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block1/unit_3/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block1/unit_3/bottleneck_v1/combine_layer: size:(?, 56, 56, 64) fn:add
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_1/bottleneck_v1/shortcut_conv: shape:[1, 1, 64, 128] strides:[1, 2, 2, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_1/bottleneck_v1/shortcut_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_1/bottleneck_v1/shortcut_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_1/bottleneck_v1/shortcut_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn1/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv1: shape:[3, 3, 64, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_1/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] PadLayer   resnet_v1_100/block2/unit_1/bottleneck_v1/padding_conv2: paddings:[[0, 0], [1, 1], [1, 1], [0, 0]] mode:CONSTANT
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 2, 2, 1] pad:VALID act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_1/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_1/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_1/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_2/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_2/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_2/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_2/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_3/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_3/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_3/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_3/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_4/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_4/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_4/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_4/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_5/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_5/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_5/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_5/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_6/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_6/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_6/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_6/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_7/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_7/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_7/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_7/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_8/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_8/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_8/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_8/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_9/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_9/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_9/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_9/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_10/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_10/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_10/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_10/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_11/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_11/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_11/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_11/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_12/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_12/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_12/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_12/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_13/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_13/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_13/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_13/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_1/bottleneck_v1/shortcut_conv: shape:[1, 1, 128, 256] strides:[1, 2, 2, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_1/bottleneck_v1/shortcut_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_1/bottleneck_v1/shortcut_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_1/bottleneck_v1/shortcut_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv1: shape:[3, 3, 128, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_1/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] PadLayer   resnet_v1_100/block3/unit_1/bottleneck_v1/padding_conv2: paddings:[[0, 0], [1, 1], [1, 1], [0, 0]] mode:CONSTANT
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 2, 2, 1] pad:VALID act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_1/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_1/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_1/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_2/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_2/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_2/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_2/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_3/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_3/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_3/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_3/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_4/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_4/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_4/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_4/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_5/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_5/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_5/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_5/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_6/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_6/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_6/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_6/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_7/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_7/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_7/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_7/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_8/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_8/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_8/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_8/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_9/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_9/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_9/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_9/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_10/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_10/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_10/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_10/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_11/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_11/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_11/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_11/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_12/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_12/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_12/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_12/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_13/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_13/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_13/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_13/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_14/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_14/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_14/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_14/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_15/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_15/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_15/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_15/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_16/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_16/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_16/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_16/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_17/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_17/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_17/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_17/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_18/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_18/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_18/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_18/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_19/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_19/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_19/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_19/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_20/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_20/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_20/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_20/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_21/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_21/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_21/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_21/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_22/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_22/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_22/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_22/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_23/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_23/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_23/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_23/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_24/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_24/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_24/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_24/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_25/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_25/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_25/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_25/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_26/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_26/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_26/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_26/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_27/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_27/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_27/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_27/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_28/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_28/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_28/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_28/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_29/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_29/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_29/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_29/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_30/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_30/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_30/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_30/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_1/bottleneck_v1/shortcut_conv: shape:[1, 1, 256, 512] strides:[1, 2, 2, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_1/bottleneck_v1/shortcut_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block4/unit_1/bottleneck_v1/shortcut_bn/BatchNorm/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_1/bottleneck_v1/shortcut_bn/BatchNorm/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv1: shape:[3, 3, 256, 512] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn2/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block4/unit_1/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] PadLayer   resnet_v1_100/block4/unit_1/bottleneck_v1/padding_conv2: paddings:[[0, 0], [1, 1], [1, 1], [0, 0]] mode:CONSTANT
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv2: shape:[3, 3, 512, 512] strides:[1, 2, 2, 1] pad:VALID act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:TrueWARNING:tensorflow:From /home/test_user06/dave/transfer/InsightFace_TF_npu_20221130111637/losses/face_losses.py:21: calling norm (from tensorflow.python.ops.linalg_ops) with keep_dims is deprecated and will be removed in a future version.
+Instructions for updating:
+keep_dims is deprecated, use keepdims instead
+WARNING:tensorflow:From /home/test_user06/dave/transfer/InsightFace_TF_npu_20221130111637/losses/face_losses.py:22: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
+Instructions for updating:
+Deprecated in favor of operator or tf.math.divide.
+WARNING:tensorflow:From /home/test_user06/dave/transfer/InsightFace_TF_npu_20221130111637/losses/face_losses.py:41: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
+Instructions for updating:
+Use tf.where in 2.0, which has the same broadcast rule as np.where
+
+Tensor("resnet_v1_100/block4/unit_1/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_1/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block4/unit_1/bottleneck_v1/combine_layer: size:(?, 7, 7, 512) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn1/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv1: shape:[3, 3, 512, 512] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn2/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block4/unit_2/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv2: shape:[3, 3, 512, 512] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block4/unit_2/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_2/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block4/unit_2/bottleneck_v1/combine_layer: size:(?, 7, 7, 512) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn1/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv1: shape:[3, 3, 512, 512] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn2/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block4/unit_3/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv2: shape:[3, 3, 512, 512] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block4/unit_3/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_3/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block4/unit_3/bottleneck_v1/combine_layer: size:(?, 7, 7, 512) fn:add
+  [TL] BatchNormLayer resnet_v1_100/E_BN1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/E_BN1/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/E_BN1/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] ReshapeLayer resnet_v1_100/E_Reshapelayer: (?, 25088)
+  [TL] DenseLayer  resnet_v1_100/E_DenseLayer: 512 identity
+  [TL] BatchNormLayer resnet_v1_100/E_BN2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/E_BN2/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/E_BN2/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] InputLayer  resnet_v1_100/input_layer: (?, 112, 112, 3)
+  [TL] Conv2dLayer resnet_v1_100/conv1: shape:[3, 3, 3, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/bn0: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/prelu0: channel_shared:False
+  [TL] PoolLayer   resnet_v1_100/block1/unit_1/bottleneck_v1/shortcut: ksize:[1, 1, 1, 1] strides:[1, 2, 2, 1] padding:SAME pool:max_pool
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv1: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block1/unit_1/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] PadLayer   resnet_v1_100/block1/unit_1/bottleneck_v1/padding_conv2: paddings:[[0, 0], [1, 1], [1, 1], [0, 0]] mode:CONSTANT
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv2: shape:[3, 3, 64, 64] strides:[1, 2, 2, 1] pad:VALID act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block1/unit_1/bottleneck_v1/combine_layer: size:(?, 56, 56, 64) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv1: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block1/unit_2/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv2: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block1/unit_2/bottleneck_v1/combine_layer: size:(?, 56, 56, 64) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv1: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block1/unit_3/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv2: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block1/unit_3/bottleneck_v1/combine_layer: size:(?, 56, 56, 64) fn:add
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_1/bottleneck_v1/shortcut_conv: shape:[1, 1, 64, 128] strides:[1, 2, 2, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_1/bottleneck_v1/shortcut_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv1: shape:[3, 3, 64, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_1/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] PadLayer   resnet_v1_100/block2/unit_1/bottleneck_v1/padding_conv2: paddings:[[0, 0], [1, 1], [1, 1], [0, 0]] mode:CONSTANT
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 2, 2, 1] pad:VALID act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_1/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_2/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_2/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_3/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_3/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_4/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_4/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_5/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_5/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_6/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_6/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_7/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_7/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_8/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_8/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_9/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_9/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_10/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_10/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_11/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_11/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_12/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_12/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_13/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_13/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_1/bottleneck_v1/shortcut_conv: shape:[1, 1, 128, 256] strides:[1, 2, 2, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_1/bottleneck_v1/shortcut_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv1: shape:[3, 3, 128, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_1/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] PadLayer   resnet_v1_100/block3/unit_1/bottleneck_v1/padding_conv2: paddings:[[0, 0], [1, 1], [1, 1], [0, 0]] mode:CONSTANT
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 2, 2, 1] pad:VALID act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_1/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_2/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_2/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_3/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_3/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_4/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_4/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_5/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_5/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_6/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_6/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_7/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_7/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_8/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_8/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_9/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_9/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_10/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_10/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_11/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_11/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_12/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_12/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_13/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_13/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_14/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_14/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_15/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_15/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_16/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_16/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_17/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_17/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_18/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_18/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_19/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_19/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_20/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_20/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_21/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_21/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_22/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_22/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_23/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_23/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_24/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_24/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_25/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_25/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_26/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_26/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_27/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_27/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_28/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_28/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_29/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_29/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_30/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_30/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_1/bottleneck_v1/shortcut_conv: shape:[1, 1, 256, 512] strides:[1, 2, 2, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_1/bottleneck_v1/shortcut_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv1: shape:[3, 3, 256, 512] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block4/unit_1/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] PadLayer   resnet_v1_100/block4/unit_1/bottleneck_v1/padding_conv2: paddings:[[0, 0], [1, 1], [1, 1], [0, 0]] mode:CONSTANT
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv2: shape:[3, 3, 512, 512] strides:[1, 2, 2, 1] pad:VALID act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block4/unit_1/bottleneck_v1/combine_layer: size:(?, 7, 7, 512) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv1: shape:[3, 3, 512, 512] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block4/unit_2/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv2: shape:[3, 3, 512, 512] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block4/unit_2/bottleneck_v1/combine_layer: size:(?, 7, 7, 512) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv1: shape:[3, 3, 512, 512] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block4/unit_3/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv2: shape:[3, 3, 512, 512] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block4/unit_3/bottleneck_v1/combine_layer: size:(?, 7, 7, 512) fn:add
+  [TL] BatchNormLayer resnet_v1_100/E_BN1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ReshapeLayer resnet_v1_100/E_Reshapelayer: (?, 25088)
+  [TL] DenseLayer  resnet_v1_100/E_DenseLayer: 512 identityWARNING:tensorflow:From /usr/local/python3.7.5/lib/python3.7/site-packages/tensorlayer/layers.py:169: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.
+
+
+  [TL] BatchNormLayer resnet_v1_100/E_BN2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [*] geting variables with W_conv2d
+  got   0: resnet_v1_100/conv1/W_conv2d:0   (3, 3, 3, 64)
+  got   1: resnet_v1_100/block1/unit_1/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 64, 64)
+  got   2: resnet_v1_100/block1/unit_1/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 64, 64)
+  got   3: resnet_v1_100/block1/unit_2/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 64, 64)
+  got   4: resnet_v1_100/block1/unit_2/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 64, 64)
+  got   5: resnet_v1_100/block1/unit_3/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 64, 64)
+  got   6: resnet_v1_100/block1/unit_3/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 64, 64)
+  got   7: resnet_v1_100/block2/unit_1/bottleneck_v1/shortcut_conv/W_conv2d:0   (1, 1, 64, 128)
+  got   8: resnet_v1_100/block2/unit_1/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 64, 128)
+  got   9: resnet_v1_100/block2/unit_1/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  10: resnet_v1_100/block2/unit_2/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  11: resnet_v1_100/block2/unit_2/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  12: resnet_v1_100/block2/unit_3/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  13: resnet_v1_100/block2/unit_3/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  14: resnet_v1_100/block2/unit_4/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  15: resnet_v1_100/block2/unit_4/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  16: resnet_v1_100/block2/unit_5/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  17: resnet_v1_100/block2/unit_5/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  18: resnet_v1_100/block2/unit_6/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  19: resnet_v1_100/block2/unit_6/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  20: resnet_v1_100/block2/unit_7/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  21: resnet_v1_100/block2/unit_7/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  22: resnet_v1_100/block2/unit_8/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  23: resnet_v1_100/block2/unit_8/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  24: resnet_v1_100/block2/unit_9/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  25: resnet_v1_100/block2/unit_9/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  26: resnet_v1_100/block2/unit_10/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  27: resnet_v1_100/block2/unit_10/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  28: resnet_v1_100/block2/unit_11/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  29: resnet_v1_100/block2/unit_11/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  30: resnet_v1_100/block2/unit_12/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  31: resnet_v1_100/block2/unit_12/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  32: resnet_v1_100/block2/unit_13/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  33: resnet_v1_100/block2/unit_13/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  34: resnet_v1_100/block3/unit_1/bottleneck_v1/shortcut_conv/W_conv2d:0   (1, 1, 128, 256)
+  got  35: resnet_v1_100/block3/unit_1/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 256)
+  got  36: resnet_v1_100/block3/unit_1/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  37: resnet_v1_100/block3/unit_2/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  38: resnet_v1_100/block3/unit_2/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  39: resnet_v1_100/block3/unit_3/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  40: resnet_v1_100/block3/unit_3/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  41: resnet_v1_100/block3/unit_4/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  42: resnet_v1_100/block3/unit_4/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  43: resnet_v1_100/block3/unit_5/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  44: resnet_v1_100/block3/unit_5/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  45: resnet_v1_100/block3/unit_6/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  46: resnet_v1_100/block3/unit_6/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  47: resnet_v1_100/block3/unit_7/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  48: resnet_v1_100/block3/unit_7/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  49: resnet_v1_100/block3/unit_8/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  50: resnet_v1_100/block3/unit_8/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  51: resnet_v1_100/block3/unit_9/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  52: resnet_v1_100/block3/unit_9/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  53: resnet_v1_100/block3/unit_10/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  54: resnet_v1_100/block3/unit_10/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  55: resnet_v1_100/block3/unit_11/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  56: resnet_v1_100/block3/unit_11/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  57: resnet_v1_100/block3/unit_12/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  58: resnet_v1_100/block3/unit_12/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  59: resnet_v1_100/block3/unit_13/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  60: resnet_v1_100/block3/unit_13/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  61: resnet_v1_100/block3/unit_14/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  62: resnet_v1_100/block3/unit_14/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  63: resnet_v1_100/block3/unit_15/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  64: resnet_v1_100/block3/unit_15/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  65: resnet_v1_100/block3/unit_16/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  66: resnet_v1_100/block3/unit_16/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  67: resnet_v1_100/block3/unit_17/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  68: resnet_v1_100/block3/unit_17/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  69: resnet_v1_100/block3/unit_18/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  70: resnet_v1_100/block3/unit_18/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  71: resnet_v1_100/block3/unit_19/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  72: resnet_v1_100/block3/unit_19/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  73: resnet_v1_100/block3/unit_20/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  74: resnet_v1_100/block3/unit_20/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  75: resnet_v1_100/block3/unit_21/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  76: resnet_v1_100/block3/unit_21/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  77: resnet_v1_100/block3/unit_22/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  78: resnet_v1_100/block3/unit_22/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  79: resnet_v1_100/block3/unit_23/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  80: resnet_v1_100/block3/unit_23/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  81: resnet_v1_100/block3/unit_24/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  82: resnet_v1_100/block3/unit_24/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  83: resnet_v1_100/block3/unit_25/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  84: resnet_v1_100/block3/unit_25/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  85: resnet_v1_100/block3/unit_26/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  86: resnet_v1_100/block3/unit_26/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  87: resnet_v1_100/block3/unit_27/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  88: resnet_v1_100/block3/unit_27/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  89: resnet_v1_100/block3/unit_28/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  90: resnet_v1_100/block3/unit_28/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  91: resnet_v1_100/block3/unit_29/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  92: resnet_v1_100/block3/unit_29/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  93: resnet_v1_100/block3/unit_30/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  94: resnet_v1_100/block3/unit_30/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  95: resnet_v1_100/block4/unit_1/bottleneck_v1/shortcut_conv/W_conv2d:0   (1, 1, 256, 512)
+  got  96: resnet_v1_100/block4/unit_1/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 512)
+  got  97: resnet_v1_100/block4/unit_1/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 512, 512)
+  got  98: resnet_v1_100/block4/unit_2/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 512, 512)
+  got  99: resnet_v1_100/block4/unit_2/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 512, 512)
+  got 100: resnet_v1_100/block4/unit_3/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 512, 512)
+  got 101: resnet_v1_100/block4/unit_3/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 512, 512)
+  [*] geting variables with resnet_v1_50/E_DenseLayer/W
+  [*] geting variables with embedding_weights
+  got   0: arcface_loss/embedding_weights:0   (512, 85164)
+  [*] geting variables with gamma
+  got   0: resnet_v1_100/bn0/gamma:0   (64,)
+  got   1: resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn1/gamma:0   (64,)
+  got   2: resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn2/gamma:0   (64,)
+  got   3: resnet_v1_100/block1/unit_1/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (64,)
+  got   4: resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn1/gamma:0   (64,)
+  got   5: resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn2/gamma:0   (64,)
+  got   6: resnet_v1_100/block1/unit_2/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (64,)
+  got   7: resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn1/gamma:0   (64,)
+  got   8: resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn2/gamma:0   (64,)
+  got   9: resnet_v1_100/block1/unit_3/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (64,)
+  got  10: resnet_v1_100/block2/unit_1/bottleneck_v1/shortcut_bn/BatchNorm/gamma:0   (128,)
+  got  11: resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn1/gamma:0   (64,)
+  got  12: resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  13: resnet_v1_100/block2/unit_1/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  14: resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  15: resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  16: resnet_v1_100/block2/unit_2/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  17: resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  18: resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  19: resnet_v1_100/block2/unit_3/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  20: resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  21: resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  22: resnet_v1_100/block2/unit_4/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  23: resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  24: resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  25: resnet_v1_100/block2/unit_5/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  26: resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  27: resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  28: resnet_v1_100/block2/unit_6/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  29: resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  30: resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  31: resnet_v1_100/block2/unit_7/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  32: resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  33: resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  34: resnet_v1_100/block2/unit_8/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  35: resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  36: resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  37: resnet_v1_100/block2/unit_9/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  38: resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  39: resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  40: resnet_v1_100/block2/unit_10/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  41: resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  42: resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  43: resnet_v1_100/block2/unit_11/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  44: resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  45: resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  46: resnet_v1_100/block2/unit_12/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  47: resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  48: resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  49: resnet_v1_100/block2/unit_13/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  50: resnet_v1_100/block3/unit_1/bottleneck_v1/shortcut_bn/BatchNorm/gamma:0   (256,)
+  got  51: resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  52: resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  53: resnet_v1_100/block3/unit_1/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  54: resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  55: resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  56: resnet_v1_100/block3/unit_2/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  57: resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  58: resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  59: resnet_v1_100/block3/unit_3/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  60: resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  61: resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  62: resnet_v1_100/block3/unit_4/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  63: resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  64: resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  65: resnet_v1_100/block3/unit_5/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  66: resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  67: resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  68: resnet_v1_100/block3/unit_6/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  69: resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  70: resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  71: resnet_v1_100/block3/unit_7/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  72: resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  73: resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  74: resnet_v1_100/block3/unit_8/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  75: resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  76: resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  77: resnet_v1_100/block3/unit_9/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  78: resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  79: resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  80: resnet_v1_100/block3/unit_10/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  81: resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  82: resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  83: resnet_v1_100/block3/unit_11/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  84: resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  85: resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  86: resnet_v1_100/block3/unit_12/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  87: resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  88: resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  89: resnet_v1_100/block3/unit_13/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  90: resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  91: resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  92: resnet_v1_100/block3/unit_14/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  93: resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  94: resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  95: resnet_v1_100/block3/unit_15/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  96: resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  97: resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  98: resnet_v1_100/block3/unit_16/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  99: resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 100: resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 101: resnet_v1_100/block3/unit_17/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 102: resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 103: resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 104: resnet_v1_100/block3/unit_18/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 105: resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 106: resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 107: resnet_v1_100/block3/unit_19/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 108: resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 109: resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 110: resnet_v1_100/block3/unit_20/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 111: resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 112: resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 113: resnet_v1_100/block3/unit_21/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 114: resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 115: resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 116: resnet_v1_100/block3/unit_22/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 117: resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 118: resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 119: resnet_v1_100/block3/unit_23/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 120: resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 121: resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 122: resnet_v1_100/block3/unit_24/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 123: resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 124: resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 125: resnet_v1_100/block3/unit_25/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 126: resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 127: resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 128: resnet_v1_100/block3/unit_26/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 129: resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 130: resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 131: resnet_v1_100/block3/unit_27/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 132: resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 133: resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 134: resnet_v1_100/block3/unit_28/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 135: resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 136: resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 137: resnet_v1_100/block3/unit_29/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 138: resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 139: resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 140: resnet_v1_100/block3/unit_30/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 141: resnet_v1_100/block4/unit_1/bottleneck_v1/shortcut_bn/BatchNorm/gamma:0   (512,)
+  got 142: resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 143: resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn2/gamma:0   (512,)
+  got 144: resnet_v1_100/block4/unit_1/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (512,)
+  got 145: resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn1/gamma:0   (512,)
+  got 146: resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn2/gamma:0   (512,)
+  got 147: resnet_v1_100/block4/unit_2/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (512,)
+  got 148: resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn1/gamma:0   (512,)
+  got 149: resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn2/gamma:0   (512,)
+  got 150: resnet_v1_100/block4/unit_3/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (512,)
+  got 151: resnet_v1_100/E_BN1/gamma:0   (512,)
+  [*] geting variables with alphas
+  got   0: resnet_v1_100/prelu0/alphas:0   (64,)
+  got   1: resnet_v1_100/block1/unit_1/bottleneck_v1/prelu_layer/alphas:0   (64,)
+  got   2: resnet_v1_100/block1/unit_2/bottleneck_v1/prelu_layer/alphas:0   (64,)
+  got   3: resnet_v1_100/block1/unit_3/bottleneck_v1/prelu_layer/alphas:0   (64,)
+  got   4: resnet_v1_100/block2/unit_1/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got   5: resnet_v1_100/block2/unit_2/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got   6: resnet_v1_100/block2/unit_3/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got   7: resnet_v1_100/block2/unit_4/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got   8: resnet_v1_100/block2/unit_5/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got   9: resnet_v1_100/block2/unit_6/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got  10: resnet_v1_100/block2/unit_7/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got  11: resnet_v1_100/block2/unit_8/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got  12: resnet_v1_100/block2/unit_9/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got  13: resnet_v1_100/block2/unit_10/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got  14: resnet_v1_100/block2/unit_11/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got  15: resnet_v1_100/block2/unit_12/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got  16: resnet_v1_100/block2/unit_13/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got  17: resnet_v1_100/block3/unit_1/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  18: resnet_v1_100/block3/unit_2/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  19: resnet_v1_100/block3/unit_3/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  20: resnet_v1_100/block3/unit_4/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  21: resnet_v1_100/block3/unit_5/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  22: resnet_v1_100/block3/unit_6/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  23: resnet_v1_100/block3/unit_7/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  24: resnet_v1_100/block3/unit_8/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  25: resnet_v1_100/block3/unit_9/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  26: resnet_v1_100/block3/unit_10/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  27: resnet_v1_100/block3/unit_11/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  28: resnet_v1_100/block3/unit_12/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  29: resnet_v1_100/block3/unit_13/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  30: resnet_v1_100/block3/unit_14/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  31: resnet_v1_100/block3/unit_15/bottleneck_v1/prelu_layer/alphas:0   (256,)WARNING:tensorflow:From train_nets.py:118: The name tf.train.piecewise_constant is deprecated. Please use tf.compat.v1.train.piecewise_constant instead.
+
+WARNING:tensorflow:From train_nets.py:120: The name tf.train.MomentumOptimizer is deprecated. Please use tf.compat.v1.train.MomentumOptimizer instead.
+
+WARNING:tensorflow:From train_nets.py:123: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead.
+
+WARNING:tensorflow:From train_nets.py:131: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.
+
+WARNING:tensorflow:From train_nets.py:134: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.
+
+2022-12-01 00:01:04.831203: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 AVX512F FMA
+2022-12-01 00:01:04.867231: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2600000000 Hz
+2022-12-01 00:01:04.873637: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55e0e7866050 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
+2022-12-01 00:01:04.873693: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
+WARNING:tensorflow:From train_nets.py:136: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.
+
+WARNING:tensorflow:From train_nets.py:141: The name tf.summary.histogram is deprecated. Please use tf.compat.v1.summary.histogram instead.
+
+WARNING:tensorflow:From train_nets.py:146: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.
+
+WARNING:tensorflow:From train_nets.py:151: The name tf.summary.merge is deprecated. Please use tf.compat.v1.summary.merge instead.
+
+WARNING:tensorflow:From train_nets.py:153: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.
+
+WARNING:tensorflow:From train_nets.py:155: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead.
+
+2022-12-01 00:01:15.663888: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:15.670381: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:15.670533: W tf_adapter/util/npu_attrs.cc:1077] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:15.670549: W tf_adapter/util/npu_attrs.cc:1207] The op_select_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:01:15.670559: W tf_adapter/util/npu_attrs.cc:1211] The optypelist_for_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:01:16.884690: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:16.884772: W tf_adapter/util/npu_attrs.cc:1077] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:16.884784: W tf_adapter/util/npu_attrs.cc:1207] The op_select_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:01:16.884789: W tf_adapter/util/npu_attrs.cc:1211] The optypelist_for_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:01:16.893457: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:16.893522: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:16.893564: W tf_adapter/util/npu_attrs.cc:1077] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:16.893574: W tf_adapter/util/npu_attrs.cc:1207] The op_select_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:01:16.893578: W tf_adapter/util/npu_attrs.cc:1211] The optypelist_for_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:01:16.893672: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:16.893708: W tf_adapter/util/npu_attrs.cc:1077] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:16.893714: W tf_adapter/util/npu_attrs.cc:1207] The op_select_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:01:16.893719: W tf_adapter/util/npu_attrs.cc:1211] The optypelist_for_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:01:16.893770: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+
+  got  32: resnet_v1_100/block3/unit_16/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  33: resnet_v1_100/block3/unit_17/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  34: resnet_v1_100/block3/unit_18/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  35: resnet_v1_100/block3/unit_19/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  36: resnet_v1_100/block3/unit_20/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  37: resnet_v1_100/block3/unit_21/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  38: resnet_v1_100/block3/unit_22/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  39: resnet_v1_100/block3/unit_23/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  40: resnet_v1_100/block3/unit_24/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  41: resnet_v1_100/block3/unit_25/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  42: resnet_v1_100/block3/unit_26/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  43: resnet_v1_100/block3/unit_27/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  44: resnet_v1_100/block3/unit_28/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  45: resnet_v1_100/block3/unit_29/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  46: resnet_v1_100/block3/unit_30/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  47: resnet_v1_100/block4/unit_1/bottleneck_v1/prelu_layer/alphas:0   (512,)
+  got  48: resnet_v1_100/block4/unit_2/bottleneck_v1/prelu_layer/alphas:0   (512,)
+  got  49: resnet_v1_100/block4/unit_3/bottleneck_v1/prelu_layer/alphas:0   (512,)
+[640000, 960000, 1280000]
+2022-12-01 00:01:35.401739: I tf_adapter/kernels/geop_npu.cc:854] The model has been compiled on the Ascend AI processor, current graph id is: 1
+2022-12-01 00:01:55.383488: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:55.383630: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:55.383700: W tf_adapter/util/npu_attrs.cc:1077] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:55.383711: W tf_adapter/util/npu_attrs.cc:1207] The op_select_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:01:55.383717: W tf_adapter/util/npu_attrs.cc:1211] The optypelist_for_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:01:55.384121: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:55.384190: W tf_adapter/util/npu_attrs.cc:1077] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:55.384200: W tf_adapter/util/npu_attrs.cc:1207] The op_select_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:01:55.384208: W tf_adapter/util/npu_attrs.cc:1211] The optypelist_for_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:01:55.395218: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:55.395278: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:55.395330: W tf_adapter/util/npu_attrs.cc:1077] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:55.395342: W tf_adapter/util/npu_attrs.cc:1207] The op_select_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:01:55.395349: W tf_adapter/util/npu_attrs.cc:1211] The optypelist_for_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:01:55.395447: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:55.395494: W tf_adapter/util/npu_attrs.cc:1077] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:55.395500: W tf_adapter/util/npu_attrs.cc:1207] The op_select_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:01:55.395505: W tf_adapter/util/npu_attrs.cc:1211] The optypelist_for_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:01:55.395569: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:56.258483: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:56.258624: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:56.258676: W tf_adapter/util/npu_attrs.cc:1077] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:56.258685: W tf_adapter/util/npu_attrs.cc:1207] The op_select_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:01:56.258690: W tf_adapter/util/npu_attrs.cc:1211] The optypelist_for_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:01:56.259146: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:56.259204: W tf_adapter/util/npu_attrs.cc:1077] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:56.259211: W tf_adapter/util/npu_attrs.cc:1207] The op_select_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:01:56.259215: W tf_adapter/util/npu_attrs.cc:1211] The optypelist_for_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:01:56.262415: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:56.262459: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:56.262508: W tf_adapter/util/npu_attrs.cc:1077] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:56.262515: W tf_adapter/util/npu_attrs.cc:1207] The op_select_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:01:56.262519: W tf_adapter/util/npu_attrs.cc:1211] The optypelist_for_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:01:56.262606: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:56.262650: W tf_adapter/util/npu_attrs.cc:1077] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:01:56.262656: W tf_adapter/util/npu_attrs.cc:1207] The op_select_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:01:56.262659: W tf_adapter/util/npu_attrs.cc:1211] The optypelist_for_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:01:56.262698: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:02:06.263893: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:145] Filling up shuffle buffer (this may take a while): 7480 of 10000
+2022-12-01 00:02:09.775751: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:195] Shuffle buffer filled.
+2022-12-01 00:02:23.358866: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:02:23.375295: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:02:23.375401: W tf_adapter/util/npu_attrs.cc:1077] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:02:23.375413: W tf_adapter/util/npu_attrs.cc:1207] The op_select_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:02:23.375427: W tf_adapter/util/npu_attrs.cc:1211] The optypelist_for_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:02:29.970795: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:02:29.970885: W tf_adapter/util/npu_attrs.cc:1077] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:02:29.970898: W tf_adapter/util/npu_attrs.cc:1207] The op_select_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:02:29.970903: W tf_adapter/util/npu_attrs.cc:1211] The optypelist_for_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:02:29.981158: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:02:29.981215: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:02:29.981259: W tf_adapter/util/npu_attrs.cc:1077] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:02:29.981268: W tf_adapter/util/npu_attrs.cc:1207] The op_select_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:02:29.981273: W tf_adapter/util/npu_attrs.cc:1211] The optypelist_for_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:02:29.981387: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:02:29.981426: W tf_adapter/util/npu_attrs.cc:1077] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:02:29.981432: W tf_adapter/util/npu_attrs.cc:1207] The op_select_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:02:29.981437: W tf_adapter/util/npu_attrs.cc:1211] The optypelist_for_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:02:29.981491: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:02:37.058312: I tf_adapter/kernels/geop_npu.cc:854] The model has been compiled on the Ascend AI processor, current graph id is: 11
+2022-12-01 00:05:50.449880: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:05:50.477049: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:05:50.477196: W tf_adapter/util/npu_attrs.cc:1077] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:05:50.477217: W tf_adapter/util/npu_attrs.cc:1207] The op_select_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:05:50.477226: W tf_adapter/util/npu_attrs.cc:1211] The optypelist_for_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:05:56.688123: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:05:56.688284: W tf_adapter/util/npu_attrs.cc:1077] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:05:56.688307: W tf_adapter/util/npu_attrs.cc:1207] The op_select_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:05:56.688318: W tf_adapter/util/npu_attrs.cc:1211] The optypelist_for_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:05:57.395126: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:05:57.396227: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:05:57.396311: W tf_adapter/util/npu_attrs.cc:1077] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:05:57.396326: W tf_adapter/util/npu_attrs.cc:1207] The op_select_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:05:57.396335: W tf_adapter/util/npu_attrs.cc:1211] The optypelist_for_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:05:57.397619: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:05:57.397696: W tf_adapter/util/npu_attrs.cc:1077] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:05:57.397708: W tf_adapter/util/npu_attrs.cc:1207] The op_select_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:05:57.397716: W tf_adapter/util/npu_attrs.cc:1211] The optypelist_for_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:05:57.398994: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:06:06.431427: I tf_adapter/kernels/geop_npu.cc:854] The model has been compiled on the Ascend AI processor, current graph id is: 21
+epoch 0, total_step 20, total loss is 58.00 , inference loss is 43.53, weight deacy loss is 14.47, training accuracy is 0.000000, time 229.218 samples/sec
+epoch 0, total_step 40, total loss is 59.88 , inference loss is 45.40, weight deacy loss is 14.48, training accuracy is 0.000000, time 225.196 samples/sec
+epoch 0, total_step 60, total loss is 57.18 , inference loss is 42.70, weight deacy loss is 14.48, training accuracy is 0.000000, time 231.740 samples/sec
+epoch 0, total_step 80, total loss is 53.08 , inference loss is 38.61, weight deacy loss is 14.48, training accuracy is 0.000000, time 215.144 samples/sec
+epoch 0, total_step 100, total loss is 52.97 , inference loss is 38.50, weight deacy loss is 14.48, training accuracy is 0.000000, time 230.647 samples/sec
+epoch 0, total_step 120, total loss is 53.69 , inference loss is 39.22, weight deacy loss is 14.48, training accuracy is 0.000000, time 205.789 samples/sec
+epoch 0, total_step 140, total loss is 50.71 , inference loss is 36.23, weight deacy loss is 14.47, training accuracy is 0.000000, time 209.200 samples/sec
+epoch 0, total_step 160, total loss is 52.98 , inference loss is 38.51, weight deacy loss is 14.47, training accuracy is 0.000000, time 211.722 samples/sec
+epoch 0, total_step 180, total loss is 51.75 , inference loss is 37.28, weight deacy loss is 14.47, training accuracy is 0.000000, time 230.534 samples/sec
+epoch 0, total_step 200, total loss is 54.64 , inference loss is 40.17, weight deacy loss is 14.47, training accuracy is 0.000000, time 217.277 samples/sec
+epoch 0, total_step 220, total loss is 51.94 , inference loss is 37.47, weight deacy loss is 14.47, training accuracy is 0.000000, time 224.246 samples/sec
+epoch 0, total_step 240, total loss is 50.74 , inference loss is 36.27, weight deacy loss is 14.47, training accuracy is 0.000000, time 225.463 samples/sec
+epoch 0, total_step 260, total loss is 51.68 , inference loss is 37.21, weight deacy loss is 14.47, training accuracy is 0.000000, time 307.132 samples/sec
+epoch 0, total_step 280, total loss is 49.30 , inference loss is 34.83, weight deacy loss is 14.46, training accuracy is 0.000000, time 307.546 samples/sec
+epoch 0, total_step 300, total loss is 50.46 , inference loss is 36.00, weight deacy loss is 14.46, training accuracy is 0.031250, time 11.770 samples/sec
+epoch 0, total_step 320, total loss is 47.11 , inference loss is 32.65, weight deacy loss is 14.46, training accuracy is 0.031250, time 231.697 samples/sec
+epoch 0, total_step 340, total loss is 50.18 , inference loss is 35.72, weight deacy loss is 14.46, training accuracy is 0.031250, time 234.566 samples/sec
+epoch 0, total_step 360, total loss is 45.88 , inference loss is 31.43, weight deacy loss is 14.46, training accuracy is 0.031250, time 216.966 samples/sec
+epoch 0, total_step 380, total loss is 49.33 , inference loss is 34.88, weight deacy loss is 14.46, training accuracy is 0.000000, time 211.972 samples/sec
+epoch 0, total_step 400, total loss is 49.87 , inference loss is 35.41, weight deacy loss is 14.46, training accuracy is 0.000000, time 230.757 samples/sec
+epoch 0, total_step 420, total loss is 48.85 , inference loss is 34.40, weight deacy loss is 14.45, training accuracy is 0.000000, time 226.519 samples/sec
+epoch 0, total_step 440, total loss is 50.17 , inference loss is 35.71, weight deacy loss is 14.45, training accuracy is 0.000000, time 227.768 samples/sec
+epoch 0, total_step 460, total loss is 45.53 , inference loss is 31.08, weight deacy loss is 14.45, training accuracy is 0.031250, time 308.002 samples/sec
+epoch 0, total_step 480, total loss is 48.24 , inference loss is 33.79, weight deacy loss is 14.45, training accuracy is 0.000000, time 230.973 samples/sec
+epoch 0, total_step 500, total loss is 48.08 , inference loss is 33.63, weight deacy loss is 14.45, training accuracy is 0.000000, time 225.139 samples/sec
+epoch 0, total_step 520, total loss is 49.79 , inference loss is 35.34, weight deacy loss is 14.45, training accuracy is 0.031250, time 223.400 samples/sec
+epoch 0, total_step 540, total loss is 48.29 , inference loss is 33.85, weight deacy loss is 14.44, training accuracy is 0.000000, time 195.154 samples/sec
+epoch 0, total_step 560, total loss is 41.78 , inference loss is 27.34, weight deacy loss is 14.44, training accuracy is 0.062500, time 232.673 samples/sec
+epoch 0, total_step 580, total loss is 50.05 , inference loss is 35.61, weight deacy loss is 14.44, training accuracy is 0.000000, time 238.076 samples/sec
+epoch 0, total_step 600, total loss is 45.07 , inference loss is 30.63, weight deacy loss is 14.44, training accuracy is 0.000000, time 11.912 samples/sec
+epoch 0, total_step 620, total loss is 47.14 , inference loss is 32.70, weight deacy loss is 14.44, training accuracy is 0.000000, time 219.179 samples/sec
+epoch 0, total_step 640, total loss is 47.14 , inference loss is 32.70, weight deacy loss is 14.44, training accuracy is 0.031250, time 226.400 samples/sec
+epoch 0, total_step 660, total loss is 46.03 , inference loss is 31.60, weight deacy loss is 14.43, training accuracy is 0.062500, time 214.944 samples/sec
+epoch 0, total_step 680, total loss is 46.66 , inference loss is 32.23, weight deacy loss is 14.43, training accuracy is 0.031250, time 214.748 samples/sec
+epoch 0, total_step 700, total loss is 42.60 , inference loss is 28.17, weight deacy loss is 14.43, training accuracy is 0.000000, time 232.199 samples/sec
+epoch 0, total_step 720, total loss is 46.23 , inference loss is 31.80, weight deacy loss is 14.43, training accuracy is 0.000000, time 307.828 samples/sec
+epoch 0, total_step 740, total loss is 49.02 , inference loss is 34.59, weight deacy loss is 14.43, training accuracy is 0.000000, time 217.507 samples/sec
+epoch 0, total_step 760, total loss is 47.08 , inference loss is 32.65, weight deacy loss is 14.43, training accuracy is 0.000000, time 208.583 samples/sec
+epoch 0, total_step 780, total loss is 49.48 , inference loss is 35.05, weight deacy loss is 14.42, training accuracy is 0.000000, time 216.031 samples/sec
+epoch 0, total_step 800, total loss is 44.07 , inference loss is 29.65, weight deacy loss is 14.42, training accuracy is 0.000000, time 219.188 samples/sec
+epoch 0, total_step 820, total loss is 44.64 , inference loss is 30.22, weight deacy loss is 14.42, training accuracy is 0.000000, time 223.743 samples/sec
+epoch 0, total_step 840, total loss is 48.95 , inference loss is 34.53, weight deacy loss is 14.42, training accuracy is 0.000000, time 216.013 samples/sec
+epoch 0, total_step 860, total loss is 49.86 , inference loss is 35.44, weight deacy loss is 14.42, training accuracy is 0.000000, time 217.496 samples/sec
+epoch 0, total_step 880, total loss is 48.14 , inference loss is 33.72, weight deacy loss is 14.42, training accuracy is 0.000000, time 229.196 samples/sec
+epoch 0, total_step 900, total loss is 48.90 , inference loss is 34.49, weight deacy loss is 14.41, training accuracy is 0.000000, time 12.869 samples/sec
+epoch 0, total_step 920, total loss is 45.39 , inference loss is 30.98, weight deacy loss is 14.41, training accuracy is 0.031250, time 217.772 samples/sec
+epoch 0, total_step 940, total loss is 46.97 , inference loss is 32.56, weight deacy loss is 14.41, training accuracy is 0.000000, time 221.886 samples/sec
+epoch 0, total_step 960, total loss is 48.81 , inference loss is 34.40, weight deacy loss is 14.41, training accuracy is 0.000000, time 231.360 samples/sec
+epoch 0, total_step 980, total loss is 47.86 , inference loss is 33.45, weight deacy loss is 14.41, training accuracy is 0.000000, time 307.514 samples/sec
+epoch 0, total_step 1000, total loss is 48.09 , inference loss is 33.68, weight deacy loss is 14.41, training accuracy is 0.000000, time 227.155 samples/sec
+epoch 0, total_step 1020, total loss is 44.16 , inference loss is 29.76, weight deacy loss is 14.40, training accuracy is 0.031250, time 226.580 samples/sec
+epoch 0, total_step 1040, total loss is 46.34 , inference loss is 31.94, weight deacy loss is 14.40, training accuracy is 0.031250, time 231.529 samples/sec
+epoch 0, total_step 1060, total loss is 49.14 , inference loss is 34.74, weight deacy loss is 14.40, training accuracy is 0.000000, time 188.304 samples/sec2022-12-01 00:14:51.865527: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:14:51.867125: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:14:51.867195: W tf_adapter/util/npu_attrs.cc:1077] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:14:51.867205: W tf_adapter/util/npu_attrs.cc:1207] The op_select_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:14:51.867210: W tf_adapter/util/npu_attrs.cc:1211] The optypelist_for_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:14:52.089229: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:14:52.089308: W tf_adapter/util/npu_attrs.cc:1077] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:14:52.089318: W tf_adapter/util/npu_attrs.cc:1207] The op_select_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:14:52.089323: W tf_adapter/util/npu_attrs.cc:1211] The optypelist_for_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:14:52.118131: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:14:52.118199: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:14:52.118241: W tf_adapter/util/npu_attrs.cc:1077] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:14:52.118249: W tf_adapter/util/npu_attrs.cc:1207] The op_select_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:14:52.118264: W tf_adapter/util/npu_attrs.cc:1211] The optypelist_for_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:14:52.118360: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:14:52.118395: W tf_adapter/util/npu_attrs.cc:1077] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:14:52.118401: W tf_adapter/util/npu_attrs.cc:1207] The op_select_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:14:52.118405: W tf_adapter/util/npu_attrs.cc:1211] The optypelist_for_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:14:52.118452: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:14:52.951850: I tf_adapter/kernels/geop_npu.cc:854] The model has been compiled on the Ascend AI processor, current graph id is: 31
+2022-12-01 00:16:08.326466: I tf_adapter/kernels/geop_npu.cc:854] The model has been compiled on the Ascend AI processor, current graph id is: 11
+
+epoch 0, total_step 1080, total loss is 46.10 , inference loss is 31.70, weight deacy loss is 14.40, training accuracy is 0.000000, time 228.987 samples/sec
+epoch 0, total_step 1100, total loss is 49.23 , inference loss is 34.83, weight deacy loss is 14.40, training accuracy is 0.031250, time 230.325 samples/sec
+epoch 0, total_step 1120, total loss is 46.71 , inference loss is 32.32, weight deacy loss is 14.40, training accuracy is 0.000000, time 203.241 samples/sec
+epoch 0, total_step 1140, total loss is 48.76 , inference loss is 34.37, weight deacy loss is 14.39, training accuracy is 0.000000, time 220.196 samples/sec
+epoch 0, total_step 1160, total loss is 45.98 , inference loss is 31.58, weight deacy loss is 14.39, training accuracy is 0.031250, time 231.192 samples/sec
+epoch 0, total_step 1180, total loss is 47.62 , inference loss is 33.23, weight deacy loss is 14.39, training accuracy is 0.000000, time 182.470 samples/sec
+epoch 0, total_step 1200, total loss is 44.98 , inference loss is 30.59, weight deacy loss is 14.39, training accuracy is 0.031250, time 13.806 samples/sec
+epoch 0, total_step 1220, total loss is 46.43 , inference loss is 32.04, weight deacy loss is 14.39, training accuracy is 0.000000, time 206.836 samples/sec
+epoch 0, total_step 1240, total loss is 45.82 , inference loss is 31.43, weight deacy loss is 14.39, training accuracy is 0.000000, time 308.028 samples/sec
+epoch 0, total_step 1260, total loss is 45.33 , inference loss is 30.95, weight deacy loss is 14.38, training accuracy is 0.031250, time 201.451 samples/sec
+epoch 0, total_step 1280, total loss is 50.14 , inference loss is 35.76, weight deacy loss is 14.38, training accuracy is 0.000000, time 241.663 samples/sec
+epoch 0, total_step 1300, total loss is 44.44 , inference loss is 30.06, weight deacy loss is 14.38, training accuracy is 0.000000, time 228.695 samples/sec
+epoch 0, total_step 1320, total loss is 46.58 , inference loss is 32.20, weight deacy loss is 14.38, training accuracy is 0.031250, time 239.792 samples/sec
+epoch 0, total_step 1340, total loss is 43.13 , inference loss is 28.75, weight deacy loss is 14.38, training accuracy is 0.062500, time 213.824 samples/sec
+epoch 0, total_step 1360, total loss is 42.82 , inference loss is 28.45, weight deacy loss is 14.38, training accuracy is 0.031250, time 210.143 samples/sec
+epoch 0, total_step 1380, total loss is 43.23 , inference loss is 28.85, weight deacy loss is 14.37, training accuracy is 0.062500, time 229.791 samples/sec
+epoch 0, total_step 1400, total loss is 44.59 , inference loss is 30.21, weight deacy loss is 14.37, training accuracy is 0.031250, time 230.106 samples/sec
+epoch 0, total_step 1420, total loss is 50.86 , inference loss is 36.49, weight deacy loss is 14.37, training accuracy is 0.000000, time 219.408 samples/sec
+epoch 0, total_step 1440, total loss is 46.86 , inference loss is 32.49, weight deacy loss is 14.37, training accuracy is 0.000000, time 237.366 samples/sec
+epoch 0, total_step 1460, total loss is 47.11 , inference loss is 32.74, weight deacy loss is 14.37, training accuracy is 0.000000, time 227.834 samples/sec
+epoch 0, total_step 1480, total loss is 49.36 , inference loss is 34.99, weight deacy loss is 14.37, training accuracy is 0.000000, time 243.561 samples/sec
+epoch 0, total_step 1500, total loss is 44.19 , inference loss is 29.83, weight deacy loss is 14.36, training accuracy is 0.000000, time 11.230 samples/sec
+epoch 0, total_step 1520, total loss is 43.71 , inference loss is 29.34, weight deacy loss is 14.36, training accuracy is 0.000000, time 224.357 samples/sec
+epoch 0, total_step 1540, total loss is 47.66 , inference loss is 33.30, weight deacy loss is 14.36, training accuracy is 0.000000, time 230.488 samples/sec
+epoch 0, total_step 1560, total loss is 46.10 , inference loss is 31.74, weight deacy loss is 14.36, training accuracy is 0.031250, time 230.514 samples/sec
+epoch 0, total_step 1580, total loss is 47.08 , inference loss is 32.72, weight deacy loss is 14.36, training accuracy is 0.000000, time 234.027 samples/sec
+epoch 0, total_step 1600, total loss is 43.86 , inference loss is 29.51, weight deacy loss is 14.36, training accuracy is 0.000000, time 242.328 samples/sec
+epoch 0, total_step 1620, total loss is 44.44 , inference loss is 30.09, weight deacy loss is 14.35, training accuracy is 0.000000, time 229.108 samples/sec
+epoch 0, total_step 1640, total loss is 43.77 , inference loss is 29.42, weight deacy loss is 14.35, training accuracy is 0.062500, time 222.003 samples/sec
+epoch 0, total_step 1660, total loss is 43.45 , inference loss is 29.10, weight deacy loss is 14.35, training accuracy is 0.031250, time 220.195 samples/sec
+epoch 0, total_step 1680, total loss is 41.13 , inference loss is 26.78, weight deacy loss is 14.35, training accuracy is 0.062500, time 234.592 samples/sec
+epoch 0, total_step 1700, total loss is 48.73 , inference loss is 34.38, weight deacy loss is 14.35, training accuracy is 0.000000, time 218.548 samples/sec
+epoch 0, total_step 1720, total loss is 41.62 , inference loss is 27.28, weight deacy loss is 14.35, training accuracy is 0.031250, time 216.976 samples/sec
+epoch 0, total_step 1740, total loss is 48.37 , inference loss is 34.03, weight deacy loss is 14.35, training accuracy is 0.000000, time 228.147 samples/sec
+epoch 0, total_step 1760, total loss is 46.83 , inference loss is 32.49, weight deacy loss is 14.34, training accuracy is 0.031250, time 219.310 samples/sec
+epoch 0, total_step 1780, total loss is 44.90 , inference loss is 30.56, weight deacy loss is 14.34, training accuracy is 0.031250, time 229.947 samples/sec
+epoch 0, total_step 1800, total loss is 43.59 , inference loss is 29.25, weight deacy loss is 14.34, training accuracy is 0.000000, time 14.094 samples/sec
+epoch 0, total_step 1820, total loss is 39.39 , inference loss is 25.05, weight deacy loss is 14.34, training accuracy is 0.062500, time 222.939 samples/sec
+epoch 0, total_step 1840, total loss is 49.11 , inference loss is 34.77, weight deacy loss is 14.34, training accuracy is 0.000000, time 234.387 samples/sec
+epoch 0, total_step 1860, total loss is 46.39 , inference loss is 32.05, weight deacy loss is 14.34, training accuracy is 0.031250, time 228.654 samples/sec
+epoch 0, total_step 1880, total loss is 45.34 , inference loss is 31.01, weight deacy loss is 14.33, training accuracy is 0.062500, time 223.622 samples/sec
+epoch 0, total_step 1900, total loss is 44.55 , inference loss is 30.22, weight deacy loss is 14.33, training accuracy is 0.062500, time 231.079 samples/sec
+epoch 0, total_step 1920, total loss is 45.71 , inference loss is 31.38, weight deacy loss is 14.33, training accuracy is 0.000000, time 308.354 samples/sec
+epoch 0, total_step 1940, total loss is 42.37 , inference loss is 28.04, weight deacy loss is 14.33, training accuracy is 0.125000, time 307.054 samples/sec
+epoch 0, total_step 1960, total loss is 42.83 , inference loss is 28.51, weight deacy loss is 14.33, training accuracy is 0.031250, time 229.505 samples/sec
+epoch 0, total_step 1980, total loss is 39.57 , inference loss is 25.24, weight deacy loss is 14.33, training accuracy is 0.031250, time 229.005 samples/sec
+testing verification..
+(12000, 512)
+infer time 51.93786499999995
+best_threshold_index 134 0.8825925925925926
+best_threshold_index 129 0.8833333333333333
+best_threshold_index 134 0.8803703703703704
+best_threshold_index 134 0.8818518518518519
+best_threshold_index 134 0.8868518518518519
+best_threshold_index 134 0.8803703703703704
+best_threshold_index 134 0.8792592592592593
+best_threshold_index 132 0.8818518518518519
+best_threshold_index 134 0.8816666666666667
+best_threshold_index 134 0.8809259259259259
+[lfw][2000]XNorm: 20.567539
+[lfw][2000]Accuracy-Flip: 0.87933+-0.01886
+test accuracy is:  0.8793333333333335
+epoch 0, total_step 2000, total loss is 45.31 , inference loss is 30.99, weight deacy loss is 14.32, training accuracy is 0.000000, time 0.189 samples/sec
+epoch 0, total_step 2020, total loss is 45.69 , inference loss is 31.36, weight deacy loss is 14.32, training accuracy is 0.031250, time 225.188 samples/sec
+epoch 0, total_step 2040, total loss is 43.91 , inference loss is 29.59, weight deacy loss is 14.32, training accuracy is 0.062500, time 236.274 samples/sec2022-12-01 00:19:17.618028: I tf_adapter/kernels/geop_npu.cc:854] The model has been compiled on the Ascend AI processor, current graph id is: 21
+
+epoch 0, total_step 2060, total loss is 41.72 , inference loss is 27.40, weight deacy loss is 14.32, training accuracy is 0.062500, time 224.270 samples/sec
+epoch 0, total_step 2080, total loss is 41.25 , inference loss is 26.93, weight deacy loss is 14.32, training accuracy is 0.093750, time 214.745 samples/sec
+epoch 0, total_step 2100, total loss is 43.23 , inference loss is 28.92, weight deacy loss is 14.32, training accuracy is 0.062500, time 11.888 samples/sec
+epoch 0, total_step 2120, total loss is 44.38 , inference loss is 30.06, weight deacy loss is 14.31, training accuracy is 0.031250, time 181.738 samples/sec
+epoch 0, total_step 2140, total loss is 41.66 , inference loss is 27.35, weight deacy loss is 14.31, training accuracy is 0.062500, time 211.725 samples/sec
+epoch 0, total_step 2160, total loss is 45.85 , inference loss is 31.54, weight deacy loss is 14.31, training accuracy is 0.000000, time 209.117 samples/sec
+epoch 0, total_step 2180, total loss is 38.56 , inference loss is 24.25, weight deacy loss is 14.31, training accuracy is 0.125000, time 223.834 samples/sec
+epoch 0, total_step 2200, total loss is 43.72 , inference loss is 29.41, weight deacy loss is 14.31, training accuracy is 0.031250, time 180.056 samples/sec
+epoch 0, total_step 2220, total loss is 39.60 , inference loss is 25.29, weight deacy loss is 14.31, training accuracy is 0.031250, time 215.383 samples/sec
+epoch 0, total_step 2240, total loss is 41.40 , inference loss is 27.10, weight deacy loss is 14.30, training accuracy is 0.093750, time 219.656 samples/sec
+epoch 0, total_step 2260, total loss is 41.64 , inference loss is 27.34, weight deacy loss is 14.30, training accuracy is 0.031250, time 224.060 samples/sec
+epoch 0, total_step 2280, total loss is 40.44 , inference loss is 26.14, weight deacy loss is 14.30, training accuracy is 0.125000, time 292.367 samples/sec
+epoch 0, total_step 2300, total loss is 43.51 , inference loss is 29.21, weight deacy loss is 14.30, training accuracy is 0.062500, time 225.071 samples/sec
+epoch 0, total_step 2320, total loss is 39.30 , inference loss is 25.00, weight deacy loss is 14.30, training accuracy is 0.031250, time 200.273 samples/sec
+epoch 0, total_step 2340, total loss is 41.71 , inference loss is 27.41, weight deacy loss is 14.30, training accuracy is 0.031250, time 204.280 samples/sec
+epoch 0, total_step 2360, total loss is 42.22 , inference loss is 27.92, weight deacy loss is 14.29, training accuracy is 0.093750, time 220.689 samples/sec
+epoch 0, total_step 2380, total loss is 41.50 , inference loss is 27.20, weight deacy loss is 14.29, training accuracy is 0.031250, time 225.219 samples/sec
+epoch 0, total_step 2400, total loss is 38.91 , inference loss is 24.62, weight deacy loss is 14.29, training accuracy is 0.031250, time 12.530 samples/sec
+epoch 0, total_step 2420, total loss is 40.06 , inference loss is 25.77, weight deacy loss is 14.29, training accuracy is 0.031250, time 207.353 samples/sec
+epoch 0, total_step 2440, total loss is 40.25 , inference loss is 25.96, weight deacy loss is 14.29, training accuracy is 0.062500, time 225.263 samples/sec
+epoch 0, total_step 2460, total loss is 41.07 , inference loss is 26.78, weight deacy loss is 14.29, training accuracy is 0.093750, time 225.432 samples/sec
+epoch 0, total_step 2480, total loss is 37.21 , inference loss is 22.92, weight deacy loss is 14.28, training accuracy is 0.062500, time 226.318 samples/sec
+epoch 0, total_step 2500, total loss is 43.92 , inference loss is 29.64, weight deacy loss is 14.28, training accuracy is 0.000000, time 227.330 samples/sec
+epoch 0, total_step 2520, total loss is 40.73 , inference loss is 26.45, weight deacy loss is 14.28, training accuracy is 0.062500, time 231.076 samples/sec
+epoch 0, total_step 2540, total loss is 39.79 , inference loss is 25.51, weight deacy loss is 14.28, training accuracy is 0.062500, time 298.846 samples/sec
+epoch 0, total_step 2560, total loss is 42.07 , inference loss is 27.79, weight deacy loss is 14.28, training accuracy is 0.031250, time 216.565 samples/sec
+epoch 0, total_step 2580, total loss is 41.09 , inference loss is 26.81, weight deacy loss is 14.28, training accuracy is 0.031250, time 219.927 samples/sec
+epoch 0, total_step 2600, total loss is 40.69 , inference loss is 26.41, weight deacy loss is 14.28, training accuracy is 0.000000, time 224.814 samples/sec
+epoch 0, total_step 2620, total loss is 40.73 , inference loss is 26.46, weight deacy loss is 14.27, training accuracy is 0.093750, time 209.706 samples/sec
+epoch 0, total_step 2640, total loss is 38.24 , inference loss is 23.97, weight deacy loss is 14.27, training accuracy is 0.062500, time 216.848 samples/sec
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+epoch 0, total_step 2680, total loss is 40.68 , inference loss is 26.41, weight deacy loss is 14.27, training accuracy is 0.000000, time 217.333 samples/sec
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+testing verification..
+(12000, 512)
+infer time 23.909249999999986
+best_threshold_index 125 0.9033333333333333
+best_threshold_index 125 0.9046296296296297
+best_threshold_index 125 0.9072222222222223
+best_threshold_index 127 0.9042592592592592
+best_threshold_index 125 0.9070370370370371
+best_threshold_index 125 0.902962962962963
+best_threshold_index 125 0.9024074074074074
+best_threshold_index 125 0.9044444444444445
+best_threshold_index 125 0.9044444444444445
+best_threshold_index 125 0.9027777777777778
+[lfw][4000]XNorm: 23.632429
+[lfw][4000]Accuracy-Flip: 0.90350+-0.01427
+test accuracy is:  0.9035
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+epoch 0, total_step 5760, total loss is 42.75 , inference loss is 28.72, weight deacy loss is 14.03, training accuracy is 0.031250, time 212.549 samples/sec
+epoch 0, total_step 5780, total loss is 41.76 , inference loss is 27.73, weight deacy loss is 14.03, training accuracy is 0.000000, time 208.070 samples/sec
+epoch 0, total_step 5800, total loss is 44.89 , inference loss is 30.86, weight deacy loss is 14.03, training accuracy is 0.000000, time 212.568 samples/sec
+epoch 0, total_step 5820, total loss is 46.20 , inference loss is 32.17, weight deacy loss is 14.03, training accuracy is 0.062500, time 212.274 samples/sec
+epoch 0, total_step 5840, total loss is 41.72 , inference loss is 27.69, weight deacy loss is 14.03, training accuracy is 0.000000, time 205.575 samples/sec
+epoch 0, total_step 5860, total loss is 44.11 , inference loss is 30.09, weight deacy loss is 14.03, training accuracy is 0.062500, time 230.538 samples/sec
+epoch 0, total_step 5880, total loss is 36.02 , inference loss is 21.99, weight deacy loss is 14.02, training accuracy is 0.093750, time 203.219 samples/sec
+epoch 0, total_step 5900, total loss is 42.94 , inference loss is 28.91, weight deacy loss is 14.02, training accuracy is 0.000000, time 297.743 samples/sec
+epoch 0, total_step 5920, total loss is 40.43 , inference loss is 26.41, weight deacy loss is 14.02, training accuracy is 0.062500, time 210.597 samples/sec
+epoch 0, total_step 5940, total loss is 42.04 , inference loss is 28.02, weight deacy loss is 14.02, training accuracy is 0.062500, time 220.860 samples/sec
+epoch 0, total_step 5960, total loss is 38.47 , inference loss is 24.45, weight deacy loss is 14.02, training accuracy is 0.093750, time 238.188 samples/sec
+epoch 0, total_step 5980, total loss is 36.82 , inference loss is 22.81, weight deacy loss is 14.02, training accuracy is 0.031250, time 238.908 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.707121000000004
+best_threshold_index 123 0.9301851851851852
+best_threshold_index 123 0.9285185185185185
+best_threshold_index 123 0.9305555555555556
+best_threshold_index 123 0.9312962962962963
+best_threshold_index 123 0.9346296296296296
+best_threshold_index 123 0.9283333333333333
+best_threshold_index 123 0.9296296296296296
+best_threshold_index 123 0.9311111111111111
+best_threshold_index 123 0.9285185185185185
+best_threshold_index 123 0.9288888888888889
+[lfw][6000]XNorm: 22.421414
+[lfw][6000]Accuracy-Flip: 0.93017+-0.01634
+test accuracy is:  0.9301666666666666
+epoch 0, total_step 6000, total loss is 39.18 , inference loss is 25.17, weight deacy loss is 14.02, training accuracy is 0.031250, time 17.873 samples/sec
+epoch 0, total_step 6020, total loss is 38.36 , inference loss is 24.34, weight deacy loss is 14.01, training accuracy is 0.000000, time 217.374 samples/sec
+epoch 0, total_step 6040, total loss is 41.46 , inference loss is 27.45, weight deacy loss is 14.01, training accuracy is 0.062500, time 219.496 samples/sec
+epoch 0, total_step 6060, total loss is 38.54 , inference loss is 24.53, weight deacy loss is 14.01, training accuracy is 0.031250, time 205.114 samples/sec
+epoch 0, total_step 6080, total loss is 45.05 , inference loss is 31.04, weight deacy loss is 14.01, training accuracy is 0.000000, time 212.164 samples/sec
+epoch 0, total_step 6100, total loss is 41.62 , inference loss is 27.61, weight deacy loss is 14.01, training accuracy is 0.125000, time 214.256 samples/sec
+epoch 0, total_step 6120, total loss is 40.57 , inference loss is 26.57, weight deacy loss is 14.01, training accuracy is 0.000000, time 230.122 samples/sec
+epoch 0, total_step 6140, total loss is 39.94 , inference loss is 25.94, weight deacy loss is 14.01, training accuracy is 0.062500, time 214.281 samples/sec
+epoch 0, total_step 6160, total loss is 38.88 , inference loss is 24.88, weight deacy loss is 14.00, training accuracy is 0.062500, time 228.936 samples/sec
+epoch 0, total_step 6180, total loss is 35.48 , inference loss is 21.48, weight deacy loss is 14.00, training accuracy is 0.062500, time 222.271 samples/sec
+epoch 0, total_step 6200, total loss is 41.25 , inference loss is 27.25, weight deacy loss is 14.00, training accuracy is 0.031250, time 214.419 samples/sec
+epoch 0, total_step 6220, total loss is 36.37 , inference loss is 22.38, weight deacy loss is 14.00, training accuracy is 0.093750, time 217.969 samples/sec
+epoch 0, total_step 6240, total loss is 42.49 , inference loss is 28.49, weight deacy loss is 14.00, training accuracy is 0.062500, time 213.052 samples/sec
+epoch 0, total_step 6260, total loss is 38.78 , inference loss is 24.79, weight deacy loss is 14.00, training accuracy is 0.000000, time 226.295 samples/sec
+epoch 0, total_step 6280, total loss is 41.40 , inference loss is 27.41, weight deacy loss is 13.99, training accuracy is 0.062500, time 299.740 samples/sec
+epoch 0, total_step 6300, total loss is 36.03 , inference loss is 22.04, weight deacy loss is 13.99, training accuracy is 0.031250, time 14.494 samples/sec
+epoch 0, total_step 6320, total loss is 39.95 , inference loss is 25.96, weight deacy loss is 13.99, training accuracy is 0.031250, time 230.482 samples/sec
+epoch 0, total_step 6340, total loss is 48.28 , inference loss is 34.29, weight deacy loss is 13.99, training accuracy is 0.000000, time 231.918 samples/sec
+epoch 0, total_step 6360, total loss is 38.58 , inference loss is 24.59, weight deacy loss is 13.99, training accuracy is 0.000000, time 215.541 samples/sec
+epoch 0, total_step 6380, total loss is 39.61 , inference loss is 25.62, weight deacy loss is 13.99, training accuracy is 0.093750, time 236.565 samples/sec
+epoch 0, total_step 6400, total loss is 39.16 , inference loss is 25.18, weight deacy loss is 13.99, training accuracy is 0.000000, time 216.468 samples/sec
+epoch 0, total_step 6420, total loss is 39.24 , inference loss is 25.25, weight deacy loss is 13.98, training accuracy is 0.062500, time 215.040 samples/sec
+epoch 0, total_step 6440, total loss is 39.75 , inference loss is 25.77, weight deacy loss is 13.98, training accuracy is 0.000000, time 209.036 samples/sec
+epoch 0, total_step 6460, total loss is 38.36 , inference loss is 24.38, weight deacy loss is 13.98, training accuracy is 0.031250, time 224.697 samples/sec
+epoch 0, total_step 6480, total loss is 37.98 , inference loss is 24.00, weight deacy loss is 13.98, training accuracy is 0.062500, time 218.077 samples/sec
+epoch 0, total_step 6500, total loss is 41.50 , inference loss is 27.52, weight deacy loss is 13.98, training accuracy is 0.062500, time 224.861 samples/sec
+epoch 0, total_step 6520, total loss is 46.43 , inference loss is 32.46, weight deacy loss is 13.98, training accuracy is 0.000000, time 226.436 samples/sec
+epoch 0, total_step 6540, total loss is 40.20 , inference loss is 26.22, weight deacy loss is 13.97, training accuracy is 0.031250, time 300.276 samples/sec
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+epoch 0, total_step 6580, total loss is 41.09 , inference loss is 27.12, weight deacy loss is 13.97, training accuracy is 0.000000, time 211.669 samples/sec
+epoch 0, total_step 6600, total loss is 40.32 , inference loss is 26.35, weight deacy loss is 13.97, training accuracy is 0.031250, time 14.139 samples/sec
+epoch 0, total_step 6620, total loss is 39.42 , inference loss is 25.45, weight deacy loss is 13.97, training accuracy is 0.031250, time 212.100 samples/sec
+epoch 0, total_step 6640, total loss is 35.62 , inference loss is 21.65, weight deacy loss is 13.97, training accuracy is 0.031250, time 226.869 samples/sec
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+epoch 0, total_step 6720, total loss is 40.66 , inference loss is 26.70, weight deacy loss is 13.96, training accuracy is 0.062500, time 226.624 samples/sec
+epoch 0, total_step 6740, total loss is 41.87 , inference loss is 27.91, weight deacy loss is 13.96, training accuracy is 0.062500, time 226.302 samples/sec
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+epoch 0, total_step 6880, total loss is 43.40 , inference loss is 29.45, weight deacy loss is 13.95, training accuracy is 0.062500, time 213.040 samples/sec
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+epoch 0, total_step 6980, total loss is 37.27 , inference loss is 23.33, weight deacy loss is 13.94, training accuracy is 0.000000, time 214.967 samples/sec
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+epoch 0, total_step 7020, total loss is 38.35 , inference loss is 24.41, weight deacy loss is 13.94, training accuracy is 0.000000, time 219.088 samples/sec
+epoch 0, total_step 7040, total loss is 41.59 , inference loss is 27.65, weight deacy loss is 13.94, training accuracy is 0.000000, time 215.101 samples/sec
+epoch 0, total_step 7060, total loss is 42.18 , inference loss is 28.24, weight deacy loss is 13.94, training accuracy is 0.000000, time 211.802 samples/sec
+epoch 0, total_step 7080, total loss is 38.11 , inference loss is 24.17, weight deacy loss is 13.93, training accuracy is 0.062500, time 213.564 samples/sec
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+epoch 0, total_step 7640, total loss is 36.73 , inference loss is 22.84, weight deacy loss is 13.89, training accuracy is 0.062500, time 223.949 samples/sec
+epoch 0, total_step 7660, total loss is 39.34 , inference loss is 25.45, weight deacy loss is 13.89, training accuracy is 0.062500, time 209.006 samples/sec
+epoch 0, total_step 7680, total loss is 37.25 , inference loss is 23.36, weight deacy loss is 13.89, training accuracy is 0.093750, time 210.645 samples/sec
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+epoch 0, total_step 7720, total loss is 34.17 , inference loss is 20.28, weight deacy loss is 13.89, training accuracy is 0.062500, time 210.075 samples/sec
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+epoch 0, total_step 7900, total loss is 38.39 , inference loss is 24.52, weight deacy loss is 13.87, training accuracy is 0.031250, time 215.247 samples/sec
+epoch 0, total_step 7920, total loss is 36.82 , inference loss is 22.95, weight deacy loss is 13.87, training accuracy is 0.031250, time 216.515 samples/sec
+epoch 0, total_step 7940, total loss is 39.11 , inference loss is 25.24, weight deacy loss is 13.87, training accuracy is 0.000000, time 229.758 samples/sec
+epoch 0, total_step 7960, total loss is 39.96 , inference loss is 26.10, weight deacy loss is 13.87, training accuracy is 0.000000, time 212.798 samples/sec
+epoch 0, total_step 7980, total loss is 46.40 , inference loss is 32.54, weight deacy loss is 13.87, training accuracy is 0.000000, time 222.117 samples/sec
+testing verification..
+(12000, 512)
+infer time 23.873974000000004
+best_threshold_index 118 0.9468518518518518
+best_threshold_index 118 0.947037037037037
+best_threshold_index 118 0.9481481481481482
+best_threshold_index 118 0.945925925925926
+best_threshold_index 118 0.9503703703703704
+best_threshold_index 118 0.9457407407407408
+best_threshold_index 118 0.945925925925926
+best_threshold_index 118 0.9477777777777778
+best_threshold_index 118 0.9455555555555556
+best_threshold_index 118 0.945
+[lfw][8000]XNorm: 23.696459
+[lfw][8000]Accuracy-Flip: 0.94683+-0.01365
+test accuracy is:  0.9468333333333334
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+epoch 0, total_step 8020, total loss is 36.85 , inference loss is 22.99, weight deacy loss is 13.86, training accuracy is 0.156250, time 221.143 samples/sec
+epoch 0, total_step 8040, total loss is 32.61 , inference loss is 18.75, weight deacy loss is 13.86, training accuracy is 0.031250, time 201.256 samples/sec
+epoch 0, total_step 8060, total loss is 39.57 , inference loss is 25.71, weight deacy loss is 13.86, training accuracy is 0.031250, time 204.625 samples/sec
+epoch 0, total_step 8080, total loss is 36.38 , inference loss is 22.52, weight deacy loss is 13.86, training accuracy is 0.031250, time 218.427 samples/sec
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+epoch 0, total_step 8120, total loss is 41.32 , inference loss is 27.47, weight deacy loss is 13.86, training accuracy is 0.093750, time 199.239 samples/sec
+epoch 0, total_step 8140, total loss is 39.74 , inference loss is 25.88, weight deacy loss is 13.85, training accuracy is 0.156250, time 297.835 samples/sec
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+epoch 0, total_step 8680, total loss is 39.30 , inference loss is 25.49, weight deacy loss is 13.81, training accuracy is 0.031250, time 212.629 samples/sec
+epoch 0, total_step 8700, total loss is 35.33 , inference loss is 21.52, weight deacy loss is 13.81, training accuracy is 0.031250, time 11.197 samples/sec
+epoch 0, total_step 8720, total loss is 37.98 , inference loss is 24.17, weight deacy loss is 13.81, training accuracy is 0.093750, time 216.084 samples/sec
+epoch 0, total_step 8740, total loss is 39.50 , inference loss is 25.69, weight deacy loss is 13.81, training accuracy is 0.000000, time 234.071 samples/sec
+epoch 0, total_step 8760, total loss is 42.16 , inference loss is 28.35, weight deacy loss is 13.81, training accuracy is 0.000000, time 222.676 samples/sec
+epoch 0, total_step 8780, total loss is 42.88 , inference loss is 29.07, weight deacy loss is 13.81, training accuracy is 0.000000, time 218.179 samples/sec
+epoch 0, total_step 8800, total loss is 36.88 , inference loss is 23.07, weight deacy loss is 13.81, training accuracy is 0.062500, time 216.198 samples/sec
+epoch 0, total_step 8820, total loss is 37.37 , inference loss is 23.57, weight deacy loss is 13.80, training accuracy is 0.062500, time 212.242 samples/sec
+epoch 0, total_step 8840, total loss is 35.60 , inference loss is 21.80, weight deacy loss is 13.80, training accuracy is 0.093750, time 220.005 samples/sec
+epoch 0, total_step 8860, total loss is 41.29 , inference loss is 27.49, weight deacy loss is 13.80, training accuracy is 0.093750, time 213.904 samples/sec
+epoch 0, total_step 8880, total loss is 40.78 , inference loss is 26.98, weight deacy loss is 13.80, training accuracy is 0.000000, time 216.909 samples/sec
+epoch 0, total_step 8900, total loss is 34.79 , inference loss is 20.99, weight deacy loss is 13.80, training accuracy is 0.062500, time 225.944 samples/sec
+epoch 0, total_step 8920, total loss is 36.73 , inference loss is 22.93, weight deacy loss is 13.80, training accuracy is 0.000000, time 297.888 samples/sec
+epoch 0, total_step 8940, total loss is 44.47 , inference loss is 30.67, weight deacy loss is 13.80, training accuracy is 0.031250, time 281.763 samples/sec
+epoch 0, total_step 8960, total loss is 34.84 , inference loss is 21.04, weight deacy loss is 13.79, training accuracy is 0.062500, time 225.300 samples/sec
+epoch 0, total_step 8980, total loss is 40.30 , inference loss is 26.51, weight deacy loss is 13.79, training accuracy is 0.000000, time 214.959 samples/sec
+epoch 0, total_step 9000, total loss is 38.29 , inference loss is 24.49, weight deacy loss is 13.79, training accuracy is 0.062500, time 11.738 samples/sec
+epoch 0, total_step 9020, total loss is 41.26 , inference loss is 27.47, weight deacy loss is 13.79, training accuracy is 0.000000, time 242.281 samples/sec
+epoch 0, total_step 9040, total loss is 39.46 , inference loss is 25.67, weight deacy loss is 13.79, training accuracy is 0.031250, time 226.454 samples/sec
+epoch 0, total_step 9060, total loss is 35.16 , inference loss is 21.37, weight deacy loss is 13.79, training accuracy is 0.031250, time 216.958 samples/sec
+epoch 0, total_step 9080, total loss is 34.47 , inference loss is 20.68, weight deacy loss is 13.79, training accuracy is 0.093750, time 224.427 samples/sec
+epoch 0, total_step 9100, total loss is 34.81 , inference loss is 21.02, weight deacy loss is 13.78, training accuracy is 0.062500, time 299.103 samples/sec
+epoch 0, total_step 9120, total loss is 42.70 , inference loss is 28.92, weight deacy loss is 13.78, training accuracy is 0.031250, time 211.688 samples/sec
+epoch 0, total_step 9140, total loss is 34.97 , inference loss is 21.19, weight deacy loss is 13.78, training accuracy is 0.031250, time 224.761 samples/sec
+epoch 0, total_step 9160, total loss is 37.53 , inference loss is 23.75, weight deacy loss is 13.78, training accuracy is 0.062500, time 211.513 samples/sec
+epoch 0, total_step 9180, total loss is 33.54 , inference loss is 19.76, weight deacy loss is 13.78, training accuracy is 0.062500, time 204.918 samples/sec
+epoch 0, total_step 9200, total loss is 37.12 , inference loss is 23.34, weight deacy loss is 13.78, training accuracy is 0.031250, time 209.608 samples/sec
+epoch 0, total_step 9220, total loss is 38.57 , inference loss is 24.79, weight deacy loss is 13.77, training accuracy is 0.062500, time 226.599 samples/sec2022-12-01 00:52:13.052448: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:52:13.053215: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:52:13.053284: W tf_adapter/util/npu_attrs.cc:1077] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:52:13.053294: W tf_adapter/util/npu_attrs.cc:1207] The op_select_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:52:13.053299: W tf_adapter/util/npu_attrs.cc:1211] The optypelist_for_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:52:14.511791: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:52:14.511874: W tf_adapter/util/npu_attrs.cc:1077] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:52:14.511885: W tf_adapter/util/npu_attrs.cc:1207] The op_select_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:52:14.511890: W tf_adapter/util/npu_attrs.cc:1211] The optypelist_for_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:52:14.528863: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:52:14.528937: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:52:14.528978: W tf_adapter/util/npu_attrs.cc:1077] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:52:14.528987: W tf_adapter/util/npu_attrs.cc:1207] The op_select_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:52:14.528991: W tf_adapter/util/npu_attrs.cc:1211] The optypelist_for_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:52:14.529087: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:52:14.529121: W tf_adapter/util/npu_attrs.cc:1077] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:52:14.529126: W tf_adapter/util/npu_attrs.cc:1207] The op_select_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:52:14.529130: W tf_adapter/util/npu_attrs.cc:1211] The optypelist_for_implmode option IS DEPRECATED. It will be removed in a future version. Please use op_precision_mode instead
+2022-12-01 00:52:14.529177: W tf_adapter/util/npu_attrs.cc:861] The enable_data_pre_proc option IS DEPRECATED. It will be removed in a future version.
+2022-12-01 00:52:15.411379: I tf_adapter/kernels/geop_npu.cc:854] The model has been compiled on the Ascend AI processor, current graph id is: 41
+
+epoch 0, total_step 9240, total loss is 42.11 , inference loss is 28.34, weight deacy loss is 13.77, training accuracy is 0.031250, time 207.626 samples/sec
+epoch 0, total_step 9260, total loss is 37.41 , inference loss is 23.64, weight deacy loss is 13.77, training accuracy is 0.000000, time 199.227 samples/sec
+epoch 0, total_step 9280, total loss is 35.69 , inference loss is 21.92, weight deacy loss is 13.77, training accuracy is 0.031250, time 219.659 samples/sec
+epoch 0, total_step 9300, total loss is 39.79 , inference loss is 26.02, weight deacy loss is 13.77, training accuracy is 0.031250, time 11.371 samples/sec
+epoch 0, total_step 9320, total loss is 42.09 , inference loss is 28.32, weight deacy loss is 13.77, training accuracy is 0.031250, time 212.577 samples/sec
+epoch 0, total_step 9340, total loss is 37.46 , inference loss is 23.69, weight deacy loss is 13.77, training accuracy is 0.031250, time 211.308 samples/sec
+epoch 0, total_step 9360, total loss is 41.62 , inference loss is 27.86, weight deacy loss is 13.76, training accuracy is 0.062500, time 299.256 samples/sec
+epoch 0, total_step 9380, total loss is 35.53 , inference loss is 21.77, weight deacy loss is 13.76, training accuracy is 0.031250, time 207.200 samples/sec
+epoch 0, total_step 9400, total loss is 37.20 , inference loss is 23.44, weight deacy loss is 13.76, training accuracy is 0.000000, time 238.390 samples/sec
+epoch 0, total_step 9420, total loss is 38.68 , inference loss is 24.92, weight deacy loss is 13.76, training accuracy is 0.093750, time 230.075 samples/sec
+epoch 0, total_step 9440, total loss is 34.62 , inference loss is 20.86, weight deacy loss is 13.76, training accuracy is 0.062500, time 229.152 samples/sec
+epoch 0, total_step 9460, total loss is 42.14 , inference loss is 28.39, weight deacy loss is 13.76, training accuracy is 0.000000, time 204.897 samples/sec
+epoch 0, total_step 9480, total loss is 37.48 , inference loss is 23.72, weight deacy loss is 13.76, training accuracy is 0.031250, time 226.051 samples/sec
+epoch 0, total_step 9500, total loss is 40.58 , inference loss is 26.82, weight deacy loss is 13.75, training accuracy is 0.062500, time 194.645 samples/sec
+epoch 0, total_step 9520, total loss is 42.56 , inference loss is 28.81, weight deacy loss is 13.75, training accuracy is 0.062500, time 225.028 samples/sec
+epoch 0, total_step 9540, total loss is 35.23 , inference loss is 21.48, weight deacy loss is 13.75, training accuracy is 0.062500, time 209.854 samples/sec
+epoch 0, total_step 9560, total loss is 37.77 , inference loss is 24.02, weight deacy loss is 13.75, training accuracy is 0.000000, time 206.543 samples/sec
+epoch 0, total_step 9580, total loss is 35.84 , inference loss is 22.10, weight deacy loss is 13.75, training accuracy is 0.031250, time 215.782 samples/sec
+epoch 0, total_step 9600, total loss is 41.05 , inference loss is 27.30, weight deacy loss is 13.75, training accuracy is 0.031250, time 14.040 samples/sec
+epoch 0, total_step 9620, total loss is 38.81 , inference loss is 25.07, weight deacy loss is 13.75, training accuracy is 0.031250, time 216.991 samples/sec
+epoch 0, total_step 9640, total loss is 37.39 , inference loss is 23.65, weight deacy loss is 13.74, training accuracy is 0.062500, time 299.394 samples/sec
+epoch 0, total_step 9660, total loss is 41.57 , inference loss is 27.83, weight deacy loss is 13.74, training accuracy is 0.031250, time 239.898 samples/sec
+epoch 0, total_step 9680, total loss is 42.13 , inference loss is 28.39, weight deacy loss is 13.74, training accuracy is 0.000000, time 224.964 samples/sec
+epoch 0, total_step 9700, total loss is 34.35 , inference loss is 20.61, weight deacy loss is 13.74, training accuracy is 0.062500, time 217.595 samples/sec
+epoch 0, total_step 9720, total loss is 37.81 , inference loss is 24.07, weight deacy loss is 13.74, training accuracy is 0.031250, time 215.046 samples/sec
+epoch 0, total_step 9740, total loss is 38.09 , inference loss is 24.36, weight deacy loss is 13.74, training accuracy is 0.125000, time 207.102 samples/sec
+epoch 0, total_step 9760, total loss is 36.76 , inference loss is 23.03, weight deacy loss is 13.73, training accuracy is 0.031250, time 195.709 samples/sec
+epoch 0, total_step 9780, total loss is 40.62 , inference loss is 26.89, weight deacy loss is 13.73, training accuracy is 0.031250, time 224.446 samples/sec
+epoch 0, total_step 9800, total loss is 40.01 , inference loss is 26.27, weight deacy loss is 13.73, training accuracy is 0.000000, time 207.848 samples/sec
+epoch 0, total_step 9820, total loss is 37.18 , inference loss is 23.45, weight deacy loss is 13.73, training accuracy is 0.000000, time 235.492 samples/sec
+epoch 0, total_step 9840, total loss is 38.27 , inference loss is 24.54, weight deacy loss is 13.73, training accuracy is 0.000000, time 212.637 samples/sec
+epoch 0, total_step 9860, total loss is 35.82 , inference loss is 22.09, weight deacy loss is 13.73, training accuracy is 0.062500, time 226.976 samples/sec
+epoch 0, total_step 9880, total loss is 38.34 , inference loss is 24.62, weight deacy loss is 13.73, training accuracy is 0.093750, time 215.915 samples/sec
+epoch 0, total_step 9900, total loss is 36.93 , inference loss is 23.21, weight deacy loss is 13.72, training accuracy is 0.093750, time 11.305 samples/sec
+epoch 0, total_step 9920, total loss is 39.27 , inference loss is 25.55, weight deacy loss is 13.72, training accuracy is 0.031250, time 227.270 samples/sec
+epoch 0, total_step 9940, total loss is 35.47 , inference loss is 21.75, weight deacy loss is 13.72, training accuracy is 0.031250, time 236.278 samples/sec
+epoch 0, total_step 9960, total loss is 42.21 , inference loss is 28.49, weight deacy loss is 13.72, training accuracy is 0.031250, time 220.243 samples/sec
+epoch 0, total_step 9980, total loss is 34.77 , inference loss is 21.05, weight deacy loss is 13.72, training accuracy is 0.125000, time 225.928 samples/sec
+testing verification..
+(12000, 512)
+infer time 23.616218000000007
+best_threshold_index 102 0.9398148148148148
+best_threshold_index 103 0.9409259259259259
+best_threshold_index 102 0.942962962962963
+best_threshold_index 101 0.9414814814814815
+best_threshold_index 101 0.9425925925925925
+best_threshold_index 100 0.9383333333333334
+best_threshold_index 100 0.94
+best_threshold_index 102 0.9411111111111111
+best_threshold_index 103 0.9390740740740741
+best_threshold_index 102 0.9394444444444444
+[lfw][10000]XNorm: 24.284825
+[lfw][10000]Accuracy-Flip: 0.93883+-0.01263
+test accuracy is:  0.9388333333333332
+epoch 0, total_step 10000, total loss is 39.15 , inference loss is 25.43, weight deacy loss is 13.72, training accuracy is 0.093750, time 16.503 samples/sec
+epoch 0, total_step 10020, total loss is 31.71 , inference loss is 18.00, weight deacy loss is 13.72, training accuracy is 0.062500, time 213.531 samples/sec
+epoch 0, total_step 10040, total loss is 37.78 , inference loss is 24.07, weight deacy loss is 13.71, training accuracy is 0.000000, time 228.629 samples/sec
+epoch 0, total_step 10060, total loss is 38.33 , inference loss is 24.62, weight deacy loss is 13.71, training accuracy is 0.000000, time 218.386 samples/sec
+epoch 0, total_step 10080, total loss is 43.24 , inference loss is 29.53, weight deacy loss is 13.71, training accuracy is 0.031250, time 217.283 samples/sec
+epoch 0, total_step 10100, total loss is 37.62 , inference loss is 23.91, weight deacy loss is 13.71, training accuracy is 0.031250, time 244.365 samples/sec
+epoch 0, total_step 10120, total loss is 33.45 , inference loss is 19.74, weight deacy loss is 13.71, training accuracy is 0.062500, time 213.513 samples/sec
+epoch 0, total_step 10140, total loss is 37.50 , inference loss is 23.80, weight deacy loss is 13.71, training accuracy is 0.000000, time 294.478 samples/sec
+epoch 0, total_step 10160, total loss is 41.03 , inference loss is 27.32, weight deacy loss is 13.70, training accuracy is 0.000000, time 221.984 samples/sec
+epoch 0, total_step 10180, total loss is 36.06 , inference loss is 22.35, weight deacy loss is 13.70, training accuracy is 0.062500, time 238.556 samples/sec
+epoch 0, total_step 10200, total loss is 35.43 , inference loss is 21.73, weight deacy loss is 13.70, training accuracy is 0.031250, time 13.604 samples/sec
+epoch 0, total_step 10220, total loss is 33.77 , inference loss is 20.06, weight deacy loss is 13.70, training accuracy is 0.093750, time 237.188 samples/sec
+epoch 0, total_step 10240, total loss is 34.97 , inference loss is 21.27, weight deacy loss is 13.70, training accuracy is 0.093750, time 219.456 samples/sec
+epoch 0, total_step 10260, total loss is 36.18 , inference loss is 22.48, weight deacy loss is 13.70, training accuracy is 0.000000, time 210.050 samples/sec
+epoch 0, total_step 10280, total loss is 41.15 , inference loss is 27.46, weight deacy loss is 13.70, training accuracy is 0.031250, time 236.385 samples/sec
+epoch 0, total_step 10300, total loss is 39.04 , inference loss is 25.35, weight deacy loss is 13.69, training accuracy is 0.062500, time 225.125 samples/sec
+epoch 0, total_step 10320, total loss is 37.30 , inference loss is 23.61, weight deacy loss is 13.69, training accuracy is 0.031250, time 209.558 samples/sec
+epoch 0, total_step 10340, total loss is 37.65 , inference loss is 23.96, weight deacy loss is 13.69, training accuracy is 0.031250, time 224.924 samples/sec
+epoch 0, total_step 10360, total loss is 36.80 , inference loss is 23.11, weight deacy loss is 13.69, training accuracy is 0.031250, time 208.361 samples/sec
+epoch 0, total_step 10380, total loss is 41.40 , inference loss is 27.71, weight deacy loss is 13.69, training accuracy is 0.000000, time 234.784 samples/sec
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+epoch 0, total_step 10420, total loss is 37.79 , inference loss is 24.10, weight deacy loss is 13.69, training accuracy is 0.093750, time 216.656 samples/sec
+epoch 0, total_step 10440, total loss is 38.64 , inference loss is 24.96, weight deacy loss is 13.68, training accuracy is 0.000000, time 205.792 samples/sec
+epoch 0, total_step 10460, total loss is 34.04 , inference loss is 20.36, weight deacy loss is 13.68, training accuracy is 0.093750, time 218.365 samples/sec
+epoch 0, total_step 10480, total loss is 37.53 , inference loss is 23.85, weight deacy loss is 13.68, training accuracy is 0.031250, time 221.370 samples/sec
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+epoch 0, total_step 10880, total loss is 40.16 , inference loss is 26.51, weight deacy loss is 13.65, training accuracy is 0.031250, time 210.773 samples/sec
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+epoch 0, total_step 11020, total loss is 36.94 , inference loss is 23.30, weight deacy loss is 13.64, training accuracy is 0.031250, time 211.616 samples/sec
+epoch 0, total_step 11040, total loss is 36.18 , inference loss is 22.54, weight deacy loss is 13.64, training accuracy is 0.062500, time 219.291 samples/sec
+epoch 0, total_step 11060, total loss is 37.76 , inference loss is 24.12, weight deacy loss is 13.64, training accuracy is 0.093750, time 213.990 samples/sec
+epoch 0, total_step 11080, total loss is 38.11 , inference loss is 24.47, weight deacy loss is 13.64, training accuracy is 0.000000, time 223.095 samples/sec
+epoch 0, total_step 11100, total loss is 37.83 , inference loss is 24.20, weight deacy loss is 13.64, training accuracy is 0.000000, time 14.066 samples/sec
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+epoch 0, total_step 11140, total loss is 35.46 , inference loss is 21.83, weight deacy loss is 13.63, training accuracy is 0.031250, time 209.111 samples/sec
+epoch 0, total_step 11160, total loss is 37.77 , inference loss is 24.14, weight deacy loss is 13.63, training accuracy is 0.125000, time 218.671 samples/sec
+epoch 0, total_step 11180, total loss is 38.87 , inference loss is 25.24, weight deacy loss is 13.63, training accuracy is 0.000000, time 209.045 samples/sec
+epoch 0, total_step 11200, total loss is 38.26 , inference loss is 24.63, weight deacy loss is 13.63, training accuracy is 0.125000, time 212.387 samples/sec
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+epoch 0, total_step 11240, total loss is 31.34 , inference loss is 17.72, weight deacy loss is 13.63, training accuracy is 0.062500, time 241.065 samples/sec
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+epoch 0, total_step 11940, total loss is 42.45 , inference loss is 28.88, weight deacy loss is 13.58, training accuracy is 0.031250, time 220.086 samples/sec
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+epoch 0, total_step 11980, total loss is 37.53 , inference loss is 23.96, weight deacy loss is 13.57, training accuracy is 0.000000, time 193.839 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.997808999999997
+best_threshold_index 95 0.9512962962962963
+best_threshold_index 96 0.9512962962962963
+best_threshold_index 95 0.952037037037037
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+best_threshold_index 95 0.9503703703703704
+best_threshold_index 95 0.9512962962962963
+best_threshold_index 95 0.9514814814814815
+best_threshold_index 95 0.9483333333333334
+best_threshold_index 100 0.9503703703703704
+[lfw][12000]XNorm: 24.727908
+[lfw][12000]Accuracy-Flip: 0.94967+-0.01190
+test accuracy is:  0.9496666666666668
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+epoch 0, total_step 13980, total loss is 39.05 , inference loss is 25.62, weight deacy loss is 13.44, training accuracy is 0.031250, time 223.692 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.117879000000013
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+best_threshold_index 90 0.9561111111111111
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+best_threshold_index 90 0.9538888888888889
+best_threshold_index 90 0.9542592592592593
+[lfw][14000]XNorm: 26.058693
+[lfw][14000]Accuracy-Flip: 0.95333+-0.01213
+test accuracy is:  0.9533333333333334
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+epoch 0, total_step 14260, total loss is 36.05 , inference loss is 22.63, weight deacy loss is 13.42, training accuracy is 0.031250, time 229.308 samples/sec
+epoch 0, total_step 14280, total loss is 37.89 , inference loss is 24.47, weight deacy loss is 13.42, training accuracy is 0.000000, time 210.691 samples/sec
+epoch 0, total_step 14300, total loss is 37.80 , inference loss is 24.38, weight deacy loss is 13.42, training accuracy is 0.000000, time 220.940 samples/sec
+epoch 0, total_step 14320, total loss is 36.62 , inference loss is 23.20, weight deacy loss is 13.41, training accuracy is 0.031250, time 217.100 samples/sec
+epoch 0, total_step 14340, total loss is 37.45 , inference loss is 24.04, weight deacy loss is 13.41, training accuracy is 0.031250, time 195.522 samples/sec
+epoch 0, total_step 14360, total loss is 39.50 , inference loss is 26.09, weight deacy loss is 13.41, training accuracy is 0.000000, time 215.555 samples/sec
+epoch 0, total_step 14380, total loss is 37.36 , inference loss is 23.95, weight deacy loss is 13.41, training accuracy is 0.000000, time 232.753 samples/sec
+epoch 0, total_step 14400, total loss is 41.96 , inference loss is 28.55, weight deacy loss is 13.41, training accuracy is 0.031250, time 14.629 samples/sec
+epoch 0, total_step 14420, total loss is 38.48 , inference loss is 25.07, weight deacy loss is 13.41, training accuracy is 0.031250, time 229.254 samples/sec
+epoch 0, total_step 14440, total loss is 38.89 , inference loss is 25.48, weight deacy loss is 13.41, training accuracy is 0.000000, time 207.990 samples/sec
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+epoch 0, total_step 14480, total loss is 40.25 , inference loss is 26.84, weight deacy loss is 13.40, training accuracy is 0.000000, time 194.393 samples/sec
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+epoch 0, total_step 14540, total loss is 40.07 , inference loss is 26.67, weight deacy loss is 13.40, training accuracy is 0.000000, time 220.591 samples/sec
+epoch 0, total_step 14560, total loss is 39.68 , inference loss is 26.28, weight deacy loss is 13.40, training accuracy is 0.031250, time 214.990 samples/sec
+epoch 0, total_step 14580, total loss is 37.08 , inference loss is 23.68, weight deacy loss is 13.40, training accuracy is 0.000000, time 222.882 samples/sec
+epoch 0, total_step 14600, total loss is 34.32 , inference loss is 20.92, weight deacy loss is 13.40, training accuracy is 0.031250, time 298.009 samples/sec
+epoch 0, total_step 14620, total loss is 38.68 , inference loss is 25.28, weight deacy loss is 13.39, training accuracy is 0.031250, time 209.670 samples/sec
+epoch 0, total_step 14640, total loss is 38.11 , inference loss is 24.71, weight deacy loss is 13.39, training accuracy is 0.000000, time 221.591 samples/sec
+epoch 0, total_step 14660, total loss is 42.21 , inference loss is 28.82, weight deacy loss is 13.39, training accuracy is 0.000000, time 211.584 samples/sec
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+epoch 0, total_step 14860, total loss is 43.56 , inference loss is 30.19, weight deacy loss is 13.38, training accuracy is 0.000000, time 297.697 samples/sec
+epoch 0, total_step 14880, total loss is 43.70 , inference loss is 30.32, weight deacy loss is 13.38, training accuracy is 0.031250, time 226.424 samples/sec
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+epoch 0, total_step 14940, total loss is 45.98 , inference loss is 32.61, weight deacy loss is 13.37, training accuracy is 0.031250, time 221.752 samples/sec
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+epoch 0, total_step 14980, total loss is 43.98 , inference loss is 30.61, weight deacy loss is 13.37, training accuracy is 0.000000, time 209.145 samples/sec
+epoch 0, total_step 15000, total loss is 37.50 , inference loss is 24.13, weight deacy loss is 13.37, training accuracy is 0.000000, time 11.566 samples/sec
+epoch 0, total_step 15020, total loss is 38.17 , inference loss is 24.80, weight deacy loss is 13.37, training accuracy is 0.000000, time 197.171 samples/sec
+epoch 0, total_step 15040, total loss is 38.61 , inference loss is 25.24, weight deacy loss is 13.37, training accuracy is 0.000000, time 182.634 samples/sec
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+epoch 0, total_step 15080, total loss is 35.00 , inference loss is 21.64, weight deacy loss is 13.36, training accuracy is 0.031250, time 193.870 samples/sec
+epoch 0, total_step 15100, total loss is 32.96 , inference loss is 19.59, weight deacy loss is 13.36, training accuracy is 0.031250, time 177.908 samples/sec
+epoch 0, total_step 15120, total loss is 36.54 , inference loss is 23.18, weight deacy loss is 13.36, training accuracy is 0.062500, time 201.945 samples/sec
+epoch 0, total_step 15140, total loss is 35.45 , inference loss is 22.10, weight deacy loss is 13.36, training accuracy is 0.031250, time 298.447 samples/sec
+epoch 0, total_step 15160, total loss is 32.68 , inference loss is 19.32, weight deacy loss is 13.36, training accuracy is 0.000000, time 194.501 samples/sec
+epoch 0, total_step 15180, total loss is 32.67 , inference loss is 19.31, weight deacy loss is 13.36, training accuracy is 0.031250, time 175.926 samples/sec
+epoch 0, total_step 15200, total loss is 33.19 , inference loss is 19.84, weight deacy loss is 13.35, training accuracy is 0.031250, time 190.640 samples/sec
+epoch 0, total_step 15220, total loss is 39.04 , inference loss is 25.69, weight deacy loss is 13.35, training accuracy is 0.000000, time 216.734 samples/sec
+epoch 0, total_step 15240, total loss is 36.01 , inference loss is 22.66, weight deacy loss is 13.35, training accuracy is 0.000000, time 225.411 samples/sec
+epoch 0, total_step 15260, total loss is 35.77 , inference loss is 22.42, weight deacy loss is 13.35, training accuracy is 0.031250, time 221.000 samples/sec
+epoch 0, total_step 15280, total loss is 38.24 , inference loss is 24.89, weight deacy loss is 13.35, training accuracy is 0.031250, time 198.375 samples/sec2022-12-01 01:13:44.953921: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:145] Filling up shuffle buffer (this may take a while): 7248 of 10000
+2022-12-01 01:13:48.269929: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:195] Shuffle buffer filled.
+
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+epoch 0, total_step 15320, total loss is 29.36 , inference loss is 16.01, weight deacy loss is 13.34, training accuracy is 0.125000, time 197.092 samples/sec
+End of epoch 0
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+epoch 1, total_step 15440, total loss is 31.34 , inference loss is 18.01, weight deacy loss is 13.33, training accuracy is 0.093750, time 207.684 samples/sec
+epoch 1, total_step 15460, total loss is 31.77 , inference loss is 18.44, weight deacy loss is 13.33, training accuracy is 0.125000, time 239.540 samples/sec
+epoch 1, total_step 15480, total loss is 34.84 , inference loss is 21.51, weight deacy loss is 13.33, training accuracy is 0.062500, time 217.104 samples/sec
+epoch 1, total_step 15500, total loss is 35.14 , inference loss is 21.82, weight deacy loss is 13.33, training accuracy is 0.187500, time 209.224 samples/sec
+epoch 1, total_step 15520, total loss is 30.15 , inference loss is 16.82, weight deacy loss is 13.32, training accuracy is 0.250000, time 215.481 samples/sec
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+epoch 1, total_step 15560, total loss is 33.69 , inference loss is 20.37, weight deacy loss is 13.32, training accuracy is 0.093750, time 212.863 samples/sec
+epoch 1, total_step 15580, total loss is 34.40 , inference loss is 21.09, weight deacy loss is 13.32, training accuracy is 0.093750, time 213.870 samples/sec
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+epoch 1, total_step 15880, total loss is 25.18 , inference loss is 11.89, weight deacy loss is 13.29, training accuracy is 0.250000, time 205.777 samples/sec
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+epoch 1, total_step 15980, total loss is 32.01 , inference loss is 18.73, weight deacy loss is 13.28, training accuracy is 0.093750, time 212.397 samples/sec
+testing verification..
+(12000, 512)
+infer time 23.228116000000014
+best_threshold_index 101 0.9598148148148148
+best_threshold_index 101 0.9601851851851851
+best_threshold_index 101 0.9598148148148148
+best_threshold_index 101 0.9611111111111111
+best_threshold_index 101 0.960925925925926
+best_threshold_index 101 0.9575925925925926
+best_threshold_index 101 0.9598148148148148
+best_threshold_index 101 0.9594444444444444
+best_threshold_index 101 0.9575925925925926
+best_threshold_index 101 0.9587037037037037
+[lfw][16000]XNorm: 24.356007
+[lfw][16000]Accuracy-Flip: 0.95950+-0.01038
+test accuracy is:  0.9595
+epoch 1, total_step 16000, total loss is 36.09 , inference loss is 22.81, weight deacy loss is 13.28, training accuracy is 0.093750, time 18.785 samples/sec
+epoch 1, total_step 16020, total loss is 32.17 , inference loss is 18.89, weight deacy loss is 13.28, training accuracy is 0.031250, time 222.024 samples/sec
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+epoch 1, total_step 16060, total loss is 31.57 , inference loss is 18.30, weight deacy loss is 13.27, training accuracy is 0.125000, time 203.901 samples/sec
+epoch 1, total_step 16080, total loss is 34.37 , inference loss is 21.10, weight deacy loss is 13.27, training accuracy is 0.031250, time 203.162 samples/sec
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+epoch 1, total_step 16380, total loss is 35.16 , inference loss is 21.91, weight deacy loss is 13.24, training accuracy is 0.031250, time 220.732 samples/sec
+epoch 1, total_step 16400, total loss is 33.21 , inference loss is 19.97, weight deacy loss is 13.24, training accuracy is 0.250000, time 213.212 samples/sec
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+epoch 1, total_step 17220, total loss is 29.41 , inference loss is 16.25, weight deacy loss is 13.16, training accuracy is 0.093750, time 207.516 samples/sec
+epoch 1, total_step 17240, total loss is 37.28 , inference loss is 24.12, weight deacy loss is 13.16, training accuracy is 0.031250, time 296.542 samples/sec
+epoch 1, total_step 17260, total loss is 30.75 , inference loss is 17.59, weight deacy loss is 13.16, training accuracy is 0.125000, time 203.746 samples/sec
+epoch 1, total_step 17280, total loss is 29.63 , inference loss is 16.48, weight deacy loss is 13.15, training accuracy is 0.218750, time 238.575 samples/sec
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+epoch 1, total_step 17320, total loss is 30.65 , inference loss is 17.50, weight deacy loss is 13.15, training accuracy is 0.187500, time 215.702 samples/sec
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+epoch 1, total_step 17420, total loss is 32.64 , inference loss is 19.50, weight deacy loss is 13.14, training accuracy is 0.156250, time 220.461 samples/sec
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+epoch 1, total_step 17540, total loss is 30.56 , inference loss is 17.43, weight deacy loss is 13.13, training accuracy is 0.250000, time 227.334 samples/sec
+epoch 1, total_step 17560, total loss is 30.23 , inference loss is 17.11, weight deacy loss is 13.13, training accuracy is 0.187500, time 215.644 samples/sec
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+epoch 1, total_step 17680, total loss is 30.94 , inference loss is 17.82, weight deacy loss is 13.12, training accuracy is 0.093750, time 210.925 samples/sec
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+epoch 1, total_step 17720, total loss is 29.24 , inference loss is 16.13, weight deacy loss is 13.11, training accuracy is 0.187500, time 224.713 samples/sec
+epoch 1, total_step 17740, total loss is 33.22 , inference loss is 20.11, weight deacy loss is 13.11, training accuracy is 0.187500, time 212.695 samples/sec
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+epoch 1, total_step 17780, total loss is 32.26 , inference loss is 19.15, weight deacy loss is 13.11, training accuracy is 0.125000, time 215.033 samples/sec
+epoch 1, total_step 17800, total loss is 30.09 , inference loss is 16.98, weight deacy loss is 13.10, training accuracy is 0.062500, time 224.624 samples/sec
+epoch 1, total_step 17820, total loss is 30.38 , inference loss is 17.28, weight deacy loss is 13.10, training accuracy is 0.093750, time 296.523 samples/sec
+epoch 1, total_step 17840, total loss is 31.83 , inference loss is 18.73, weight deacy loss is 13.10, training accuracy is 0.093750, time 215.483 samples/sec
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+epoch 1, total_step 17880, total loss is 30.08 , inference loss is 16.98, weight deacy loss is 13.10, training accuracy is 0.125000, time 204.382 samples/sec
+epoch 1, total_step 17900, total loss is 30.03 , inference loss is 16.94, weight deacy loss is 13.09, training accuracy is 0.093750, time 211.301 samples/sec
+epoch 1, total_step 17920, total loss is 29.45 , inference loss is 16.36, weight deacy loss is 13.09, training accuracy is 0.062500, time 216.942 samples/sec
+epoch 1, total_step 17940, total loss is 26.55 , inference loss is 13.45, weight deacy loss is 13.09, training accuracy is 0.281250, time 298.411 samples/sec
+epoch 1, total_step 17960, total loss is 28.76 , inference loss is 15.67, weight deacy loss is 13.09, training accuracy is 0.250000, time 218.965 samples/sec
+epoch 1, total_step 17980, total loss is 25.48 , inference loss is 12.39, weight deacy loss is 13.09, training accuracy is 0.218750, time 217.743 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.763921999999994
+best_threshold_index 105 0.9587037037037037
+best_threshold_index 101 0.9598148148148148
+best_threshold_index 105 0.9598148148148148
+best_threshold_index 102 0.9598148148148148
+best_threshold_index 101 0.960925925925926
+best_threshold_index 104 0.9594444444444444
+best_threshold_index 105 0.9585185185185185
+best_threshold_index 102 0.9587037037037037
+best_threshold_index 101 0.9568518518518518
+best_threshold_index 105 0.9581481481481482
+[lfw][18000]XNorm: 24.336557
+[lfw][18000]Accuracy-Flip: 0.95717+-0.00997
+test accuracy is:  0.9571666666666667
+epoch 1, total_step 18000, total loss is 28.34 , inference loss is 15.26, weight deacy loss is 13.09, training accuracy is 0.187500, time 17.224 samples/sec
+epoch 1, total_step 18020, total loss is 27.79 , inference loss is 14.70, weight deacy loss is 13.08, training accuracy is 0.250000, time 296.239 samples/sec
+epoch 1, total_step 18040, total loss is 32.74 , inference loss is 19.66, weight deacy loss is 13.08, training accuracy is 0.156250, time 211.569 samples/sec
+epoch 1, total_step 18060, total loss is 34.46 , inference loss is 21.38, weight deacy loss is 13.08, training accuracy is 0.062500, time 228.442 samples/sec
+epoch 1, total_step 18080, total loss is 31.07 , inference loss is 17.99, weight deacy loss is 13.08, training accuracy is 0.093750, time 218.223 samples/sec
+epoch 1, total_step 18100, total loss is 29.94 , inference loss is 16.87, weight deacy loss is 13.08, training accuracy is 0.062500, time 237.720 samples/sec
+epoch 1, total_step 18120, total loss is 32.48 , inference loss is 19.41, weight deacy loss is 13.07, training accuracy is 0.156250, time 223.268 samples/sec
+epoch 1, total_step 18140, total loss is 29.82 , inference loss is 16.75, weight deacy loss is 13.07, training accuracy is 0.093750, time 227.359 samples/sec
+epoch 1, total_step 18160, total loss is 28.18 , inference loss is 15.11, weight deacy loss is 13.07, training accuracy is 0.187500, time 206.859 samples/sec
+epoch 1, total_step 18180, total loss is 31.24 , inference loss is 18.17, weight deacy loss is 13.07, training accuracy is 0.062500, time 217.803 samples/sec
+epoch 1, total_step 18200, total loss is 31.60 , inference loss is 18.53, weight deacy loss is 13.07, training accuracy is 0.125000, time 233.869 samples/sec
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+epoch 1, total_step 18240, total loss is 30.81 , inference loss is 17.75, weight deacy loss is 13.06, training accuracy is 0.156250, time 224.742 samples/sec
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+epoch 1, total_step 18300, total loss is 35.05 , inference loss is 22.00, weight deacy loss is 13.06, training accuracy is 0.031250, time 13.570 samples/sec
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+epoch 1, total_step 18340, total loss is 30.29 , inference loss is 17.24, weight deacy loss is 13.05, training accuracy is 0.156250, time 232.431 samples/sec
+epoch 1, total_step 18360, total loss is 28.78 , inference loss is 15.73, weight deacy loss is 13.05, training accuracy is 0.281250, time 225.573 samples/sec
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+epoch 1, total_step 18440, total loss is 32.62 , inference loss is 19.58, weight deacy loss is 13.04, training accuracy is 0.125000, time 220.941 samples/sec
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+epoch 1, total_step 19980, total loss is 31.14 , inference loss is 18.24, weight deacy loss is 12.90, training accuracy is 0.156250, time 219.775 samples/sec
+testing verification..
+(12000, 512)
+infer time 23.618454000000025
+best_threshold_index 117 0.9679629629629629
+best_threshold_index 116 0.9655555555555555
+best_threshold_index 116 0.9675925925925926
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+best_threshold_index 116 0.9666666666666667
+best_threshold_index 116 0.9644444444444444
+best_threshold_index 117 0.9664814814814815
+[lfw][20000]XNorm: 22.208202
+[lfw][20000]Accuracy-Flip: 0.96467+-0.01256
+test accuracy is:  0.9646666666666667
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+epoch 1, total_step 20020, total loss is 30.23 , inference loss is 17.33, weight deacy loss is 12.90, training accuracy is 0.062500, time 209.742 samples/sec
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+epoch 1, total_step 20080, total loss is 29.39 , inference loss is 16.49, weight deacy loss is 12.89, training accuracy is 0.093750, time 204.760 samples/sec
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+testing verification..
+(12000, 512)
+infer time 25.185083000000006
+best_threshold_index 105 0.9612962962962963
+best_threshold_index 105 0.962037037037037
+best_threshold_index 104 0.9631481481481482
+best_threshold_index 105 0.9638888888888889
+best_threshold_index 105 0.9624074074074074
+best_threshold_index 105 0.9601851851851851
+best_threshold_index 105 0.9616666666666667
+best_threshold_index 105 0.9614814814814815
+best_threshold_index 105 0.9596296296296296
+best_threshold_index 105 0.960925925925926
+[lfw][22000]XNorm: 24.974489
+[lfw][22000]Accuracy-Flip: 0.96117+-0.01167
+test accuracy is:  0.9611666666666668
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+epoch 1, total_step 23180, total loss is 30.36 , inference loss is 17.74, weight deacy loss is 12.62, training accuracy is 0.156250, time 218.275 samples/sec
+epoch 1, total_step 23200, total loss is 34.79 , inference loss is 22.17, weight deacy loss is 12.62, training accuracy is 0.031250, time 210.483 samples/sec
+epoch 1, total_step 23220, total loss is 29.40 , inference loss is 16.78, weight deacy loss is 12.62, training accuracy is 0.187500, time 235.802 samples/sec
+epoch 1, total_step 23240, total loss is 31.51 , inference loss is 18.90, weight deacy loss is 12.61, training accuracy is 0.062500, time 206.154 samples/sec
+epoch 1, total_step 23260, total loss is 31.76 , inference loss is 19.15, weight deacy loss is 12.61, training accuracy is 0.093750, time 297.434 samples/sec
+epoch 1, total_step 23280, total loss is 27.82 , inference loss is 15.21, weight deacy loss is 12.61, training accuracy is 0.218750, time 206.028 samples/sec
+epoch 1, total_step 23300, total loss is 27.39 , inference loss is 14.79, weight deacy loss is 12.61, training accuracy is 0.156250, time 202.531 samples/sec
+epoch 1, total_step 23320, total loss is 31.77 , inference loss is 19.16, weight deacy loss is 12.61, training accuracy is 0.093750, time 230.253 samples/sec
+epoch 1, total_step 23340, total loss is 33.41 , inference loss is 20.80, weight deacy loss is 12.61, training accuracy is 0.062500, time 221.920 samples/sec
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+epoch 1, total_step 23380, total loss is 30.68 , inference loss is 18.08, weight deacy loss is 12.60, training accuracy is 0.187500, time 219.731 samples/sec
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+epoch 1, total_step 23440, total loss is 29.04 , inference loss is 16.44, weight deacy loss is 12.60, training accuracy is 0.125000, time 193.328 samples/sec
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+epoch 1, total_step 23480, total loss is 32.74 , inference loss is 20.14, weight deacy loss is 12.59, training accuracy is 0.125000, time 222.713 samples/sec
+epoch 1, total_step 23500, total loss is 28.54 , inference loss is 15.95, weight deacy loss is 12.59, training accuracy is 0.156250, time 221.890 samples/sec
+epoch 1, total_step 23520, total loss is 31.45 , inference loss is 18.86, weight deacy loss is 12.59, training accuracy is 0.093750, time 294.763 samples/sec
+epoch 1, total_step 23540, total loss is 33.98 , inference loss is 21.39, weight deacy loss is 12.59, training accuracy is 0.062500, time 212.063 samples/sec
+epoch 1, total_step 23560, total loss is 33.93 , inference loss is 21.34, weight deacy loss is 12.59, training accuracy is 0.062500, time 217.808 samples/sec
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+epoch 1, total_step 23620, total loss is 31.56 , inference loss is 18.98, weight deacy loss is 12.58, training accuracy is 0.093750, time 215.816 samples/sec
+epoch 1, total_step 23640, total loss is 28.35 , inference loss is 15.77, weight deacy loss is 12.58, training accuracy is 0.218750, time 217.760 samples/sec
+epoch 1, total_step 23660, total loss is 31.24 , inference loss is 18.66, weight deacy loss is 12.58, training accuracy is 0.093750, time 208.904 samples/sec
+epoch 1, total_step 23680, total loss is 31.58 , inference loss is 19.00, weight deacy loss is 12.58, training accuracy is 0.031250, time 215.575 samples/sec
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+epoch 1, total_step 23720, total loss is 36.90 , inference loss is 24.33, weight deacy loss is 12.57, training accuracy is 0.093750, time 205.549 samples/sec
+epoch 1, total_step 23740, total loss is 32.09 , inference loss is 19.52, weight deacy loss is 12.57, training accuracy is 0.156250, time 220.374 samples/sec
+epoch 1, total_step 23760, total loss is 30.59 , inference loss is 18.03, weight deacy loss is 12.57, training accuracy is 0.125000, time 227.424 samples/sec
+epoch 1, total_step 23780, total loss is 35.93 , inference loss is 23.37, weight deacy loss is 12.57, training accuracy is 0.031250, time 299.464 samples/sec
+epoch 1, total_step 23800, total loss is 28.32 , inference loss is 15.75, weight deacy loss is 12.57, training accuracy is 0.093750, time 212.224 samples/sec
+epoch 1, total_step 23820, total loss is 33.11 , inference loss is 20.54, weight deacy loss is 12.56, training accuracy is 0.125000, time 226.031 samples/sec
+epoch 1, total_step 23840, total loss is 33.89 , inference loss is 21.33, weight deacy loss is 12.56, training accuracy is 0.062500, time 238.076 samples/sec
+epoch 1, total_step 23860, total loss is 31.33 , inference loss is 18.77, weight deacy loss is 12.56, training accuracy is 0.125000, time 216.058 samples/sec
+epoch 1, total_step 23880, total loss is 30.39 , inference loss is 17.83, weight deacy loss is 12.56, training accuracy is 0.093750, time 217.098 samples/sec
+epoch 1, total_step 23900, total loss is 34.13 , inference loss is 21.58, weight deacy loss is 12.56, training accuracy is 0.031250, time 216.325 samples/sec
+epoch 1, total_step 23920, total loss is 31.63 , inference loss is 19.08, weight deacy loss is 12.55, training accuracy is 0.062500, time 204.985 samples/sec
+epoch 1, total_step 23940, total loss is 25.92 , inference loss is 13.37, weight deacy loss is 12.55, training accuracy is 0.156250, time 211.961 samples/sec
+epoch 1, total_step 23960, total loss is 29.69 , inference loss is 17.14, weight deacy loss is 12.55, training accuracy is 0.062500, time 235.051 samples/sec
+epoch 1, total_step 23980, total loss is 32.37 , inference loss is 19.82, weight deacy loss is 12.55, training accuracy is 0.187500, time 206.935 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.48513200000002
+best_threshold_index 110 0.9711111111111111
+best_threshold_index 110 0.9718518518518519
+best_threshold_index 110 0.9716666666666667
+best_threshold_index 108 0.9714814814814815
+best_threshold_index 110 0.9718518518518519
+best_threshold_index 110 0.9698148148148148
+best_threshold_index 110 0.9705555555555555
+best_threshold_index 107 0.9705555555555555
+best_threshold_index 110 0.9688888888888889
+best_threshold_index 108 0.9694444444444444
+[lfw][24000]XNorm: 24.657568
+[lfw][24000]Accuracy-Flip: 0.96933+-0.00898
+test accuracy is:  0.9693333333333334
+epoch 1, total_step 24000, total loss is 31.20 , inference loss is 18.65, weight deacy loss is 12.55, training accuracy is 0.125000, time 17.094 samples/sec
+epoch 1, total_step 24020, total loss is 34.47 , inference loss is 21.92, weight deacy loss is 12.55, training accuracy is 0.031250, time 247.498 samples/sec
+epoch 1, total_step 24040, total loss is 32.44 , inference loss is 19.90, weight deacy loss is 12.54, training accuracy is 0.093750, time 234.916 samples/sec
+epoch 1, total_step 24060, total loss is 32.15 , inference loss is 19.60, weight deacy loss is 12.54, training accuracy is 0.031250, time 211.436 samples/sec
+epoch 1, total_step 24080, total loss is 30.73 , inference loss is 18.19, weight deacy loss is 12.54, training accuracy is 0.031250, time 207.138 samples/sec
+epoch 1, total_step 24100, total loss is 30.52 , inference loss is 17.98, weight deacy loss is 12.54, training accuracy is 0.093750, time 219.368 samples/sec
+epoch 1, total_step 24120, total loss is 27.73 , inference loss is 15.20, weight deacy loss is 12.54, training accuracy is 0.062500, time 221.377 samples/sec
+epoch 1, total_step 24140, total loss is 31.12 , inference loss is 18.59, weight deacy loss is 12.54, training accuracy is 0.125000, time 209.273 samples/sec
+epoch 1, total_step 24160, total loss is 30.82 , inference loss is 18.28, weight deacy loss is 12.53, training accuracy is 0.062500, time 298.947 samples/sec
+epoch 1, total_step 24180, total loss is 31.73 , inference loss is 19.19, weight deacy loss is 12.53, training accuracy is 0.062500, time 225.784 samples/sec
+epoch 1, total_step 24200, total loss is 33.53 , inference loss is 21.00, weight deacy loss is 12.53, training accuracy is 0.062500, time 206.721 samples/sec
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+epoch 1, total_step 24240, total loss is 34.52 , inference loss is 22.00, weight deacy loss is 12.53, training accuracy is 0.000000, time 218.944 samples/sec
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+epoch 1, total_step 24300, total loss is 32.20 , inference loss is 19.68, weight deacy loss is 12.52, training accuracy is 0.000000, time 14.234 samples/sec
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+epoch 1, total_step 24340, total loss is 30.18 , inference loss is 17.66, weight deacy loss is 12.52, training accuracy is 0.187500, time 213.204 samples/sec
+epoch 1, total_step 24360, total loss is 30.04 , inference loss is 17.53, weight deacy loss is 12.52, training accuracy is 0.125000, time 196.317 samples/sec
+epoch 1, total_step 24380, total loss is 30.59 , inference loss is 18.07, weight deacy loss is 12.52, training accuracy is 0.125000, time 225.382 samples/sec
+epoch 1, total_step 24400, total loss is 28.43 , inference loss is 15.92, weight deacy loss is 12.51, training accuracy is 0.156250, time 225.355 samples/sec
+epoch 1, total_step 24420, total loss is 28.74 , inference loss is 16.23, weight deacy loss is 12.51, training accuracy is 0.031250, time 298.954 samples/sec
+epoch 1, total_step 24440, total loss is 33.55 , inference loss is 21.04, weight deacy loss is 12.51, training accuracy is 0.062500, time 208.531 samples/sec
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+epoch 1, total_step 24500, total loss is 33.23 , inference loss is 20.72, weight deacy loss is 12.51, training accuracy is 0.062500, time 216.106 samples/sec
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+epoch 1, total_step 25980, total loss is 27.47 , inference loss is 15.09, weight deacy loss is 12.38, training accuracy is 0.125000, time 221.700 samples/sec
+testing verification..
+(12000, 512)
+infer time 25.37247
+best_threshold_index 109 0.9681481481481482
+best_threshold_index 109 0.9674074074074074
+best_threshold_index 103 0.9692592592592593
+best_threshold_index 104 0.9681481481481482
+best_threshold_index 109 0.9679629629629629
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+best_threshold_index 103 0.9672222222222222
+best_threshold_index 103 0.9661111111111111
+best_threshold_index 101 0.9672222222222222
+[lfw][26000]XNorm: 23.189070
+[lfw][26000]Accuracy-Flip: 0.96483+-0.00835
+test accuracy is:  0.9648333333333333
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+epoch 1, total_step 27980, total loss is 30.76 , inference loss is 18.55, weight deacy loss is 12.22, training accuracy is 0.093750, time 218.997 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.042339
+best_threshold_index 100 0.9685185185185186
+best_threshold_index 100 0.9677777777777777
+best_threshold_index 100 0.9685185185185186
+best_threshold_index 100 0.9696296296296296
+best_threshold_index 100 0.9687037037037037
+best_threshold_index 100 0.9674074074074074
+best_threshold_index 100 0.9687037037037037
+best_threshold_index 100 0.9679629629629629
+best_threshold_index 100 0.9662962962962963
+best_threshold_index 100 0.9664814814814815
+[lfw][28000]XNorm: 24.494244
+[lfw][28000]Accuracy-Flip: 0.96800+-0.00891
+test accuracy is:  0.968
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+epoch 1, total_step 28100, total loss is 29.37 , inference loss is 17.17, weight deacy loss is 12.21, training accuracy is 0.125000, time 220.549 samples/sec
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+epoch 1, total_step 28140, total loss is 28.12 , inference loss is 15.92, weight deacy loss is 12.20, training accuracy is 0.125000, time 207.794 samples/sec
+epoch 1, total_step 28160, total loss is 30.70 , inference loss is 18.50, weight deacy loss is 12.20, training accuracy is 0.062500, time 300.351 samples/sec
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+epoch 1, total_step 29120, total loss is 28.72 , inference loss is 16.60, weight deacy loss is 12.13, training accuracy is 0.093750, time 223.226 samples/sec
+epoch 1, total_step 29140, total loss is 33.37 , inference loss is 21.25, weight deacy loss is 12.12, training accuracy is 0.093750, time 210.906 samples/sec
+epoch 1, total_step 29160, total loss is 34.40 , inference loss is 22.28, weight deacy loss is 12.12, training accuracy is 0.062500, time 205.446 samples/sec
+epoch 1, total_step 29180, total loss is 28.07 , inference loss is 15.95, weight deacy loss is 12.12, training accuracy is 0.093750, time 213.200 samples/sec
+epoch 1, total_step 29200, total loss is 30.54 , inference loss is 18.42, weight deacy loss is 12.12, training accuracy is 0.125000, time 227.029 samples/sec
+epoch 1, total_step 29220, total loss is 29.75 , inference loss is 17.64, weight deacy loss is 12.12, training accuracy is 0.031250, time 216.589 samples/sec
+epoch 1, total_step 29240, total loss is 28.55 , inference loss is 16.43, weight deacy loss is 12.12, training accuracy is 0.156250, time 227.861 samples/sec
+epoch 1, total_step 29260, total loss is 31.79 , inference loss is 19.68, weight deacy loss is 12.11, training accuracy is 0.031250, time 217.159 samples/sec
+epoch 1, total_step 29280, total loss is 35.23 , inference loss is 23.12, weight deacy loss is 12.11, training accuracy is 0.062500, time 203.270 samples/sec
+epoch 1, total_step 29300, total loss is 25.53 , inference loss is 13.42, weight deacy loss is 12.11, training accuracy is 0.125000, time 235.927 samples/sec
+epoch 1, total_step 29320, total loss is 35.20 , inference loss is 23.09, weight deacy loss is 12.11, training accuracy is 0.062500, time 215.308 samples/sec
+epoch 1, total_step 29340, total loss is 35.73 , inference loss is 23.62, weight deacy loss is 12.11, training accuracy is 0.031250, time 206.156 samples/sec
+epoch 1, total_step 29360, total loss is 29.01 , inference loss is 16.91, weight deacy loss is 12.11, training accuracy is 0.000000, time 224.986 samples/sec
+epoch 1, total_step 29380, total loss is 30.29 , inference loss is 18.18, weight deacy loss is 12.10, training accuracy is 0.062500, time 215.202 samples/sec
+epoch 1, total_step 29400, total loss is 30.90 , inference loss is 18.80, weight deacy loss is 12.10, training accuracy is 0.000000, time 13.588 samples/sec
+epoch 1, total_step 29420, total loss is 25.07 , inference loss is 12.97, weight deacy loss is 12.10, training accuracy is 0.093750, time 211.330 samples/sec
+epoch 1, total_step 29440, total loss is 30.08 , inference loss is 17.98, weight deacy loss is 12.10, training accuracy is 0.093750, time 218.052 samples/sec
+epoch 1, total_step 29460, total loss is 27.73 , inference loss is 15.63, weight deacy loss is 12.10, training accuracy is 0.093750, time 207.029 samples/sec
+epoch 1, total_step 29480, total loss is 33.78 , inference loss is 21.69, weight deacy loss is 12.10, training accuracy is 0.062500, time 205.267 samples/sec
+epoch 1, total_step 29500, total loss is 29.15 , inference loss is 17.06, weight deacy loss is 12.10, training accuracy is 0.093750, time 220.361 samples/sec
+epoch 1, total_step 29520, total loss is 32.68 , inference loss is 20.58, weight deacy loss is 12.09, training accuracy is 0.062500, time 221.796 samples/sec
+epoch 1, total_step 29540, total loss is 32.46 , inference loss is 20.37, weight deacy loss is 12.09, training accuracy is 0.031250, time 227.689 samples/sec
+epoch 1, total_step 29560, total loss is 29.07 , inference loss is 16.98, weight deacy loss is 12.09, training accuracy is 0.062500, time 235.399 samples/sec
+epoch 1, total_step 29580, total loss is 28.78 , inference loss is 16.69, weight deacy loss is 12.09, training accuracy is 0.062500, time 224.722 samples/sec
+epoch 1, total_step 29600, total loss is 33.04 , inference loss is 20.95, weight deacy loss is 12.09, training accuracy is 0.031250, time 177.349 samples/sec
+epoch 1, total_step 29620, total loss is 30.97 , inference loss is 18.88, weight deacy loss is 12.09, training accuracy is 0.125000, time 197.462 samples/sec
+epoch 1, total_step 29640, total loss is 31.49 , inference loss is 19.41, weight deacy loss is 12.08, training accuracy is 0.062500, time 223.133 samples/sec
+epoch 1, total_step 29660, total loss is 29.71 , inference loss is 17.63, weight deacy loss is 12.08, training accuracy is 0.062500, time 296.068 samples/sec
+epoch 1, total_step 29680, total loss is 27.81 , inference loss is 15.73, weight deacy loss is 12.08, training accuracy is 0.093750, time 202.727 samples/sec
+epoch 1, total_step 29700, total loss is 31.85 , inference loss is 19.77, weight deacy loss is 12.08, training accuracy is 0.062500, time 11.713 samples/sec
+epoch 1, total_step 29720, total loss is 32.35 , inference loss is 20.27, weight deacy loss is 12.08, training accuracy is 0.062500, time 207.637 samples/sec
+epoch 1, total_step 29740, total loss is 32.47 , inference loss is 20.39, weight deacy loss is 12.08, training accuracy is 0.000000, time 217.585 samples/sec
+epoch 1, total_step 29760, total loss is 26.80 , inference loss is 14.73, weight deacy loss is 12.07, training accuracy is 0.093750, time 219.711 samples/sec
+epoch 1, total_step 29780, total loss is 32.77 , inference loss is 20.70, weight deacy loss is 12.07, training accuracy is 0.000000, time 215.001 samples/sec
+epoch 1, total_step 29800, total loss is 28.38 , inference loss is 16.31, weight deacy loss is 12.07, training accuracy is 0.062500, time 220.964 samples/sec
+epoch 1, total_step 29820, total loss is 29.10 , inference loss is 17.03, weight deacy loss is 12.07, training accuracy is 0.031250, time 196.275 samples/sec
+epoch 1, total_step 29840, total loss is 28.39 , inference loss is 16.32, weight deacy loss is 12.07, training accuracy is 0.000000, time 216.648 samples/sec
+epoch 1, total_step 29860, total loss is 30.25 , inference loss is 18.19, weight deacy loss is 12.07, training accuracy is 0.093750, time 214.559 samples/sec
+epoch 1, total_step 29880, total loss is 33.74 , inference loss is 21.67, weight deacy loss is 12.07, training accuracy is 0.062500, time 214.658 samples/sec
+epoch 1, total_step 29900, total loss is 29.14 , inference loss is 17.08, weight deacy loss is 12.06, training accuracy is 0.000000, time 218.819 samples/sec
+epoch 1, total_step 29920, total loss is 31.86 , inference loss is 19.80, weight deacy loss is 12.06, training accuracy is 0.000000, time 295.834 samples/sec
+epoch 1, total_step 29940, total loss is 29.26 , inference loss is 17.20, weight deacy loss is 12.06, training accuracy is 0.031250, time 207.408 samples/sec
+epoch 1, total_step 29960, total loss is 29.38 , inference loss is 17.32, weight deacy loss is 12.06, training accuracy is 0.125000, time 208.918 samples/sec
+epoch 1, total_step 29980, total loss is 26.87 , inference loss is 14.81, weight deacy loss is 12.06, training accuracy is 0.031250, time 203.281 samples/sec
+testing verification..
+(12000, 512)
+infer time 23.820756999999972
+best_threshold_index 98 0.9662962962962963
+best_threshold_index 101 0.9653703703703703
+best_threshold_index 98 0.9666666666666667
+best_threshold_index 101 0.967037037037037
+best_threshold_index 98 0.9662962962962963
+best_threshold_index 101 0.9640740740740741
+best_threshold_index 101 0.9662962962962963
+best_threshold_index 98 0.9651851851851851
+best_threshold_index 101 0.9640740740740741
+best_threshold_index 101 0.9642592592592593
+[lfw][30000]XNorm: 24.312305
+[lfw][30000]Accuracy-Flip: 0.96367+-0.01038
+test accuracy is:  0.9636666666666667
+epoch 1, total_step 30000, total loss is 30.46 , inference loss is 18.41, weight deacy loss is 12.06, training accuracy is 0.062500, time 17.490 samples/sec
+epoch 1, total_step 30020, total loss is 31.43 , inference loss is 19.37, weight deacy loss is 12.05, training accuracy is 0.031250, time 207.195 samples/sec
+epoch 1, total_step 30040, total loss is 29.87 , inference loss is 17.81, weight deacy loss is 12.05, training accuracy is 0.031250, time 282.438 samples/sec
+epoch 1, total_step 30060, total loss is 26.80 , inference loss is 14.75, weight deacy loss is 12.05, training accuracy is 0.187500, time 297.333 samples/sec
+epoch 1, total_step 30080, total loss is 30.61 , inference loss is 18.56, weight deacy loss is 12.05, training accuracy is 0.031250, time 217.101 samples/sec
+epoch 1, total_step 30100, total loss is 30.83 , inference loss is 18.78, weight deacy loss is 12.05, training accuracy is 0.093750, time 218.035 samples/sec
+epoch 1, total_step 30120, total loss is 30.24 , inference loss is 18.20, weight deacy loss is 12.05, training accuracy is 0.062500, time 215.719 samples/sec
+epoch 1, total_step 30140, total loss is 32.53 , inference loss is 20.49, weight deacy loss is 12.04, training accuracy is 0.000000, time 223.021 samples/sec
+epoch 1, total_step 30160, total loss is 28.02 , inference loss is 15.98, weight deacy loss is 12.04, training accuracy is 0.062500, time 211.141 samples/sec
+epoch 1, total_step 30180, total loss is 32.75 , inference loss is 20.71, weight deacy loss is 12.04, training accuracy is 0.000000, time 208.010 samples/sec
+epoch 1, total_step 30200, total loss is 27.11 , inference loss is 15.07, weight deacy loss is 12.04, training accuracy is 0.093750, time 225.703 samples/sec
+epoch 1, total_step 30220, total loss is 31.33 , inference loss is 19.29, weight deacy loss is 12.04, training accuracy is 0.093750, time 292.195 samples/sec
+epoch 1, total_step 30240, total loss is 32.14 , inference loss is 20.10, weight deacy loss is 12.04, training accuracy is 0.031250, time 297.867 samples/sec
+epoch 1, total_step 30260, total loss is 31.51 , inference loss is 19.47, weight deacy loss is 12.04, training accuracy is 0.031250, time 223.619 samples/sec
+epoch 1, total_step 30280, total loss is 32.03 , inference loss is 19.99, weight deacy loss is 12.03, training accuracy is 0.062500, time 225.005 samples/sec2022-12-01 02:14:12.420604: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:145] Filling up shuffle buffer (this may take a while): 7472 of 10000
+2022-12-01 02:14:15.188531: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:195] Shuffle buffer filled.
+
+epoch 1, total_step 30300, total loss is 30.89 , inference loss is 18.86, weight deacy loss is 12.03, training accuracy is 0.062500, time 13.844 samples/sec
+epoch 1, total_step 30320, total loss is 30.80 , inference loss is 18.77, weight deacy loss is 12.03, training accuracy is 0.062500, time 218.722 samples/sec
+epoch 1, total_step 30340, total loss is 32.98 , inference loss is 20.95, weight deacy loss is 12.03, training accuracy is 0.031250, time 224.483 samples/sec
+epoch 1, total_step 30360, total loss is 27.75 , inference loss is 15.73, weight deacy loss is 12.03, training accuracy is 0.062500, time 198.662 samples/sec
+epoch 1, total_step 30380, total loss is 30.56 , inference loss is 18.53, weight deacy loss is 12.03, training accuracy is 0.093750, time 209.265 samples/sec
+epoch 1, total_step 30400, total loss is 28.45 , inference loss is 16.43, weight deacy loss is 12.02, training accuracy is 0.062500, time 179.320 samples/sec
+epoch 1, total_step 30420, total loss is 29.75 , inference loss is 17.73, weight deacy loss is 12.02, training accuracy is 0.031250, time 238.921 samples/sec
+epoch 1, total_step 30440, total loss is 28.31 , inference loss is 16.29, weight deacy loss is 12.02, training accuracy is 0.062500, time 296.394 samples/sec
+epoch 1, total_step 30460, total loss is 28.40 , inference loss is 16.38, weight deacy loss is 12.02, training accuracy is 0.156250, time 196.670 samples/sec
+epoch 1, total_step 30480, total loss is 29.95 , inference loss is 17.93, weight deacy loss is 12.02, training accuracy is 0.125000, time 194.031 samples/sec
+epoch 1, total_step 30500, total loss is 31.63 , inference loss is 19.61, weight deacy loss is 12.02, training accuracy is 0.000000, time 215.180 samples/sec
+epoch 1, total_step 30520, total loss is 27.49 , inference loss is 15.48, weight deacy loss is 12.02, training accuracy is 0.093750, time 200.289 samples/sec
+epoch 1, total_step 30540, total loss is 29.39 , inference loss is 17.37, weight deacy loss is 12.01, training accuracy is 0.031250, time 198.545 samples/sec
+epoch 1, total_step 30560, total loss is 31.39 , inference loss is 19.38, weight deacy loss is 12.01, training accuracy is 0.062500, time 175.325 samples/sec
+epoch 1, total_step 30580, total loss is 28.24 , inference loss is 16.23, weight deacy loss is 12.01, training accuracy is 0.062500, time 203.883 samples/sec
+epoch 1, total_step 30600, total loss is 28.27 , inference loss is 16.26, weight deacy loss is 12.01, training accuracy is 0.000000, time 12.438 samples/sec
+epoch 1, total_step 30620, total loss is 31.64 , inference loss is 19.63, weight deacy loss is 12.01, training accuracy is 0.031250, time 186.294 samples/sec
+epoch 1, total_step 30640, total loss is 30.54 , inference loss is 18.54, weight deacy loss is 12.01, training accuracy is 0.031250, time 206.744 samples/sec
+epoch 1, total_step 30660, total loss is 28.98 , inference loss is 16.98, weight deacy loss is 12.00, training accuracy is 0.031250, time 194.950 samples/sec
+End of epoch 1
+epoch 2, total_step 30680, total loss is 35.71 , inference loss is 23.71, weight deacy loss is 12.00, training accuracy is 0.000000, time 216.983 samples/sec
+epoch 2, total_step 30700, total loss is 24.20 , inference loss is 12.20, weight deacy loss is 12.00, training accuracy is 0.281250, time 207.450 samples/sec
+epoch 2, total_step 30720, total loss is 25.12 , inference loss is 13.12, weight deacy loss is 12.00, training accuracy is 0.187500, time 224.445 samples/sec
+epoch 2, total_step 30740, total loss is 26.46 , inference loss is 14.47, weight deacy loss is 12.00, training accuracy is 0.187500, time 220.525 samples/sec
+epoch 2, total_step 30760, total loss is 29.88 , inference loss is 17.88, weight deacy loss is 12.00, training accuracy is 0.093750, time 205.587 samples/sec
+epoch 2, total_step 30780, total loss is 23.87 , inference loss is 11.87, weight deacy loss is 12.00, training accuracy is 0.312500, time 207.547 samples/sec
+epoch 2, total_step 30800, total loss is 22.84 , inference loss is 10.85, weight deacy loss is 11.99, training accuracy is 0.375000, time 219.085 samples/sec
+epoch 2, total_step 30820, total loss is 18.33 , inference loss is 6.34, weight deacy loss is 11.99, training accuracy is 0.562500, time 211.922 samples/sec
+epoch 2, total_step 30840, total loss is 21.21 , inference loss is 9.22, weight deacy loss is 11.99, training accuracy is 0.437500, time 206.380 samples/sec
+epoch 2, total_step 30860, total loss is 23.89 , inference loss is 11.90, weight deacy loss is 11.99, training accuracy is 0.218750, time 210.943 samples/sec
+epoch 2, total_step 30880, total loss is 23.34 , inference loss is 11.35, weight deacy loss is 11.99, training accuracy is 0.343750, time 206.891 samples/sec
+epoch 2, total_step 30900, total loss is 22.32 , inference loss is 10.33, weight deacy loss is 11.98, training accuracy is 0.375000, time 13.399 samples/sec
+epoch 2, total_step 30920, total loss is 27.53 , inference loss is 15.55, weight deacy loss is 11.98, training accuracy is 0.250000, time 297.722 samples/sec
+epoch 2, total_step 30940, total loss is 29.51 , inference loss is 17.53, weight deacy loss is 11.98, training accuracy is 0.125000, time 210.588 samples/sec
+epoch 2, total_step 30960, total loss is 30.87 , inference loss is 18.89, weight deacy loss is 11.98, training accuracy is 0.218750, time 213.653 samples/sec
+epoch 2, total_step 30980, total loss is 28.52 , inference loss is 16.54, weight deacy loss is 11.98, training accuracy is 0.156250, time 225.150 samples/sec
+epoch 2, total_step 31000, total loss is 26.86 , inference loss is 14.89, weight deacy loss is 11.98, training accuracy is 0.218750, time 204.898 samples/sec
+epoch 2, total_step 31020, total loss is 26.63 , inference loss is 14.66, weight deacy loss is 11.97, training accuracy is 0.250000, time 220.556 samples/sec
+epoch 2, total_step 31040, total loss is 23.08 , inference loss is 11.10, weight deacy loss is 11.97, training accuracy is 0.343750, time 232.760 samples/sec
+epoch 2, total_step 31060, total loss is 24.44 , inference loss is 12.47, weight deacy loss is 11.97, training accuracy is 0.281250, time 216.370 samples/sec
+epoch 2, total_step 31080, total loss is 27.86 , inference loss is 15.89, weight deacy loss is 11.97, training accuracy is 0.218750, time 228.707 samples/sec
+epoch 2, total_step 31100, total loss is 31.89 , inference loss is 19.92, weight deacy loss is 11.97, training accuracy is 0.218750, time 220.840 samples/sec
+epoch 2, total_step 31120, total loss is 26.57 , inference loss is 14.60, weight deacy loss is 11.97, training accuracy is 0.187500, time 195.224 samples/sec
+epoch 2, total_step 31140, total loss is 21.69 , inference loss is 9.73, weight deacy loss is 11.96, training accuracy is 0.218750, time 224.079 samples/sec
+epoch 2, total_step 31160, total loss is 27.19 , inference loss is 15.23, weight deacy loss is 11.96, training accuracy is 0.250000, time 212.757 samples/sec
+epoch 2, total_step 31180, total loss is 28.02 , inference loss is 16.06, weight deacy loss is 11.96, training accuracy is 0.281250, time 234.813 samples/sec
+epoch 2, total_step 31200, total loss is 22.04 , inference loss is 10.08, weight deacy loss is 11.96, training accuracy is 0.375000, time 15.186 samples/sec
+epoch 2, total_step 31220, total loss is 26.49 , inference loss is 14.53, weight deacy loss is 11.96, training accuracy is 0.156250, time 225.066 samples/sec
+epoch 2, total_step 31240, total loss is 22.28 , inference loss is 10.33, weight deacy loss is 11.96, training accuracy is 0.375000, time 244.222 samples/sec
+epoch 2, total_step 31260, total loss is 27.30 , inference loss is 15.34, weight deacy loss is 11.96, training accuracy is 0.187500, time 225.819 samples/sec
+epoch 2, total_step 31280, total loss is 26.87 , inference loss is 14.92, weight deacy loss is 11.95, training accuracy is 0.281250, time 212.931 samples/sec
+epoch 2, total_step 31300, total loss is 28.42 , inference loss is 16.47, weight deacy loss is 11.95, training accuracy is 0.156250, time 213.421 samples/sec
+epoch 2, total_step 31320, total loss is 30.20 , inference loss is 18.25, weight deacy loss is 11.95, training accuracy is 0.187500, time 224.438 samples/sec
+epoch 2, total_step 31340, total loss is 29.52 , inference loss is 17.57, weight deacy loss is 11.95, training accuracy is 0.250000, time 225.336 samples/sec
+epoch 2, total_step 31360, total loss is 26.66 , inference loss is 14.72, weight deacy loss is 11.95, training accuracy is 0.187500, time 217.539 samples/sec
+epoch 2, total_step 31380, total loss is 25.04 , inference loss is 13.09, weight deacy loss is 11.95, training accuracy is 0.156250, time 224.403 samples/sec
+epoch 2, total_step 31400, total loss is 26.99 , inference loss is 15.05, weight deacy loss is 11.94, training accuracy is 0.218750, time 230.749 samples/sec
+epoch 2, total_step 31420, total loss is 25.35 , inference loss is 13.41, weight deacy loss is 11.94, training accuracy is 0.156250, time 229.296 samples/sec
+epoch 2, total_step 31440, total loss is 29.32 , inference loss is 17.38, weight deacy loss is 11.94, training accuracy is 0.187500, time 222.774 samples/sec
+epoch 2, total_step 31460, total loss is 25.64 , inference loss is 13.70, weight deacy loss is 11.94, training accuracy is 0.156250, time 199.530 samples/sec
+epoch 2, total_step 31480, total loss is 29.48 , inference loss is 17.54, weight deacy loss is 11.94, training accuracy is 0.093750, time 214.993 samples/sec
+epoch 2, total_step 31500, total loss is 30.98 , inference loss is 19.05, weight deacy loss is 11.94, training accuracy is 0.218750, time 13.630 samples/sec
+epoch 2, total_step 31520, total loss is 26.80 , inference loss is 14.87, weight deacy loss is 11.93, training accuracy is 0.125000, time 218.853 samples/sec
+epoch 2, total_step 31540, total loss is 29.59 , inference loss is 17.66, weight deacy loss is 11.93, training accuracy is 0.093750, time 228.407 samples/sec
+epoch 2, total_step 31560, total loss is 28.54 , inference loss is 16.61, weight deacy loss is 11.93, training accuracy is 0.062500, time 220.003 samples/sec
+epoch 2, total_step 31580, total loss is 27.78 , inference loss is 15.85, weight deacy loss is 11.93, training accuracy is 0.250000, time 215.451 samples/sec
+epoch 2, total_step 31600, total loss is 31.20 , inference loss is 19.28, weight deacy loss is 11.93, training accuracy is 0.125000, time 215.499 samples/sec
+epoch 2, total_step 31620, total loss is 30.30 , inference loss is 18.38, weight deacy loss is 11.93, training accuracy is 0.156250, time 217.469 samples/sec
+epoch 2, total_step 31640, total loss is 26.17 , inference loss is 14.25, weight deacy loss is 11.92, training accuracy is 0.125000, time 196.000 samples/sec
+epoch 2, total_step 31660, total loss is 25.96 , inference loss is 14.04, weight deacy loss is 11.92, training accuracy is 0.218750, time 218.664 samples/sec
+epoch 2, total_step 31680, total loss is 30.42 , inference loss is 18.50, weight deacy loss is 11.92, training accuracy is 0.093750, time 217.967 samples/sec
+epoch 2, total_step 31700, total loss is 25.41 , inference loss is 13.49, weight deacy loss is 11.92, training accuracy is 0.187500, time 216.818 samples/sec
+epoch 2, total_step 31720, total loss is 26.19 , inference loss is 14.27, weight deacy loss is 11.92, training accuracy is 0.281250, time 220.922 samples/sec
+epoch 2, total_step 31740, total loss is 26.10 , inference loss is 14.18, weight deacy loss is 11.92, training accuracy is 0.250000, time 224.182 samples/sec
+epoch 2, total_step 31760, total loss is 25.51 , inference loss is 13.59, weight deacy loss is 11.91, training accuracy is 0.218750, time 293.086 samples/sec
+epoch 2, total_step 31780, total loss is 29.58 , inference loss is 17.67, weight deacy loss is 11.91, training accuracy is 0.125000, time 200.006 samples/sec
+epoch 2, total_step 31800, total loss is 25.81 , inference loss is 13.90, weight deacy loss is 11.91, training accuracy is 0.281250, time 11.854 samples/sec
+epoch 2, total_step 31820, total loss is 28.50 , inference loss is 16.59, weight deacy loss is 11.91, training accuracy is 0.156250, time 218.688 samples/sec
+epoch 2, total_step 31840, total loss is 31.86 , inference loss is 19.95, weight deacy loss is 11.91, training accuracy is 0.125000, time 222.744 samples/sec
+epoch 2, total_step 31860, total loss is 28.36 , inference loss is 16.45, weight deacy loss is 11.91, training accuracy is 0.093750, time 213.316 samples/sec
+epoch 2, total_step 31880, total loss is 25.86 , inference loss is 13.96, weight deacy loss is 11.90, training accuracy is 0.250000, time 215.053 samples/sec
+epoch 2, total_step 31900, total loss is 26.90 , inference loss is 15.00, weight deacy loss is 11.90, training accuracy is 0.250000, time 223.435 samples/sec
+epoch 2, total_step 31920, total loss is 28.03 , inference loss is 16.13, weight deacy loss is 11.90, training accuracy is 0.187500, time 216.700 samples/sec
+epoch 2, total_step 31940, total loss is 28.20 , inference loss is 16.30, weight deacy loss is 11.90, training accuracy is 0.218750, time 216.802 samples/sec
+epoch 2, total_step 31960, total loss is 30.18 , inference loss is 18.28, weight deacy loss is 11.90, training accuracy is 0.093750, time 232.843 samples/sec
+epoch 2, total_step 31980, total loss is 23.79 , inference loss is 11.90, weight deacy loss is 11.90, training accuracy is 0.312500, time 211.393 samples/sec
+testing verification..
+(12000, 512)
+infer time 22.625037000000017
+best_threshold_index 110 0.975925925925926
+best_threshold_index 110 0.9755555555555555
+best_threshold_index 110 0.9757407407407407
+best_threshold_index 110 0.9762962962962963
+best_threshold_index 110 0.9761111111111112
+best_threshold_index 110 0.9744444444444444
+best_threshold_index 105 0.9753703703703703
+best_threshold_index 110 0.9748148148148148
+best_threshold_index 110 0.9733333333333334
+best_threshold_index 110 0.9740740740740741
+[lfw][32000]XNorm: 23.802170
+[lfw][32000]Accuracy-Flip: 0.97467+-0.00852
+test accuracy is:  0.9746666666666666
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+epoch 2, total_step 32020, total loss is 27.59 , inference loss is 15.70, weight deacy loss is 11.89, training accuracy is 0.156250, time 213.787 samples/sec
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+testing verification..
+(12000, 512)
+infer time 24.268443000000005
+best_threshold_index 107 0.9788888888888889
+best_threshold_index 108 0.9785185185185186
+best_threshold_index 108 0.9798148148148148
+best_threshold_index 108 0.9792592592592593
+best_threshold_index 108 0.9792592592592593
+best_threshold_index 108 0.9781481481481481
+best_threshold_index 108 0.9788888888888889
+best_threshold_index 107 0.9787037037037037
+best_threshold_index 107 0.9774074074074074
+best_threshold_index 108 0.9777777777777777
+[lfw][34000]XNorm: 23.754778
+[lfw][34000]Accuracy-Flip: 0.97817+-0.00603
+test accuracy is:  0.9781666666666666
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+epoch 2, total_step 35080, total loss is 25.87 , inference loss is 14.22, weight deacy loss is 11.65, training accuracy is 0.187500, time 204.746 samples/sec
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+epoch 2, total_step 35140, total loss is 27.14 , inference loss is 15.50, weight deacy loss is 11.64, training accuracy is 0.125000, time 224.657 samples/sec
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+epoch 2, total_step 35220, total loss is 24.81 , inference loss is 13.18, weight deacy loss is 11.64, training accuracy is 0.218750, time 212.972 samples/sec
+epoch 2, total_step 35240, total loss is 29.31 , inference loss is 17.68, weight deacy loss is 11.63, training accuracy is 0.093750, time 215.310 samples/sec
+epoch 2, total_step 35260, total loss is 25.46 , inference loss is 13.83, weight deacy loss is 11.63, training accuracy is 0.156250, time 240.144 samples/sec
+epoch 2, total_step 35280, total loss is 26.84 , inference loss is 15.21, weight deacy loss is 11.63, training accuracy is 0.125000, time 218.373 samples/sec
+epoch 2, total_step 35300, total loss is 30.04 , inference loss is 18.41, weight deacy loss is 11.63, training accuracy is 0.125000, time 215.018 samples/sec
+epoch 2, total_step 35320, total loss is 24.24 , inference loss is 12.61, weight deacy loss is 11.63, training accuracy is 0.093750, time 221.791 samples/sec
+epoch 2, total_step 35340, total loss is 25.61 , inference loss is 13.98, weight deacy loss is 11.63, training accuracy is 0.187500, time 224.892 samples/sec
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+epoch 2, total_step 35460, total loss is 31.84 , inference loss is 20.23, weight deacy loss is 11.62, training accuracy is 0.062500, time 298.054 samples/sec
+epoch 2, total_step 35480, total loss is 28.32 , inference loss is 16.70, weight deacy loss is 11.62, training accuracy is 0.156250, time 224.416 samples/sec
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+epoch 2, total_step 35520, total loss is 32.37 , inference loss is 20.75, weight deacy loss is 11.61, training accuracy is 0.031250, time 225.311 samples/sec
+epoch 2, total_step 35540, total loss is 33.50 , inference loss is 21.89, weight deacy loss is 11.61, training accuracy is 0.125000, time 227.882 samples/sec
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+epoch 2, total_step 35620, total loss is 29.33 , inference loss is 17.72, weight deacy loss is 11.60, training accuracy is 0.250000, time 212.652 samples/sec
+epoch 2, total_step 35640, total loss is 26.67 , inference loss is 15.06, weight deacy loss is 11.60, training accuracy is 0.156250, time 222.144 samples/sec
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+epoch 2, total_step 35680, total loss is 26.09 , inference loss is 14.49, weight deacy loss is 11.60, training accuracy is 0.125000, time 215.880 samples/sec
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+epoch 2, total_step 35720, total loss is 28.10 , inference loss is 16.51, weight deacy loss is 11.60, training accuracy is 0.156250, time 209.246 samples/sec
+epoch 2, total_step 35740, total loss is 25.08 , inference loss is 13.49, weight deacy loss is 11.60, training accuracy is 0.218750, time 208.333 samples/sec
+epoch 2, total_step 35760, total loss is 28.71 , inference loss is 17.12, weight deacy loss is 11.59, training accuracy is 0.062500, time 218.194 samples/sec
+epoch 2, total_step 35780, total loss is 24.79 , inference loss is 13.20, weight deacy loss is 11.59, training accuracy is 0.187500, time 226.426 samples/sec
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+epoch 2, total_step 35820, total loss is 28.50 , inference loss is 16.91, weight deacy loss is 11.59, training accuracy is 0.031250, time 219.351 samples/sec
+epoch 2, total_step 35840, total loss is 23.90 , inference loss is 12.31, weight deacy loss is 11.59, training accuracy is 0.187500, time 216.594 samples/sec
+epoch 2, total_step 35860, total loss is 23.17 , inference loss is 11.58, weight deacy loss is 11.59, training accuracy is 0.187500, time 211.101 samples/sec
+epoch 2, total_step 35880, total loss is 26.92 , inference loss is 15.34, weight deacy loss is 11.58, training accuracy is 0.125000, time 210.713 samples/sec
+epoch 2, total_step 35900, total loss is 23.05 , inference loss is 11.47, weight deacy loss is 11.58, training accuracy is 0.218750, time 227.129 samples/sec
+epoch 2, total_step 35920, total loss is 20.96 , inference loss is 9.38, weight deacy loss is 11.58, training accuracy is 0.375000, time 248.166 samples/sec
+epoch 2, total_step 35940, total loss is 23.60 , inference loss is 12.02, weight deacy loss is 11.58, training accuracy is 0.218750, time 224.761 samples/sec
+epoch 2, total_step 35960, total loss is 25.90 , inference loss is 14.32, weight deacy loss is 11.58, training accuracy is 0.250000, time 208.056 samples/sec
+epoch 2, total_step 35980, total loss is 31.71 , inference loss is 20.14, weight deacy loss is 11.58, training accuracy is 0.062500, time 214.415 samples/sec
+testing verification..
+(12000, 512)
+infer time 25.260622999999985
+best_threshold_index 113 0.9812962962962963
+best_threshold_index 113 0.98
+best_threshold_index 113 0.9811111111111112
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+best_threshold_index 113 0.9812962962962963
+best_threshold_index 113 0.98
+best_threshold_index 113 0.9794444444444445
+best_threshold_index 113 0.9803703703703703
+best_threshold_index 113 0.9783333333333334
+best_threshold_index 113 0.9796296296296296
+[lfw][36000]XNorm: 22.761330
+[lfw][36000]Accuracy-Flip: 0.98033+-0.00912
+test accuracy is:  0.9803333333333333
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+epoch 2, total_step 36040, total loss is 27.32 , inference loss is 15.75, weight deacy loss is 11.57, training accuracy is 0.218750, time 211.837 samples/sec
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+epoch 2, total_step 36100, total loss is 23.27 , inference loss is 11.70, weight deacy loss is 11.57, training accuracy is 0.250000, time 216.528 samples/sec
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+epoch 2, total_step 36320, total loss is 26.52 , inference loss is 14.97, weight deacy loss is 11.55, training accuracy is 0.250000, time 224.696 samples/sec
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+epoch 2, total_step 36380, total loss is 22.98 , inference loss is 11.43, weight deacy loss is 11.55, training accuracy is 0.312500, time 224.844 samples/sec
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+epoch 2, total_step 36420, total loss is 28.63 , inference loss is 17.09, weight deacy loss is 11.54, training accuracy is 0.187500, time 228.083 samples/sec
+epoch 2, total_step 36440, total loss is 26.10 , inference loss is 14.55, weight deacy loss is 11.54, training accuracy is 0.156250, time 223.734 samples/sec
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+epoch 2, total_step 37920, total loss is 26.26 , inference loss is 14.84, weight deacy loss is 11.43, training accuracy is 0.156250, time 204.816 samples/sec
+epoch 2, total_step 37940, total loss is 28.01 , inference loss is 16.58, weight deacy loss is 11.43, training accuracy is 0.125000, time 212.521 samples/sec
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+epoch 2, total_step 37980, total loss is 25.67 , inference loss is 14.24, weight deacy loss is 11.42, training accuracy is 0.156250, time 190.927 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.93752799999998
+best_threshold_index 110 0.982037037037037
+best_threshold_index 110 0.9814814814814815
+best_threshold_index 110 0.9825925925925926
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+best_threshold_index 110 0.9801851851851852
+best_threshold_index 110 0.9794444444444445
+best_threshold_index 110 0.9801851851851852
+[lfw][38000]XNorm: 23.016690
+[lfw][38000]Accuracy-Flip: 0.98050+-0.00869
+test accuracy is:  0.9804999999999999
+epoch 2, total_step 38000, total loss is 24.47 , inference loss is 13.05, weight deacy loss is 11.42, training accuracy is 0.156250, time 17.136 samples/sec
+epoch 2, total_step 38020, total loss is 24.48 , inference loss is 13.06, weight deacy loss is 11.42, training accuracy is 0.375000, time 223.320 samples/sec
+epoch 2, total_step 38040, total loss is 25.43 , inference loss is 14.01, weight deacy loss is 11.42, training accuracy is 0.281250, time 218.780 samples/sec
+epoch 2, total_step 38060, total loss is 21.79 , inference loss is 10.37, weight deacy loss is 11.42, training accuracy is 0.375000, time 216.071 samples/sec
+epoch 2, total_step 38080, total loss is 26.94 , inference loss is 15.52, weight deacy loss is 11.42, training accuracy is 0.093750, time 240.367 samples/sec
+epoch 2, total_step 38100, total loss is 27.74 , inference loss is 16.33, weight deacy loss is 11.41, training accuracy is 0.156250, time 14.230 samples/sec
+epoch 2, total_step 38120, total loss is 26.74 , inference loss is 15.32, weight deacy loss is 11.41, training accuracy is 0.156250, time 221.452 samples/sec
+epoch 2, total_step 38140, total loss is 26.10 , inference loss is 14.69, weight deacy loss is 11.41, training accuracy is 0.250000, time 216.688 samples/sec
+epoch 2, total_step 38160, total loss is 28.58 , inference loss is 17.17, weight deacy loss is 11.41, training accuracy is 0.125000, time 226.633 samples/sec
+epoch 2, total_step 38180, total loss is 34.03 , inference loss is 22.62, weight deacy loss is 11.41, training accuracy is 0.125000, time 207.112 samples/sec
+epoch 2, total_step 38200, total loss is 26.42 , inference loss is 15.02, weight deacy loss is 11.41, training accuracy is 0.281250, time 207.203 samples/sec
+epoch 2, total_step 38220, total loss is 25.08 , inference loss is 13.67, weight deacy loss is 11.41, training accuracy is 0.187500, time 219.013 samples/sec
+epoch 2, total_step 38240, total loss is 22.07 , inference loss is 10.67, weight deacy loss is 11.40, training accuracy is 0.343750, time 223.375 samples/sec
+epoch 2, total_step 38260, total loss is 30.93 , inference loss is 19.52, weight deacy loss is 11.40, training accuracy is 0.218750, time 232.626 samples/sec
+epoch 2, total_step 38280, total loss is 26.07 , inference loss is 14.67, weight deacy loss is 11.40, training accuracy is 0.156250, time 215.462 samples/sec
+epoch 2, total_step 38300, total loss is 25.62 , inference loss is 14.22, weight deacy loss is 11.40, training accuracy is 0.218750, time 213.611 samples/sec
+epoch 2, total_step 38320, total loss is 23.20 , inference loss is 11.80, weight deacy loss is 11.40, training accuracy is 0.343750, time 208.785 samples/sec
+epoch 2, total_step 38340, total loss is 24.57 , inference loss is 13.17, weight deacy loss is 11.40, training accuracy is 0.156250, time 203.039 samples/sec
+epoch 2, total_step 38360, total loss is 29.38 , inference loss is 17.98, weight deacy loss is 11.40, training accuracy is 0.187500, time 227.999 samples/sec
+epoch 2, total_step 38380, total loss is 25.71 , inference loss is 14.32, weight deacy loss is 11.39, training accuracy is 0.281250, time 215.621 samples/sec
+epoch 2, total_step 38400, total loss is 30.49 , inference loss is 19.10, weight deacy loss is 11.39, training accuracy is 0.125000, time 12.259 samples/sec
+epoch 2, total_step 38420, total loss is 27.85 , inference loss is 16.46, weight deacy loss is 11.39, training accuracy is 0.187500, time 243.127 samples/sec
+epoch 2, total_step 38440, total loss is 27.88 , inference loss is 16.49, weight deacy loss is 11.39, training accuracy is 0.031250, time 211.336 samples/sec
+epoch 2, total_step 38460, total loss is 23.54 , inference loss is 12.15, weight deacy loss is 11.39, training accuracy is 0.250000, time 235.566 samples/sec
+epoch 2, total_step 38480, total loss is 28.81 , inference loss is 17.43, weight deacy loss is 11.39, training accuracy is 0.062500, time 197.801 samples/sec
+epoch 2, total_step 38500, total loss is 23.21 , inference loss is 11.83, weight deacy loss is 11.38, training accuracy is 0.343750, time 211.467 samples/sec
+epoch 2, total_step 38520, total loss is 23.62 , inference loss is 12.24, weight deacy loss is 11.38, training accuracy is 0.250000, time 213.478 samples/sec
+epoch 2, total_step 38540, total loss is 23.44 , inference loss is 12.06, weight deacy loss is 11.38, training accuracy is 0.218750, time 217.429 samples/sec
+epoch 2, total_step 38560, total loss is 24.50 , inference loss is 13.12, weight deacy loss is 11.38, training accuracy is 0.125000, time 198.758 samples/sec
+epoch 2, total_step 38580, total loss is 28.94 , inference loss is 17.56, weight deacy loss is 11.38, training accuracy is 0.156250, time 220.445 samples/sec
+epoch 2, total_step 38600, total loss is 28.56 , inference loss is 17.18, weight deacy loss is 11.38, training accuracy is 0.031250, time 214.959 samples/sec
+epoch 2, total_step 38620, total loss is 24.88 , inference loss is 13.51, weight deacy loss is 11.38, training accuracy is 0.187500, time 226.844 samples/sec
+epoch 2, total_step 38640, total loss is 27.41 , inference loss is 16.04, weight deacy loss is 11.37, training accuracy is 0.125000, time 226.107 samples/sec
+epoch 2, total_step 38660, total loss is 27.42 , inference loss is 16.05, weight deacy loss is 11.37, training accuracy is 0.218750, time 212.009 samples/sec
+epoch 2, total_step 38680, total loss is 26.63 , inference loss is 15.26, weight deacy loss is 11.37, training accuracy is 0.093750, time 299.939 samples/sec
+epoch 2, total_step 38700, total loss is 27.10 , inference loss is 15.73, weight deacy loss is 11.37, training accuracy is 0.218750, time 13.618 samples/sec
+epoch 2, total_step 38720, total loss is 27.58 , inference loss is 16.21, weight deacy loss is 11.37, training accuracy is 0.156250, time 224.841 samples/sec
+epoch 2, total_step 38740, total loss is 27.01 , inference loss is 15.64, weight deacy loss is 11.37, training accuracy is 0.187500, time 233.043 samples/sec
+epoch 2, total_step 38760, total loss is 26.68 , inference loss is 15.32, weight deacy loss is 11.36, training accuracy is 0.187500, time 197.741 samples/sec
+epoch 2, total_step 38780, total loss is 28.11 , inference loss is 16.74, weight deacy loss is 11.36, training accuracy is 0.187500, time 223.354 samples/sec
+epoch 2, total_step 38800, total loss is 24.73 , inference loss is 13.37, weight deacy loss is 11.36, training accuracy is 0.125000, time 217.293 samples/sec
+epoch 2, total_step 38820, total loss is 27.15 , inference loss is 15.79, weight deacy loss is 11.36, training accuracy is 0.156250, time 222.321 samples/sec
+epoch 2, total_step 38840, total loss is 30.42 , inference loss is 19.06, weight deacy loss is 11.36, training accuracy is 0.125000, time 222.362 samples/sec
+epoch 2, total_step 38860, total loss is 22.84 , inference loss is 11.48, weight deacy loss is 11.36, training accuracy is 0.281250, time 219.704 samples/sec
+epoch 2, total_step 38880, total loss is 23.89 , inference loss is 12.53, weight deacy loss is 11.36, training accuracy is 0.281250, time 212.326 samples/sec
+epoch 2, total_step 38900, total loss is 28.10 , inference loss is 16.75, weight deacy loss is 11.35, training accuracy is 0.156250, time 206.570 samples/sec
+epoch 2, total_step 38920, total loss is 29.14 , inference loss is 17.79, weight deacy loss is 11.35, training accuracy is 0.125000, time 212.069 samples/sec
+epoch 2, total_step 38940, total loss is 24.64 , inference loss is 13.29, weight deacy loss is 11.35, training accuracy is 0.218750, time 218.879 samples/sec
+epoch 2, total_step 38960, total loss is 26.00 , inference loss is 14.65, weight deacy loss is 11.35, training accuracy is 0.156250, time 220.097 samples/sec
+epoch 2, total_step 38980, total loss is 25.03 , inference loss is 13.68, weight deacy loss is 11.35, training accuracy is 0.187500, time 202.255 samples/sec
+epoch 2, total_step 39000, total loss is 27.59 , inference loss is 16.24, weight deacy loss is 11.35, training accuracy is 0.062500, time 13.786 samples/sec
+epoch 2, total_step 39020, total loss is 28.50 , inference loss is 17.15, weight deacy loss is 11.35, training accuracy is 0.218750, time 226.682 samples/sec
+epoch 2, total_step 39040, total loss is 27.94 , inference loss is 16.59, weight deacy loss is 11.34, training accuracy is 0.156250, time 214.084 samples/sec
+epoch 2, total_step 39060, total loss is 26.54 , inference loss is 15.19, weight deacy loss is 11.34, training accuracy is 0.156250, time 234.316 samples/sec
+epoch 2, total_step 39080, total loss is 28.40 , inference loss is 17.05, weight deacy loss is 11.34, training accuracy is 0.281250, time 220.562 samples/sec
+epoch 2, total_step 39100, total loss is 30.65 , inference loss is 19.31, weight deacy loss is 11.34, training accuracy is 0.093750, time 225.966 samples/sec
+epoch 2, total_step 39120, total loss is 29.41 , inference loss is 18.07, weight deacy loss is 11.34, training accuracy is 0.125000, time 300.480 samples/sec
+epoch 2, total_step 39140, total loss is 29.84 , inference loss is 18.50, weight deacy loss is 11.34, training accuracy is 0.062500, time 214.578 samples/sec
+epoch 2, total_step 39160, total loss is 26.49 , inference loss is 15.15, weight deacy loss is 11.33, training accuracy is 0.125000, time 211.491 samples/sec
+epoch 2, total_step 39180, total loss is 32.20 , inference loss is 20.86, weight deacy loss is 11.33, training accuracy is 0.093750, time 214.761 samples/sec
+epoch 2, total_step 39200, total loss is 25.47 , inference loss is 14.14, weight deacy loss is 11.33, training accuracy is 0.156250, time 216.376 samples/sec
+epoch 2, total_step 39220, total loss is 24.07 , inference loss is 12.74, weight deacy loss is 11.33, training accuracy is 0.250000, time 213.858 samples/sec
+epoch 2, total_step 39240, total loss is 27.12 , inference loss is 15.79, weight deacy loss is 11.33, training accuracy is 0.156250, time 218.192 samples/sec
+epoch 2, total_step 39260, total loss is 26.76 , inference loss is 15.43, weight deacy loss is 11.33, training accuracy is 0.093750, time 225.225 samples/sec
+epoch 2, total_step 39280, total loss is 30.11 , inference loss is 18.79, weight deacy loss is 11.33, training accuracy is 0.125000, time 298.510 samples/sec
+epoch 2, total_step 39300, total loss is 27.01 , inference loss is 15.68, weight deacy loss is 11.32, training accuracy is 0.281250, time 13.669 samples/sec
+epoch 2, total_step 39320, total loss is 28.26 , inference loss is 16.93, weight deacy loss is 11.32, training accuracy is 0.156250, time 220.837 samples/sec
+epoch 2, total_step 39340, total loss is 23.41 , inference loss is 12.08, weight deacy loss is 11.32, training accuracy is 0.218750, time 222.195 samples/sec
+epoch 2, total_step 39360, total loss is 29.15 , inference loss is 17.83, weight deacy loss is 11.32, training accuracy is 0.062500, time 214.812 samples/sec
+epoch 2, total_step 39380, total loss is 29.10 , inference loss is 17.78, weight deacy loss is 11.32, training accuracy is 0.125000, time 188.454 samples/sec
+epoch 2, total_step 39400, total loss is 28.05 , inference loss is 16.74, weight deacy loss is 11.32, training accuracy is 0.093750, time 227.146 samples/sec
+epoch 2, total_step 39420, total loss is 31.98 , inference loss is 20.66, weight deacy loss is 11.32, training accuracy is 0.062500, time 224.554 samples/sec
+epoch 2, total_step 39440, total loss is 31.47 , inference loss is 20.16, weight deacy loss is 11.31, training accuracy is 0.062500, time 224.896 samples/sec
+epoch 2, total_step 39460, total loss is 26.87 , inference loss is 15.55, weight deacy loss is 11.31, training accuracy is 0.125000, time 215.788 samples/sec
+epoch 2, total_step 39480, total loss is 23.52 , inference loss is 12.20, weight deacy loss is 11.31, training accuracy is 0.218750, time 231.343 samples/sec
+epoch 2, total_step 39500, total loss is 23.58 , inference loss is 12.27, weight deacy loss is 11.31, training accuracy is 0.125000, time 192.734 samples/sec
+epoch 2, total_step 39520, total loss is 28.97 , inference loss is 17.66, weight deacy loss is 11.31, training accuracy is 0.187500, time 211.721 samples/sec
+epoch 2, total_step 39540, total loss is 23.23 , inference loss is 11.92, weight deacy loss is 11.31, training accuracy is 0.187500, time 209.378 samples/sec
+epoch 2, total_step 39560, total loss is 30.41 , inference loss is 19.10, weight deacy loss is 11.31, training accuracy is 0.125000, time 221.438 samples/sec
+epoch 2, total_step 39580, total loss is 25.69 , inference loss is 14.39, weight deacy loss is 11.30, training accuracy is 0.187500, time 213.033 samples/sec
+epoch 2, total_step 39600, total loss is 28.76 , inference loss is 17.46, weight deacy loss is 11.30, training accuracy is 0.093750, time 12.458 samples/sec
+epoch 2, total_step 39620, total loss is 25.20 , inference loss is 13.90, weight deacy loss is 11.30, training accuracy is 0.156250, time 221.042 samples/sec
+epoch 2, total_step 39640, total loss is 29.23 , inference loss is 17.93, weight deacy loss is 11.30, training accuracy is 0.156250, time 182.377 samples/sec
+epoch 2, total_step 39660, total loss is 29.59 , inference loss is 18.29, weight deacy loss is 11.30, training accuracy is 0.062500, time 201.876 samples/sec
+epoch 2, total_step 39680, total loss is 27.10 , inference loss is 15.80, weight deacy loss is 11.30, training accuracy is 0.156250, time 220.769 samples/sec
+epoch 2, total_step 39700, total loss is 31.29 , inference loss is 19.99, weight deacy loss is 11.30, training accuracy is 0.093750, time 205.536 samples/sec
+epoch 2, total_step 39720, total loss is 24.56 , inference loss is 13.27, weight deacy loss is 11.29, training accuracy is 0.156250, time 237.898 samples/sec
+epoch 2, total_step 39740, total loss is 23.66 , inference loss is 12.36, weight deacy loss is 11.29, training accuracy is 0.250000, time 215.966 samples/sec
+epoch 2, total_step 39760, total loss is 26.59 , inference loss is 15.29, weight deacy loss is 11.29, training accuracy is 0.156250, time 218.738 samples/sec
+epoch 2, total_step 39780, total loss is 31.99 , inference loss is 20.70, weight deacy loss is 11.29, training accuracy is 0.062500, time 215.965 samples/sec
+epoch 2, total_step 39800, total loss is 25.41 , inference loss is 14.12, weight deacy loss is 11.29, training accuracy is 0.187500, time 210.656 samples/sec
+epoch 2, total_step 39820, total loss is 30.38 , inference loss is 19.10, weight deacy loss is 11.29, training accuracy is 0.125000, time 218.797 samples/sec
+epoch 2, total_step 39840, total loss is 28.34 , inference loss is 17.05, weight deacy loss is 11.28, training accuracy is 0.156250, time 218.308 samples/sec
+epoch 2, total_step 39860, total loss is 29.01 , inference loss is 17.73, weight deacy loss is 11.28, training accuracy is 0.125000, time 225.496 samples/sec
+epoch 2, total_step 39880, total loss is 28.57 , inference loss is 17.29, weight deacy loss is 11.28, training accuracy is 0.062500, time 229.364 samples/sec
+epoch 2, total_step 39900, total loss is 25.52 , inference loss is 14.24, weight deacy loss is 11.28, training accuracy is 0.156250, time 13.534 samples/sec
+epoch 2, total_step 39920, total loss is 26.28 , inference loss is 15.00, weight deacy loss is 11.28, training accuracy is 0.125000, time 207.895 samples/sec
+epoch 2, total_step 39940, total loss is 24.69 , inference loss is 13.41, weight deacy loss is 11.28, training accuracy is 0.093750, time 222.202 samples/sec
+epoch 2, total_step 39960, total loss is 22.17 , inference loss is 10.89, weight deacy loss is 11.28, training accuracy is 0.250000, time 235.257 samples/sec
+epoch 2, total_step 39980, total loss is 23.13 , inference loss is 11.85, weight deacy loss is 11.27, training accuracy is 0.187500, time 210.554 samples/sec
+testing verification..
+(12000, 512)
+infer time 23.965379000000006
+best_threshold_index 108 0.9783333333333334
+best_threshold_index 108 0.9785185185185186
+best_threshold_index 108 0.9788888888888889
+best_threshold_index 107 0.9790740740740741
+best_threshold_index 108 0.9787037037037037
+best_threshold_index 108 0.9777777777777777
+best_threshold_index 108 0.9781481481481481
+best_threshold_index 108 0.9775925925925926
+best_threshold_index 107 0.9772222222222222
+best_threshold_index 107 0.9777777777777777
+[lfw][40000]XNorm: 23.710506
+[lfw][40000]Accuracy-Flip: 0.97733+-0.00507
+test accuracy is:  0.9773333333333334
+epoch 2, total_step 40000, total loss is 22.98 , inference loss is 11.71, weight deacy loss is 11.27, training accuracy is 0.187500, time 16.411 samples/sec
+epoch 2, total_step 40020, total loss is 30.18 , inference loss is 18.91, weight deacy loss is 11.27, training accuracy is 0.156250, time 216.557 samples/sec
+epoch 2, total_step 40040, total loss is 26.36 , inference loss is 15.09, weight deacy loss is 11.27, training accuracy is 0.187500, time 216.566 samples/sec
+epoch 2, total_step 40060, total loss is 29.58 , inference loss is 18.31, weight deacy loss is 11.27, training accuracy is 0.156250, time 217.105 samples/sec
+epoch 2, total_step 40080, total loss is 26.55 , inference loss is 15.28, weight deacy loss is 11.27, training accuracy is 0.156250, time 226.347 samples/sec
+epoch 2, total_step 40100, total loss is 26.19 , inference loss is 14.92, weight deacy loss is 11.27, training accuracy is 0.187500, time 236.098 samples/sec
+epoch 2, total_step 40120, total loss is 26.95 , inference loss is 15.69, weight deacy loss is 11.26, training accuracy is 0.187500, time 236.007 samples/sec
+epoch 2, total_step 40140, total loss is 28.03 , inference loss is 16.77, weight deacy loss is 11.26, training accuracy is 0.093750, time 243.692 samples/sec
+epoch 2, total_step 40160, total loss is 27.43 , inference loss is 16.17, weight deacy loss is 11.26, training accuracy is 0.125000, time 218.948 samples/sec
+epoch 2, total_step 40180, total loss is 26.81 , inference loss is 15.55, weight deacy loss is 11.26, training accuracy is 0.281250, time 206.183 samples/sec
+epoch 2, total_step 40200, total loss is 20.99 , inference loss is 9.73, weight deacy loss is 11.26, training accuracy is 0.250000, time 13.174 samples/sec
+epoch 2, total_step 40220, total loss is 28.55 , inference loss is 17.30, weight deacy loss is 11.26, training accuracy is 0.125000, time 296.233 samples/sec
+epoch 2, total_step 40240, total loss is 24.27 , inference loss is 13.01, weight deacy loss is 11.26, training accuracy is 0.250000, time 211.662 samples/sec
+epoch 2, total_step 40260, total loss is 26.51 , inference loss is 15.26, weight deacy loss is 11.25, training accuracy is 0.125000, time 201.888 samples/sec
+epoch 2, total_step 40280, total loss is 28.57 , inference loss is 17.32, weight deacy loss is 11.25, training accuracy is 0.187500, time 224.839 samples/sec
+epoch 2, total_step 40300, total loss is 24.61 , inference loss is 13.36, weight deacy loss is 11.25, training accuracy is 0.156250, time 211.181 samples/sec
+epoch 2, total_step 40320, total loss is 23.63 , inference loss is 12.38, weight deacy loss is 11.25, training accuracy is 0.156250, time 217.287 samples/sec
+epoch 2, total_step 40340, total loss is 28.41 , inference loss is 17.16, weight deacy loss is 11.25, training accuracy is 0.250000, time 220.647 samples/sec
+epoch 2, total_step 40360, total loss is 25.88 , inference loss is 14.63, weight deacy loss is 11.25, training accuracy is 0.281250, time 219.882 samples/sec
+epoch 2, total_step 40380, total loss is 24.50 , inference loss is 13.26, weight deacy loss is 11.25, training accuracy is 0.093750, time 217.709 samples/sec
+epoch 2, total_step 40400, total loss is 24.83 , inference loss is 13.59, weight deacy loss is 11.24, training accuracy is 0.187500, time 202.497 samples/sec
+epoch 2, total_step 40420, total loss is 22.03 , inference loss is 10.78, weight deacy loss is 11.24, training accuracy is 0.187500, time 219.686 samples/sec
+epoch 2, total_step 40440, total loss is 28.94 , inference loss is 17.70, weight deacy loss is 11.24, training accuracy is 0.031250, time 219.134 samples/sec
+epoch 2, total_step 40460, total loss is 24.76 , inference loss is 13.52, weight deacy loss is 11.24, training accuracy is 0.250000, time 216.221 samples/sec
+epoch 2, total_step 40480, total loss is 27.31 , inference loss is 16.07, weight deacy loss is 11.24, training accuracy is 0.156250, time 236.007 samples/sec
+epoch 2, total_step 40500, total loss is 21.74 , inference loss is 10.50, weight deacy loss is 11.24, training accuracy is 0.250000, time 13.742 samples/sec
+epoch 2, total_step 40520, total loss is 25.27 , inference loss is 14.03, weight deacy loss is 11.24, training accuracy is 0.218750, time 221.147 samples/sec
+epoch 2, total_step 40540, total loss is 25.18 , inference loss is 13.94, weight deacy loss is 11.23, training accuracy is 0.187500, time 212.499 samples/sec
+epoch 2, total_step 40560, total loss is 24.86 , inference loss is 13.63, weight deacy loss is 11.23, training accuracy is 0.250000, time 218.863 samples/sec
+epoch 2, total_step 40580, total loss is 23.74 , inference loss is 12.51, weight deacy loss is 11.23, training accuracy is 0.218750, time 215.845 samples/sec
+epoch 2, total_step 40600, total loss is 27.76 , inference loss is 16.53, weight deacy loss is 11.23, training accuracy is 0.093750, time 219.174 samples/sec
+epoch 2, total_step 40620, total loss is 26.24 , inference loss is 15.02, weight deacy loss is 11.23, training accuracy is 0.250000, time 214.549 samples/sec
+epoch 2, total_step 40640, total loss is 27.40 , inference loss is 16.18, weight deacy loss is 11.23, training accuracy is 0.156250, time 224.070 samples/sec
+epoch 2, total_step 40660, total loss is 24.20 , inference loss is 12.97, weight deacy loss is 11.23, training accuracy is 0.125000, time 211.467 samples/sec
+epoch 2, total_step 40680, total loss is 22.13 , inference loss is 10.91, weight deacy loss is 11.22, training accuracy is 0.281250, time 228.771 samples/sec
+epoch 2, total_step 40700, total loss is 26.16 , inference loss is 14.94, weight deacy loss is 11.22, training accuracy is 0.156250, time 215.227 samples/sec
+epoch 2, total_step 40720, total loss is 30.43 , inference loss is 19.21, weight deacy loss is 11.22, training accuracy is 0.093750, time 222.348 samples/sec
+epoch 2, total_step 40740, total loss is 26.62 , inference loss is 15.40, weight deacy loss is 11.22, training accuracy is 0.156250, time 218.601 samples/sec
+epoch 2, total_step 40760, total loss is 21.70 , inference loss is 10.48, weight deacy loss is 11.22, training accuracy is 0.218750, time 202.471 samples/sec
+epoch 2, total_step 40780, total loss is 25.81 , inference loss is 14.59, weight deacy loss is 11.22, training accuracy is 0.156250, time 202.088 samples/sec
+epoch 2, total_step 40800, total loss is 24.03 , inference loss is 12.81, weight deacy loss is 11.21, training accuracy is 0.156250, time 13.628 samples/sec
+epoch 2, total_step 40820, total loss is 24.76 , inference loss is 13.55, weight deacy loss is 11.21, training accuracy is 0.156250, time 211.979 samples/sec
+epoch 2, total_step 40840, total loss is 25.28 , inference loss is 14.07, weight deacy loss is 11.21, training accuracy is 0.218750, time 213.057 samples/sec
+epoch 2, total_step 40860, total loss is 29.10 , inference loss is 17.89, weight deacy loss is 11.21, training accuracy is 0.187500, time 212.966 samples/sec
+epoch 2, total_step 40880, total loss is 24.89 , inference loss is 13.68, weight deacy loss is 11.21, training accuracy is 0.125000, time 213.732 samples/sec
+epoch 2, total_step 40900, total loss is 26.33 , inference loss is 15.12, weight deacy loss is 11.21, training accuracy is 0.218750, time 213.406 samples/sec
+epoch 2, total_step 40920, total loss is 22.18 , inference loss is 10.97, weight deacy loss is 11.21, training accuracy is 0.187500, time 211.456 samples/sec
+epoch 2, total_step 40940, total loss is 28.17 , inference loss is 16.97, weight deacy loss is 11.20, training accuracy is 0.156250, time 215.716 samples/sec
+epoch 2, total_step 40960, total loss is 31.54 , inference loss is 20.34, weight deacy loss is 11.20, training accuracy is 0.093750, time 213.995 samples/sec
+epoch 2, total_step 40980, total loss is 24.69 , inference loss is 13.49, weight deacy loss is 11.20, training accuracy is 0.187500, time 219.721 samples/sec
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+epoch 2, total_step 41020, total loss is 26.77 , inference loss is 15.57, weight deacy loss is 11.20, training accuracy is 0.218750, time 297.856 samples/sec
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+epoch 2, total_step 41980, total loss is 25.75 , inference loss is 14.62, weight deacy loss is 11.13, training accuracy is 0.093750, time 219.052 samples/sec
+testing verification..
+(12000, 512)
+infer time 25.759942999999993
+best_threshold_index 109 0.977037037037037
+best_threshold_index 109 0.9774074074074074
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+best_threshold_index 109 0.9755555555555555
+[lfw][42000]XNorm: 23.254394
+[lfw][42000]Accuracy-Flip: 0.97650+-0.00835
+test accuracy is:  0.9765
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+epoch 2, total_step 43980, total loss is 26.71 , inference loss is 15.72, weight deacy loss is 10.99, training accuracy is 0.093750, time 236.938 samples/sec
+testing verification..
+(12000, 512)
+infer time 25.225942999999994
+best_threshold_index 106 0.9748148148148148
+best_threshold_index 106 0.9742592592592593
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+[lfw][44000]XNorm: 23.642517
+[lfw][44000]Accuracy-Flip: 0.97383+-0.01093
+test accuracy is:  0.9738333333333333
+epoch 2, total_step 44000, total loss is 24.48 , inference loss is 13.49, weight deacy loss is 10.99, training accuracy is 0.156250, time 17.082 samples/sec
+epoch 2, total_step 44020, total loss is 23.19 , inference loss is 12.20, weight deacy loss is 10.99, training accuracy is 0.218750, time 218.541 samples/sec
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+epoch 2, total_step 44060, total loss is 24.84 , inference loss is 13.85, weight deacy loss is 10.98, training accuracy is 0.156250, time 217.179 samples/sec
+epoch 2, total_step 44080, total loss is 30.74 , inference loss is 19.75, weight deacy loss is 10.98, training accuracy is 0.093750, time 224.710 samples/sec
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+epoch 2, total_step 44160, total loss is 26.44 , inference loss is 15.46, weight deacy loss is 10.98, training accuracy is 0.156250, time 209.771 samples/sec
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+epoch 2, total_step 44220, total loss is 33.56 , inference loss is 22.59, weight deacy loss is 10.97, training accuracy is 0.031250, time 296.417 samples/sec
+epoch 2, total_step 44240, total loss is 25.78 , inference loss is 14.81, weight deacy loss is 10.97, training accuracy is 0.125000, time 212.823 samples/sec
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+epoch 2, total_step 44280, total loss is 35.26 , inference loss is 24.29, weight deacy loss is 10.97, training accuracy is 0.062500, time 222.037 samples/sec
+epoch 2, total_step 44300, total loss is 23.44 , inference loss is 12.48, weight deacy loss is 10.97, training accuracy is 0.125000, time 218.194 samples/sec
+epoch 2, total_step 44320, total loss is 24.98 , inference loss is 14.02, weight deacy loss is 10.97, training accuracy is 0.093750, time 226.714 samples/sec
+epoch 2, total_step 44340, total loss is 23.63 , inference loss is 12.67, weight deacy loss is 10.97, training accuracy is 0.125000, time 228.193 samples/sec
+epoch 2, total_step 44360, total loss is 26.56 , inference loss is 15.60, weight deacy loss is 10.96, training accuracy is 0.156250, time 232.996 samples/sec
+epoch 2, total_step 44380, total loss is 24.27 , inference loss is 13.31, weight deacy loss is 10.96, training accuracy is 0.093750, time 240.099 samples/sec
+epoch 2, total_step 44400, total loss is 26.08 , inference loss is 15.12, weight deacy loss is 10.96, training accuracy is 0.156250, time 14.425 samples/sec
+epoch 2, total_step 44420, total loss is 24.44 , inference loss is 13.48, weight deacy loss is 10.96, training accuracy is 0.093750, time 214.421 samples/sec
+epoch 2, total_step 44440, total loss is 28.43 , inference loss is 17.47, weight deacy loss is 10.96, training accuracy is 0.093750, time 224.206 samples/sec
+epoch 2, total_step 44460, total loss is 26.64 , inference loss is 15.68, weight deacy loss is 10.96, training accuracy is 0.125000, time 218.358 samples/sec
+epoch 2, total_step 44480, total loss is 25.02 , inference loss is 14.06, weight deacy loss is 10.96, training accuracy is 0.187500, time 214.061 samples/sec
+epoch 2, total_step 44500, total loss is 25.67 , inference loss is 14.72, weight deacy loss is 10.95, training accuracy is 0.031250, time 197.940 samples/sec
+epoch 2, total_step 44520, total loss is 27.38 , inference loss is 16.43, weight deacy loss is 10.95, training accuracy is 0.093750, time 217.490 samples/sec
+epoch 2, total_step 44540, total loss is 29.58 , inference loss is 18.62, weight deacy loss is 10.95, training accuracy is 0.031250, time 298.159 samples/sec
+epoch 2, total_step 44560, total loss is 24.03 , inference loss is 13.08, weight deacy loss is 10.95, training accuracy is 0.156250, time 219.728 samples/sec
+epoch 2, total_step 44580, total loss is 25.98 , inference loss is 15.04, weight deacy loss is 10.95, training accuracy is 0.125000, time 207.666 samples/sec
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+epoch 2, total_step 44620, total loss is 25.57 , inference loss is 14.63, weight deacy loss is 10.95, training accuracy is 0.187500, time 219.650 samples/sec
+epoch 2, total_step 44640, total loss is 29.01 , inference loss is 18.07, weight deacy loss is 10.94, training accuracy is 0.062500, time 224.974 samples/sec
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+epoch 2, total_step 44720, total loss is 23.48 , inference loss is 12.55, weight deacy loss is 10.94, training accuracy is 0.156250, time 210.739 samples/sec
+epoch 2, total_step 44740, total loss is 26.35 , inference loss is 15.41, weight deacy loss is 10.94, training accuracy is 0.093750, time 224.680 samples/sec
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+epoch 2, total_step 44840, total loss is 31.44 , inference loss is 20.51, weight deacy loss is 10.93, training accuracy is 0.062500, time 216.483 samples/sec
+epoch 2, total_step 44860, total loss is 25.70 , inference loss is 14.77, weight deacy loss is 10.93, training accuracy is 0.031250, time 226.648 samples/sec
+epoch 2, total_step 44880, total loss is 28.97 , inference loss is 18.04, weight deacy loss is 10.93, training accuracy is 0.093750, time 190.466 samples/sec
+epoch 2, total_step 44900, total loss is 24.52 , inference loss is 13.60, weight deacy loss is 10.93, training accuracy is 0.187500, time 214.038 samples/sec
+epoch 2, total_step 44920, total loss is 29.73 , inference loss is 18.80, weight deacy loss is 10.93, training accuracy is 0.125000, time 212.107 samples/sec
+epoch 2, total_step 44940, total loss is 28.97 , inference loss is 18.05, weight deacy loss is 10.92, training accuracy is 0.125000, time 298.705 samples/sec
+epoch 2, total_step 44960, total loss is 28.14 , inference loss is 17.21, weight deacy loss is 10.92, training accuracy is 0.125000, time 223.500 samples/sec
+epoch 2, total_step 44980, total loss is 26.63 , inference loss is 15.71, weight deacy loss is 10.92, training accuracy is 0.125000, time 220.453 samples/sec
+epoch 2, total_step 45000, total loss is 23.93 , inference loss is 13.01, weight deacy loss is 10.92, training accuracy is 0.281250, time 13.094 samples/sec
+epoch 2, total_step 45020, total loss is 24.56 , inference loss is 13.64, weight deacy loss is 10.92, training accuracy is 0.156250, time 222.383 samples/sec
+epoch 2, total_step 45040, total loss is 25.99 , inference loss is 15.07, weight deacy loss is 10.92, training accuracy is 0.125000, time 209.439 samples/sec
+epoch 2, total_step 45060, total loss is 27.24 , inference loss is 16.33, weight deacy loss is 10.92, training accuracy is 0.062500, time 209.586 samples/sec
+epoch 2, total_step 45080, total loss is 27.58 , inference loss is 16.67, weight deacy loss is 10.91, training accuracy is 0.156250, time 216.354 samples/sec
+epoch 2, total_step 45100, total loss is 28.07 , inference loss is 17.16, weight deacy loss is 10.91, training accuracy is 0.093750, time 219.030 samples/sec
+epoch 2, total_step 45120, total loss is 26.61 , inference loss is 15.70, weight deacy loss is 10.91, training accuracy is 0.062500, time 202.324 samples/sec
+epoch 2, total_step 45140, total loss is 26.15 , inference loss is 15.24, weight deacy loss is 10.91, training accuracy is 0.125000, time 207.607 samples/sec
+epoch 2, total_step 45160, total loss is 28.70 , inference loss is 17.79, weight deacy loss is 10.91, training accuracy is 0.093750, time 235.063 samples/sec
+epoch 2, total_step 45180, total loss is 21.72 , inference loss is 10.81, weight deacy loss is 10.91, training accuracy is 0.187500, time 214.309 samples/sec
+epoch 2, total_step 45200, total loss is 25.34 , inference loss is 14.43, weight deacy loss is 10.91, training accuracy is 0.062500, time 295.621 samples/sec
+epoch 2, total_step 45220, total loss is 24.76 , inference loss is 13.86, weight deacy loss is 10.91, training accuracy is 0.062500, time 207.832 samples/sec
+epoch 2, total_step 45240, total loss is 26.61 , inference loss is 15.71, weight deacy loss is 10.90, training accuracy is 0.031250, time 194.155 samples/sec
+epoch 2, total_step 45260, total loss is 29.16 , inference loss is 18.26, weight deacy loss is 10.90, training accuracy is 0.093750, time 237.971 samples/sec
+epoch 2, total_step 45280, total loss is 25.81 , inference loss is 14.91, weight deacy loss is 10.90, training accuracy is 0.125000, time 216.063 samples/sec
+epoch 2, total_step 45300, total loss is 29.33 , inference loss is 18.43, weight deacy loss is 10.90, training accuracy is 0.031250, time 13.530 samples/sec
+epoch 2, total_step 45320, total loss is 24.63 , inference loss is 13.73, weight deacy loss is 10.90, training accuracy is 0.187500, time 221.718 samples/sec
+epoch 2, total_step 45340, total loss is 25.05 , inference loss is 14.15, weight deacy loss is 10.90, training accuracy is 0.187500, time 217.925 samples/sec
+epoch 2, total_step 45360, total loss is 27.45 , inference loss is 16.56, weight deacy loss is 10.90, training accuracy is 0.031250, time 200.859 samples/sec2022-12-01 03:14:02.230640: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:145] Filling up shuffle buffer (this may take a while): 7376 of 10000
+2022-12-01 03:14:05.579513: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:195] Shuffle buffer filled.
+
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+epoch 2, total_step 45400, total loss is 28.83 , inference loss is 17.94, weight deacy loss is 10.89, training accuracy is 0.093750, time 225.831 samples/sec
+epoch 2, total_step 45420, total loss is 28.38 , inference loss is 17.49, weight deacy loss is 10.89, training accuracy is 0.062500, time 210.361 samples/sec
+epoch 2, total_step 45440, total loss is 30.40 , inference loss is 19.51, weight deacy loss is 10.89, training accuracy is 0.031250, time 197.054 samples/sec
+epoch 2, total_step 45460, total loss is 22.11 , inference loss is 11.22, weight deacy loss is 10.89, training accuracy is 0.187500, time 294.617 samples/sec
+epoch 2, total_step 45480, total loss is 25.36 , inference loss is 14.47, weight deacy loss is 10.89, training accuracy is 0.062500, time 220.740 samples/sec
+epoch 2, total_step 45500, total loss is 28.52 , inference loss is 17.63, weight deacy loss is 10.89, training accuracy is 0.031250, time 212.927 samples/sec
+epoch 2, total_step 45520, total loss is 23.70 , inference loss is 12.81, weight deacy loss is 10.88, training accuracy is 0.093750, time 228.209 samples/sec
+epoch 2, total_step 45540, total loss is 25.83 , inference loss is 14.94, weight deacy loss is 10.88, training accuracy is 0.031250, time 224.012 samples/sec
+epoch 2, total_step 45560, total loss is 29.68 , inference loss is 18.80, weight deacy loss is 10.88, training accuracy is 0.093750, time 219.698 samples/sec
+epoch 2, total_step 45580, total loss is 28.76 , inference loss is 17.88, weight deacy loss is 10.88, training accuracy is 0.062500, time 214.306 samples/sec
+epoch 2, total_step 45600, total loss is 25.75 , inference loss is 14.87, weight deacy loss is 10.88, training accuracy is 0.062500, time 13.560 samples/sec
+epoch 2, total_step 45620, total loss is 30.91 , inference loss is 20.03, weight deacy loss is 10.88, training accuracy is 0.062500, time 214.518 samples/sec
+epoch 2, total_step 45640, total loss is 25.64 , inference loss is 14.76, weight deacy loss is 10.88, training accuracy is 0.093750, time 221.252 samples/sec
+epoch 2, total_step 45660, total loss is 28.23 , inference loss is 17.35, weight deacy loss is 10.88, training accuracy is 0.125000, time 190.292 samples/sec
+epoch 2, total_step 45680, total loss is 26.08 , inference loss is 15.21, weight deacy loss is 10.87, training accuracy is 0.093750, time 222.940 samples/sec
+epoch 2, total_step 45700, total loss is 25.94 , inference loss is 15.07, weight deacy loss is 10.87, training accuracy is 0.125000, time 202.088 samples/sec
+epoch 2, total_step 45720, total loss is 28.47 , inference loss is 17.60, weight deacy loss is 10.87, training accuracy is 0.062500, time 299.953 samples/sec
+epoch 2, total_step 45740, total loss is 24.46 , inference loss is 13.59, weight deacy loss is 10.87, training accuracy is 0.093750, time 176.966 samples/sec
+epoch 2, total_step 45760, total loss is 25.66 , inference loss is 14.79, weight deacy loss is 10.87, training accuracy is 0.187500, time 191.996 samples/sec
+epoch 2, total_step 45780, total loss is 28.40 , inference loss is 17.54, weight deacy loss is 10.87, training accuracy is 0.093750, time 192.718 samples/sec
+epoch 2, total_step 45800, total loss is 21.97 , inference loss is 11.11, weight deacy loss is 10.87, training accuracy is 0.093750, time 193.492 samples/sec
+epoch 2, total_step 45820, total loss is 29.86 , inference loss is 18.99, weight deacy loss is 10.86, training accuracy is 0.000000, time 202.951 samples/sec
+epoch 2, total_step 45840, total loss is 26.99 , inference loss is 16.12, weight deacy loss is 10.86, training accuracy is 0.093750, time 197.187 samples/sec
+epoch 2, total_step 45860, total loss is 27.77 , inference loss is 16.91, weight deacy loss is 10.86, training accuracy is 0.062500, time 220.406 samples/sec
+epoch 2, total_step 45880, total loss is 24.91 , inference loss is 14.05, weight deacy loss is 10.86, training accuracy is 0.156250, time 216.407 samples/sec
+epoch 2, total_step 45900, total loss is 27.63 , inference loss is 16.77, weight deacy loss is 10.86, training accuracy is 0.125000, time 12.206 samples/sec
+epoch 2, total_step 45920, total loss is 25.13 , inference loss is 14.27, weight deacy loss is 10.86, training accuracy is 0.125000, time 179.046 samples/sec
+epoch 2, total_step 45940, total loss is 28.34 , inference loss is 17.48, weight deacy loss is 10.86, training accuracy is 0.093750, time 203.183 samples/sec
+epoch 2, total_step 45960, total loss is 25.52 , inference loss is 14.67, weight deacy loss is 10.86, training accuracy is 0.156250, time 192.430 samples/sec
+epoch 2, total_step 45980, total loss is 25.47 , inference loss is 14.62, weight deacy loss is 10.85, training accuracy is 0.093750, time 216.788 samples/sec
+End of epoch 2
+testing verification..
+(12000, 512)
+infer time 25.18361599999998
+best_threshold_index 121 0.9805555555555555
+best_threshold_index 121 0.98
+best_threshold_index 121 0.9807407407407407
+best_threshold_index 121 0.980925925925926
+best_threshold_index 121 0.9803703703703703
+best_threshold_index 121 0.9792592592592593
+best_threshold_index 121 0.9801851851851852
+best_threshold_index 121 0.98
+best_threshold_index 121 0.9785185185185186
+best_threshold_index 121 0.9794444444444445
+[lfw][46000]XNorm: 22.070035
+[lfw][46000]Accuracy-Flip: 0.98000+-0.00632
+test accuracy is:  0.9800000000000001
+epoch 3, total_step 46000, total loss is 39.35 , inference loss is 28.50, weight deacy loss is 10.85, training accuracy is 0.000000, time 17.950 samples/sec
+epoch 3, total_step 46020, total loss is 27.54 , inference loss is 16.68, weight deacy loss is 10.85, training accuracy is 0.093750, time 219.626 samples/sec
+epoch 3, total_step 46040, total loss is 19.72 , inference loss is 8.87, weight deacy loss is 10.85, training accuracy is 0.312500, time 202.468 samples/sec
+epoch 3, total_step 46060, total loss is 25.30 , inference loss is 14.45, weight deacy loss is 10.85, training accuracy is 0.187500, time 296.915 samples/sec
+epoch 3, total_step 46080, total loss is 21.36 , inference loss is 10.51, weight deacy loss is 10.85, training accuracy is 0.281250, time 224.337 samples/sec
+epoch 3, total_step 46100, total loss is 28.54 , inference loss is 17.70, weight deacy loss is 10.85, training accuracy is 0.156250, time 227.302 samples/sec
+epoch 3, total_step 46120, total loss is 20.92 , inference loss is 10.07, weight deacy loss is 10.84, training accuracy is 0.312500, time 221.655 samples/sec
+epoch 3, total_step 46140, total loss is 19.73 , inference loss is 8.89, weight deacy loss is 10.84, training accuracy is 0.468750, time 214.535 samples/sec
+epoch 3, total_step 46160, total loss is 22.27 , inference loss is 11.43, weight deacy loss is 10.84, training accuracy is 0.343750, time 213.008 samples/sec
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+epoch 3, total_step 46220, total loss is 19.05 , inference loss is 8.21, weight deacy loss is 10.84, training accuracy is 0.531250, time 214.573 samples/sec
+epoch 3, total_step 46240, total loss is 22.10 , inference loss is 11.26, weight deacy loss is 10.84, training accuracy is 0.281250, time 215.024 samples/sec
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+epoch 3, total_step 46280, total loss is 22.57 , inference loss is 11.73, weight deacy loss is 10.83, training accuracy is 0.250000, time 298.344 samples/sec
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+epoch 3, total_step 46320, total loss is 22.07 , inference loss is 11.24, weight deacy loss is 10.83, training accuracy is 0.437500, time 209.391 samples/sec
+epoch 3, total_step 46340, total loss is 22.93 , inference loss is 12.10, weight deacy loss is 10.83, training accuracy is 0.281250, time 219.490 samples/sec
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+epoch 3, total_step 46380, total loss is 22.62 , inference loss is 11.80, weight deacy loss is 10.83, training accuracy is 0.250000, time 216.340 samples/sec
+epoch 3, total_step 46400, total loss is 22.90 , inference loss is 12.07, weight deacy loss is 10.82, training accuracy is 0.125000, time 204.805 samples/sec
+epoch 3, total_step 46420, total loss is 23.93 , inference loss is 13.11, weight deacy loss is 10.82, training accuracy is 0.312500, time 202.134 samples/sec
+epoch 3, total_step 46440, total loss is 21.70 , inference loss is 10.88, weight deacy loss is 10.82, training accuracy is 0.218750, time 202.617 samples/sec
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+epoch 3, total_step 46480, total loss is 25.30 , inference loss is 14.48, weight deacy loss is 10.82, training accuracy is 0.218750, time 217.921 samples/sec
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+epoch 3, total_step 46580, total loss is 22.41 , inference loss is 11.60, weight deacy loss is 10.81, training accuracy is 0.343750, time 214.805 samples/sec
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+epoch 3, total_step 46640, total loss is 21.39 , inference loss is 10.58, weight deacy loss is 10.81, training accuracy is 0.281250, time 220.361 samples/sec
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+epoch 3, total_step 46700, total loss is 20.99 , inference loss is 10.18, weight deacy loss is 10.80, training accuracy is 0.343750, time 225.501 samples/sec
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+epoch 3, total_step 46900, total loss is 23.42 , inference loss is 12.63, weight deacy loss is 10.79, training accuracy is 0.218750, time 181.340 samples/sec
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+epoch 3, total_step 47980, total loss is 25.70 , inference loss is 14.98, weight deacy loss is 10.72, training accuracy is 0.250000, time 212.279 samples/sec
+testing verification..
+(12000, 512)
+infer time 23.176746999999988
+best_threshold_index 111 0.9833333333333333
+best_threshold_index 111 0.9825925925925926
+best_threshold_index 111 0.9827777777777778
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+best_threshold_index 111 0.9818518518518519
+best_threshold_index 111 0.9818518518518519
+best_threshold_index 111 0.9812962962962963
+[lfw][48000]XNorm: 22.560015
+[lfw][48000]Accuracy-Flip: 0.98183+-0.00565
+test accuracy is:  0.9818333333333336
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+epoch 3, total_step 49980, total loss is 23.19 , inference loss is 12.61, weight deacy loss is 10.58, training accuracy is 0.250000, time 229.826 samples/sec
+testing verification..
+(12000, 512)
+infer time 21.998904000000003
+best_threshold_index 117 0.9833333333333333
+best_threshold_index 117 0.9827777777777778
+best_threshold_index 117 0.9840740740740741
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+best_threshold_index 117 0.9824074074074074
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+best_threshold_index 117 0.9833333333333333
+best_threshold_index 117 0.982037037037037
+best_threshold_index 117 0.9825925925925926
+[lfw][50000]XNorm: 22.703694
+[lfw][50000]Accuracy-Flip: 0.98317+-0.00660
+test accuracy is:  0.9831666666666667
+epoch 3, total_step 50000, total loss is 22.44 , inference loss is 11.86, weight deacy loss is 10.58, training accuracy is 0.250000, time 16.648 samples/sec
+epoch 3, total_step 50020, total loss is 21.51 , inference loss is 10.93, weight deacy loss is 10.58, training accuracy is 0.156250, time 217.473 samples/sec
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+epoch 3, total_step 50060, total loss is 24.53 , inference loss is 13.95, weight deacy loss is 10.58, training accuracy is 0.156250, time 221.355 samples/sec
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+epoch 3, total_step 50120, total loss is 20.52 , inference loss is 9.95, weight deacy loss is 10.57, training accuracy is 0.187500, time 210.186 samples/sec
+epoch 3, total_step 50140, total loss is 26.09 , inference loss is 15.52, weight deacy loss is 10.57, training accuracy is 0.187500, time 212.493 samples/sec
+epoch 3, total_step 50160, total loss is 21.67 , inference loss is 11.10, weight deacy loss is 10.57, training accuracy is 0.281250, time 214.689 samples/sec
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+epoch 3, total_step 50200, total loss is 21.97 , inference loss is 11.41, weight deacy loss is 10.57, training accuracy is 0.312500, time 208.470 samples/sec
+epoch 3, total_step 50220, total loss is 23.10 , inference loss is 12.53, weight deacy loss is 10.56, training accuracy is 0.156250, time 297.073 samples/sec
+epoch 3, total_step 50240, total loss is 22.10 , inference loss is 11.54, weight deacy loss is 10.56, training accuracy is 0.218750, time 210.140 samples/sec
+epoch 3, total_step 50260, total loss is 22.03 , inference loss is 11.47, weight deacy loss is 10.56, training accuracy is 0.093750, time 219.868 samples/sec
+epoch 3, total_step 50280, total loss is 20.45 , inference loss is 9.89, weight deacy loss is 10.56, training accuracy is 0.156250, time 217.099 samples/sec
+epoch 3, total_step 50300, total loss is 24.50 , inference loss is 13.95, weight deacy loss is 10.56, training accuracy is 0.250000, time 212.905 samples/sec
+epoch 3, total_step 50320, total loss is 21.79 , inference loss is 11.23, weight deacy loss is 10.56, training accuracy is 0.156250, time 216.711 samples/sec
+epoch 3, total_step 50340, total loss is 22.92 , inference loss is 12.36, weight deacy loss is 10.56, training accuracy is 0.218750, time 205.197 samples/sec
+epoch 3, total_step 50360, total loss is 26.60 , inference loss is 16.04, weight deacy loss is 10.56, training accuracy is 0.156250, time 219.453 samples/sec
+epoch 3, total_step 50380, total loss is 21.30 , inference loss is 10.74, weight deacy loss is 10.55, training accuracy is 0.125000, time 224.872 samples/sec
+epoch 3, total_step 50400, total loss is 26.19 , inference loss is 15.64, weight deacy loss is 10.55, training accuracy is 0.218750, time 13.414 samples/sec
+epoch 3, total_step 50420, total loss is 21.84 , inference loss is 11.29, weight deacy loss is 10.55, training accuracy is 0.187500, time 226.737 samples/sec
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+epoch 3, total_step 51200, total loss is 26.55 , inference loss is 16.05, weight deacy loss is 10.50, training accuracy is 0.218750, time 215.441 samples/sec
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+epoch 3, total_step 51480, total loss is 23.02 , inference loss is 12.54, weight deacy loss is 10.48, training accuracy is 0.187500, time 243.513 samples/sec
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+epoch 3, total_step 51640, total loss is 20.75 , inference loss is 10.28, weight deacy loss is 10.47, training accuracy is 0.281250, time 199.792 samples/sec
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+epoch 3, total_step 51680, total loss is 27.09 , inference loss is 16.62, weight deacy loss is 10.47, training accuracy is 0.218750, time 209.173 samples/sec
+epoch 3, total_step 51700, total loss is 22.19 , inference loss is 11.72, weight deacy loss is 10.47, training accuracy is 0.187500, time 213.757 samples/sec
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+epoch 3, total_step 51780, total loss is 26.49 , inference loss is 16.02, weight deacy loss is 10.46, training accuracy is 0.218750, time 207.745 samples/sec
+epoch 3, total_step 51800, total loss is 27.29 , inference loss is 16.83, weight deacy loss is 10.46, training accuracy is 0.187500, time 215.613 samples/sec
+epoch 3, total_step 51820, total loss is 25.96 , inference loss is 15.50, weight deacy loss is 10.46, training accuracy is 0.187500, time 297.582 samples/sec
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+epoch 3, total_step 51920, total loss is 24.87 , inference loss is 14.41, weight deacy loss is 10.45, training accuracy is 0.281250, time 222.735 samples/sec
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+epoch 3, total_step 51980, total loss is 26.31 , inference loss is 15.86, weight deacy loss is 10.45, training accuracy is 0.156250, time 201.035 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.471604000000013
+best_threshold_index 118 0.9846296296296296
+best_threshold_index 118 0.9833333333333333
+best_threshold_index 118 0.9844444444444445
+best_threshold_index 118 0.9848148148148148
+best_threshold_index 118 0.9844444444444445
+best_threshold_index 118 0.9835185185185186
+best_threshold_index 118 0.9837037037037037
+best_threshold_index 118 0.9835185185185186
+best_threshold_index 118 0.9825925925925926
+best_threshold_index 118 0.9833333333333333
+[lfw][52000]XNorm: 21.851396
+[lfw][52000]Accuracy-Flip: 0.98383+-0.00610
+test accuracy is:  0.9838333333333333
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+epoch 3, total_step 52140, total loss is 23.44 , inference loss is 13.00, weight deacy loss is 10.44, training accuracy is 0.156250, time 220.972 samples/sec
+epoch 3, total_step 52160, total loss is 30.43 , inference loss is 19.99, weight deacy loss is 10.44, training accuracy is 0.125000, time 227.049 samples/sec
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+epoch 3, total_step 53000, total loss is 24.04 , inference loss is 13.65, weight deacy loss is 10.38, training accuracy is 0.218750, time 212.628 samples/sec
+epoch 3, total_step 53020, total loss is 25.14 , inference loss is 14.76, weight deacy loss is 10.38, training accuracy is 0.156250, time 214.488 samples/sec
+epoch 3, total_step 53040, total loss is 28.60 , inference loss is 18.21, weight deacy loss is 10.38, training accuracy is 0.187500, time 210.932 samples/sec
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+epoch 3, total_step 53080, total loss is 21.54 , inference loss is 11.16, weight deacy loss is 10.38, training accuracy is 0.281250, time 211.358 samples/sec
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+epoch 3, total_step 53140, total loss is 22.15 , inference loss is 11.77, weight deacy loss is 10.37, training accuracy is 0.125000, time 209.775 samples/sec
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+epoch 3, total_step 53260, total loss is 29.13 , inference loss is 18.76, weight deacy loss is 10.37, training accuracy is 0.062500, time 215.327 samples/sec
+epoch 3, total_step 53280, total loss is 25.15 , inference loss is 14.79, weight deacy loss is 10.37, training accuracy is 0.218750, time 208.897 samples/sec
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+epoch 3, total_step 53780, total loss is 21.91 , inference loss is 11.57, weight deacy loss is 10.33, training accuracy is 0.281250, time 202.975 samples/sec
+epoch 3, total_step 53800, total loss is 24.60 , inference loss is 14.27, weight deacy loss is 10.33, training accuracy is 0.218750, time 214.654 samples/sec
+epoch 3, total_step 53820, total loss is 22.56 , inference loss is 12.23, weight deacy loss is 10.33, training accuracy is 0.250000, time 209.227 samples/sec
+epoch 3, total_step 53840, total loss is 25.50 , inference loss is 15.17, weight deacy loss is 10.33, training accuracy is 0.250000, time 213.524 samples/sec
+epoch 3, total_step 53860, total loss is 21.70 , inference loss is 11.37, weight deacy loss is 10.33, training accuracy is 0.187500, time 209.898 samples/sec
+epoch 3, total_step 53880, total loss is 21.36 , inference loss is 11.03, weight deacy loss is 10.33, training accuracy is 0.156250, time 206.158 samples/sec
+epoch 3, total_step 53900, total loss is 21.15 , inference loss is 10.82, weight deacy loss is 10.32, training accuracy is 0.406250, time 301.036 samples/sec
+epoch 3, total_step 53920, total loss is 25.05 , inference loss is 14.72, weight deacy loss is 10.32, training accuracy is 0.125000, time 209.366 samples/sec
+epoch 3, total_step 53940, total loss is 24.58 , inference loss is 14.26, weight deacy loss is 10.32, training accuracy is 0.281250, time 209.044 samples/sec
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+epoch 3, total_step 53980, total loss is 25.07 , inference loss is 14.75, weight deacy loss is 10.32, training accuracy is 0.187500, time 217.779 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.69664699999997
+best_threshold_index 114 0.9835185185185186
+best_threshold_index 117 0.9825925925925926
+best_threshold_index 114 0.9835185185185186
+best_threshold_index 114 0.9833333333333333
+best_threshold_index 114 0.9827777777777778
+best_threshold_index 114 0.9822222222222222
+best_threshold_index 114 0.9827777777777778
+best_threshold_index 114 0.982037037037037
+best_threshold_index 117 0.9812962962962963
+best_threshold_index 117 0.9818518518518519
+[lfw][54000]XNorm: 22.598574
+[lfw][54000]Accuracy-Flip: 0.98167+-0.00637
+test accuracy is:  0.9816666666666667
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+epoch 3, total_step 54020, total loss is 19.46 , inference loss is 9.14, weight deacy loss is 10.32, training accuracy is 0.343750, time 215.059 samples/sec
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+epoch 3, total_step 54120, total loss is 23.83 , inference loss is 13.52, weight deacy loss is 10.31, training accuracy is 0.218750, time 215.472 samples/sec
+epoch 3, total_step 54140, total loss is 24.52 , inference loss is 14.21, weight deacy loss is 10.31, training accuracy is 0.250000, time 213.635 samples/sec
+epoch 3, total_step 54160, total loss is 23.18 , inference loss is 12.87, weight deacy loss is 10.31, training accuracy is 0.250000, time 224.994 samples/sec
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+epoch 3, total_step 54200, total loss is 24.37 , inference loss is 14.06, weight deacy loss is 10.31, training accuracy is 0.281250, time 216.748 samples/sec
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+epoch 3, total_step 54240, total loss is 24.39 , inference loss is 14.09, weight deacy loss is 10.30, training accuracy is 0.250000, time 209.317 samples/sec
+epoch 3, total_step 54260, total loss is 27.16 , inference loss is 16.86, weight deacy loss is 10.30, training accuracy is 0.156250, time 213.257 samples/sec
+epoch 3, total_step 54280, total loss is 20.23 , inference loss is 9.93, weight deacy loss is 10.30, training accuracy is 0.281250, time 298.230 samples/sec
+epoch 3, total_step 54300, total loss is 21.89 , inference loss is 11.59, weight deacy loss is 10.30, training accuracy is 0.187500, time 14.153 samples/sec
+epoch 3, total_step 54320, total loss is 25.19 , inference loss is 14.89, weight deacy loss is 10.30, training accuracy is 0.156250, time 212.431 samples/sec
+epoch 3, total_step 54340, total loss is 25.85 , inference loss is 15.55, weight deacy loss is 10.30, training accuracy is 0.187500, time 226.201 samples/sec
+epoch 3, total_step 54360, total loss is 25.78 , inference loss is 15.48, weight deacy loss is 10.30, training accuracy is 0.218750, time 208.094 samples/sec
+epoch 3, total_step 54380, total loss is 20.89 , inference loss is 10.60, weight deacy loss is 10.29, training accuracy is 0.312500, time 212.146 samples/sec
+epoch 3, total_step 54400, total loss is 24.20 , inference loss is 13.91, weight deacy loss is 10.29, training accuracy is 0.156250, time 207.884 samples/sec
+epoch 3, total_step 54420, total loss is 21.86 , inference loss is 11.56, weight deacy loss is 10.29, training accuracy is 0.156250, time 225.779 samples/sec
+epoch 3, total_step 54440, total loss is 21.02 , inference loss is 10.73, weight deacy loss is 10.29, training accuracy is 0.250000, time 213.528 samples/sec
+epoch 3, total_step 54460, total loss is 27.75 , inference loss is 17.46, weight deacy loss is 10.29, training accuracy is 0.125000, time 204.679 samples/sec
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+epoch 3, total_step 55040, total loss is 25.03 , inference loss is 14.78, weight deacy loss is 10.25, training accuracy is 0.218750, time 204.172 samples/sec
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+epoch 3, total_step 55720, total loss is 20.35 , inference loss is 10.14, weight deacy loss is 10.21, training accuracy is 0.343750, time 210.072 samples/sec
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+epoch 3, total_step 55900, total loss is 19.51 , inference loss is 9.31, weight deacy loss is 10.20, training accuracy is 0.406250, time 209.620 samples/sec
+epoch 3, total_step 55920, total loss is 26.26 , inference loss is 16.06, weight deacy loss is 10.20, training accuracy is 0.187500, time 217.474 samples/sec
+epoch 3, total_step 55940, total loss is 20.65 , inference loss is 10.45, weight deacy loss is 10.20, training accuracy is 0.250000, time 211.354 samples/sec
+epoch 3, total_step 55960, total loss is 19.23 , inference loss is 9.03, weight deacy loss is 10.20, training accuracy is 0.375000, time 213.944 samples/sec
+epoch 3, total_step 55980, total loss is 23.33 , inference loss is 13.14, weight deacy loss is 10.19, training accuracy is 0.218750, time 208.737 samples/sec
+testing verification..
+(12000, 512)
+infer time 23.40075800000001
+best_threshold_index 107 0.9827777777777778
+best_threshold_index 107 0.9824074074074074
+best_threshold_index 107 0.9835185185185186
+best_threshold_index 107 0.9831481481481481
+best_threshold_index 108 0.9824074074074074
+best_threshold_index 107 0.9814814814814815
+best_threshold_index 107 0.9824074074074074
+best_threshold_index 107 0.9816666666666667
+best_threshold_index 107 0.9807407407407407
+best_threshold_index 107 0.9812962962962963
+[lfw][56000]XNorm: 24.309557
+[lfw][56000]Accuracy-Flip: 0.98183+-0.00751
+test accuracy is:  0.9818333333333333
+epoch 3, total_step 56000, total loss is 27.95 , inference loss is 17.76, weight deacy loss is 10.19, training accuracy is 0.250000, time 15.434 samples/sec
+epoch 3, total_step 56020, total loss is 22.03 , inference loss is 11.84, weight deacy loss is 10.19, training accuracy is 0.250000, time 208.128 samples/sec
+epoch 3, total_step 56040, total loss is 27.94 , inference loss is 17.75, weight deacy loss is 10.19, training accuracy is 0.156250, time 206.196 samples/sec
+epoch 3, total_step 56060, total loss is 18.14 , inference loss is 7.95, weight deacy loss is 10.19, training accuracy is 0.375000, time 213.745 samples/sec
+epoch 3, total_step 56080, total loss is 21.15 , inference loss is 10.96, weight deacy loss is 10.19, training accuracy is 0.281250, time 208.846 samples/sec
+epoch 3, total_step 56100, total loss is 21.45 , inference loss is 11.26, weight deacy loss is 10.19, training accuracy is 0.343750, time 12.493 samples/sec
+epoch 3, total_step 56120, total loss is 21.11 , inference loss is 10.93, weight deacy loss is 10.19, training accuracy is 0.406250, time 223.539 samples/sec
+epoch 3, total_step 56140, total loss is 22.07 , inference loss is 11.89, weight deacy loss is 10.18, training accuracy is 0.093750, time 212.550 samples/sec
+epoch 3, total_step 56160, total loss is 26.77 , inference loss is 16.59, weight deacy loss is 10.18, training accuracy is 0.218750, time 207.386 samples/sec
+epoch 3, total_step 56180, total loss is 26.43 , inference loss is 16.25, weight deacy loss is 10.18, training accuracy is 0.125000, time 225.705 samples/sec
+epoch 3, total_step 56200, total loss is 18.92 , inference loss is 8.74, weight deacy loss is 10.18, training accuracy is 0.343750, time 218.255 samples/sec
+epoch 3, total_step 56220, total loss is 23.29 , inference loss is 13.11, weight deacy loss is 10.18, training accuracy is 0.125000, time 212.813 samples/sec
+epoch 3, total_step 56240, total loss is 22.94 , inference loss is 12.77, weight deacy loss is 10.18, training accuracy is 0.218750, time 208.845 samples/sec
+epoch 3, total_step 56260, total loss is 21.08 , inference loss is 10.90, weight deacy loss is 10.18, training accuracy is 0.312500, time 204.745 samples/sec
+epoch 3, total_step 56280, total loss is 22.13 , inference loss is 11.95, weight deacy loss is 10.18, training accuracy is 0.156250, time 211.962 samples/sec
+epoch 3, total_step 56300, total loss is 19.64 , inference loss is 9.47, weight deacy loss is 10.17, training accuracy is 0.312500, time 218.307 samples/sec
+epoch 3, total_step 56320, total loss is 18.81 , inference loss is 8.64, weight deacy loss is 10.17, training accuracy is 0.375000, time 216.258 samples/sec
+epoch 3, total_step 56340, total loss is 26.43 , inference loss is 16.26, weight deacy loss is 10.17, training accuracy is 0.062500, time 205.202 samples/sec
+epoch 3, total_step 56360, total loss is 23.10 , inference loss is 12.93, weight deacy loss is 10.17, training accuracy is 0.218750, time 207.998 samples/sec
+epoch 3, total_step 56380, total loss is 20.25 , inference loss is 10.08, weight deacy loss is 10.17, training accuracy is 0.250000, time 212.841 samples/sec
+epoch 3, total_step 56400, total loss is 19.53 , inference loss is 9.36, weight deacy loss is 10.17, training accuracy is 0.218750, time 14.053 samples/sec
+epoch 3, total_step 56420, total loss is 25.73 , inference loss is 15.57, weight deacy loss is 10.17, training accuracy is 0.093750, time 295.701 samples/sec
+epoch 3, total_step 56440, total loss is 25.20 , inference loss is 15.03, weight deacy loss is 10.17, training accuracy is 0.312500, time 208.606 samples/sec
+epoch 3, total_step 56460, total loss is 22.67 , inference loss is 12.50, weight deacy loss is 10.16, training accuracy is 0.218750, time 231.120 samples/sec
+epoch 3, total_step 56480, total loss is 25.23 , inference loss is 15.07, weight deacy loss is 10.16, training accuracy is 0.218750, time 216.078 samples/sec
+epoch 3, total_step 56500, total loss is 21.81 , inference loss is 11.64, weight deacy loss is 10.16, training accuracy is 0.281250, time 211.928 samples/sec
+epoch 3, total_step 56520, total loss is 20.50 , inference loss is 10.34, weight deacy loss is 10.16, training accuracy is 0.375000, time 209.744 samples/sec
+epoch 3, total_step 56540, total loss is 20.56 , inference loss is 10.40, weight deacy loss is 10.16, training accuracy is 0.312500, time 210.240 samples/sec
+epoch 3, total_step 56560, total loss is 26.73 , inference loss is 16.58, weight deacy loss is 10.16, training accuracy is 0.093750, time 208.386 samples/sec
+epoch 3, total_step 56580, total loss is 26.65 , inference loss is 16.49, weight deacy loss is 10.16, training accuracy is 0.156250, time 215.379 samples/sec
+epoch 3, total_step 56600, total loss is 22.58 , inference loss is 12.42, weight deacy loss is 10.16, training accuracy is 0.250000, time 212.256 samples/sec
+epoch 3, total_step 56620, total loss is 20.93 , inference loss is 10.77, weight deacy loss is 10.15, training accuracy is 0.312500, time 209.335 samples/sec
+epoch 3, total_step 56640, total loss is 22.80 , inference loss is 12.64, weight deacy loss is 10.15, training accuracy is 0.218750, time 218.445 samples/sec
+epoch 3, total_step 56660, total loss is 24.28 , inference loss is 14.13, weight deacy loss is 10.15, training accuracy is 0.187500, time 215.994 samples/sec
+epoch 3, total_step 56680, total loss is 27.07 , inference loss is 16.92, weight deacy loss is 10.15, training accuracy is 0.187500, time 201.223 samples/sec
+epoch 3, total_step 56700, total loss is 25.04 , inference loss is 14.89, weight deacy loss is 10.15, training accuracy is 0.125000, time 14.578 samples/sec
+epoch 3, total_step 56720, total loss is 23.82 , inference loss is 13.67, weight deacy loss is 10.15, training accuracy is 0.281250, time 225.604 samples/sec
+epoch 3, total_step 56740, total loss is 20.59 , inference loss is 10.44, weight deacy loss is 10.15, training accuracy is 0.187500, time 214.258 samples/sec
+epoch 3, total_step 56760, total loss is 23.86 , inference loss is 13.71, weight deacy loss is 10.15, training accuracy is 0.156250, time 207.882 samples/sec
+epoch 3, total_step 56780, total loss is 28.02 , inference loss is 17.88, weight deacy loss is 10.14, training accuracy is 0.125000, time 217.876 samples/sec
+epoch 3, total_step 56800, total loss is 27.45 , inference loss is 17.31, weight deacy loss is 10.14, training accuracy is 0.093750, time 211.252 samples/sec
+epoch 3, total_step 56820, total loss is 22.29 , inference loss is 12.14, weight deacy loss is 10.14, training accuracy is 0.218750, time 210.081 samples/sec
+epoch 3, total_step 56840, total loss is 23.55 , inference loss is 13.41, weight deacy loss is 10.14, training accuracy is 0.187500, time 215.743 samples/sec
+epoch 3, total_step 56860, total loss is 21.92 , inference loss is 11.78, weight deacy loss is 10.14, training accuracy is 0.187500, time 244.003 samples/sec
+epoch 3, total_step 56880, total loss is 25.13 , inference loss is 15.00, weight deacy loss is 10.14, training accuracy is 0.125000, time 210.589 samples/sec
+epoch 3, total_step 56900, total loss is 27.86 , inference loss is 17.72, weight deacy loss is 10.14, training accuracy is 0.125000, time 203.979 samples/sec
+epoch 3, total_step 56920, total loss is 23.00 , inference loss is 12.87, weight deacy loss is 10.14, training accuracy is 0.218750, time 218.584 samples/sec
+epoch 3, total_step 56940, total loss is 22.05 , inference loss is 11.92, weight deacy loss is 10.14, training accuracy is 0.156250, time 214.970 samples/sec
+epoch 3, total_step 56960, total loss is 22.96 , inference loss is 12.83, weight deacy loss is 10.13, training accuracy is 0.218750, time 211.035 samples/sec
+epoch 3, total_step 56980, total loss is 19.67 , inference loss is 9.54, weight deacy loss is 10.13, training accuracy is 0.218750, time 214.573 samples/sec
+epoch 3, total_step 57000, total loss is 21.10 , inference loss is 10.97, weight deacy loss is 10.13, training accuracy is 0.218750, time 13.956 samples/sec
+epoch 3, total_step 57020, total loss is 21.46 , inference loss is 11.33, weight deacy loss is 10.13, training accuracy is 0.218750, time 212.179 samples/sec
+epoch 3, total_step 57040, total loss is 24.14 , inference loss is 14.01, weight deacy loss is 10.13, training accuracy is 0.218750, time 203.910 samples/sec
+epoch 3, total_step 57060, total loss is 23.14 , inference loss is 13.02, weight deacy loss is 10.13, training accuracy is 0.250000, time 211.323 samples/sec
+epoch 3, total_step 57080, total loss is 23.42 , inference loss is 13.29, weight deacy loss is 10.13, training accuracy is 0.125000, time 296.582 samples/sec
+epoch 3, total_step 57100, total loss is 21.88 , inference loss is 11.76, weight deacy loss is 10.13, training accuracy is 0.250000, time 215.587 samples/sec
+epoch 3, total_step 57120, total loss is 22.71 , inference loss is 12.59, weight deacy loss is 10.12, training accuracy is 0.187500, time 219.341 samples/sec
+epoch 3, total_step 57140, total loss is 27.46 , inference loss is 17.33, weight deacy loss is 10.12, training accuracy is 0.093750, time 210.312 samples/sec
+epoch 3, total_step 57160, total loss is 22.87 , inference loss is 12.75, weight deacy loss is 10.12, training accuracy is 0.250000, time 225.758 samples/sec
+epoch 3, total_step 57180, total loss is 25.38 , inference loss is 15.26, weight deacy loss is 10.12, training accuracy is 0.156250, time 213.356 samples/sec
+epoch 3, total_step 57200, total loss is 27.13 , inference loss is 17.01, weight deacy loss is 10.12, training accuracy is 0.187500, time 214.814 samples/sec
+epoch 3, total_step 57220, total loss is 23.86 , inference loss is 13.74, weight deacy loss is 10.12, training accuracy is 0.156250, time 211.684 samples/sec
+epoch 3, total_step 57240, total loss is 22.80 , inference loss is 12.68, weight deacy loss is 10.12, training accuracy is 0.281250, time 225.127 samples/sec
+epoch 3, total_step 57260, total loss is 23.22 , inference loss is 13.10, weight deacy loss is 10.12, training accuracy is 0.218750, time 298.980 samples/sec
+epoch 3, total_step 57280, total loss is 21.68 , inference loss is 11.56, weight deacy loss is 10.11, training accuracy is 0.218750, time 210.226 samples/sec
+epoch 3, total_step 57300, total loss is 22.50 , inference loss is 12.39, weight deacy loss is 10.11, training accuracy is 0.218750, time 14.115 samples/sec
+epoch 3, total_step 57320, total loss is 22.30 , inference loss is 12.19, weight deacy loss is 10.11, training accuracy is 0.250000, time 214.450 samples/sec
+epoch 3, total_step 57340, total loss is 23.15 , inference loss is 13.04, weight deacy loss is 10.11, training accuracy is 0.281250, time 220.652 samples/sec
+epoch 3, total_step 57360, total loss is 22.40 , inference loss is 12.29, weight deacy loss is 10.11, training accuracy is 0.187500, time 202.550 samples/sec
+epoch 3, total_step 57380, total loss is 26.51 , inference loss is 16.40, weight deacy loss is 10.11, training accuracy is 0.125000, time 218.540 samples/sec
+epoch 3, total_step 57400, total loss is 20.97 , inference loss is 10.86, weight deacy loss is 10.11, training accuracy is 0.125000, time 207.886 samples/sec
+epoch 3, total_step 57420, total loss is 25.09 , inference loss is 14.98, weight deacy loss is 10.11, training accuracy is 0.218750, time 208.673 samples/sec
+epoch 3, total_step 57440, total loss is 24.27 , inference loss is 14.16, weight deacy loss is 10.10, training accuracy is 0.156250, time 297.035 samples/sec
+epoch 3, total_step 57460, total loss is 25.03 , inference loss is 14.93, weight deacy loss is 10.10, training accuracy is 0.156250, time 211.489 samples/sec
+epoch 3, total_step 57480, total loss is 22.61 , inference loss is 12.50, weight deacy loss is 10.10, training accuracy is 0.343750, time 208.289 samples/sec
+epoch 3, total_step 57500, total loss is 22.35 , inference loss is 12.24, weight deacy loss is 10.10, training accuracy is 0.218750, time 215.125 samples/sec
+epoch 3, total_step 57520, total loss is 24.49 , inference loss is 14.39, weight deacy loss is 10.10, training accuracy is 0.187500, time 207.918 samples/sec
+epoch 3, total_step 57540, total loss is 21.97 , inference loss is 11.87, weight deacy loss is 10.10, training accuracy is 0.281250, time 220.018 samples/sec
+epoch 3, total_step 57560, total loss is 27.52 , inference loss is 17.42, weight deacy loss is 10.10, training accuracy is 0.218750, time 214.933 samples/sec
+epoch 3, total_step 57580, total loss is 22.38 , inference loss is 12.28, weight deacy loss is 10.10, training accuracy is 0.187500, time 215.984 samples/sec
+epoch 3, total_step 57600, total loss is 25.17 , inference loss is 15.07, weight deacy loss is 10.10, training accuracy is 0.125000, time 13.530 samples/sec
+epoch 3, total_step 57620, total loss is 21.72 , inference loss is 11.63, weight deacy loss is 10.09, training accuracy is 0.093750, time 212.542 samples/sec
+epoch 3, total_step 57640, total loss is 22.71 , inference loss is 12.62, weight deacy loss is 10.09, training accuracy is 0.343750, time 212.636 samples/sec
+epoch 3, total_step 57660, total loss is 24.24 , inference loss is 14.15, weight deacy loss is 10.09, training accuracy is 0.187500, time 206.430 samples/sec
+epoch 3, total_step 57680, total loss is 26.57 , inference loss is 16.48, weight deacy loss is 10.09, training accuracy is 0.250000, time 201.544 samples/sec
+epoch 3, total_step 57700, total loss is 23.17 , inference loss is 13.08, weight deacy loss is 10.09, training accuracy is 0.156250, time 299.500 samples/sec
+epoch 3, total_step 57720, total loss is 24.62 , inference loss is 14.53, weight deacy loss is 10.09, training accuracy is 0.218750, time 205.810 samples/sec
+epoch 3, total_step 57740, total loss is 25.80 , inference loss is 15.72, weight deacy loss is 10.09, training accuracy is 0.125000, time 214.190 samples/sec
+epoch 3, total_step 57760, total loss is 20.34 , inference loss is 10.25, weight deacy loss is 10.09, training accuracy is 0.312500, time 213.708 samples/sec
+epoch 3, total_step 57780, total loss is 22.04 , inference loss is 11.96, weight deacy loss is 10.08, training accuracy is 0.343750, time 212.810 samples/sec
+epoch 3, total_step 57800, total loss is 27.83 , inference loss is 17.75, weight deacy loss is 10.08, training accuracy is 0.062500, time 209.979 samples/sec
+epoch 3, total_step 57820, total loss is 30.03 , inference loss is 19.95, weight deacy loss is 10.08, training accuracy is 0.187500, time 199.471 samples/sec
+epoch 3, total_step 57840, total loss is 22.18 , inference loss is 12.10, weight deacy loss is 10.08, training accuracy is 0.187500, time 212.807 samples/sec
+epoch 3, total_step 57860, total loss is 21.70 , inference loss is 11.62, weight deacy loss is 10.08, training accuracy is 0.187500, time 209.018 samples/sec
+epoch 3, total_step 57880, total loss is 23.09 , inference loss is 13.01, weight deacy loss is 10.08, training accuracy is 0.250000, time 207.530 samples/sec
+epoch 3, total_step 57900, total loss is 25.01 , inference loss is 14.93, weight deacy loss is 10.08, training accuracy is 0.187500, time 12.366 samples/sec
+epoch 3, total_step 57920, total loss is 25.12 , inference loss is 15.04, weight deacy loss is 10.08, training accuracy is 0.125000, time 225.875 samples/sec
+epoch 3, total_step 57940, total loss is 23.14 , inference loss is 13.06, weight deacy loss is 10.07, training accuracy is 0.218750, time 209.292 samples/sec
+epoch 3, total_step 57960, total loss is 22.07 , inference loss is 11.99, weight deacy loss is 10.07, training accuracy is 0.250000, time 292.740 samples/sec
+epoch 3, total_step 57980, total loss is 24.20 , inference loss is 14.13, weight deacy loss is 10.07, training accuracy is 0.250000, time 202.021 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.88652300000002
+best_threshold_index 101 0.9764814814814815
+best_threshold_index 101 0.9761111111111112
+best_threshold_index 98 0.9772222222222222
+best_threshold_index 98 0.9762962962962963
+best_threshold_index 101 0.9764814814814815
+best_threshold_index 98 0.9748148148148148
+best_threshold_index 101 0.9762962962962963
+best_threshold_index 107 0.9748148148148148
+best_threshold_index 101 0.9737037037037037
+best_threshold_index 100 0.975
+[lfw][58000]XNorm: 24.264329
+[lfw][58000]Accuracy-Flip: 0.97367+-0.00933
+test accuracy is:  0.9736666666666668
+epoch 3, total_step 58000, total loss is 21.43 , inference loss is 11.36, weight deacy loss is 10.07, training accuracy is 0.218750, time 18.185 samples/sec
+epoch 3, total_step 58020, total loss is 24.50 , inference loss is 14.43, weight deacy loss is 10.07, training accuracy is 0.187500, time 219.217 samples/sec
+epoch 3, total_step 58040, total loss is 26.23 , inference loss is 16.16, weight deacy loss is 10.07, training accuracy is 0.187500, time 216.040 samples/sec
+epoch 3, total_step 58060, total loss is 22.01 , inference loss is 11.94, weight deacy loss is 10.07, training accuracy is 0.093750, time 196.815 samples/sec
+epoch 3, total_step 58080, total loss is 25.00 , inference loss is 14.93, weight deacy loss is 10.07, training accuracy is 0.156250, time 296.931 samples/sec
+epoch 3, total_step 58100, total loss is 22.13 , inference loss is 12.07, weight deacy loss is 10.07, training accuracy is 0.187500, time 204.410 samples/sec
+epoch 3, total_step 58120, total loss is 23.03 , inference loss is 12.97, weight deacy loss is 10.06, training accuracy is 0.156250, time 213.103 samples/sec
+epoch 3, total_step 58140, total loss is 25.05 , inference loss is 14.99, weight deacy loss is 10.06, training accuracy is 0.250000, time 217.499 samples/sec
+epoch 3, total_step 58160, total loss is 24.69 , inference loss is 14.63, weight deacy loss is 10.06, training accuracy is 0.375000, time 223.215 samples/sec
+epoch 3, total_step 58180, total loss is 22.12 , inference loss is 12.06, weight deacy loss is 10.06, training accuracy is 0.187500, time 208.316 samples/sec
+epoch 3, total_step 58200, total loss is 22.49 , inference loss is 12.43, weight deacy loss is 10.06, training accuracy is 0.187500, time 12.403 samples/sec
+epoch 3, total_step 58220, total loss is 20.30 , inference loss is 10.25, weight deacy loss is 10.06, training accuracy is 0.218750, time 208.029 samples/sec
+epoch 3, total_step 58240, total loss is 19.47 , inference loss is 9.41, weight deacy loss is 10.06, training accuracy is 0.312500, time 214.750 samples/sec
+epoch 3, total_step 58260, total loss is 23.14 , inference loss is 13.09, weight deacy loss is 10.06, training accuracy is 0.093750, time 192.744 samples/sec
+epoch 3, total_step 58280, total loss is 22.73 , inference loss is 12.68, weight deacy loss is 10.05, training accuracy is 0.250000, time 214.261 samples/sec
+epoch 3, total_step 58300, total loss is 21.93 , inference loss is 11.88, weight deacy loss is 10.05, training accuracy is 0.187500, time 223.546 samples/sec
+epoch 3, total_step 58320, total loss is 24.05 , inference loss is 14.00, weight deacy loss is 10.05, training accuracy is 0.218750, time 213.409 samples/sec
+epoch 3, total_step 58340, total loss is 22.02 , inference loss is 11.97, weight deacy loss is 10.05, training accuracy is 0.218750, time 297.394 samples/sec
+epoch 3, total_step 58360, total loss is 21.68 , inference loss is 11.63, weight deacy loss is 10.05, training accuracy is 0.343750, time 216.114 samples/sec
+epoch 3, total_step 58380, total loss is 20.31 , inference loss is 10.26, weight deacy loss is 10.05, training accuracy is 0.343750, time 218.765 samples/sec
+epoch 3, total_step 58400, total loss is 19.28 , inference loss is 9.24, weight deacy loss is 10.05, training accuracy is 0.187500, time 211.388 samples/sec
+epoch 3, total_step 58420, total loss is 23.07 , inference loss is 13.02, weight deacy loss is 10.05, training accuracy is 0.125000, time 217.216 samples/sec
+epoch 3, total_step 58440, total loss is 22.86 , inference loss is 12.81, weight deacy loss is 10.04, training accuracy is 0.187500, time 210.149 samples/sec
+epoch 3, total_step 58460, total loss is 21.37 , inference loss is 11.32, weight deacy loss is 10.04, training accuracy is 0.156250, time 205.114 samples/sec
+epoch 3, total_step 58480, total loss is 21.35 , inference loss is 11.31, weight deacy loss is 10.04, training accuracy is 0.218750, time 208.983 samples/sec
+epoch 3, total_step 58500, total loss is 23.86 , inference loss is 13.81, weight deacy loss is 10.04, training accuracy is 0.218750, time 13.584 samples/sec
+epoch 3, total_step 58520, total loss is 24.37 , inference loss is 14.33, weight deacy loss is 10.04, training accuracy is 0.093750, time 211.713 samples/sec
+epoch 3, total_step 58540, total loss is 21.54 , inference loss is 11.50, weight deacy loss is 10.04, training accuracy is 0.156250, time 208.733 samples/sec
+epoch 3, total_step 58560, total loss is 28.31 , inference loss is 18.27, weight deacy loss is 10.04, training accuracy is 0.062500, time 212.841 samples/sec
+epoch 3, total_step 58580, total loss is 22.87 , inference loss is 12.84, weight deacy loss is 10.04, training accuracy is 0.156250, time 203.965 samples/sec
+epoch 3, total_step 58600, total loss is 20.58 , inference loss is 10.54, weight deacy loss is 10.04, training accuracy is 0.375000, time 295.719 samples/sec
+epoch 3, total_step 58620, total loss is 19.22 , inference loss is 9.18, weight deacy loss is 10.03, training accuracy is 0.250000, time 208.358 samples/sec
+epoch 3, total_step 58640, total loss is 23.54 , inference loss is 13.51, weight deacy loss is 10.03, training accuracy is 0.156250, time 208.438 samples/sec
+epoch 3, total_step 58660, total loss is 22.35 , inference loss is 12.32, weight deacy loss is 10.03, training accuracy is 0.250000, time 215.171 samples/sec
+epoch 3, total_step 58680, total loss is 23.26 , inference loss is 13.23, weight deacy loss is 10.03, training accuracy is 0.031250, time 226.096 samples/sec
+epoch 3, total_step 58700, total loss is 23.63 , inference loss is 13.60, weight deacy loss is 10.03, training accuracy is 0.250000, time 207.309 samples/sec
+epoch 3, total_step 58720, total loss is 27.03 , inference loss is 17.01, weight deacy loss is 10.03, training accuracy is 0.031250, time 215.984 samples/sec
+epoch 3, total_step 58740, total loss is 27.52 , inference loss is 17.49, weight deacy loss is 10.03, training accuracy is 0.187500, time 224.984 samples/sec
+epoch 3, total_step 58760, total loss is 23.88 , inference loss is 13.85, weight deacy loss is 10.03, training accuracy is 0.187500, time 202.449 samples/sec
+epoch 3, total_step 58780, total loss is 26.39 , inference loss is 16.37, weight deacy loss is 10.02, training accuracy is 0.125000, time 218.542 samples/sec
+epoch 3, total_step 58800, total loss is 27.53 , inference loss is 17.51, weight deacy loss is 10.02, training accuracy is 0.062500, time 13.525 samples/sec
+epoch 3, total_step 58820, total loss is 21.82 , inference loss is 11.79, weight deacy loss is 10.02, training accuracy is 0.187500, time 211.635 samples/sec
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+epoch 3, total_step 58900, total loss is 26.42 , inference loss is 16.40, weight deacy loss is 10.02, training accuracy is 0.156250, time 221.923 samples/sec
+epoch 3, total_step 58920, total loss is 21.95 , inference loss is 11.94, weight deacy loss is 10.02, training accuracy is 0.093750, time 220.176 samples/sec
+epoch 3, total_step 58940, total loss is 25.37 , inference loss is 15.36, weight deacy loss is 10.02, training accuracy is 0.125000, time 221.835 samples/sec
+epoch 3, total_step 58960, total loss is 27.88 , inference loss is 17.87, weight deacy loss is 10.01, training accuracy is 0.125000, time 210.843 samples/sec
+epoch 3, total_step 58980, total loss is 24.51 , inference loss is 14.50, weight deacy loss is 10.01, training accuracy is 0.125000, time 212.147 samples/sec
+epoch 3, total_step 59000, total loss is 22.53 , inference loss is 12.52, weight deacy loss is 10.01, training accuracy is 0.187500, time 212.048 samples/sec
+epoch 3, total_step 59020, total loss is 26.37 , inference loss is 16.36, weight deacy loss is 10.01, training accuracy is 0.031250, time 219.529 samples/sec
+epoch 3, total_step 59040, total loss is 18.48 , inference loss is 8.47, weight deacy loss is 10.01, training accuracy is 0.281250, time 297.170 samples/sec
+epoch 3, total_step 59060, total loss is 25.69 , inference loss is 15.68, weight deacy loss is 10.01, training accuracy is 0.156250, time 297.186 samples/sec
+epoch 3, total_step 59080, total loss is 26.56 , inference loss is 16.55, weight deacy loss is 10.01, training accuracy is 0.125000, time 213.157 samples/sec
+epoch 3, total_step 59100, total loss is 24.17 , inference loss is 14.17, weight deacy loss is 10.01, training accuracy is 0.187500, time 13.702 samples/sec
+epoch 3, total_step 59120, total loss is 24.22 , inference loss is 14.22, weight deacy loss is 10.00, training accuracy is 0.187500, time 210.930 samples/sec
+epoch 3, total_step 59140, total loss is 22.57 , inference loss is 12.57, weight deacy loss is 10.00, training accuracy is 0.218750, time 225.598 samples/sec
+epoch 3, total_step 59160, total loss is 23.33 , inference loss is 13.32, weight deacy loss is 10.00, training accuracy is 0.125000, time 212.647 samples/sec
+epoch 3, total_step 59180, total loss is 23.90 , inference loss is 13.90, weight deacy loss is 10.00, training accuracy is 0.156250, time 212.396 samples/sec
+epoch 3, total_step 59200, total loss is 28.88 , inference loss is 18.88, weight deacy loss is 10.00, training accuracy is 0.125000, time 214.644 samples/sec
+epoch 3, total_step 59220, total loss is 24.80 , inference loss is 14.80, weight deacy loss is 10.00, training accuracy is 0.187500, time 212.395 samples/sec
+epoch 3, total_step 59240, total loss is 26.97 , inference loss is 16.97, weight deacy loss is 10.00, training accuracy is 0.156250, time 211.570 samples/sec
+epoch 3, total_step 59260, total loss is 28.22 , inference loss is 18.23, weight deacy loss is 10.00, training accuracy is 0.156250, time 208.127 samples/sec
+epoch 3, total_step 59280, total loss is 29.47 , inference loss is 19.48, weight deacy loss is 10.00, training accuracy is 0.031250, time 214.782 samples/sec
+epoch 3, total_step 59300, total loss is 23.58 , inference loss is 13.58, weight deacy loss is 9.99, training accuracy is 0.156250, time 214.735 samples/sec
+epoch 3, total_step 59320, total loss is 22.80 , inference loss is 12.81, weight deacy loss is 9.99, training accuracy is 0.187500, time 300.110 samples/sec
+epoch 3, total_step 59340, total loss is 27.57 , inference loss is 17.58, weight deacy loss is 9.99, training accuracy is 0.218750, time 212.221 samples/sec
+epoch 3, total_step 59360, total loss is 26.91 , inference loss is 16.92, weight deacy loss is 9.99, training accuracy is 0.125000, time 215.707 samples/sec
+epoch 3, total_step 59380, total loss is 27.33 , inference loss is 17.35, weight deacy loss is 9.99, training accuracy is 0.156250, time 207.383 samples/sec
+epoch 3, total_step 59400, total loss is 26.35 , inference loss is 16.36, weight deacy loss is 9.99, training accuracy is 0.093750, time 12.541 samples/sec
+epoch 3, total_step 59420, total loss is 25.53 , inference loss is 15.54, weight deacy loss is 9.99, training accuracy is 0.062500, time 212.918 samples/sec
+epoch 3, total_step 59440, total loss is 29.70 , inference loss is 19.72, weight deacy loss is 9.99, training accuracy is 0.062500, time 208.809 samples/sec
+epoch 3, total_step 59460, total loss is 29.55 , inference loss is 19.57, weight deacy loss is 9.98, training accuracy is 0.156250, time 219.398 samples/sec
+epoch 3, total_step 59480, total loss is 22.57 , inference loss is 12.59, weight deacy loss is 9.98, training accuracy is 0.218750, time 218.531 samples/sec
+epoch 3, total_step 59500, total loss is 26.30 , inference loss is 16.32, weight deacy loss is 9.98, training accuracy is 0.156250, time 207.338 samples/sec
+epoch 3, total_step 59520, total loss is 26.37 , inference loss is 16.39, weight deacy loss is 9.98, training accuracy is 0.218750, time 233.549 samples/sec
+epoch 3, total_step 59540, total loss is 23.14 , inference loss is 13.16, weight deacy loss is 9.98, training accuracy is 0.156250, time 207.260 samples/sec
+epoch 3, total_step 59560, total loss is 23.69 , inference loss is 13.72, weight deacy loss is 9.98, training accuracy is 0.125000, time 213.683 samples/sec
+epoch 3, total_step 59580, total loss is 25.99 , inference loss is 16.02, weight deacy loss is 9.98, training accuracy is 0.093750, time 181.078 samples/sec
+epoch 3, total_step 59600, total loss is 24.41 , inference loss is 14.43, weight deacy loss is 9.98, training accuracy is 0.250000, time 174.045 samples/sec
+epoch 3, total_step 59620, total loss is 21.15 , inference loss is 11.17, weight deacy loss is 9.98, training accuracy is 0.093750, time 209.710 samples/sec
+epoch 3, total_step 59640, total loss is 22.96 , inference loss is 12.99, weight deacy loss is 9.97, training accuracy is 0.156250, time 236.937 samples/sec
+epoch 3, total_step 59660, total loss is 21.46 , inference loss is 11.49, weight deacy loss is 9.97, training accuracy is 0.125000, time 220.536 samples/sec
+epoch 3, total_step 59680, total loss is 25.02 , inference loss is 15.05, weight deacy loss is 9.97, training accuracy is 0.187500, time 207.932 samples/sec
+epoch 3, total_step 59700, total loss is 23.19 , inference loss is 13.22, weight deacy loss is 9.97, training accuracy is 0.187500, time 14.152 samples/sec
+epoch 3, total_step 59720, total loss is 21.29 , inference loss is 11.32, weight deacy loss is 9.97, training accuracy is 0.218750, time 224.930 samples/sec
+epoch 3, total_step 59740, total loss is 24.61 , inference loss is 14.64, weight deacy loss is 9.97, training accuracy is 0.156250, time 208.543 samples/sec
+epoch 3, total_step 59760, total loss is 23.08 , inference loss is 13.11, weight deacy loss is 9.97, training accuracy is 0.156250, time 212.350 samples/sec
+epoch 3, total_step 59780, total loss is 25.28 , inference loss is 15.32, weight deacy loss is 9.97, training accuracy is 0.156250, time 206.517 samples/sec
+epoch 3, total_step 59800, total loss is 21.92 , inference loss is 11.96, weight deacy loss is 9.96, training accuracy is 0.218750, time 215.792 samples/sec
+epoch 3, total_step 59820, total loss is 25.18 , inference loss is 15.22, weight deacy loss is 9.96, training accuracy is 0.062500, time 210.341 samples/sec
+epoch 3, total_step 59840, total loss is 22.72 , inference loss is 12.76, weight deacy loss is 9.96, training accuracy is 0.156250, time 206.708 samples/sec
+epoch 3, total_step 59860, total loss is 18.44 , inference loss is 8.48, weight deacy loss is 9.96, training accuracy is 0.375000, time 297.194 samples/sec
+epoch 3, total_step 59880, total loss is 25.97 , inference loss is 16.01, weight deacy loss is 9.96, training accuracy is 0.125000, time 208.156 samples/sec
+epoch 3, total_step 59900, total loss is 24.84 , inference loss is 14.88, weight deacy loss is 9.96, training accuracy is 0.093750, time 209.414 samples/sec
+epoch 3, total_step 59920, total loss is 27.42 , inference loss is 17.46, weight deacy loss is 9.96, training accuracy is 0.031250, time 205.892 samples/sec
+epoch 3, total_step 59940, total loss is 21.14 , inference loss is 11.18, weight deacy loss is 9.96, training accuracy is 0.187500, time 213.452 samples/sec
+epoch 3, total_step 59960, total loss is 25.08 , inference loss is 15.13, weight deacy loss is 9.96, training accuracy is 0.218750, time 200.084 samples/sec
+epoch 3, total_step 59980, total loss is 27.10 , inference loss is 17.14, weight deacy loss is 9.95, training accuracy is 0.156250, time 208.825 samples/sec
+testing verification..
+(12000, 512)
+infer time 23.201575999999996
+best_threshold_index 106 0.9796296296296296
+best_threshold_index 106 0.9790740740740741
+best_threshold_index 105 0.9801851851851852
+best_threshold_index 106 0.9805555555555555
+best_threshold_index 106 0.9790740740740741
+best_threshold_index 106 0.9777777777777777
+best_threshold_index 105 0.9788888888888889
+best_threshold_index 106 0.9779629629629629
+best_threshold_index 106 0.9775925925925926
+best_threshold_index 105 0.9779629629629629
+[lfw][60000]XNorm: 23.403148
+[lfw][60000]Accuracy-Flip: 0.97800+-0.00897
+test accuracy is:  0.978
+epoch 3, total_step 60000, total loss is 19.63 , inference loss is 9.68, weight deacy loss is 9.95, training accuracy is 0.125000, time 14.662 samples/sec
+epoch 3, total_step 60020, total loss is 22.09 , inference loss is 12.14, weight deacy loss is 9.95, training accuracy is 0.156250, time 218.549 samples/sec
+epoch 3, total_step 60040, total loss is 26.43 , inference loss is 16.48, weight deacy loss is 9.95, training accuracy is 0.062500, time 217.476 samples/sec
+epoch 3, total_step 60060, total loss is 23.67 , inference loss is 13.72, weight deacy loss is 9.95, training accuracy is 0.156250, time 222.073 samples/sec
+epoch 3, total_step 60080, total loss is 21.87 , inference loss is 11.92, weight deacy loss is 9.95, training accuracy is 0.281250, time 225.580 samples/sec
+epoch 3, total_step 60100, total loss is 24.97 , inference loss is 15.03, weight deacy loss is 9.95, training accuracy is 0.062500, time 218.698 samples/sec
+epoch 3, total_step 60120, total loss is 24.72 , inference loss is 14.77, weight deacy loss is 9.95, training accuracy is 0.187500, time 209.054 samples/sec
+epoch 3, total_step 60140, total loss is 27.26 , inference loss is 17.31, weight deacy loss is 9.94, training accuracy is 0.125000, time 215.499 samples/sec
+epoch 3, total_step 60160, total loss is 24.51 , inference loss is 14.57, weight deacy loss is 9.94, training accuracy is 0.125000, time 219.537 samples/sec
+epoch 3, total_step 60180, total loss is 22.24 , inference loss is 12.30, weight deacy loss is 9.94, training accuracy is 0.125000, time 214.643 samples/sec
+epoch 3, total_step 60200, total loss is 24.88 , inference loss is 14.93, weight deacy loss is 9.94, training accuracy is 0.125000, time 294.520 samples/sec
+epoch 3, total_step 60220, total loss is 22.58 , inference loss is 12.64, weight deacy loss is 9.94, training accuracy is 0.156250, time 216.597 samples/sec
+epoch 3, total_step 60240, total loss is 25.54 , inference loss is 15.60, weight deacy loss is 9.94, training accuracy is 0.125000, time 212.929 samples/sec
+epoch 3, total_step 60260, total loss is 22.45 , inference loss is 12.51, weight deacy loss is 9.94, training accuracy is 0.218750, time 210.500 samples/sec
+epoch 3, total_step 60280, total loss is 23.91 , inference loss is 13.98, weight deacy loss is 9.94, training accuracy is 0.062500, time 218.369 samples/sec
+epoch 3, total_step 60300, total loss is 26.84 , inference loss is 16.91, weight deacy loss is 9.94, training accuracy is 0.250000, time 12.361 samples/sec
+epoch 3, total_step 60320, total loss is 23.79 , inference loss is 13.85, weight deacy loss is 9.93, training accuracy is 0.156250, time 214.502 samples/sec
+epoch 3, total_step 60340, total loss is 20.18 , inference loss is 10.24, weight deacy loss is 9.93, training accuracy is 0.156250, time 211.835 samples/sec
+epoch 3, total_step 60360, total loss is 21.75 , inference loss is 11.82, weight deacy loss is 9.93, training accuracy is 0.125000, time 222.775 samples/sec
+epoch 3, total_step 60380, total loss is 24.77 , inference loss is 14.84, weight deacy loss is 9.93, training accuracy is 0.218750, time 211.461 samples/sec
+epoch 3, total_step 60400, total loss is 25.85 , inference loss is 15.92, weight deacy loss is 9.93, training accuracy is 0.062500, time 216.611 samples/sec
+epoch 3, total_step 60420, total loss is 24.07 , inference loss is 14.14, weight deacy loss is 9.93, training accuracy is 0.218750, time 213.761 samples/sec
+epoch 3, total_step 60440, total loss is 25.14 , inference loss is 15.21, weight deacy loss is 9.93, training accuracy is 0.093750, time 217.798 samples/sec
+epoch 3, total_step 60460, total loss is 26.88 , inference loss is 16.95, weight deacy loss is 9.93, training accuracy is 0.125000, time 209.637 samples/sec2022-12-01 04:14:57.484367: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:145] Filling up shuffle buffer (this may take a while): 8645 of 10000
+2022-12-01 04:14:58.921808: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:195] Shuffle buffer filled.
+
+epoch 3, total_step 60480, total loss is 27.26 , inference loss is 17.34, weight deacy loss is 9.93, training accuracy is 0.125000, time 211.701 samples/sec
+epoch 3, total_step 60500, total loss is 25.39 , inference loss is 15.47, weight deacy loss is 9.92, training accuracy is 0.062500, time 218.411 samples/sec
+epoch 3, total_step 60520, total loss is 25.95 , inference loss is 16.03, weight deacy loss is 9.92, training accuracy is 0.250000, time 206.010 samples/sec
+epoch 3, total_step 60540, total loss is 20.44 , inference loss is 10.52, weight deacy loss is 9.92, training accuracy is 0.156250, time 210.299 samples/sec
+epoch 3, total_step 60560, total loss is 25.03 , inference loss is 15.11, weight deacy loss is 9.92, training accuracy is 0.187500, time 210.450 samples/sec
+epoch 3, total_step 60580, total loss is 23.63 , inference loss is 13.71, weight deacy loss is 9.92, training accuracy is 0.125000, time 194.757 samples/sec
+epoch 3, total_step 60600, total loss is 25.32 , inference loss is 15.40, weight deacy loss is 9.92, training accuracy is 0.093750, time 13.587 samples/sec
+epoch 3, total_step 60620, total loss is 23.69 , inference loss is 13.78, weight deacy loss is 9.92, training accuracy is 0.156250, time 206.951 samples/sec
+epoch 3, total_step 60640, total loss is 22.12 , inference loss is 12.21, weight deacy loss is 9.92, training accuracy is 0.093750, time 297.691 samples/sec
+epoch 3, total_step 60660, total loss is 25.07 , inference loss is 15.15, weight deacy loss is 9.91, training accuracy is 0.250000, time 216.992 samples/sec
+epoch 3, total_step 60680, total loss is 25.76 , inference loss is 15.85, weight deacy loss is 9.91, training accuracy is 0.125000, time 219.451 samples/sec
+epoch 3, total_step 60700, total loss is 26.05 , inference loss is 16.14, weight deacy loss is 9.91, training accuracy is 0.250000, time 219.532 samples/sec
+epoch 3, total_step 60720, total loss is 23.87 , inference loss is 13.96, weight deacy loss is 9.91, training accuracy is 0.093750, time 216.809 samples/sec
+epoch 3, total_step 60740, total loss is 22.85 , inference loss is 12.94, weight deacy loss is 9.91, training accuracy is 0.218750, time 217.202 samples/sec
+epoch 3, total_step 60760, total loss is 19.75 , inference loss is 9.84, weight deacy loss is 9.91, training accuracy is 0.187500, time 215.753 samples/sec
+epoch 3, total_step 60780, total loss is 23.76 , inference loss is 13.86, weight deacy loss is 9.91, training accuracy is 0.156250, time 216.767 samples/sec
+epoch 3, total_step 60800, total loss is 24.42 , inference loss is 14.51, weight deacy loss is 9.91, training accuracy is 0.125000, time 297.548 samples/sec
+epoch 3, total_step 60820, total loss is 26.76 , inference loss is 16.86, weight deacy loss is 9.91, training accuracy is 0.125000, time 298.276 samples/sec
+epoch 3, total_step 60840, total loss is 20.98 , inference loss is 11.08, weight deacy loss is 9.90, training accuracy is 0.281250, time 212.261 samples/sec
+epoch 3, total_step 60860, total loss is 26.71 , inference loss is 16.81, weight deacy loss is 9.90, training accuracy is 0.156250, time 208.701 samples/sec
+epoch 3, total_step 60880, total loss is 23.02 , inference loss is 13.12, weight deacy loss is 9.90, training accuracy is 0.093750, time 209.290 samples/sec
+epoch 3, total_step 60900, total loss is 26.98 , inference loss is 17.08, weight deacy loss is 9.90, training accuracy is 0.093750, time 13.764 samples/sec
+epoch 3, total_step 60920, total loss is 22.87 , inference loss is 12.97, weight deacy loss is 9.90, training accuracy is 0.093750, time 204.375 samples/sec
+epoch 3, total_step 60940, total loss is 29.50 , inference loss is 19.60, weight deacy loss is 9.90, training accuracy is 0.062500, time 214.858 samples/sec
+epoch 3, total_step 60960, total loss is 24.51 , inference loss is 14.61, weight deacy loss is 9.90, training accuracy is 0.093750, time 216.091 samples/sec
+epoch 3, total_step 60980, total loss is 25.64 , inference loss is 15.74, weight deacy loss is 9.90, training accuracy is 0.062500, time 212.235 samples/sec
+epoch 3, total_step 61000, total loss is 20.64 , inference loss is 10.74, weight deacy loss is 9.90, training accuracy is 0.125000, time 207.772 samples/sec
+epoch 3, total_step 61020, total loss is 25.14 , inference loss is 15.25, weight deacy loss is 9.89, training accuracy is 0.093750, time 234.162 samples/sec
+epoch 3, total_step 61040, total loss is 24.15 , inference loss is 14.26, weight deacy loss is 9.89, training accuracy is 0.062500, time 193.567 samples/sec
+epoch 3, total_step 61060, total loss is 21.68 , inference loss is 11.78, weight deacy loss is 9.89, training accuracy is 0.187500, time 201.981 samples/sec
+epoch 3, total_step 61080, total loss is 24.56 , inference loss is 14.67, weight deacy loss is 9.89, training accuracy is 0.093750, time 194.187 samples/sec
+epoch 3, total_step 61100, total loss is 26.55 , inference loss is 16.66, weight deacy loss is 9.89, training accuracy is 0.031250, time 235.216 samples/sec
+epoch 3, total_step 61120, total loss is 29.13 , inference loss is 19.24, weight deacy loss is 9.89, training accuracy is 0.093750, time 213.667 samples/sec
+epoch 3, total_step 61140, total loss is 25.41 , inference loss is 15.52, weight deacy loss is 9.89, training accuracy is 0.031250, time 192.868 samples/sec
+epoch 3, total_step 61160, total loss is 21.58 , inference loss is 11.69, weight deacy loss is 9.89, training accuracy is 0.218750, time 222.225 samples/sec
+epoch 3, total_step 61180, total loss is 23.53 , inference loss is 13.64, weight deacy loss is 9.89, training accuracy is 0.125000, time 207.349 samples/sec
+epoch 3, total_step 61200, total loss is 22.68 , inference loss is 12.80, weight deacy loss is 9.88, training accuracy is 0.125000, time 14.015 samples/sec
+epoch 3, total_step 61220, total loss is 23.48 , inference loss is 13.59, weight deacy loss is 9.88, training accuracy is 0.125000, time 208.185 samples/sec
+epoch 3, total_step 61240, total loss is 24.91 , inference loss is 15.03, weight deacy loss is 9.88, training accuracy is 0.156250, time 175.062 samples/sec
+epoch 3, total_step 61260, total loss is 21.19 , inference loss is 11.31, weight deacy loss is 9.88, training accuracy is 0.187500, time 212.831 samples/sec
+epoch 3, total_step 61280, total loss is 23.40 , inference loss is 13.52, weight deacy loss is 9.88, training accuracy is 0.187500, time 209.331 samples/sec
+epoch 3, total_step 61300, total loss is 22.64 , inference loss is 12.76, weight deacy loss is 9.88, training accuracy is 0.218750, time 176.777 samples/sec
+epoch 3, total_step 61320, total loss is 24.02 , inference loss is 14.15, weight deacy loss is 9.88, training accuracy is 0.156250, time 200.805 samples/sec
+End of epoch 3
+epoch 4, total_step 61340, total loss is 26.94 , inference loss is 17.06, weight deacy loss is 9.88, training accuracy is 0.093750, time 211.942 samples/sec
+epoch 4, total_step 61360, total loss is 17.45 , inference loss is 7.58, weight deacy loss is 9.88, training accuracy is 0.406250, time 212.576 samples/sec
+epoch 4, total_step 61380, total loss is 19.58 , inference loss is 9.70, weight deacy loss is 9.87, training accuracy is 0.343750, time 208.945 samples/sec
+epoch 4, total_step 61400, total loss is 19.48 , inference loss is 9.60, weight deacy loss is 9.87, training accuracy is 0.312500, time 208.441 samples/sec
+epoch 4, total_step 61420, total loss is 17.10 , inference loss is 7.23, weight deacy loss is 9.87, training accuracy is 0.500000, time 211.913 samples/sec
+epoch 4, total_step 61440, total loss is 17.26 , inference loss is 7.39, weight deacy loss is 9.87, training accuracy is 0.562500, time 221.969 samples/sec
+epoch 4, total_step 61460, total loss is 19.47 , inference loss is 9.60, weight deacy loss is 9.87, training accuracy is 0.468750, time 211.368 samples/sec
+epoch 4, total_step 61480, total loss is 15.25 , inference loss is 5.38, weight deacy loss is 9.87, training accuracy is 0.687500, time 177.057 samples/sec
+epoch 4, total_step 61500, total loss is 19.82 , inference loss is 9.95, weight deacy loss is 9.87, training accuracy is 0.437500, time 13.610 samples/sec
+epoch 4, total_step 61520, total loss is 20.48 , inference loss is 10.62, weight deacy loss is 9.87, training accuracy is 0.406250, time 217.896 samples/sec
+epoch 4, total_step 61540, total loss is 18.45 , inference loss is 8.59, weight deacy loss is 9.86, training accuracy is 0.250000, time 211.065 samples/sec
+epoch 4, total_step 61560, total loss is 18.01 , inference loss is 8.15, weight deacy loss is 9.86, training accuracy is 0.468750, time 212.976 samples/sec
+epoch 4, total_step 61580, total loss is 17.27 , inference loss is 7.41, weight deacy loss is 9.86, training accuracy is 0.281250, time 216.948 samples/sec
+epoch 4, total_step 61600, total loss is 19.39 , inference loss is 9.53, weight deacy loss is 9.86, training accuracy is 0.312500, time 230.759 samples/sec
+epoch 4, total_step 61620, total loss is 22.01 , inference loss is 12.15, weight deacy loss is 9.86, training accuracy is 0.312500, time 223.154 samples/sec
+epoch 4, total_step 61640, total loss is 24.24 , inference loss is 14.38, weight deacy loss is 9.86, training accuracy is 0.218750, time 207.491 samples/sec
+epoch 4, total_step 61660, total loss is 20.75 , inference loss is 10.90, weight deacy loss is 9.86, training accuracy is 0.343750, time 208.771 samples/sec
+epoch 4, total_step 61680, total loss is 16.34 , inference loss is 6.48, weight deacy loss is 9.86, training accuracy is 0.437500, time 208.968 samples/sec
+epoch 4, total_step 61700, total loss is 24.07 , inference loss is 14.22, weight deacy loss is 9.85, training accuracy is 0.312500, time 209.478 samples/sec
+epoch 4, total_step 61720, total loss is 18.29 , inference loss is 8.44, weight deacy loss is 9.85, training accuracy is 0.375000, time 198.770 samples/sec
+epoch 4, total_step 61740, total loss is 21.60 , inference loss is 11.74, weight deacy loss is 9.85, training accuracy is 0.312500, time 295.416 samples/sec
+epoch 4, total_step 61760, total loss is 19.59 , inference loss is 9.74, weight deacy loss is 9.85, training accuracy is 0.312500, time 212.860 samples/sec
+epoch 4, total_step 61780, total loss is 21.27 , inference loss is 11.42, weight deacy loss is 9.85, training accuracy is 0.312500, time 208.538 samples/sec
+epoch 4, total_step 61800, total loss is 19.49 , inference loss is 9.64, weight deacy loss is 9.85, training accuracy is 0.437500, time 13.624 samples/sec
+epoch 4, total_step 61820, total loss is 19.82 , inference loss is 9.97, weight deacy loss is 9.85, training accuracy is 0.343750, time 212.118 samples/sec
+epoch 4, total_step 61840, total loss is 21.63 , inference loss is 11.78, weight deacy loss is 9.85, training accuracy is 0.468750, time 208.278 samples/sec
+epoch 4, total_step 61860, total loss is 18.93 , inference loss is 9.09, weight deacy loss is 9.84, training accuracy is 0.312500, time 212.528 samples/sec
+epoch 4, total_step 61880, total loss is 21.54 , inference loss is 11.69, weight deacy loss is 9.84, training accuracy is 0.312500, time 204.672 samples/sec
+epoch 4, total_step 61900, total loss is 20.56 , inference loss is 10.71, weight deacy loss is 9.84, training accuracy is 0.218750, time 220.112 samples/sec
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+epoch 4, total_step 61980, total loss is 24.57 , inference loss is 14.73, weight deacy loss is 9.84, training accuracy is 0.281250, time 210.146 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.426797999999998
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+[lfw][62000]XNorm: 22.299559
+[lfw][62000]Accuracy-Flip: 0.97983+-0.00664
+test accuracy is:  0.9798333333333333
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+testing verification..
+(12000, 512)
+infer time 24.74967400000001
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+[lfw][64000]XNorm: 23.180064
+[lfw][64000]Accuracy-Flip: 0.98350+-0.00545
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+testing verification..
+(12000, 512)
+infer time 24.817127999999986
+best_threshold_index 110 0.9848148148148148
+best_threshold_index 110 0.9842592592592593
+best_threshold_index 110 0.9859259259259259
+best_threshold_index 110 0.9861111111111112
+best_threshold_index 110 0.9857407407407407
+best_threshold_index 110 0.9837037037037037
+best_threshold_index 110 0.985
+best_threshold_index 110 0.9840740740740741
+best_threshold_index 110 0.9833333333333333
+best_threshold_index 110 0.9837037037037037
+[lfw][66000]XNorm: 20.801256
+[lfw][66000]Accuracy-Flip: 0.98467+-0.00859
+test accuracy is:  0.9846666666666668
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+epoch 4, total_step 67980, total loss is 22.37 , inference loss is 12.87, weight deacy loss is 9.50, training accuracy is 0.250000, time 214.216 samples/sec
+testing verification..
+(12000, 512)
+infer time 23.55919499999997
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+[lfw][68000]XNorm: 23.280288
+[lfw][68000]Accuracy-Flip: 0.98317+-0.00751
+test accuracy is:  0.9831666666666667
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+testing verification..
+(12000, 512)
+infer time 24.47982799999998
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+[lfw][70000]XNorm: 22.954492
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+testing verification..
+(12000, 512)
+infer time 24.629170999999975
+best_threshold_index 106 0.9829629629629629
+best_threshold_index 106 0.982037037037037
+best_threshold_index 106 0.9831481481481481
+best_threshold_index 106 0.9829629629629629
+best_threshold_index 107 0.9824074074074074
+best_threshold_index 107 0.9811111111111112
+best_threshold_index 106 0.9822222222222222
+best_threshold_index 106 0.9812962962962963
+best_threshold_index 107 0.9811111111111112
+best_threshold_index 106 0.9812962962962963
+[lfw][72000]XNorm: 22.500911
+[lfw][72000]Accuracy-Flip: 0.98150+-0.00693
+test accuracy is:  0.9814999999999999
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+epoch 4, total_step 73980, total loss is 21.99 , inference loss is 12.80, weight deacy loss is 9.19, training accuracy is 0.093750, time 207.820 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.833625999999995
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+[lfw][74000]XNorm: 23.233927
+[lfw][74000]Accuracy-Flip: 0.98200+-0.00802
+test accuracy is:  0.982
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+epoch 4, total_step 75700, total loss is 19.55 , inference loss is 10.45, weight deacy loss is 9.10, training accuracy is 0.250000, time 208.519 samples/sec
+epoch 4, total_step 75720, total loss is 20.25 , inference loss is 11.15, weight deacy loss is 9.10, training accuracy is 0.281250, time 199.502 samples/sec
+epoch 4, total_step 75740, total loss is 22.55 , inference loss is 13.45, weight deacy loss is 9.10, training accuracy is 0.218750, time 215.827 samples/sec
+epoch 4, total_step 75760, total loss is 23.86 , inference loss is 14.76, weight deacy loss is 9.10, training accuracy is 0.156250, time 216.010 samples/sec
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+epoch 4, total_step 75800, total loss is 23.31 , inference loss is 14.22, weight deacy loss is 9.10, training accuracy is 0.125000, time 210.612 samples/sec2022-12-01 05:16:12.338689: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:145] Filling up shuffle buffer (this may take a while): 8334 of 10000
+2022-12-01 05:16:14.286529: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:195] Shuffle buffer filled.
+
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+epoch 4, total_step 75980, total loss is 19.49 , inference loss is 10.40, weight deacy loss is 9.09, training accuracy is 0.156250, time 212.016 samples/sec
+testing verification..
+(12000, 512)
+infer time 23.674079000000017
+best_threshold_index 105 0.9838888888888889
+best_threshold_index 109 0.9816666666666667
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+best_threshold_index 109 0.9807407407407407
+best_threshold_index 109 0.9812962962962963
+[lfw][76000]XNorm: 22.789207
+[lfw][76000]Accuracy-Flip: 0.98083+-0.00973
+test accuracy is:  0.9808333333333333
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+epoch 4, total_step 76500, total loss is 23.75 , inference loss is 14.68, weight deacy loss is 9.06, training accuracy is 0.156250, time 13.643 samples/sec
+epoch 4, total_step 76520, total loss is 24.01 , inference loss is 14.94, weight deacy loss is 9.06, training accuracy is 0.125000, time 207.371 samples/sec
+epoch 4, total_step 76540, total loss is 19.30 , inference loss is 10.24, weight deacy loss is 9.06, training accuracy is 0.312500, time 204.962 samples/sec
+epoch 4, total_step 76560, total loss is 23.74 , inference loss is 14.68, weight deacy loss is 9.06, training accuracy is 0.156250, time 195.098 samples/sec
+epoch 4, total_step 76580, total loss is 18.79 , inference loss is 9.73, weight deacy loss is 9.06, training accuracy is 0.250000, time 197.146 samples/sec
+epoch 4, total_step 76600, total loss is 21.62 , inference loss is 12.56, weight deacy loss is 9.06, training accuracy is 0.156250, time 232.408 samples/sec
+epoch 4, total_step 76620, total loss is 19.43 , inference loss is 10.37, weight deacy loss is 9.06, training accuracy is 0.187500, time 195.929 samples/sec
+epoch 4, total_step 76640, total loss is 20.58 , inference loss is 11.53, weight deacy loss is 9.06, training accuracy is 0.343750, time 207.733 samples/sec
+End of epoch 4
+epoch 5, total_step 76660, total loss is 31.67 , inference loss is 22.62, weight deacy loss is 9.06, training accuracy is 0.000000, time 213.622 samples/sec
+epoch 5, total_step 76680, total loss is 23.10 , inference loss is 14.05, weight deacy loss is 9.05, training accuracy is 0.218750, time 203.897 samples/sec
+epoch 5, total_step 76700, total loss is 18.12 , inference loss is 9.06, weight deacy loss is 9.05, training accuracy is 0.375000, time 298.183 samples/sec
+epoch 5, total_step 76720, total loss is 18.84 , inference loss is 9.79, weight deacy loss is 9.05, training accuracy is 0.375000, time 210.864 samples/sec
+epoch 5, total_step 76740, total loss is 16.49 , inference loss is 7.44, weight deacy loss is 9.05, training accuracy is 0.437500, time 212.479 samples/sec
+epoch 5, total_step 76760, total loss is 17.22 , inference loss is 8.17, weight deacy loss is 9.05, training accuracy is 0.531250, time 207.457 samples/sec
+epoch 5, total_step 76780, total loss is 21.53 , inference loss is 12.48, weight deacy loss is 9.05, training accuracy is 0.468750, time 215.807 samples/sec
+epoch 5, total_step 76800, total loss is 17.74 , inference loss is 8.70, weight deacy loss is 9.05, training accuracy is 0.375000, time 10.852 samples/sec
+epoch 5, total_step 76820, total loss is 17.56 , inference loss is 8.51, weight deacy loss is 9.05, training accuracy is 0.437500, time 208.987 samples/sec
+epoch 5, total_step 76840, total loss is 19.21 , inference loss is 10.17, weight deacy loss is 9.05, training accuracy is 0.406250, time 212.495 samples/sec
+epoch 5, total_step 76860, total loss is 17.16 , inference loss is 8.11, weight deacy loss is 9.05, training accuracy is 0.500000, time 217.900 samples/sec
+epoch 5, total_step 76880, total loss is 15.28 , inference loss is 6.23, weight deacy loss is 9.04, training accuracy is 0.468750, time 215.481 samples/sec
+epoch 5, total_step 76900, total loss is 19.68 , inference loss is 10.64, weight deacy loss is 9.04, training accuracy is 0.343750, time 217.722 samples/sec
+epoch 5, total_step 76920, total loss is 17.84 , inference loss is 8.80, weight deacy loss is 9.04, training accuracy is 0.406250, time 248.485 samples/sec
+epoch 5, total_step 76940, total loss is 15.16 , inference loss is 6.12, weight deacy loss is 9.04, training accuracy is 0.500000, time 219.597 samples/sec
+epoch 5, total_step 76960, total loss is 15.96 , inference loss is 6.92, weight deacy loss is 9.04, training accuracy is 0.437500, time 215.837 samples/sec
+epoch 5, total_step 76980, total loss is 20.31 , inference loss is 11.27, weight deacy loss is 9.04, training accuracy is 0.343750, time 225.624 samples/sec
+epoch 5, total_step 77000, total loss is 20.89 , inference loss is 11.85, weight deacy loss is 9.04, training accuracy is 0.312500, time 296.996 samples/sec
+epoch 5, total_step 77020, total loss is 19.68 , inference loss is 10.64, weight deacy loss is 9.04, training accuracy is 0.406250, time 296.996 samples/sec
+epoch 5, total_step 77040, total loss is 17.62 , inference loss is 8.59, weight deacy loss is 9.04, training accuracy is 0.375000, time 208.130 samples/sec
+epoch 5, total_step 77060, total loss is 21.78 , inference loss is 12.74, weight deacy loss is 9.04, training accuracy is 0.375000, time 202.823 samples/sec
+epoch 5, total_step 77080, total loss is 15.11 , inference loss is 6.08, weight deacy loss is 9.03, training accuracy is 0.593750, time 210.509 samples/sec
+epoch 5, total_step 77100, total loss is 16.54 , inference loss is 7.51, weight deacy loss is 9.03, training accuracy is 0.468750, time 14.546 samples/sec
+epoch 5, total_step 77120, total loss is 16.42 , inference loss is 7.39, weight deacy loss is 9.03, training accuracy is 0.375000, time 215.860 samples/sec
+epoch 5, total_step 77140, total loss is 23.42 , inference loss is 14.39, weight deacy loss is 9.03, training accuracy is 0.375000, time 212.919 samples/sec
+epoch 5, total_step 77160, total loss is 20.61 , inference loss is 11.58, weight deacy loss is 9.03, training accuracy is 0.343750, time 209.160 samples/sec
+epoch 5, total_step 77180, total loss is 21.29 , inference loss is 12.26, weight deacy loss is 9.03, training accuracy is 0.437500, time 209.839 samples/sec
+epoch 5, total_step 77200, total loss is 16.74 , inference loss is 7.71, weight deacy loss is 9.03, training accuracy is 0.562500, time 210.974 samples/sec
+epoch 5, total_step 77220, total loss is 18.99 , inference loss is 9.96, weight deacy loss is 9.03, training accuracy is 0.406250, time 204.344 samples/sec
+epoch 5, total_step 77240, total loss is 17.95 , inference loss is 8.92, weight deacy loss is 9.03, training accuracy is 0.562500, time 224.226 samples/sec
+epoch 5, total_step 77260, total loss is 20.28 , inference loss is 11.25, weight deacy loss is 9.03, training accuracy is 0.343750, time 218.705 samples/sec
+epoch 5, total_step 77280, total loss is 19.58 , inference loss is 10.55, weight deacy loss is 9.02, training accuracy is 0.343750, time 220.296 samples/sec
+epoch 5, total_step 77300, total loss is 18.25 , inference loss is 9.23, weight deacy loss is 9.02, training accuracy is 0.281250, time 206.817 samples/sec
+epoch 5, total_step 77320, total loss is 16.27 , inference loss is 7.25, weight deacy loss is 9.02, training accuracy is 0.468750, time 215.311 samples/sec
+epoch 5, total_step 77340, total loss is 20.14 , inference loss is 11.12, weight deacy loss is 9.02, training accuracy is 0.437500, time 216.381 samples/sec
+epoch 5, total_step 77360, total loss is 20.94 , inference loss is 11.92, weight deacy loss is 9.02, training accuracy is 0.312500, time 209.049 samples/sec
+epoch 5, total_step 77380, total loss is 18.78 , inference loss is 9.75, weight deacy loss is 9.02, training accuracy is 0.312500, time 213.756 samples/sec
+epoch 5, total_step 77400, total loss is 24.18 , inference loss is 15.16, weight deacy loss is 9.02, training accuracy is 0.250000, time 13.049 samples/sec
+epoch 5, total_step 77420, total loss is 15.72 , inference loss is 6.70, weight deacy loss is 9.02, training accuracy is 0.500000, time 208.155 samples/sec
+epoch 5, total_step 77440, total loss is 17.10 , inference loss is 8.08, weight deacy loss is 9.02, training accuracy is 0.531250, time 214.234 samples/sec
+epoch 5, total_step 77460, total loss is 19.82 , inference loss is 10.80, weight deacy loss is 9.02, training accuracy is 0.343750, time 216.362 samples/sec
+epoch 5, total_step 77480, total loss is 19.22 , inference loss is 10.21, weight deacy loss is 9.02, training accuracy is 0.437500, time 219.295 samples/sec
+epoch 5, total_step 77500, total loss is 17.47 , inference loss is 8.46, weight deacy loss is 9.01, training accuracy is 0.375000, time 215.626 samples/sec
+epoch 5, total_step 77520, total loss is 21.09 , inference loss is 12.07, weight deacy loss is 9.01, training accuracy is 0.375000, time 208.552 samples/sec
+epoch 5, total_step 77540, total loss is 17.03 , inference loss is 8.02, weight deacy loss is 9.01, training accuracy is 0.406250, time 218.608 samples/sec
+epoch 5, total_step 77560, total loss is 17.47 , inference loss is 8.46, weight deacy loss is 9.01, training accuracy is 0.375000, time 211.183 samples/sec
+epoch 5, total_step 77580, total loss is 16.46 , inference loss is 7.44, weight deacy loss is 9.01, training accuracy is 0.437500, time 222.090 samples/sec
+epoch 5, total_step 77600, total loss is 17.38 , inference loss is 8.37, weight deacy loss is 9.01, training accuracy is 0.375000, time 223.990 samples/sec
+epoch 5, total_step 77620, total loss is 18.16 , inference loss is 9.15, weight deacy loss is 9.01, training accuracy is 0.406250, time 212.098 samples/sec
+epoch 5, total_step 77640, total loss is 22.05 , inference loss is 13.05, weight deacy loss is 9.01, training accuracy is 0.250000, time 215.930 samples/sec
+epoch 5, total_step 77660, total loss is 20.27 , inference loss is 11.26, weight deacy loss is 9.01, training accuracy is 0.406250, time 213.789 samples/sec
+epoch 5, total_step 77680, total loss is 19.79 , inference loss is 10.78, weight deacy loss is 9.01, training accuracy is 0.437500, time 297.705 samples/sec
+epoch 5, total_step 77700, total loss is 19.16 , inference loss is 10.16, weight deacy loss is 9.01, training accuracy is 0.406250, time 12.984 samples/sec
+epoch 5, total_step 77720, total loss is 17.13 , inference loss is 8.13, weight deacy loss is 9.01, training accuracy is 0.312500, time 204.853 samples/sec
+epoch 5, total_step 77740, total loss is 18.14 , inference loss is 9.14, weight deacy loss is 9.00, training accuracy is 0.312500, time 203.791 samples/sec
+epoch 5, total_step 77760, total loss is 18.57 , inference loss is 9.57, weight deacy loss is 9.00, training accuracy is 0.406250, time 215.460 samples/sec
+epoch 5, total_step 77780, total loss is 18.59 , inference loss is 9.59, weight deacy loss is 9.00, training accuracy is 0.312500, time 219.025 samples/sec
+epoch 5, total_step 77800, total loss is 23.77 , inference loss is 14.77, weight deacy loss is 9.00, training accuracy is 0.406250, time 219.898 samples/sec
+epoch 5, total_step 77820, total loss is 18.39 , inference loss is 9.39, weight deacy loss is 9.00, training accuracy is 0.406250, time 225.943 samples/sec
+epoch 5, total_step 77840, total loss is 18.48 , inference loss is 9.48, weight deacy loss is 9.00, training accuracy is 0.343750, time 217.374 samples/sec
+epoch 5, total_step 77860, total loss is 17.69 , inference loss is 8.70, weight deacy loss is 9.00, training accuracy is 0.312500, time 222.483 samples/sec
+epoch 5, total_step 77880, total loss is 18.75 , inference loss is 9.75, weight deacy loss is 9.00, training accuracy is 0.312500, time 217.298 samples/sec
+epoch 5, total_step 77900, total loss is 25.37 , inference loss is 16.38, weight deacy loss is 9.00, training accuracy is 0.250000, time 210.807 samples/sec
+epoch 5, total_step 77920, total loss is 19.66 , inference loss is 10.66, weight deacy loss is 9.00, training accuracy is 0.250000, time 211.170 samples/sec
+epoch 5, total_step 77940, total loss is 21.31 , inference loss is 12.32, weight deacy loss is 9.00, training accuracy is 0.250000, time 187.322 samples/sec
+epoch 5, total_step 77960, total loss is 16.04 , inference loss is 7.05, weight deacy loss is 8.99, training accuracy is 0.500000, time 193.934 samples/sec
+epoch 5, total_step 77980, total loss is 21.69 , inference loss is 12.70, weight deacy loss is 8.99, training accuracy is 0.312500, time 214.822 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.307763999999995
+best_threshold_index 123 0.985
+best_threshold_index 121 0.9844444444444445
+best_threshold_index 114 0.9846296296296296
+best_threshold_index 121 0.9851851851851852
+best_threshold_index 121 0.9840740740740741
+best_threshold_index 121 0.9842592592592593
+best_threshold_index 114 0.9844444444444445
+best_threshold_index 121 0.9844444444444445
+best_threshold_index 121 0.9838888888888889
+best_threshold_index 121 0.9838888888888889
+[lfw][78000]XNorm: 22.340605
+[lfw][78000]Accuracy-Flip: 0.98250+-0.00455
+test accuracy is:  0.9824999999999999
+epoch 5, total_step 78000, total loss is 21.74 , inference loss is 12.75, weight deacy loss is 8.99, training accuracy is 0.187500, time 19.523 samples/sec
+epoch 5, total_step 78020, total loss is 19.12 , inference loss is 10.13, weight deacy loss is 8.99, training accuracy is 0.375000, time 210.381 samples/sec
+epoch 5, total_step 78040, total loss is 19.96 , inference loss is 10.97, weight deacy loss is 8.99, training accuracy is 0.406250, time 207.489 samples/sec
+epoch 5, total_step 78060, total loss is 19.60 , inference loss is 10.61, weight deacy loss is 8.99, training accuracy is 0.468750, time 200.268 samples/sec
+epoch 5, total_step 78080, total loss is 22.98 , inference loss is 14.00, weight deacy loss is 8.99, training accuracy is 0.156250, time 213.947 samples/sec
+epoch 5, total_step 78100, total loss is 19.19 , inference loss is 10.21, weight deacy loss is 8.99, training accuracy is 0.250000, time 205.675 samples/sec
+epoch 5, total_step 78120, total loss is 19.56 , inference loss is 10.58, weight deacy loss is 8.99, training accuracy is 0.281250, time 210.067 samples/sec
+epoch 5, total_step 78140, total loss is 22.65 , inference loss is 13.66, weight deacy loss is 8.99, training accuracy is 0.250000, time 210.496 samples/sec
+epoch 5, total_step 78160, total loss is 23.87 , inference loss is 14.89, weight deacy loss is 8.99, training accuracy is 0.343750, time 206.434 samples/sec
+epoch 5, total_step 78180, total loss is 18.96 , inference loss is 9.98, weight deacy loss is 8.98, training accuracy is 0.312500, time 213.220 samples/sec
+epoch 5, total_step 78200, total loss is 17.47 , inference loss is 8.49, weight deacy loss is 8.98, training accuracy is 0.375000, time 206.211 samples/sec
+epoch 5, total_step 78220, total loss is 19.83 , inference loss is 10.84, weight deacy loss is 8.98, training accuracy is 0.187500, time 211.665 samples/sec
+epoch 5, total_step 78240, total loss is 17.37 , inference loss is 8.39, weight deacy loss is 8.98, training accuracy is 0.375000, time 211.595 samples/sec
+epoch 5, total_step 78260, total loss is 20.93 , inference loss is 11.95, weight deacy loss is 8.98, training accuracy is 0.343750, time 217.109 samples/sec
+epoch 5, total_step 78280, total loss is 22.33 , inference loss is 13.35, weight deacy loss is 8.98, training accuracy is 0.218750, time 214.921 samples/sec
+epoch 5, total_step 78300, total loss is 16.52 , inference loss is 7.54, weight deacy loss is 8.98, training accuracy is 0.312500, time 14.682 samples/sec
+epoch 5, total_step 78320, total loss is 21.98 , inference loss is 13.00, weight deacy loss is 8.98, training accuracy is 0.250000, time 208.664 samples/sec
+epoch 5, total_step 78340, total loss is 21.37 , inference loss is 12.39, weight deacy loss is 8.98, training accuracy is 0.250000, time 218.106 samples/sec
+epoch 5, total_step 78360, total loss is 21.38 , inference loss is 12.40, weight deacy loss is 8.98, training accuracy is 0.312500, time 207.955 samples/sec
+epoch 5, total_step 78380, total loss is 19.86 , inference loss is 10.89, weight deacy loss is 8.98, training accuracy is 0.375000, time 209.823 samples/sec
+epoch 5, total_step 78400, total loss is 18.95 , inference loss is 9.98, weight deacy loss is 8.97, training accuracy is 0.312500, time 218.679 samples/sec
+epoch 5, total_step 78420, total loss is 13.97 , inference loss is 4.99, weight deacy loss is 8.97, training accuracy is 0.375000, time 219.884 samples/sec
+epoch 5, total_step 78440, total loss is 17.92 , inference loss is 8.94, weight deacy loss is 8.97, training accuracy is 0.312500, time 209.983 samples/sec
+epoch 5, total_step 78460, total loss is 18.77 , inference loss is 9.80, weight deacy loss is 8.97, training accuracy is 0.406250, time 216.026 samples/sec
+epoch 5, total_step 78480, total loss is 17.83 , inference loss is 8.86, weight deacy loss is 8.97, training accuracy is 0.343750, time 214.239 samples/sec
+epoch 5, total_step 78500, total loss is 21.51 , inference loss is 12.54, weight deacy loss is 8.97, training accuracy is 0.281250, time 204.766 samples/sec
+epoch 5, total_step 78520, total loss is 19.78 , inference loss is 10.81, weight deacy loss is 8.97, training accuracy is 0.375000, time 215.831 samples/sec
+epoch 5, total_step 78540, total loss is 19.30 , inference loss is 10.33, weight deacy loss is 8.97, training accuracy is 0.406250, time 206.078 samples/sec
+epoch 5, total_step 78560, total loss is 17.69 , inference loss is 8.72, weight deacy loss is 8.97, training accuracy is 0.343750, time 202.067 samples/sec
+epoch 5, total_step 78580, total loss is 16.73 , inference loss is 7.76, weight deacy loss is 8.97, training accuracy is 0.375000, time 209.276 samples/sec
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+epoch 5, total_step 78620, total loss is 17.32 , inference loss is 8.36, weight deacy loss is 8.96, training accuracy is 0.468750, time 206.758 samples/sec
+epoch 5, total_step 78640, total loss is 18.39 , inference loss is 9.43, weight deacy loss is 8.96, training accuracy is 0.312500, time 210.164 samples/sec
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+epoch 5, total_step 78680, total loss is 17.50 , inference loss is 8.53, weight deacy loss is 8.96, training accuracy is 0.437500, time 214.743 samples/sec
+epoch 5, total_step 78700, total loss is 19.33 , inference loss is 10.37, weight deacy loss is 8.96, training accuracy is 0.406250, time 215.551 samples/sec
+epoch 5, total_step 78720, total loss is 17.93 , inference loss is 8.97, weight deacy loss is 8.96, training accuracy is 0.343750, time 212.323 samples/sec
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+epoch 5, total_step 78980, total loss is 17.27 , inference loss is 8.33, weight deacy loss is 8.95, training accuracy is 0.406250, time 211.261 samples/sec
+epoch 5, total_step 79000, total loss is 20.34 , inference loss is 11.40, weight deacy loss is 8.94, training accuracy is 0.281250, time 296.425 samples/sec
+epoch 5, total_step 79020, total loss is 15.47 , inference loss is 6.52, weight deacy loss is 8.94, training accuracy is 0.531250, time 236.218 samples/sec
+epoch 5, total_step 79040, total loss is 17.53 , inference loss is 8.59, weight deacy loss is 8.94, training accuracy is 0.312500, time 235.826 samples/sec
+epoch 5, total_step 79060, total loss is 15.43 , inference loss is 6.49, weight deacy loss is 8.94, training accuracy is 0.406250, time 202.508 samples/sec
+epoch 5, total_step 79080, total loss is 18.36 , inference loss is 9.42, weight deacy loss is 8.94, training accuracy is 0.406250, time 204.431 samples/sec
+epoch 5, total_step 79100, total loss is 16.72 , inference loss is 7.78, weight deacy loss is 8.94, training accuracy is 0.437500, time 202.012 samples/sec
+epoch 5, total_step 79120, total loss is 24.02 , inference loss is 15.09, weight deacy loss is 8.94, training accuracy is 0.218750, time 218.558 samples/sec
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+epoch 5, total_step 79300, total loss is 14.43 , inference loss is 5.50, weight deacy loss is 8.93, training accuracy is 0.562500, time 227.276 samples/sec
+epoch 5, total_step 79320, total loss is 13.81 , inference loss is 4.88, weight deacy loss is 8.93, training accuracy is 0.468750, time 209.536 samples/sec
+epoch 5, total_step 79340, total loss is 17.35 , inference loss is 8.42, weight deacy loss is 8.93, training accuracy is 0.312500, time 210.131 samples/sec
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+epoch 5, total_step 79380, total loss is 16.43 , inference loss is 7.51, weight deacy loss is 8.93, training accuracy is 0.468750, time 250.200 samples/sec
+epoch 5, total_step 79400, total loss is 19.63 , inference loss is 10.70, weight deacy loss is 8.92, training accuracy is 0.375000, time 214.664 samples/sec
+epoch 5, total_step 79420, total loss is 21.84 , inference loss is 12.91, weight deacy loss is 8.92, training accuracy is 0.406250, time 212.466 samples/sec
+epoch 5, total_step 79440, total loss is 12.36 , inference loss is 3.44, weight deacy loss is 8.92, training accuracy is 0.562500, time 214.253 samples/sec
+epoch 5, total_step 79460, total loss is 15.45 , inference loss is 6.52, weight deacy loss is 8.92, training accuracy is 0.437500, time 225.843 samples/sec
+epoch 5, total_step 79480, total loss is 14.55 , inference loss is 5.63, weight deacy loss is 8.92, training accuracy is 0.531250, time 222.778 samples/sec
+epoch 5, total_step 79500, total loss is 16.94 , inference loss is 8.02, weight deacy loss is 8.92, training accuracy is 0.437500, time 13.845 samples/sec
+epoch 5, total_step 79520, total loss is 20.78 , inference loss is 11.86, weight deacy loss is 8.92, training accuracy is 0.375000, time 204.853 samples/sec
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+epoch 5, total_step 79560, total loss is 16.61 , inference loss is 7.69, weight deacy loss is 8.92, training accuracy is 0.562500, time 220.802 samples/sec
+epoch 5, total_step 79580, total loss is 17.04 , inference loss is 8.12, weight deacy loss is 8.92, training accuracy is 0.531250, time 227.021 samples/sec
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+epoch 5, total_step 79680, total loss is 14.47 , inference loss is 5.56, weight deacy loss is 8.91, training accuracy is 0.437500, time 207.415 samples/sec
+epoch 5, total_step 79700, total loss is 13.96 , inference loss is 5.05, weight deacy loss is 8.91, training accuracy is 0.468750, time 211.189 samples/sec
+epoch 5, total_step 79720, total loss is 18.51 , inference loss is 9.60, weight deacy loss is 8.91, training accuracy is 0.406250, time 206.477 samples/sec
+epoch 5, total_step 79740, total loss is 17.03 , inference loss is 8.12, weight deacy loss is 8.91, training accuracy is 0.406250, time 212.369 samples/sec
+epoch 5, total_step 79760, total loss is 19.20 , inference loss is 10.30, weight deacy loss is 8.91, training accuracy is 0.406250, time 217.264 samples/sec
+epoch 5, total_step 79780, total loss is 15.99 , inference loss is 7.09, weight deacy loss is 8.91, training accuracy is 0.531250, time 217.510 samples/sec
+epoch 5, total_step 79800, total loss is 18.08 , inference loss is 9.18, weight deacy loss is 8.91, training accuracy is 0.468750, time 13.539 samples/sec
+epoch 5, total_step 79820, total loss is 18.44 , inference loss is 9.54, weight deacy loss is 8.90, training accuracy is 0.437500, time 299.756 samples/sec
+epoch 5, total_step 79840, total loss is 17.15 , inference loss is 8.25, weight deacy loss is 8.90, training accuracy is 0.500000, time 207.454 samples/sec
+epoch 5, total_step 79860, total loss is 20.40 , inference loss is 11.50, weight deacy loss is 8.90, training accuracy is 0.406250, time 211.719 samples/sec
+epoch 5, total_step 79880, total loss is 19.43 , inference loss is 10.53, weight deacy loss is 8.90, training accuracy is 0.343750, time 211.551 samples/sec
+epoch 5, total_step 79900, total loss is 16.55 , inference loss is 7.65, weight deacy loss is 8.90, training accuracy is 0.468750, time 211.317 samples/sec
+epoch 5, total_step 79920, total loss is 15.04 , inference loss is 6.14, weight deacy loss is 8.90, training accuracy is 0.406250, time 210.247 samples/sec
+epoch 5, total_step 79940, total loss is 20.27 , inference loss is 11.37, weight deacy loss is 8.90, training accuracy is 0.375000, time 206.533 samples/sec
+epoch 5, total_step 79960, total loss is 17.27 , inference loss is 8.38, weight deacy loss is 8.90, training accuracy is 0.375000, time 216.667 samples/sec
+epoch 5, total_step 79980, total loss is 17.50 , inference loss is 8.61, weight deacy loss is 8.90, training accuracy is 0.437500, time 213.664 samples/sec
+testing verification..
+(12000, 512)
+infer time 22.947141999999985
+best_threshold_index 115 0.9879629629629629
+best_threshold_index 115 0.9859259259259259
+best_threshold_index 115 0.9875925925925926
+best_threshold_index 115 0.9874074074074074
+best_threshold_index 115 0.9875925925925926
+best_threshold_index 115 0.9862962962962963
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+best_threshold_index 115 0.9866666666666667
+best_threshold_index 115 0.9855555555555555
+best_threshold_index 115 0.9864814814814815
+[lfw][80000]XNorm: 21.455762
+[lfw][80000]Accuracy-Flip: 0.98650+-0.00685
+test accuracy is:  0.9865
+epoch 5, total_step 80000, total loss is 22.04 , inference loss is 13.14, weight deacy loss is 8.90, training accuracy is 0.156250, time 18.047 samples/sec
+epoch 5, total_step 80020, total loss is 17.62 , inference loss is 8.72, weight deacy loss is 8.90, training accuracy is 0.312500, time 213.477 samples/sec
+epoch 5, total_step 80040, total loss is 13.88 , inference loss is 4.99, weight deacy loss is 8.89, training accuracy is 0.437500, time 212.209 samples/sec
+epoch 5, total_step 80060, total loss is 20.21 , inference loss is 11.32, weight deacy loss is 8.89, training accuracy is 0.375000, time 219.263 samples/sec
+epoch 5, total_step 80080, total loss is 14.35 , inference loss is 5.45, weight deacy loss is 8.89, training accuracy is 0.375000, time 210.237 samples/sec
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+epoch 5, total_step 80160, total loss is 18.77 , inference loss is 9.88, weight deacy loss is 8.89, training accuracy is 0.437500, time 294.382 samples/sec
+epoch 5, total_step 80180, total loss is 19.12 , inference loss is 10.23, weight deacy loss is 8.89, training accuracy is 0.250000, time 220.023 samples/sec
+epoch 5, total_step 80200, total loss is 16.93 , inference loss is 8.05, weight deacy loss is 8.89, training accuracy is 0.437500, time 205.523 samples/sec
+epoch 5, total_step 80220, total loss is 17.58 , inference loss is 8.69, weight deacy loss is 8.89, training accuracy is 0.312500, time 209.554 samples/sec
+epoch 5, total_step 80240, total loss is 20.81 , inference loss is 11.92, weight deacy loss is 8.89, training accuracy is 0.218750, time 225.556 samples/sec
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+epoch 5, total_step 80320, total loss is 16.70 , inference loss is 7.82, weight deacy loss is 8.88, training accuracy is 0.343750, time 213.945 samples/sec
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+epoch 5, total_step 80360, total loss is 17.32 , inference loss is 8.44, weight deacy loss is 8.88, training accuracy is 0.343750, time 207.961 samples/sec
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+epoch 5, total_step 80440, total loss is 22.19 , inference loss is 13.31, weight deacy loss is 8.88, training accuracy is 0.250000, time 216.669 samples/sec
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+epoch 5, total_step 81920, total loss is 15.64 , inference loss is 6.83, weight deacy loss is 8.81, training accuracy is 0.625000, time 211.787 samples/sec
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+epoch 5, total_step 81980, total loss is 21.41 , inference loss is 12.60, weight deacy loss is 8.81, training accuracy is 0.375000, time 219.159 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.742426000000005
+best_threshold_index 116 0.9859259259259259
+best_threshold_index 116 0.9842592592592593
+best_threshold_index 116 0.9855555555555555
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+best_threshold_index 116 0.985
+best_threshold_index 116 0.9838888888888889
+best_threshold_index 116 0.9842592592592593
+[lfw][82000]XNorm: 21.771844
+[lfw][82000]Accuracy-Flip: 0.98483+-0.00713
+test accuracy is:  0.9848333333333334
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+testing verification..
+(12000, 512)
+infer time 23.650451999999994
+best_threshold_index 113 0.9866666666666667
+best_threshold_index 119 0.9861111111111112
+best_threshold_index 112 0.9857407407407407
+best_threshold_index 114 0.9866666666666667
+best_threshold_index 113 0.9864814814814815
+best_threshold_index 119 0.9853703703703703
+best_threshold_index 113 0.9859259259259259
+best_threshold_index 119 0.9857407407407407
+best_threshold_index 119 0.9848148148148148
+best_threshold_index 119 0.9855555555555555
+[lfw][84000]XNorm: 21.618022
+[lfw][84000]Accuracy-Flip: 0.98417+-0.00569
+test accuracy is:  0.9841666666666666
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+epoch 5, total_step 85980, total loss is 19.28 , inference loss is 10.66, weight deacy loss is 8.63, training accuracy is 0.250000, time 214.890 samples/sec
+testing verification..
+(12000, 512)
+infer time 22.956328000000006
+best_threshold_index 112 0.9864814814814815
+best_threshold_index 112 0.9853703703703703
+best_threshold_index 112 0.9861111111111112
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+best_threshold_index 112 0.9851851851851852
+best_threshold_index 112 0.985
+[lfw][86000]XNorm: 21.944362
+[lfw][86000]Accuracy-Flip: 0.98567+-0.00442
+test accuracy is:  0.9856666666666667
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+testing verification..
+(12000, 512)
+infer time 23.87712899999998
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+best_threshold_index 106 0.9827777777777778
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+[lfw][88000]XNorm: 21.735142
+[lfw][88000]Accuracy-Flip: 0.98350+-0.00758
+test accuracy is:  0.9834999999999999
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+epoch 5, total_step 89980, total loss is 20.29 , inference loss is 11.83, weight deacy loss is 8.46, training accuracy is 0.218750, time 211.766 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.101429000000028
+best_threshold_index 108 0.9840740740740741
+best_threshold_index 108 0.9824074074074074
+best_threshold_index 107 0.9831481481481481
+best_threshold_index 107 0.9851851851851852
+best_threshold_index 104 0.9838888888888889
+best_threshold_index 107 0.9816666666666667
+best_threshold_index 107 0.9829629629629629
+best_threshold_index 107 0.9827777777777778
+best_threshold_index 104 0.9811111111111112
+best_threshold_index 104 0.9814814814814815
+[lfw][90000]XNorm: 22.199885
+[lfw][90000]Accuracy-Flip: 0.98200+-0.01061
+test accuracy is:  0.982
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+epoch 5, total_step 90860, total loss is 19.01 , inference loss is 10.59, weight deacy loss is 8.42, training accuracy is 0.156250, time 210.108 samples/sec
+epoch 5, total_step 90880, total loss is 22.59 , inference loss is 14.17, weight deacy loss is 8.42, training accuracy is 0.187500, time 227.056 samples/sec
+epoch 5, total_step 90900, total loss is 19.89 , inference loss is 11.48, weight deacy loss is 8.42, training accuracy is 0.125000, time 11.871 samples/sec
+epoch 5, total_step 90920, total loss is 19.74 , inference loss is 11.32, weight deacy loss is 8.42, training accuracy is 0.187500, time 218.489 samples/sec
+epoch 5, total_step 90940, total loss is 20.86 , inference loss is 12.44, weight deacy loss is 8.42, training accuracy is 0.187500, time 210.335 samples/sec
+epoch 5, total_step 90960, total loss is 19.70 , inference loss is 11.28, weight deacy loss is 8.42, training accuracy is 0.218750, time 207.335 samples/sec
+epoch 5, total_step 90980, total loss is 18.18 , inference loss is 9.76, weight deacy loss is 8.42, training accuracy is 0.250000, time 206.987 samples/sec
+epoch 5, total_step 91000, total loss is 21.51 , inference loss is 13.10, weight deacy loss is 8.41, training accuracy is 0.156250, time 202.590 samples/sec2022-12-01 06:16:13.992547: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:145] Filling up shuffle buffer (this may take a while): 8593 of 10000
+2022-12-01 06:16:15.671612: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:195] Shuffle buffer filled.
+
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+epoch 5, total_step 91040, total loss is 21.23 , inference loss is 12.81, weight deacy loss is 8.41, training accuracy is 0.218750, time 209.338 samples/sec
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+epoch 5, total_step 91080, total loss is 18.11 , inference loss is 9.70, weight deacy loss is 8.41, training accuracy is 0.281250, time 217.107 samples/sec
+epoch 5, total_step 91100, total loss is 21.63 , inference loss is 13.22, weight deacy loss is 8.41, training accuracy is 0.062500, time 216.131 samples/sec
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+epoch 5, total_step 91180, total loss is 19.17 , inference loss is 10.76, weight deacy loss is 8.41, training accuracy is 0.093750, time 218.078 samples/sec
+epoch 5, total_step 91200, total loss is 22.90 , inference loss is 14.49, weight deacy loss is 8.41, training accuracy is 0.281250, time 13.971 samples/sec
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+epoch 5, total_step 91780, total loss is 19.98 , inference loss is 11.59, weight deacy loss is 8.38, training accuracy is 0.218750, time 182.077 samples/sec
+epoch 5, total_step 91800, total loss is 30.14 , inference loss is 21.76, weight deacy loss is 8.38, training accuracy is 0.062500, time 14.560 samples/sec
+epoch 5, total_step 91820, total loss is 21.75 , inference loss is 13.37, weight deacy loss is 8.38, training accuracy is 0.125000, time 205.571 samples/sec
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+epoch 5, total_step 91880, total loss is 25.17 , inference loss is 16.79, weight deacy loss is 8.38, training accuracy is 0.187500, time 298.985 samples/sec
+epoch 5, total_step 91900, total loss is 24.00 , inference loss is 15.62, weight deacy loss is 8.38, training accuracy is 0.187500, time 204.228 samples/sec
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+epoch 5, total_step 91980, total loss is 23.01 , inference loss is 14.64, weight deacy loss is 8.37, training accuracy is 0.281250, time 212.634 samples/sec
+End of epoch 5
+testing verification..
+(12000, 512)
+infer time 23.317968
+best_threshold_index 123 0.9851851851851852
+best_threshold_index 122 0.9840740740740741
+best_threshold_index 122 0.9844444444444445
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+best_threshold_index 122 0.9837037037037037
+best_threshold_index 122 0.9829629629629629
+best_threshold_index 122 0.9829629629629629
+[lfw][92000]XNorm: 21.673197
+[lfw][92000]Accuracy-Flip: 0.98350+-0.00705
+test accuracy is:  0.9835
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+epoch 6, total_step 92420, total loss is 13.91 , inference loss is 5.55, weight deacy loss is 8.36, training accuracy is 0.562500, time 213.168 samples/sec
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+epoch 6, total_step 93920, total loss is 23.22 , inference loss is 14.92, weight deacy loss is 8.30, training accuracy is 0.218750, time 214.118 samples/sec
+epoch 6, total_step 93940, total loss is 22.24 , inference loss is 13.94, weight deacy loss is 8.30, training accuracy is 0.218750, time 212.386 samples/sec
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+epoch 6, total_step 93980, total loss is 18.11 , inference loss is 9.81, weight deacy loss is 8.30, training accuracy is 0.437500, time 210.539 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.04129800000001
+best_threshold_index 113 0.9872222222222222
+best_threshold_index 113 0.9857407407407407
+best_threshold_index 113 0.9868518518518519
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+best_threshold_index 113 0.9857407407407407
+best_threshold_index 113 0.9857407407407407
+best_threshold_index 113 0.9855555555555555
+[lfw][94000]XNorm: 23.176093
+[lfw][94000]Accuracy-Flip: 0.98633+-0.00618
+test accuracy is:  0.9863333333333335
+epoch 6, total_step 94000, total loss is 19.87 , inference loss is 11.57, weight deacy loss is 8.30, training accuracy is 0.406250, time 14.693 samples/sec
+epoch 6, total_step 94020, total loss is 25.36 , inference loss is 17.06, weight deacy loss is 8.30, training accuracy is 0.187500, time 211.918 samples/sec
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+testing verification..
+(12000, 512)
+infer time 24.31812000000001
+best_threshold_index 119 0.9874074074074074
+best_threshold_index 119 0.9862962962962963
+best_threshold_index 119 0.9872222222222222
+best_threshold_index 119 0.987037037037037
+best_threshold_index 119 0.9875925925925926
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+best_threshold_index 120 0.9866666666666667
+best_threshold_index 119 0.9861111111111112
+best_threshold_index 119 0.9853703703703703
+best_threshold_index 120 0.9861111111111112
+[lfw][96000]XNorm: 21.803750
+[lfw][96000]Accuracy-Flip: 0.98600+-0.00638
+test accuracy is:  0.9860000000000001
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+testing verification..
+(12000, 512)
+infer time 24.03744600000001
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+[lfw][98000]XNorm: 21.514770
+[lfw][98000]Accuracy-Flip: 0.98650+-0.00652
+test accuracy is:  0.9864999999999998
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+testing verification..
+(12000, 512)
+infer time 24.091820999999996
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+best_threshold_index 112 0.9868518518518519
+[lfw][100000]XNorm: 22.323275
+[lfw][100000]Accuracy-Flip: 0.98667+-0.00775
+test accuracy is:  0.9866666666666667
+epoch 6, total_step 100000, total loss is 16.97 , inference loss is 8.90, weight deacy loss is 8.07, training accuracy is 0.281250, time 18.650 samples/sec
+epoch 6, total_step 100020, total loss is 13.99 , inference loss is 5.91, weight deacy loss is 8.07, training accuracy is 0.531250, time 191.798 samples/sec
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+epoch 6, total_step 100060, total loss is 21.14 , inference loss is 13.06, weight deacy loss is 8.07, training accuracy is 0.125000, time 214.984 samples/sec
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+epoch 6, total_step 100100, total loss is 16.49 , inference loss is 8.42, weight deacy loss is 8.07, training accuracy is 0.375000, time 204.329 samples/sec
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+epoch 6, total_step 100200, total loss is 19.85 , inference loss is 11.78, weight deacy loss is 8.07, training accuracy is 0.437500, time 14.594 samples/sec
+epoch 6, total_step 100220, total loss is 20.78 , inference loss is 12.71, weight deacy loss is 8.07, training accuracy is 0.343750, time 204.571 samples/sec
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+epoch 6, total_step 100280, total loss is 17.72 , inference loss is 9.66, weight deacy loss is 8.06, training accuracy is 0.343750, time 210.096 samples/sec
+epoch 6, total_step 100300, total loss is 18.48 , inference loss is 10.41, weight deacy loss is 8.06, training accuracy is 0.281250, time 215.752 samples/sec
+epoch 6, total_step 100320, total loss is 19.31 , inference loss is 11.24, weight deacy loss is 8.06, training accuracy is 0.343750, time 208.767 samples/sec
+epoch 6, total_step 100340, total loss is 20.12 , inference loss is 12.06, weight deacy loss is 8.06, training accuracy is 0.218750, time 218.706 samples/sec
+epoch 6, total_step 100360, total loss is 16.87 , inference loss is 8.81, weight deacy loss is 8.06, training accuracy is 0.250000, time 213.157 samples/sec
+epoch 6, total_step 100380, total loss is 18.71 , inference loss is 10.65, weight deacy loss is 8.06, training accuracy is 0.406250, time 214.891 samples/sec
+epoch 6, total_step 100400, total loss is 17.98 , inference loss is 9.92, weight deacy loss is 8.06, training accuracy is 0.343750, time 225.817 samples/sec
+epoch 6, total_step 100420, total loss is 16.18 , inference loss is 8.12, weight deacy loss is 8.06, training accuracy is 0.343750, time 211.377 samples/sec
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+epoch 6, total_step 101620, total loss is 16.13 , inference loss is 8.11, weight deacy loss is 8.02, training accuracy is 0.437500, time 207.824 samples/sec
+epoch 6, total_step 101640, total loss is 17.07 , inference loss is 9.05, weight deacy loss is 8.02, training accuracy is 0.250000, time 211.445 samples/sec
+epoch 6, total_step 101660, total loss is 18.50 , inference loss is 10.48, weight deacy loss is 8.02, training accuracy is 0.281250, time 215.663 samples/sec
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+testing verification..
+(12000, 512)
+infer time 24.323135999999984
+best_threshold_index 105 0.9877777777777778
+best_threshold_index 105 0.9864814814814815
+best_threshold_index 105 0.9875925925925926
+best_threshold_index 105 0.9887037037037038
+best_threshold_index 105 0.9875925925925926
+best_threshold_index 105 0.9862962962962963
+best_threshold_index 105 0.9874074074074074
+best_threshold_index 105 0.9861111111111112
+best_threshold_index 105 0.9857407407407407
+best_threshold_index 105 0.9862962962962963
+[lfw][102000]XNorm: 23.505937
+[lfw][102000]Accuracy-Flip: 0.98700+-0.00806
+test accuracy is:  0.9870000000000001
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+epoch 6, total_step 103900, total loss is 16.72 , inference loss is 8.78, weight deacy loss is 7.94, training accuracy is 0.468750, time 222.145 samples/sec
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+epoch 6, total_step 103980, total loss is 14.95 , inference loss is 7.01, weight deacy loss is 7.93, training accuracy is 0.343750, time 296.243 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.89715400000003
+best_threshold_index 116 0.987037037037037
+best_threshold_index 117 0.9861111111111112
+best_threshold_index 117 0.9866666666666667
+best_threshold_index 108 0.987037037037037
+best_threshold_index 117 0.9875925925925926
+best_threshold_index 117 0.9866666666666667
+best_threshold_index 117 0.9862962962962963
+best_threshold_index 117 0.9866666666666667
+best_threshold_index 117 0.9851851851851852
+best_threshold_index 117 0.9857407407407407
+[lfw][104000]XNorm: 21.685005
+[lfw][104000]Accuracy-Flip: 0.98483+-0.00845
+test accuracy is:  0.9848333333333334
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+epoch 6, total_step 105920, total loss is 20.05 , inference loss is 12.18, weight deacy loss is 7.87, training accuracy is 0.156250, time 204.381 samples/sec
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+epoch 6, total_step 105980, total loss is 20.92 , inference loss is 13.05, weight deacy loss is 7.86, training accuracy is 0.218750, time 212.771 samples/sec
+testing verification..
+(12000, 512)
+infer time 20.667990999999997
+best_threshold_index 107 0.9838888888888889
+best_threshold_index 104 0.9824074074074074
+best_threshold_index 107 0.9835185185185186
+best_threshold_index 104 0.9853703703703703
+best_threshold_index 104 0.9837037037037037
+best_threshold_index 105 0.9822222222222222
+best_threshold_index 104 0.9825925925925926
+best_threshold_index 107 0.9822222222222222
+best_threshold_index 107 0.9816666666666667
+best_threshold_index 104 0.9816666666666667
+[lfw][106000]XNorm: 23.818581
+[lfw][106000]Accuracy-Flip: 0.98200+-0.01002
+test accuracy is:  0.982
+epoch 6, total_step 106000, total loss is 16.87 , inference loss is 9.00, weight deacy loss is 7.86, training accuracy is 0.375000, time 16.437 samples/sec
+epoch 6, total_step 106020, total loss is 21.65 , inference loss is 13.79, weight deacy loss is 7.86, training accuracy is 0.281250, time 209.609 samples/sec
+epoch 6, total_step 106040, total loss is 20.58 , inference loss is 12.72, weight deacy loss is 7.86, training accuracy is 0.218750, time 213.702 samples/sec
+epoch 6, total_step 106060, total loss is 18.22 , inference loss is 10.36, weight deacy loss is 7.86, training accuracy is 0.250000, time 217.979 samples/sec
+epoch 6, total_step 106080, total loss is 16.68 , inference loss is 8.82, weight deacy loss is 7.86, training accuracy is 0.281250, time 216.684 samples/sec
+epoch 6, total_step 106100, total loss is 20.19 , inference loss is 12.33, weight deacy loss is 7.86, training accuracy is 0.281250, time 216.532 samples/sec
+epoch 6, total_step 106120, total loss is 17.17 , inference loss is 9.31, weight deacy loss is 7.86, training accuracy is 0.281250, time 215.052 samples/sec
+epoch 6, total_step 106140, total loss is 13.70 , inference loss is 5.84, weight deacy loss is 7.86, training accuracy is 0.406250, time 207.821 samples/sec
+epoch 6, total_step 106160, total loss is 16.42 , inference loss is 8.56, weight deacy loss is 7.86, training accuracy is 0.250000, time 298.369 samples/sec
+epoch 6, total_step 106180, total loss is 19.82 , inference loss is 11.97, weight deacy loss is 7.86, training accuracy is 0.312500, time 206.754 samples/sec
+epoch 6, total_step 106200, total loss is 17.75 , inference loss is 9.89, weight deacy loss is 7.86, training accuracy is 0.250000, time 13.562 samples/sec
+epoch 6, total_step 106220, total loss is 19.58 , inference loss is 11.73, weight deacy loss is 7.86, training accuracy is 0.250000, time 213.033 samples/sec
+epoch 6, total_step 106240, total loss is 19.24 , inference loss is 11.39, weight deacy loss is 7.86, training accuracy is 0.218750, time 209.048 samples/sec
+epoch 6, total_step 106260, total loss is 17.22 , inference loss is 9.36, weight deacy loss is 7.86, training accuracy is 0.406250, time 212.452 samples/sec
+epoch 6, total_step 106280, total loss is 16.42 , inference loss is 8.56, weight deacy loss is 7.85, training accuracy is 0.375000, time 201.826 samples/sec
+epoch 6, total_step 106300, total loss is 23.17 , inference loss is 15.32, weight deacy loss is 7.85, training accuracy is 0.281250, time 212.614 samples/sec
+epoch 6, total_step 106320, total loss is 19.90 , inference loss is 12.05, weight deacy loss is 7.85, training accuracy is 0.031250, time 218.069 samples/sec
+epoch 6, total_step 106340, total loss is 19.79 , inference loss is 11.94, weight deacy loss is 7.85, training accuracy is 0.125000, time 207.632 samples/sec
+epoch 6, total_step 106360, total loss is 15.56 , inference loss is 7.71, weight deacy loss is 7.85, training accuracy is 0.281250, time 212.654 samples/sec
+epoch 6, total_step 106380, total loss is 19.42 , inference loss is 11.56, weight deacy loss is 7.85, training accuracy is 0.218750, time 209.325 samples/sec
+epoch 6, total_step 106400, total loss is 20.02 , inference loss is 12.17, weight deacy loss is 7.85, training accuracy is 0.156250, time 214.341 samples/sec
+epoch 6, total_step 106420, total loss is 23.27 , inference loss is 15.42, weight deacy loss is 7.85, training accuracy is 0.125000, time 297.970 samples/sec
+epoch 6, total_step 106440, total loss is 21.87 , inference loss is 14.02, weight deacy loss is 7.85, training accuracy is 0.218750, time 221.329 samples/sec
+epoch 6, total_step 106460, total loss is 18.92 , inference loss is 11.07, weight deacy loss is 7.85, training accuracy is 0.250000, time 217.815 samples/sec
+epoch 6, total_step 106480, total loss is 18.55 , inference loss is 10.70, weight deacy loss is 7.85, training accuracy is 0.281250, time 214.278 samples/sec
+epoch 6, total_step 106500, total loss is 15.46 , inference loss is 7.61, weight deacy loss is 7.85, training accuracy is 0.250000, time 13.311 samples/sec
+epoch 6, total_step 106520, total loss is 21.61 , inference loss is 13.76, weight deacy loss is 7.85, training accuracy is 0.187500, time 211.924 samples/sec
+epoch 6, total_step 106540, total loss is 19.33 , inference loss is 11.48, weight deacy loss is 7.85, training accuracy is 0.125000, time 209.133 samples/sec
+epoch 6, total_step 106560, total loss is 19.46 , inference loss is 11.62, weight deacy loss is 7.85, training accuracy is 0.250000, time 207.225 samples/sec
+epoch 6, total_step 106580, total loss is 22.76 , inference loss is 14.91, weight deacy loss is 7.84, training accuracy is 0.125000, time 208.577 samples/sec
+epoch 6, total_step 106600, total loss is 22.83 , inference loss is 14.99, weight deacy loss is 7.84, training accuracy is 0.218750, time 207.762 samples/sec
+epoch 6, total_step 106620, total loss is 18.75 , inference loss is 10.90, weight deacy loss is 7.84, training accuracy is 0.250000, time 207.989 samples/sec
+epoch 6, total_step 106640, total loss is 15.56 , inference loss is 7.72, weight deacy loss is 7.84, training accuracy is 0.250000, time 213.801 samples/sec
+epoch 6, total_step 106660, total loss is 19.53 , inference loss is 11.69, weight deacy loss is 7.84, training accuracy is 0.156250, time 217.848 samples/sec
+epoch 6, total_step 106680, total loss is 15.53 , inference loss is 7.69, weight deacy loss is 7.84, training accuracy is 0.218750, time 299.552 samples/sec
+epoch 6, total_step 106700, total loss is 19.15 , inference loss is 11.31, weight deacy loss is 7.84, training accuracy is 0.281250, time 211.491 samples/sec
+epoch 6, total_step 106720, total loss is 19.57 , inference loss is 11.73, weight deacy loss is 7.84, training accuracy is 0.281250, time 202.100 samples/sec
+epoch 6, total_step 106740, total loss is 14.78 , inference loss is 6.94, weight deacy loss is 7.84, training accuracy is 0.156250, time 205.305 samples/sec
+epoch 6, total_step 106760, total loss is 20.91 , inference loss is 13.07, weight deacy loss is 7.84, training accuracy is 0.281250, time 226.054 samples/sec
+epoch 6, total_step 106780, total loss is 19.08 , inference loss is 11.25, weight deacy loss is 7.84, training accuracy is 0.187500, time 207.940 samples/sec
+epoch 6, total_step 106800, total loss is 16.20 , inference loss is 8.36, weight deacy loss is 7.84, training accuracy is 0.187500, time 11.728 samples/sec
+epoch 6, total_step 106820, total loss is 23.31 , inference loss is 15.48, weight deacy loss is 7.84, training accuracy is 0.250000, time 214.398 samples/sec
+epoch 6, total_step 106840, total loss is 18.45 , inference loss is 10.62, weight deacy loss is 7.84, training accuracy is 0.281250, time 217.853 samples/sec
+epoch 6, total_step 106860, total loss is 20.76 , inference loss is 12.92, weight deacy loss is 7.84, training accuracy is 0.281250, time 209.929 samples/sec
+epoch 6, total_step 106880, total loss is 20.03 , inference loss is 12.20, weight deacy loss is 7.83, training accuracy is 0.218750, time 231.916 samples/sec
+epoch 6, total_step 106900, total loss is 17.66 , inference loss is 9.83, weight deacy loss is 7.83, training accuracy is 0.343750, time 210.186 samples/sec
+epoch 6, total_step 106920, total loss is 18.63 , inference loss is 10.80, weight deacy loss is 7.83, training accuracy is 0.156250, time 219.650 samples/sec
+epoch 6, total_step 106940, total loss is 16.93 , inference loss is 9.09, weight deacy loss is 7.83, training accuracy is 0.187500, time 214.162 samples/sec
+epoch 6, total_step 106960, total loss is 21.32 , inference loss is 13.48, weight deacy loss is 7.83, training accuracy is 0.218750, time 296.964 samples/sec
+epoch 6, total_step 106980, total loss is 23.23 , inference loss is 15.40, weight deacy loss is 7.83, training accuracy is 0.125000, time 213.026 samples/sec
+epoch 6, total_step 107000, total loss is 17.08 , inference loss is 9.25, weight deacy loss is 7.83, training accuracy is 0.156250, time 213.434 samples/sec
+epoch 6, total_step 107020, total loss is 23.40 , inference loss is 15.57, weight deacy loss is 7.83, training accuracy is 0.125000, time 195.969 samples/sec
+epoch 6, total_step 107040, total loss is 27.31 , inference loss is 19.48, weight deacy loss is 7.83, training accuracy is 0.125000, time 233.212 samples/sec
+epoch 6, total_step 107060, total loss is 25.38 , inference loss is 17.55, weight deacy loss is 7.83, training accuracy is 0.187500, time 201.413 samples/sec
+epoch 6, total_step 107080, total loss is 18.51 , inference loss is 10.68, weight deacy loss is 7.83, training accuracy is 0.312500, time 175.609 samples/sec
+epoch 6, total_step 107100, total loss is 20.87 , inference loss is 13.04, weight deacy loss is 7.83, training accuracy is 0.156250, time 13.293 samples/sec
+epoch 6, total_step 107120, total loss is 24.66 , inference loss is 16.84, weight deacy loss is 7.83, training accuracy is 0.250000, time 200.424 samples/sec
+epoch 6, total_step 107140, total loss is 20.15 , inference loss is 12.32, weight deacy loss is 7.83, training accuracy is 0.250000, time 226.020 samples/sec
+epoch 6, total_step 107160, total loss is 19.19 , inference loss is 11.37, weight deacy loss is 7.83, training accuracy is 0.187500, time 177.118 samples/sec
+epoch 6, total_step 107180, total loss is 18.67 , inference loss is 10.85, weight deacy loss is 7.82, training accuracy is 0.218750, time 217.004 samples/sec
+epoch 6, total_step 107200, total loss is 23.04 , inference loss is 15.22, weight deacy loss is 7.82, training accuracy is 0.250000, time 241.482 samples/sec
+epoch 6, total_step 107220, total loss is 17.40 , inference loss is 9.58, weight deacy loss is 7.82, training accuracy is 0.281250, time 205.811 samples/sec
+epoch 6, total_step 107240, total loss is 20.67 , inference loss is 12.84, weight deacy loss is 7.82, training accuracy is 0.093750, time 226.347 samples/sec2022-12-01 07:16:54.714470: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:145] Filling up shuffle buffer (this may take a while): 9024 of 10000
+2022-12-01 07:16:55.915096: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:195] Shuffle buffer filled.
+
+epoch 6, total_step 107260, total loss is 19.36 , inference loss is 11.54, weight deacy loss is 7.82, training accuracy is 0.187500, time 194.474 samples/sec
+epoch 6, total_step 107280, total loss is 20.35 , inference loss is 12.53, weight deacy loss is 7.82, training accuracy is 0.250000, time 209.671 samples/sec
+epoch 6, total_step 107300, total loss is 18.02 , inference loss is 10.20, weight deacy loss is 7.82, training accuracy is 0.312500, time 296.484 samples/sec
+End of epoch 6
+epoch 7, total_step 107320, total loss is 30.03 , inference loss is 22.21, weight deacy loss is 7.82, training accuracy is 0.000000, time 219.256 samples/sec
+epoch 7, total_step 107340, total loss is 19.04 , inference loss is 11.22, weight deacy loss is 7.82, training accuracy is 0.375000, time 226.953 samples/sec
+epoch 7, total_step 107360, total loss is 14.33 , inference loss is 6.51, weight deacy loss is 7.82, training accuracy is 0.437500, time 207.672 samples/sec
+epoch 7, total_step 107380, total loss is 10.93 , inference loss is 3.11, weight deacy loss is 7.82, training accuracy is 0.687500, time 216.039 samples/sec
+epoch 7, total_step 107400, total loss is 12.23 , inference loss is 4.41, weight deacy loss is 7.82, training accuracy is 0.656250, time 14.322 samples/sec
+epoch 7, total_step 107420, total loss is 14.30 , inference loss is 6.48, weight deacy loss is 7.82, training accuracy is 0.593750, time 210.541 samples/sec
+epoch 7, total_step 107440, total loss is 11.86 , inference loss is 4.05, weight deacy loss is 7.82, training accuracy is 0.562500, time 207.999 samples/sec
+epoch 7, total_step 107460, total loss is 16.26 , inference loss is 8.45, weight deacy loss is 7.81, training accuracy is 0.406250, time 212.970 samples/sec
+epoch 7, total_step 107480, total loss is 13.69 , inference loss is 5.88, weight deacy loss is 7.81, training accuracy is 0.531250, time 201.623 samples/sec
+epoch 7, total_step 107500, total loss is 12.50 , inference loss is 4.69, weight deacy loss is 7.81, training accuracy is 0.687500, time 299.162 samples/sec
+epoch 7, total_step 107520, total loss is 18.97 , inference loss is 11.16, weight deacy loss is 7.81, training accuracy is 0.468750, time 213.485 samples/sec
+epoch 7, total_step 107540, total loss is 13.80 , inference loss is 5.99, weight deacy loss is 7.81, training accuracy is 0.562500, time 207.819 samples/sec
+epoch 7, total_step 107560, total loss is 14.53 , inference loss is 6.71, weight deacy loss is 7.81, training accuracy is 0.500000, time 210.432 samples/sec
+epoch 7, total_step 107580, total loss is 17.78 , inference loss is 9.97, weight deacy loss is 7.81, training accuracy is 0.468750, time 210.763 samples/sec
+epoch 7, total_step 107600, total loss is 16.16 , inference loss is 8.35, weight deacy loss is 7.81, training accuracy is 0.593750, time 215.907 samples/sec
+epoch 7, total_step 107620, total loss is 14.72 , inference loss is 6.91, weight deacy loss is 7.81, training accuracy is 0.468750, time 205.062 samples/sec
+epoch 7, total_step 107640, total loss is 14.95 , inference loss is 7.14, weight deacy loss is 7.81, training accuracy is 0.593750, time 208.001 samples/sec
+epoch 7, total_step 107660, total loss is 11.80 , inference loss is 3.99, weight deacy loss is 7.81, training accuracy is 0.593750, time 212.164 samples/sec
+epoch 7, total_step 107680, total loss is 16.62 , inference loss is 8.81, weight deacy loss is 7.81, training accuracy is 0.375000, time 211.955 samples/sec
+epoch 7, total_step 107700, total loss is 19.78 , inference loss is 11.98, weight deacy loss is 7.81, training accuracy is 0.343750, time 11.674 samples/sec
+epoch 7, total_step 107720, total loss is 16.49 , inference loss is 8.69, weight deacy loss is 7.81, training accuracy is 0.562500, time 214.126 samples/sec
+epoch 7, total_step 107740, total loss is 14.26 , inference loss is 6.45, weight deacy loss is 7.81, training accuracy is 0.375000, time 206.912 samples/sec
+epoch 7, total_step 107760, total loss is 12.86 , inference loss is 5.06, weight deacy loss is 7.80, training accuracy is 0.500000, time 298.842 samples/sec
+epoch 7, total_step 107780, total loss is 13.99 , inference loss is 6.19, weight deacy loss is 7.80, training accuracy is 0.406250, time 218.526 samples/sec
+epoch 7, total_step 107800, total loss is 15.20 , inference loss is 7.40, weight deacy loss is 7.80, training accuracy is 0.468750, time 214.597 samples/sec
+epoch 7, total_step 107820, total loss is 13.86 , inference loss is 6.06, weight deacy loss is 7.80, training accuracy is 0.687500, time 212.824 samples/sec
+epoch 7, total_step 107840, total loss is 13.21 , inference loss is 5.41, weight deacy loss is 7.80, training accuracy is 0.500000, time 214.249 samples/sec
+epoch 7, total_step 107860, total loss is 15.44 , inference loss is 7.64, weight deacy loss is 7.80, training accuracy is 0.531250, time 217.352 samples/sec
+epoch 7, total_step 107880, total loss is 15.01 , inference loss is 7.21, weight deacy loss is 7.80, training accuracy is 0.656250, time 215.140 samples/sec
+epoch 7, total_step 107900, total loss is 21.37 , inference loss is 13.57, weight deacy loss is 7.80, training accuracy is 0.406250, time 215.807 samples/sec
+epoch 7, total_step 107920, total loss is 22.50 , inference loss is 14.70, weight deacy loss is 7.80, training accuracy is 0.375000, time 298.621 samples/sec
+epoch 7, total_step 107940, total loss is 18.53 , inference loss is 10.73, weight deacy loss is 7.80, training accuracy is 0.375000, time 298.745 samples/sec
+epoch 7, total_step 107960, total loss is 20.21 , inference loss is 12.41, weight deacy loss is 7.80, training accuracy is 0.375000, time 238.847 samples/sec
+epoch 7, total_step 107980, total loss is 16.85 , inference loss is 9.05, weight deacy loss is 7.80, training accuracy is 0.562500, time 207.507 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.57679399999998
+best_threshold_index 113 0.9859259259259259
+best_threshold_index 113 0.9857407407407407
+best_threshold_index 113 0.985
+best_threshold_index 113 0.9866666666666667
+best_threshold_index 113 0.9859259259259259
+best_threshold_index 113 0.9851851851851852
+best_threshold_index 113 0.9851851851851852
+best_threshold_index 113 0.9861111111111112
+best_threshold_index 113 0.9848148148148148
+best_threshold_index 113 0.9844444444444445
+[lfw][108000]XNorm: 20.910872
+[lfw][108000]Accuracy-Flip: 0.98550+-0.00582
+test accuracy is:  0.9854999999999998
+epoch 7, total_step 108000, total loss is 14.22 , inference loss is 6.42, weight deacy loss is 7.80, training accuracy is 0.531250, time 18.350 samples/sec
+epoch 7, total_step 108020, total loss is 13.78 , inference loss is 5.98, weight deacy loss is 7.80, training accuracy is 0.531250, time 207.027 samples/sec
+epoch 7, total_step 108040, total loss is 18.27 , inference loss is 10.47, weight deacy loss is 7.80, training accuracy is 0.437500, time 297.012 samples/sec
+epoch 7, total_step 108060, total loss is 15.21 , inference loss is 7.42, weight deacy loss is 7.80, training accuracy is 0.500000, time 203.156 samples/sec
+epoch 7, total_step 108080, total loss is 17.68 , inference loss is 9.89, weight deacy loss is 7.80, training accuracy is 0.468750, time 209.997 samples/sec
+epoch 7, total_step 108100, total loss is 14.02 , inference loss is 6.22, weight deacy loss is 7.80, training accuracy is 0.593750, time 218.798 samples/sec
+epoch 7, total_step 108120, total loss is 17.05 , inference loss is 9.25, weight deacy loss is 7.80, training accuracy is 0.312500, time 210.956 samples/sec
+epoch 7, total_step 108140, total loss is 12.82 , inference loss is 5.03, weight deacy loss is 7.80, training accuracy is 0.531250, time 211.622 samples/sec
+epoch 7, total_step 108160, total loss is 14.92 , inference loss is 7.13, weight deacy loss is 7.79, training accuracy is 0.593750, time 193.067 samples/sec
+epoch 7, total_step 108180, total loss is 18.49 , inference loss is 10.70, weight deacy loss is 7.79, training accuracy is 0.406250, time 214.190 samples/sec
+epoch 7, total_step 108200, total loss is 16.51 , inference loss is 8.72, weight deacy loss is 7.79, training accuracy is 0.343750, time 218.299 samples/sec
+epoch 7, total_step 108220, total loss is 18.21 , inference loss is 10.42, weight deacy loss is 7.79, training accuracy is 0.375000, time 211.585 samples/sec
+epoch 7, total_step 108240, total loss is 23.51 , inference loss is 15.72, weight deacy loss is 7.79, training accuracy is 0.281250, time 199.998 samples/sec
+epoch 7, total_step 108260, total loss is 18.90 , inference loss is 11.11, weight deacy loss is 7.79, training accuracy is 0.437500, time 212.222 samples/sec
+epoch 7, total_step 108280, total loss is 15.87 , inference loss is 8.08, weight deacy loss is 7.79, training accuracy is 0.375000, time 217.045 samples/sec
+epoch 7, total_step 108300, total loss is 16.26 , inference loss is 8.47, weight deacy loss is 7.79, training accuracy is 0.468750, time 14.596 samples/sec
+epoch 7, total_step 108320, total loss is 16.44 , inference loss is 8.65, weight deacy loss is 7.79, training accuracy is 0.343750, time 211.787 samples/sec
+epoch 7, total_step 108340, total loss is 16.27 , inference loss is 8.48, weight deacy loss is 7.79, training accuracy is 0.500000, time 214.233 samples/sec
+epoch 7, total_step 108360, total loss is 12.79 , inference loss is 5.00, weight deacy loss is 7.79, training accuracy is 0.406250, time 213.267 samples/sec
+epoch 7, total_step 108380, total loss is 16.76 , inference loss is 8.97, weight deacy loss is 7.79, training accuracy is 0.468750, time 206.338 samples/sec
+epoch 7, total_step 108400, total loss is 14.64 , inference loss is 6.85, weight deacy loss is 7.79, training accuracy is 0.312500, time 211.896 samples/sec
+epoch 7, total_step 108420, total loss is 17.79 , inference loss is 10.00, weight deacy loss is 7.79, training accuracy is 0.468750, time 216.288 samples/sec
+epoch 7, total_step 108440, total loss is 16.94 , inference loss is 9.15, weight deacy loss is 7.79, training accuracy is 0.437500, time 211.340 samples/sec
+epoch 7, total_step 108460, total loss is 12.65 , inference loss is 4.86, weight deacy loss is 7.79, training accuracy is 0.531250, time 226.212 samples/sec
+epoch 7, total_step 108480, total loss is 17.04 , inference loss is 9.26, weight deacy loss is 7.79, training accuracy is 0.375000, time 217.466 samples/sec
+epoch 7, total_step 108500, total loss is 22.42 , inference loss is 14.63, weight deacy loss is 7.79, training accuracy is 0.187500, time 219.256 samples/sec
+epoch 7, total_step 108520, total loss is 16.74 , inference loss is 8.96, weight deacy loss is 7.79, training accuracy is 0.406250, time 207.076 samples/sec
+epoch 7, total_step 108540, total loss is 15.58 , inference loss is 7.80, weight deacy loss is 7.79, training accuracy is 0.468750, time 211.617 samples/sec
+epoch 7, total_step 108560, total loss is 17.68 , inference loss is 9.90, weight deacy loss is 7.78, training accuracy is 0.593750, time 212.551 samples/sec
+epoch 7, total_step 108580, total loss is 19.08 , inference loss is 11.29, weight deacy loss is 7.78, training accuracy is 0.250000, time 207.967 samples/sec
+epoch 7, total_step 108600, total loss is 14.66 , inference loss is 6.88, weight deacy loss is 7.78, training accuracy is 0.406250, time 13.492 samples/sec
+epoch 7, total_step 108620, total loss is 14.18 , inference loss is 6.39, weight deacy loss is 7.78, training accuracy is 0.468750, time 210.316 samples/sec
+epoch 7, total_step 108640, total loss is 16.27 , inference loss is 8.49, weight deacy loss is 7.78, training accuracy is 0.375000, time 212.997 samples/sec
+epoch 7, total_step 108660, total loss is 16.19 , inference loss is 8.40, weight deacy loss is 7.78, training accuracy is 0.500000, time 215.761 samples/sec
+epoch 7, total_step 108680, total loss is 16.83 , inference loss is 9.04, weight deacy loss is 7.78, training accuracy is 0.500000, time 218.294 samples/sec
+epoch 7, total_step 108700, total loss is 19.69 , inference loss is 11.91, weight deacy loss is 7.78, training accuracy is 0.406250, time 209.988 samples/sec
+epoch 7, total_step 108720, total loss is 17.35 , inference loss is 9.57, weight deacy loss is 7.78, training accuracy is 0.375000, time 206.297 samples/sec
+epoch 7, total_step 108740, total loss is 19.28 , inference loss is 11.50, weight deacy loss is 7.78, training accuracy is 0.406250, time 212.911 samples/sec
+epoch 7, total_step 108760, total loss is 20.37 , inference loss is 12.59, weight deacy loss is 7.78, training accuracy is 0.375000, time 210.096 samples/sec
+epoch 7, total_step 108780, total loss is 15.74 , inference loss is 7.96, weight deacy loss is 7.78, training accuracy is 0.437500, time 211.417 samples/sec
+epoch 7, total_step 108800, total loss is 14.97 , inference loss is 7.19, weight deacy loss is 7.78, training accuracy is 0.406250, time 211.917 samples/sec
+epoch 7, total_step 108820, total loss is 14.92 , inference loss is 7.14, weight deacy loss is 7.78, training accuracy is 0.468750, time 211.273 samples/sec
+epoch 7, total_step 108840, total loss is 19.17 , inference loss is 11.39, weight deacy loss is 7.78, training accuracy is 0.375000, time 192.123 samples/sec
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+epoch 7, total_step 108880, total loss is 15.51 , inference loss is 7.73, weight deacy loss is 7.78, training accuracy is 0.468750, time 298.945 samples/sec
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+epoch 7, total_step 109780, total loss is 12.06 , inference loss is 4.31, weight deacy loss is 7.75, training accuracy is 0.625000, time 211.527 samples/sec
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+epoch 7, total_step 109820, total loss is 12.80 , inference loss is 5.05, weight deacy loss is 7.75, training accuracy is 0.531250, time 213.616 samples/sec
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+epoch 7, total_step 109880, total loss is 11.56 , inference loss is 3.81, weight deacy loss is 7.75, training accuracy is 0.625000, time 211.825 samples/sec
+epoch 7, total_step 109900, total loss is 18.18 , inference loss is 10.43, weight deacy loss is 7.75, training accuracy is 0.281250, time 213.507 samples/sec
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+epoch 7, total_step 109980, total loss is 14.83 , inference loss is 7.08, weight deacy loss is 7.74, training accuracy is 0.625000, time 207.450 samples/sec
+testing verification..
+(12000, 512)
+infer time 23.236351999999975
+best_threshold_index 116 0.9868518518518519
+best_threshold_index 115 0.9853703703703703
+best_threshold_index 115 0.9853703703703703
+best_threshold_index 116 0.9864814814814815
+best_threshold_index 118 0.9861111111111112
+best_threshold_index 116 0.9844444444444445
+best_threshold_index 114 0.985
+best_threshold_index 116 0.9848148148148148
+best_threshold_index 116 0.9846296296296296
+best_threshold_index 116 0.9844444444444445
+[lfw][110000]XNorm: 21.664058
+[lfw][110000]Accuracy-Flip: 0.98433+-0.00746
+test accuracy is:  0.9843333333333334
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+epoch 7, total_step 111880, total loss is 15.81 , inference loss is 8.12, weight deacy loss is 7.69, training accuracy is 0.468750, time 216.954 samples/sec
+epoch 7, total_step 111900, total loss is 21.14 , inference loss is 13.46, weight deacy loss is 7.69, training accuracy is 0.281250, time 13.477 samples/sec
+epoch 7, total_step 111920, total loss is 14.60 , inference loss is 6.92, weight deacy loss is 7.69, training accuracy is 0.375000, time 214.960 samples/sec
+epoch 7, total_step 111940, total loss is 17.81 , inference loss is 10.12, weight deacy loss is 7.69, training accuracy is 0.343750, time 210.420 samples/sec
+epoch 7, total_step 111960, total loss is 17.34 , inference loss is 9.65, weight deacy loss is 7.68, training accuracy is 0.312500, time 297.613 samples/sec
+epoch 7, total_step 111980, total loss is 22.08 , inference loss is 14.39, weight deacy loss is 7.68, training accuracy is 0.343750, time 209.116 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.725526000000013
+best_threshold_index 114 0.9875925925925926
+best_threshold_index 113 0.9866666666666667
+best_threshold_index 113 0.9877777777777778
+best_threshold_index 111 0.9872222222222222
+best_threshold_index 113 0.9874074074074074
+best_threshold_index 120 0.9861111111111112
+best_threshold_index 113 0.987037037037037
+best_threshold_index 113 0.9862962962962963
+best_threshold_index 125 0.9857407407407407
+best_threshold_index 113 0.9861111111111112
+[lfw][112000]XNorm: 20.903893
+[lfw][112000]Accuracy-Flip: 0.98433+-0.00539
+test accuracy is:  0.9843333333333334
+epoch 7, total_step 112000, total loss is 16.30 , inference loss is 8.62, weight deacy loss is 7.68, training accuracy is 0.406250, time 19.422 samples/sec
+epoch 7, total_step 112020, total loss is 16.06 , inference loss is 8.38, weight deacy loss is 7.68, training accuracy is 0.343750, time 210.623 samples/sec
+epoch 7, total_step 112040, total loss is 17.03 , inference loss is 9.34, weight deacy loss is 7.68, training accuracy is 0.343750, time 226.819 samples/sec
+epoch 7, total_step 112060, total loss is 16.00 , inference loss is 8.32, weight deacy loss is 7.68, training accuracy is 0.468750, time 210.541 samples/sec
+epoch 7, total_step 112080, total loss is 19.27 , inference loss is 11.59, weight deacy loss is 7.68, training accuracy is 0.343750, time 297.415 samples/sec
+epoch 7, total_step 112100, total loss is 13.21 , inference loss is 5.53, weight deacy loss is 7.68, training accuracy is 0.718750, time 216.774 samples/sec
+epoch 7, total_step 112120, total loss is 14.41 , inference loss is 6.73, weight deacy loss is 7.68, training accuracy is 0.406250, time 221.622 samples/sec
+epoch 7, total_step 112140, total loss is 23.45 , inference loss is 15.77, weight deacy loss is 7.68, training accuracy is 0.312500, time 211.169 samples/sec
+epoch 7, total_step 112160, total loss is 18.73 , inference loss is 11.05, weight deacy loss is 7.68, training accuracy is 0.312500, time 211.399 samples/sec
+epoch 7, total_step 112180, total loss is 16.56 , inference loss is 8.88, weight deacy loss is 7.68, training accuracy is 0.250000, time 226.847 samples/sec
+epoch 7, total_step 112200, total loss is 18.98 , inference loss is 11.30, weight deacy loss is 7.68, training accuracy is 0.218750, time 14.799 samples/sec
+epoch 7, total_step 112220, total loss is 18.19 , inference loss is 10.51, weight deacy loss is 7.68, training accuracy is 0.343750, time 212.619 samples/sec
+epoch 7, total_step 112240, total loss is 21.57 , inference loss is 13.90, weight deacy loss is 7.68, training accuracy is 0.250000, time 213.782 samples/sec
+epoch 7, total_step 112260, total loss is 17.56 , inference loss is 9.88, weight deacy loss is 7.68, training accuracy is 0.375000, time 224.354 samples/sec
+epoch 7, total_step 112280, total loss is 21.80 , inference loss is 14.12, weight deacy loss is 7.68, training accuracy is 0.250000, time 213.563 samples/sec
+epoch 7, total_step 112300, total loss is 18.87 , inference loss is 11.19, weight deacy loss is 7.67, training accuracy is 0.343750, time 204.559 samples/sec
+epoch 7, total_step 112320, total loss is 16.28 , inference loss is 8.60, weight deacy loss is 7.67, training accuracy is 0.343750, time 209.626 samples/sec
+epoch 7, total_step 112340, total loss is 19.20 , inference loss is 11.53, weight deacy loss is 7.67, training accuracy is 0.250000, time 298.581 samples/sec
+epoch 7, total_step 112360, total loss is 15.78 , inference loss is 8.11, weight deacy loss is 7.67, training accuracy is 0.468750, time 177.748 samples/sec
+epoch 7, total_step 112380, total loss is 16.97 , inference loss is 9.30, weight deacy loss is 7.67, training accuracy is 0.312500, time 213.102 samples/sec
+epoch 7, total_step 112400, total loss is 15.42 , inference loss is 7.74, weight deacy loss is 7.67, training accuracy is 0.468750, time 230.575 samples/sec
+epoch 7, total_step 112420, total loss is 14.17 , inference loss is 6.50, weight deacy loss is 7.67, training accuracy is 0.593750, time 200.621 samples/sec
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+epoch 7, total_step 112880, total loss is 14.72 , inference loss is 7.06, weight deacy loss is 7.66, training accuracy is 0.468750, time 298.604 samples/sec
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+epoch 7, total_step 113040, total loss is 16.25 , inference loss is 8.60, weight deacy loss is 7.65, training accuracy is 0.343750, time 215.534 samples/sec
+epoch 7, total_step 113060, total loss is 20.10 , inference loss is 12.45, weight deacy loss is 7.65, training accuracy is 0.312500, time 202.708 samples/sec
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+epoch 7, total_step 113980, total loss is 16.69 , inference loss is 9.07, weight deacy loss is 7.63, training accuracy is 0.281250, time 219.136 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.383555999999963
+best_threshold_index 112 0.9877777777777778
+best_threshold_index 112 0.9866666666666667
+best_threshold_index 112 0.987037037037037
+best_threshold_index 112 0.9881481481481481
+best_threshold_index 112 0.987037037037037
+best_threshold_index 112 0.9862962962962963
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+best_threshold_index 112 0.9872222222222222
+best_threshold_index 112 0.9859259259259259
+best_threshold_index 112 0.9864814814814815
+[lfw][114000]XNorm: 22.818682
+[lfw][114000]Accuracy-Flip: 0.98700+-0.00581
+test accuracy is:  0.9870000000000001
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+testing verification..
+(12000, 512)
+infer time 23.656479
+best_threshold_index 117 0.9894444444444445
+best_threshold_index 117 0.9888888888888889
+best_threshold_index 117 0.9896296296296296
+best_threshold_index 117 0.9894444444444445
+best_threshold_index 117 0.9894444444444445
+best_threshold_index 117 0.9885185185185185
+best_threshold_index 117 0.9894444444444445
+best_threshold_index 117 0.9888888888888889
+best_threshold_index 117 0.9881481481481481
+best_threshold_index 117 0.9881481481481481
+[lfw][116000]XNorm: 22.080170
+[lfw][116000]Accuracy-Flip: 0.98900+-0.00484
+test accuracy is:  0.9889999999999999
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+epoch 7, total_step 117860, total loss is 16.48 , inference loss is 8.97, weight deacy loss is 7.51, training accuracy is 0.406250, time 207.124 samples/sec
+epoch 7, total_step 117880, total loss is 18.98 , inference loss is 11.46, weight deacy loss is 7.51, training accuracy is 0.218750, time 209.232 samples/sec
+epoch 7, total_step 117900, total loss is 13.98 , inference loss is 6.47, weight deacy loss is 7.51, training accuracy is 0.593750, time 14.066 samples/sec
+epoch 7, total_step 117920, total loss is 20.14 , inference loss is 12.63, weight deacy loss is 7.51, training accuracy is 0.375000, time 211.740 samples/sec
+epoch 7, total_step 117940, total loss is 18.19 , inference loss is 10.68, weight deacy loss is 7.51, training accuracy is 0.406250, time 206.385 samples/sec
+epoch 7, total_step 117960, total loss is 17.91 , inference loss is 10.40, weight deacy loss is 7.51, training accuracy is 0.437500, time 206.364 samples/sec
+epoch 7, total_step 117980, total loss is 16.66 , inference loss is 9.15, weight deacy loss is 7.51, training accuracy is 0.281250, time 209.195 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.620454000000024
+best_threshold_index 110 0.9887037037037038
+best_threshold_index 110 0.9875925925925926
+best_threshold_index 110 0.9874074074074074
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+best_threshold_index 111 0.9874074074074074
+best_threshold_index 110 0.9879629629629629
+best_threshold_index 110 0.9866666666666667
+best_threshold_index 110 0.9862962962962963
+best_threshold_index 110 0.987037037037037
+[lfw][118000]XNorm: 21.525841
+[lfw][118000]Accuracy-Flip: 0.98717+-0.00633
+test accuracy is:  0.9871666666666667
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+epoch 7, total_step 119980, total loss is 18.19 , inference loss is 10.73, weight deacy loss is 7.45, training accuracy is 0.343750, time 207.300 samples/sec
+testing verification..
+(12000, 512)
+infer time 23.04926099999999
+best_threshold_index 110 0.9855555555555555
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+best_threshold_index 110 0.9853703703703703
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+best_threshold_index 109 0.9846296296296296
+best_threshold_index 110 0.9840740740740741
+best_threshold_index 110 0.9840740740740741
+[lfw][120000]XNorm: 21.798404
+[lfw][120000]Accuracy-Flip: 0.98450+-0.00723
+test accuracy is:  0.9844999999999999
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+epoch 7, total_step 120760, total loss is 18.03 , inference loss is 10.60, weight deacy loss is 7.43, training accuracy is 0.281250, time 204.306 samples/sec
+epoch 7, total_step 120780, total loss is 19.41 , inference loss is 11.98, weight deacy loss is 7.43, training accuracy is 0.250000, time 206.131 samples/sec
+epoch 7, total_step 120800, total loss is 14.61 , inference loss is 7.18, weight deacy loss is 7.43, training accuracy is 0.281250, time 211.573 samples/sec
+epoch 7, total_step 120820, total loss is 15.66 , inference loss is 8.23, weight deacy loss is 7.43, training accuracy is 0.531250, time 213.423 samples/sec
+epoch 7, total_step 120840, total loss is 16.85 , inference loss is 9.42, weight deacy loss is 7.43, training accuracy is 0.281250, time 207.488 samples/sec
+epoch 7, total_step 120860, total loss is 16.80 , inference loss is 9.37, weight deacy loss is 7.43, training accuracy is 0.343750, time 209.213 samples/sec
+epoch 7, total_step 120880, total loss is 17.26 , inference loss is 9.83, weight deacy loss is 7.43, training accuracy is 0.250000, time 215.577 samples/sec
+epoch 7, total_step 120900, total loss is 18.14 , inference loss is 10.71, weight deacy loss is 7.43, training accuracy is 0.187500, time 13.678 samples/sec
+epoch 7, total_step 120920, total loss is 14.27 , inference loss is 6.84, weight deacy loss is 7.43, training accuracy is 0.250000, time 214.855 samples/sec
+epoch 7, total_step 120940, total loss is 16.23 , inference loss is 8.80, weight deacy loss is 7.43, training accuracy is 0.437500, time 218.796 samples/sec
+epoch 7, total_step 120960, total loss is 20.49 , inference loss is 13.07, weight deacy loss is 7.43, training accuracy is 0.218750, time 211.412 samples/sec
+epoch 7, total_step 120980, total loss is 19.84 , inference loss is 12.41, weight deacy loss is 7.43, training accuracy is 0.125000, time 235.312 samples/sec
+epoch 7, total_step 121000, total loss is 14.99 , inference loss is 7.56, weight deacy loss is 7.42, training accuracy is 0.375000, time 213.100 samples/sec
+epoch 7, total_step 121020, total loss is 20.58 , inference loss is 13.15, weight deacy loss is 7.42, training accuracy is 0.187500, time 209.561 samples/sec
+epoch 7, total_step 121040, total loss is 20.71 , inference loss is 13.28, weight deacy loss is 7.42, training accuracy is 0.125000, time 206.625 samples/sec
+epoch 7, total_step 121060, total loss is 19.02 , inference loss is 11.59, weight deacy loss is 7.42, training accuracy is 0.281250, time 218.322 samples/sec
+epoch 7, total_step 121080, total loss is 15.29 , inference loss is 7.87, weight deacy loss is 7.42, training accuracy is 0.406250, time 207.857 samples/sec
+epoch 7, total_step 121100, total loss is 20.09 , inference loss is 12.67, weight deacy loss is 7.42, training accuracy is 0.218750, time 208.907 samples/sec
+epoch 7, total_step 121120, total loss is 17.88 , inference loss is 10.46, weight deacy loss is 7.42, training accuracy is 0.250000, time 216.119 samples/sec
+epoch 7, total_step 121140, total loss is 14.49 , inference loss is 7.07, weight deacy loss is 7.42, training accuracy is 0.406250, time 217.163 samples/sec
+epoch 7, total_step 121160, total loss is 16.09 , inference loss is 8.67, weight deacy loss is 7.42, training accuracy is 0.468750, time 209.871 samples/sec
+epoch 7, total_step 121180, total loss is 16.36 , inference loss is 8.94, weight deacy loss is 7.42, training accuracy is 0.187500, time 297.633 samples/sec
+epoch 7, total_step 121200, total loss is 17.45 , inference loss is 10.03, weight deacy loss is 7.42, training accuracy is 0.250000, time 11.851 samples/sec
+epoch 7, total_step 121220, total loss is 21.85 , inference loss is 14.43, weight deacy loss is 7.42, training accuracy is 0.218750, time 230.963 samples/sec
+epoch 7, total_step 121240, total loss is 16.74 , inference loss is 9.32, weight deacy loss is 7.42, training accuracy is 0.281250, time 212.681 samples/sec
+epoch 7, total_step 121260, total loss is 14.49 , inference loss is 7.07, weight deacy loss is 7.42, training accuracy is 0.437500, time 210.789 samples/sec
+epoch 7, total_step 121280, total loss is 17.79 , inference loss is 10.37, weight deacy loss is 7.42, training accuracy is 0.343750, time 209.031 samples/sec
+epoch 7, total_step 121300, total loss is 19.37 , inference loss is 11.95, weight deacy loss is 7.42, training accuracy is 0.125000, time 210.911 samples/sec
+epoch 7, total_step 121320, total loss is 23.30 , inference loss is 15.89, weight deacy loss is 7.42, training accuracy is 0.125000, time 208.544 samples/sec
+epoch 7, total_step 121340, total loss is 18.69 , inference loss is 11.27, weight deacy loss is 7.42, training accuracy is 0.218750, time 211.007 samples/sec
+epoch 7, total_step 121360, total loss is 18.88 , inference loss is 11.46, weight deacy loss is 7.41, training accuracy is 0.343750, time 212.848 samples/sec
+epoch 7, total_step 121380, total loss is 15.24 , inference loss is 7.82, weight deacy loss is 7.41, training accuracy is 0.281250, time 211.992 samples/sec
+epoch 7, total_step 121400, total loss is 13.08 , inference loss is 5.67, weight deacy loss is 7.41, training accuracy is 0.375000, time 228.770 samples/sec
+epoch 7, total_step 121420, total loss is 16.64 , inference loss is 9.23, weight deacy loss is 7.41, training accuracy is 0.312500, time 219.451 samples/sec
+epoch 7, total_step 121440, total loss is 19.70 , inference loss is 12.28, weight deacy loss is 7.41, training accuracy is 0.281250, time 296.778 samples/sec
+epoch 7, total_step 121460, total loss is 21.77 , inference loss is 14.36, weight deacy loss is 7.41, training accuracy is 0.218750, time 215.105 samples/sec
+epoch 7, total_step 121480, total loss is 16.81 , inference loss is 9.40, weight deacy loss is 7.41, training accuracy is 0.312500, time 209.456 samples/sec
+epoch 7, total_step 121500, total loss is 19.77 , inference loss is 12.35, weight deacy loss is 7.41, training accuracy is 0.187500, time 14.257 samples/sec
+epoch 7, total_step 121520, total loss is 17.14 , inference loss is 9.73, weight deacy loss is 7.41, training accuracy is 0.187500, time 222.183 samples/sec
+epoch 7, total_step 121540, total loss is 18.43 , inference loss is 11.02, weight deacy loss is 7.41, training accuracy is 0.218750, time 213.878 samples/sec
+epoch 7, total_step 121560, total loss is 15.66 , inference loss is 8.25, weight deacy loss is 7.41, training accuracy is 0.250000, time 211.737 samples/sec
+epoch 7, total_step 121580, total loss is 16.23 , inference loss is 8.82, weight deacy loss is 7.41, training accuracy is 0.343750, time 220.241 samples/sec
+epoch 7, total_step 121600, total loss is 16.29 , inference loss is 8.89, weight deacy loss is 7.41, training accuracy is 0.187500, time 221.342 samples/sec
+epoch 7, total_step 121620, total loss is 16.70 , inference loss is 9.30, weight deacy loss is 7.41, training accuracy is 0.218750, time 209.667 samples/sec
+epoch 7, total_step 121640, total loss is 24.72 , inference loss is 17.31, weight deacy loss is 7.41, training accuracy is 0.156250, time 204.383 samples/sec
+epoch 7, total_step 121660, total loss is 23.16 , inference loss is 15.76, weight deacy loss is 7.41, training accuracy is 0.156250, time 208.388 samples/sec
+epoch 7, total_step 121680, total loss is 17.16 , inference loss is 9.75, weight deacy loss is 7.41, training accuracy is 0.343750, time 206.845 samples/sec
+epoch 7, total_step 121700, total loss is 19.06 , inference loss is 11.65, weight deacy loss is 7.41, training accuracy is 0.218750, time 217.467 samples/sec
+epoch 7, total_step 121720, total loss is 15.71 , inference loss is 8.30, weight deacy loss is 7.40, training accuracy is 0.281250, time 299.104 samples/sec
+epoch 7, total_step 121740, total loss is 14.31 , inference loss is 6.90, weight deacy loss is 7.40, training accuracy is 0.375000, time 205.215 samples/sec
+epoch 7, total_step 121760, total loss is 18.26 , inference loss is 10.85, weight deacy loss is 7.40, training accuracy is 0.187500, time 211.223 samples/sec
+epoch 7, total_step 121780, total loss is 15.34 , inference loss is 7.93, weight deacy loss is 7.40, training accuracy is 0.375000, time 203.054 samples/sec
+epoch 7, total_step 121800, total loss is 22.20 , inference loss is 14.80, weight deacy loss is 7.40, training accuracy is 0.125000, time 13.914 samples/sec
+epoch 7, total_step 121820, total loss is 21.86 , inference loss is 14.46, weight deacy loss is 7.40, training accuracy is 0.093750, time 218.496 samples/sec
+epoch 7, total_step 121840, total loss is 13.47 , inference loss is 6.07, weight deacy loss is 7.40, training accuracy is 0.312500, time 213.509 samples/sec
+epoch 7, total_step 121860, total loss is 17.31 , inference loss is 9.91, weight deacy loss is 7.40, training accuracy is 0.218750, time 213.570 samples/sec
+epoch 7, total_step 121880, total loss is 17.10 , inference loss is 9.70, weight deacy loss is 7.40, training accuracy is 0.375000, time 210.690 samples/sec
+epoch 7, total_step 121900, total loss is 18.17 , inference loss is 10.77, weight deacy loss is 7.40, training accuracy is 0.250000, time 200.753 samples/sec
+epoch 7, total_step 121920, total loss is 14.94 , inference loss is 7.54, weight deacy loss is 7.40, training accuracy is 0.250000, time 208.302 samples/sec
+epoch 7, total_step 121940, total loss is 16.99 , inference loss is 9.59, weight deacy loss is 7.40, training accuracy is 0.187500, time 226.663 samples/sec
+epoch 7, total_step 121960, total loss is 19.38 , inference loss is 11.98, weight deacy loss is 7.40, training accuracy is 0.218750, time 220.502 samples/sec
+epoch 7, total_step 121980, total loss is 19.14 , inference loss is 11.74, weight deacy loss is 7.40, training accuracy is 0.281250, time 299.189 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.963117000000018
+best_threshold_index 110 0.9862962962962963
+best_threshold_index 111 0.9848148148148148
+best_threshold_index 110 0.9853703703703703
+best_threshold_index 107 0.9862962962962963
+best_threshold_index 107 0.9859259259259259
+best_threshold_index 110 0.9846296296296296
+best_threshold_index 110 0.9864814814814815
+best_threshold_index 110 0.9853703703703703
+best_threshold_index 110 0.9844444444444445
+best_threshold_index 110 0.9842592592592593
+[lfw][122000]XNorm: 22.335920
+[lfw][122000]Accuracy-Flip: 0.98450+-0.00742
+test accuracy is:  0.9845
+epoch 7, total_step 122000, total loss is 20.19 , inference loss is 12.79, weight deacy loss is 7.40, training accuracy is 0.125000, time 19.116 samples/sec
+epoch 7, total_step 122020, total loss is 23.67 , inference loss is 16.27, weight deacy loss is 7.40, training accuracy is 0.218750, time 212.665 samples/sec
+epoch 7, total_step 122040, total loss is 22.75 , inference loss is 15.35, weight deacy loss is 7.40, training accuracy is 0.218750, time 214.633 samples/sec
+epoch 7, total_step 122060, total loss is 20.47 , inference loss is 13.07, weight deacy loss is 7.40, training accuracy is 0.312500, time 209.743 samples/sec
+epoch 7, total_step 122080, total loss is 19.13 , inference loss is 11.74, weight deacy loss is 7.39, training accuracy is 0.187500, time 224.678 samples/sec
+epoch 7, total_step 122100, total loss is 20.36 , inference loss is 12.96, weight deacy loss is 7.39, training accuracy is 0.218750, time 11.748 samples/sec
+epoch 7, total_step 122120, total loss is 23.33 , inference loss is 15.93, weight deacy loss is 7.39, training accuracy is 0.187500, time 212.667 samples/sec
+epoch 7, total_step 122140, total loss is 16.72 , inference loss is 9.33, weight deacy loss is 7.39, training accuracy is 0.218750, time 206.926 samples/sec
+epoch 7, total_step 122160, total loss is 18.96 , inference loss is 11.57, weight deacy loss is 7.39, training accuracy is 0.218750, time 213.022 samples/sec
+epoch 7, total_step 122180, total loss is 21.14 , inference loss is 13.75, weight deacy loss is 7.39, training accuracy is 0.218750, time 217.303 samples/sec
+epoch 7, total_step 122200, total loss is 23.26 , inference loss is 15.87, weight deacy loss is 7.39, training accuracy is 0.218750, time 218.469 samples/sec
+epoch 7, total_step 122220, total loss is 20.25 , inference loss is 12.86, weight deacy loss is 7.39, training accuracy is 0.125000, time 214.792 samples/sec
+epoch 7, total_step 122240, total loss is 19.23 , inference loss is 11.84, weight deacy loss is 7.39, training accuracy is 0.156250, time 213.469 samples/sec
+epoch 7, total_step 122260, total loss is 16.33 , inference loss is 8.94, weight deacy loss is 7.39, training accuracy is 0.250000, time 215.907 samples/sec
+epoch 7, total_step 122280, total loss is 19.53 , inference loss is 12.14, weight deacy loss is 7.39, training accuracy is 0.250000, time 217.718 samples/sec
+epoch 7, total_step 122300, total loss is 17.30 , inference loss is 9.91, weight deacy loss is 7.39, training accuracy is 0.187500, time 297.464 samples/sec
+epoch 7, total_step 122320, total loss is 22.78 , inference loss is 15.39, weight deacy loss is 7.39, training accuracy is 0.125000, time 300.427 samples/sec
+epoch 7, total_step 122340, total loss is 16.67 , inference loss is 9.28, weight deacy loss is 7.39, training accuracy is 0.312500, time 196.780 samples/sec
+epoch 7, total_step 122360, total loss is 16.06 , inference loss is 8.67, weight deacy loss is 7.39, training accuracy is 0.281250, time 194.821 samples/sec
+epoch 7, total_step 122380, total loss is 18.19 , inference loss is 10.80, weight deacy loss is 7.39, training accuracy is 0.187500, time 209.884 samples/sec
+epoch 7, total_step 122400, total loss is 19.16 , inference loss is 11.78, weight deacy loss is 7.39, training accuracy is 0.156250, time 13.687 samples/sec
+epoch 7, total_step 122420, total loss is 19.06 , inference loss is 11.67, weight deacy loss is 7.39, training accuracy is 0.187500, time 210.084 samples/sec
+epoch 7, total_step 122440, total loss is 20.02 , inference loss is 12.64, weight deacy loss is 7.39, training accuracy is 0.343750, time 196.261 samples/sec
+epoch 7, total_step 122460, total loss is 18.96 , inference loss is 11.58, weight deacy loss is 7.38, training accuracy is 0.343750, time 218.332 samples/sec
+epoch 7, total_step 122480, total loss is 18.48 , inference loss is 11.09, weight deacy loss is 7.38, training accuracy is 0.187500, time 224.435 samples/sec
+epoch 7, total_step 122500, total loss is 16.31 , inference loss is 8.92, weight deacy loss is 7.38, training accuracy is 0.343750, time 196.233 samples/sec2022-12-01 08:17:22.536957: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:145] Filling up shuffle buffer (this may take a while): 9046 of 10000
+2022-12-01 08:17:23.568423: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:195] Shuffle buffer filled.
+
+epoch 7, total_step 122520, total loss is 20.41 , inference loss is 13.03, weight deacy loss is 7.38, training accuracy is 0.281250, time 189.402 samples/sec
+epoch 7, total_step 122540, total loss is 17.78 , inference loss is 10.40, weight deacy loss is 7.38, training accuracy is 0.281250, time 300.194 samples/sec
+epoch 7, total_step 122560, total loss is 16.35 , inference loss is 8.96, weight deacy loss is 7.38, training accuracy is 0.281250, time 229.672 samples/sec
+epoch 7, total_step 122580, total loss is 19.06 , inference loss is 11.68, weight deacy loss is 7.38, training accuracy is 0.250000, time 208.457 samples/sec
+epoch 7, total_step 122600, total loss is 19.42 , inference loss is 12.04, weight deacy loss is 7.38, training accuracy is 0.281250, time 209.413 samples/sec
+epoch 7, total_step 122620, total loss is 16.52 , inference loss is 9.14, weight deacy loss is 7.38, training accuracy is 0.250000, time 193.940 samples/sec
+epoch 7, total_step 122640, total loss is 14.84 , inference loss is 7.46, weight deacy loss is 7.38, training accuracy is 0.343750, time 227.172 samples/sec
+End of epoch 7
+epoch 8, total_step 122660, total loss is 20.51 , inference loss is 13.13, weight deacy loss is 7.38, training accuracy is 0.125000, time 202.326 samples/sec
+epoch 8, total_step 122680, total loss is 12.99 , inference loss is 5.62, weight deacy loss is 7.38, training accuracy is 0.593750, time 210.562 samples/sec
+epoch 8, total_step 122700, total loss is 13.47 , inference loss is 6.09, weight deacy loss is 7.38, training accuracy is 0.500000, time 14.430 samples/sec
+epoch 8, total_step 122720, total loss is 11.38 , inference loss is 4.00, weight deacy loss is 7.38, training accuracy is 0.593750, time 205.670 samples/sec
+epoch 8, total_step 122740, total loss is 11.26 , inference loss is 3.89, weight deacy loss is 7.38, training accuracy is 0.625000, time 214.173 samples/sec
+epoch 8, total_step 122760, total loss is 14.25 , inference loss is 6.87, weight deacy loss is 7.38, training accuracy is 0.562500, time 300.020 samples/sec
+epoch 8, total_step 122780, total loss is 12.50 , inference loss is 5.13, weight deacy loss is 7.38, training accuracy is 0.562500, time 219.517 samples/sec
+epoch 8, total_step 122800, total loss is 13.41 , inference loss is 6.04, weight deacy loss is 7.38, training accuracy is 0.656250, time 210.527 samples/sec
+epoch 8, total_step 122820, total loss is 9.79 , inference loss is 2.42, weight deacy loss is 7.37, training accuracy is 0.718750, time 213.831 samples/sec
+epoch 8, total_step 122840, total loss is 10.29 , inference loss is 2.91, weight deacy loss is 7.37, training accuracy is 0.656250, time 234.488 samples/sec
+epoch 8, total_step 122860, total loss is 11.19 , inference loss is 3.82, weight deacy loss is 7.37, training accuracy is 0.750000, time 208.949 samples/sec
+epoch 8, total_step 122880, total loss is 13.89 , inference loss is 6.52, weight deacy loss is 7.37, training accuracy is 0.593750, time 212.636 samples/sec
+epoch 8, total_step 122900, total loss is 11.93 , inference loss is 4.55, weight deacy loss is 7.37, training accuracy is 0.625000, time 209.905 samples/sec
+epoch 8, total_step 122920, total loss is 13.98 , inference loss is 6.61, weight deacy loss is 7.37, training accuracy is 0.437500, time 215.059 samples/sec
+epoch 8, total_step 122940, total loss is 14.38 , inference loss is 7.01, weight deacy loss is 7.37, training accuracy is 0.562500, time 210.012 samples/sec
+epoch 8, total_step 122960, total loss is 13.68 , inference loss is 6.31, weight deacy loss is 7.37, training accuracy is 0.531250, time 214.229 samples/sec
+epoch 8, total_step 122980, total loss is 15.07 , inference loss is 7.70, weight deacy loss is 7.37, training accuracy is 0.531250, time 209.925 samples/sec
+epoch 8, total_step 123000, total loss is 10.56 , inference loss is 3.19, weight deacy loss is 7.37, training accuracy is 0.625000, time 10.655 samples/sec
+epoch 8, total_step 123020, total loss is 13.80 , inference loss is 6.43, weight deacy loss is 7.37, training accuracy is 0.562500, time 297.028 samples/sec
+epoch 8, total_step 123040, total loss is 18.71 , inference loss is 11.35, weight deacy loss is 7.37, training accuracy is 0.468750, time 214.377 samples/sec
+epoch 8, total_step 123060, total loss is 12.80 , inference loss is 5.43, weight deacy loss is 7.37, training accuracy is 0.718750, time 205.147 samples/sec
+epoch 8, total_step 123080, total loss is 15.90 , inference loss is 8.53, weight deacy loss is 7.37, training accuracy is 0.562500, time 234.673 samples/sec
+epoch 8, total_step 123100, total loss is 11.16 , inference loss is 3.79, weight deacy loss is 7.37, training accuracy is 0.593750, time 211.263 samples/sec
+epoch 8, total_step 123120, total loss is 14.93 , inference loss is 7.56, weight deacy loss is 7.37, training accuracy is 0.593750, time 210.879 samples/sec
+epoch 8, total_step 123140, total loss is 12.92 , inference loss is 5.56, weight deacy loss is 7.37, training accuracy is 0.500000, time 209.859 samples/sec
+epoch 8, total_step 123160, total loss is 16.31 , inference loss is 8.95, weight deacy loss is 7.37, training accuracy is 0.437500, time 214.915 samples/sec
+epoch 8, total_step 123180, total loss is 15.03 , inference loss is 7.66, weight deacy loss is 7.37, training accuracy is 0.593750, time 212.966 samples/sec
+epoch 8, total_step 123200, total loss is 14.65 , inference loss is 7.29, weight deacy loss is 7.37, training accuracy is 0.500000, time 210.858 samples/sec
+epoch 8, total_step 123220, total loss is 14.80 , inference loss is 7.44, weight deacy loss is 7.36, training accuracy is 0.562500, time 202.038 samples/sec
+epoch 8, total_step 123240, total loss is 13.65 , inference loss is 6.28, weight deacy loss is 7.36, training accuracy is 0.437500, time 210.772 samples/sec
+epoch 8, total_step 123260, total loss is 14.60 , inference loss is 7.24, weight deacy loss is 7.36, training accuracy is 0.593750, time 211.044 samples/sec
+epoch 8, total_step 123280, total loss is 14.91 , inference loss is 7.55, weight deacy loss is 7.36, training accuracy is 0.500000, time 214.589 samples/sec
+epoch 8, total_step 123300, total loss is 14.75 , inference loss is 7.38, weight deacy loss is 7.36, training accuracy is 0.468750, time 15.279 samples/sec
+epoch 8, total_step 123320, total loss is 21.55 , inference loss is 14.19, weight deacy loss is 7.36, training accuracy is 0.375000, time 213.312 samples/sec
+epoch 8, total_step 123340, total loss is 12.83 , inference loss is 5.47, weight deacy loss is 7.36, training accuracy is 0.593750, time 206.897 samples/sec
+epoch 8, total_step 123360, total loss is 17.71 , inference loss is 10.34, weight deacy loss is 7.36, training accuracy is 0.343750, time 209.184 samples/sec
+epoch 8, total_step 123380, total loss is 13.23 , inference loss is 5.87, weight deacy loss is 7.36, training accuracy is 0.656250, time 225.952 samples/sec
+epoch 8, total_step 123400, total loss is 15.59 , inference loss is 8.23, weight deacy loss is 7.36, training accuracy is 0.531250, time 207.079 samples/sec
+epoch 8, total_step 123420, total loss is 17.33 , inference loss is 9.97, weight deacy loss is 7.36, training accuracy is 0.375000, time 211.529 samples/sec
+epoch 8, total_step 123440, total loss is 13.57 , inference loss is 6.21, weight deacy loss is 7.36, training accuracy is 0.500000, time 212.368 samples/sec
+epoch 8, total_step 123460, total loss is 18.52 , inference loss is 11.16, weight deacy loss is 7.36, training accuracy is 0.562500, time 211.216 samples/sec
+epoch 8, total_step 123480, total loss is 13.92 , inference loss is 6.56, weight deacy loss is 7.36, training accuracy is 0.656250, time 208.737 samples/sec
+epoch 8, total_step 123500, total loss is 12.74 , inference loss is 5.38, weight deacy loss is 7.36, training accuracy is 0.562500, time 204.970 samples/sec
+epoch 8, total_step 123520, total loss is 13.94 , inference loss is 6.58, weight deacy loss is 7.36, training accuracy is 0.437500, time 210.898 samples/sec
+epoch 8, total_step 123540, total loss is 13.34 , inference loss is 5.98, weight deacy loss is 7.36, training accuracy is 0.593750, time 213.112 samples/sec
+epoch 8, total_step 123560, total loss is 17.87 , inference loss is 10.51, weight deacy loss is 7.36, training accuracy is 0.437500, time 207.080 samples/sec
+epoch 8, total_step 123580, total loss is 15.65 , inference loss is 8.29, weight deacy loss is 7.36, training accuracy is 0.468750, time 221.340 samples/sec
+epoch 8, total_step 123600, total loss is 15.28 , inference loss is 7.92, weight deacy loss is 7.36, training accuracy is 0.468750, time 13.709 samples/sec
+epoch 8, total_step 123620, total loss is 14.07 , inference loss is 6.71, weight deacy loss is 7.36, training accuracy is 0.437500, time 215.771 samples/sec
+epoch 8, total_step 123640, total loss is 11.86 , inference loss is 4.50, weight deacy loss is 7.36, training accuracy is 0.406250, time 212.389 samples/sec
+epoch 8, total_step 123660, total loss is 15.14 , inference loss is 7.78, weight deacy loss is 7.36, training accuracy is 0.468750, time 219.810 samples/sec
+epoch 8, total_step 123680, total loss is 15.08 , inference loss is 7.73, weight deacy loss is 7.36, training accuracy is 0.437500, time 211.719 samples/sec
+epoch 8, total_step 123700, total loss is 15.22 , inference loss is 7.87, weight deacy loss is 7.36, training accuracy is 0.562500, time 207.387 samples/sec
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+epoch 8, total_step 123980, total loss is 15.83 , inference loss is 8.47, weight deacy loss is 7.35, training accuracy is 0.437500, time 217.854 samples/sec
+testing verification..
+(12000, 512)
+infer time 23.891823
+best_threshold_index 122 0.9862962962962963
+best_threshold_index 122 0.9853703703703703
+best_threshold_index 122 0.9848148148148148
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+best_threshold_index 122 0.9853703703703703
+[lfw][124000]XNorm: 20.415726
+[lfw][124000]Accuracy-Flip: 0.98483+-0.00462
+test accuracy is:  0.9848333333333332
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+testing verification..
+(12000, 512)
+infer time 22.384251999999975
+best_threshold_index 119 0.9877777777777778
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+best_threshold_index 117 0.9864814814814815
+[lfw][126000]XNorm: 22.383718
+[lfw][126000]Accuracy-Flip: 0.98583+-0.00634
+test accuracy is:  0.9858333333333335
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+testing verification..
+(12000, 512)
+infer time 24.348349000000045
+best_threshold_index 115 0.9879629629629629
+best_threshold_index 114 0.987037037037037
+best_threshold_index 114 0.9872222222222222
+best_threshold_index 115 0.9885185185185185
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+best_threshold_index 115 0.9877777777777778
+best_threshold_index 114 0.9875925925925926
+best_threshold_index 115 0.9874074074074074
+best_threshold_index 115 0.9866666666666667
+best_threshold_index 113 0.9864814814814815
+[lfw][128000]XNorm: 22.221234
+[lfw][128000]Accuracy-Flip: 0.98667+-0.00511
+test accuracy is:  0.9866666666666667
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+epoch 8, total_step 129680, total loss is 17.44 , inference loss is 10.21, weight deacy loss is 7.23, training accuracy is 0.281250, time 299.307 samples/sec
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+epoch 8, total_step 129840, total loss is 18.14 , inference loss is 10.92, weight deacy loss is 7.22, training accuracy is 0.312500, time 215.384 samples/sec
+epoch 8, total_step 129860, total loss is 17.31 , inference loss is 10.08, weight deacy loss is 7.22, training accuracy is 0.312500, time 213.703 samples/sec
+epoch 8, total_step 129880, total loss is 14.38 , inference loss is 7.15, weight deacy loss is 7.22, training accuracy is 0.312500, time 211.731 samples/sec
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+epoch 8, total_step 129920, total loss is 14.94 , inference loss is 7.72, weight deacy loss is 7.22, training accuracy is 0.375000, time 214.200 samples/sec
+epoch 8, total_step 129940, total loss is 13.62 , inference loss is 6.40, weight deacy loss is 7.22, training accuracy is 0.468750, time 212.274 samples/sec
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+epoch 8, total_step 129980, total loss is 13.53 , inference loss is 6.31, weight deacy loss is 7.22, training accuracy is 0.500000, time 212.259 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.55036099999998
+best_threshold_index 117 0.9887037037037038
+best_threshold_index 117 0.9881481481481481
+best_threshold_index 117 0.9890740740740741
+best_threshold_index 116 0.9888888888888889
+best_threshold_index 117 0.9888888888888889
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+best_threshold_index 117 0.9887037037037038
+best_threshold_index 116 0.9883333333333333
+best_threshold_index 117 0.9875925925925926
+best_threshold_index 117 0.9879629629629629
+[lfw][130000]XNorm: 21.137521
+[lfw][130000]Accuracy-Flip: 0.98817+-0.00418
+test accuracy is:  0.9881666666666667
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+epoch 8, total_step 131980, total loss is 13.66 , inference loss is 6.48, weight deacy loss is 7.18, training accuracy is 0.312500, time 219.238 samples/sec
+testing verification..
+(12000, 512)
+infer time 23.54797099999998
+best_threshold_index 116 0.9879629629629629
+best_threshold_index 113 0.9866666666666667
+best_threshold_index 113 0.9874074074074074
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+best_threshold_index 113 0.9866666666666667
+best_threshold_index 116 0.9861111111111112
+best_threshold_index 113 0.9864814814814815
+[lfw][132000]XNorm: 22.230508
+[lfw][132000]Accuracy-Flip: 0.98633+-0.00493
+test accuracy is:  0.9863333333333333
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+epoch 8, total_step 133980, total loss is 16.76 , inference loss is 9.63, weight deacy loss is 7.13, training accuracy is 0.343750, time 208.575 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.568871000000026
+best_threshold_index 109 0.985
+best_threshold_index 109 0.9846296296296296
+best_threshold_index 108 0.9842592592592593
+best_threshold_index 107 0.9855555555555555
+best_threshold_index 109 0.9859259259259259
+best_threshold_index 109 0.9842592592592593
+best_threshold_index 109 0.985
+best_threshold_index 109 0.9842592592592593
+best_threshold_index 109 0.9835185185185186
+best_threshold_index 109 0.9844444444444445
+[lfw][134000]XNorm: 19.811025
+[lfw][134000]Accuracy-Flip: 0.98367+-0.00666
+test accuracy is:  0.9836666666666666
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+epoch 8, total_step 135740, total loss is 14.07 , inference loss is 6.97, weight deacy loss is 7.09, training accuracy is 0.468750, time 197.448 samples/sec
+epoch 8, total_step 135760, total loss is 16.02 , inference loss is 8.93, weight deacy loss is 7.09, training accuracy is 0.406250, time 207.186 samples/sec
+epoch 8, total_step 135780, total loss is 15.73 , inference loss is 8.63, weight deacy loss is 7.09, training accuracy is 0.218750, time 219.006 samples/sec
+epoch 8, total_step 135800, total loss is 18.91 , inference loss is 11.82, weight deacy loss is 7.09, training accuracy is 0.156250, time 218.455 samples/sec
+epoch 8, total_step 135820, total loss is 13.85 , inference loss is 6.76, weight deacy loss is 7.09, training accuracy is 0.406250, time 209.249 samples/sec
+epoch 8, total_step 135840, total loss is 16.15 , inference loss is 9.06, weight deacy loss is 7.09, training accuracy is 0.312500, time 298.611 samples/sec
+epoch 8, total_step 135860, total loss is 15.52 , inference loss is 8.43, weight deacy loss is 7.09, training accuracy is 0.312500, time 299.635 samples/sec
+epoch 8, total_step 135880, total loss is 14.02 , inference loss is 6.92, weight deacy loss is 7.09, training accuracy is 0.343750, time 299.417 samples/sec
+epoch 8, total_step 135900, total loss is 14.51 , inference loss is 7.42, weight deacy loss is 7.09, training accuracy is 0.406250, time 3.223 samples/sec
+epoch 8, total_step 135920, total loss is 18.16 , inference loss is 11.07, weight deacy loss is 7.09, training accuracy is 0.406250, time 203.267 samples/sec
+epoch 8, total_step 135940, total loss is 17.64 , inference loss is 10.55, weight deacy loss is 7.09, training accuracy is 0.281250, time 298.731 samples/sec
+epoch 8, total_step 135960, total loss is 19.86 , inference loss is 12.77, weight deacy loss is 7.09, training accuracy is 0.250000, time 299.484 samples/sec
+epoch 8, total_step 135980, total loss is 16.51 , inference loss is 9.42, weight deacy loss is 7.09, training accuracy is 0.406250, time 298.677 samples/sec
+testing verification..
+(12000, 512)
+infer time 31.388446
+best_threshold_index 105 0.9851851851851852
+best_threshold_index 105 0.9835185185185186
+best_threshold_index 103 0.9844444444444445
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+best_threshold_index 101 0.9840740740740741
+best_threshold_index 105 0.9829629629629629
+best_threshold_index 105 0.9829629629629629
+[lfw][136000]XNorm: 23.026528
+[lfw][136000]Accuracy-Flip: 0.98300+-0.00781
+test accuracy is:  0.9829999999999999
+epoch 8, total_step 136000, total loss is 18.21 , inference loss is 11.12, weight deacy loss is 7.09, training accuracy is 0.187500, time 7.456 samples/sec
+epoch 8, total_step 136020, total loss is 17.97 , inference loss is 10.88, weight deacy loss is 7.09, training accuracy is 0.375000, time 222.716 samples/sec
+epoch 8, total_step 136040, total loss is 15.43 , inference loss is 8.34, weight deacy loss is 7.09, training accuracy is 0.312500, time 299.211 samples/sec
+epoch 8, total_step 136060, total loss is 14.15 , inference loss is 7.06, weight deacy loss is 7.09, training accuracy is 0.468750, time 298.187 samples/sec
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+epoch 8, total_step 136100, total loss is 15.72 , inference loss is 8.64, weight deacy loss is 7.09, training accuracy is 0.406250, time 299.046 samples/sec
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+epoch 8, total_step 136140, total loss is 18.69 , inference loss is 11.60, weight deacy loss is 7.09, training accuracy is 0.218750, time 179.871 samples/sec
+epoch 8, total_step 136160, total loss is 19.09 , inference loss is 12.01, weight deacy loss is 7.09, training accuracy is 0.281250, time 192.883 samples/sec
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+epoch 8, total_step 136220, total loss is 16.09 , inference loss is 9.00, weight deacy loss is 7.08, training accuracy is 0.281250, time 299.953 samples/sec
+epoch 8, total_step 136240, total loss is 19.99 , inference loss is 12.91, weight deacy loss is 7.08, training accuracy is 0.187500, time 216.356 samples/sec
+epoch 8, total_step 136260, total loss is 14.38 , inference loss is 7.29, weight deacy loss is 7.08, training accuracy is 0.406250, time 217.060 samples/sec
+epoch 8, total_step 136280, total loss is 16.05 , inference loss is 8.96, weight deacy loss is 7.08, training accuracy is 0.250000, time 208.088 samples/sec
+epoch 8, total_step 136300, total loss is 14.00 , inference loss is 6.92, weight deacy loss is 7.08, training accuracy is 0.312500, time 215.741 samples/sec
+epoch 8, total_step 136320, total loss is 14.11 , inference loss is 7.03, weight deacy loss is 7.08, training accuracy is 0.468750, time 223.530 samples/sec
+epoch 8, total_step 136340, total loss is 17.06 , inference loss is 9.98, weight deacy loss is 7.08, training accuracy is 0.250000, time 219.332 samples/sec
+epoch 8, total_step 136360, total loss is 15.08 , inference loss is 8.00, weight deacy loss is 7.08, training accuracy is 0.281250, time 210.525 samples/sec
+epoch 8, total_step 136380, total loss is 20.37 , inference loss is 13.29, weight deacy loss is 7.08, training accuracy is 0.156250, time 210.455 samples/sec
+epoch 8, total_step 136400, total loss is 13.55 , inference loss is 6.47, weight deacy loss is 7.08, training accuracy is 0.343750, time 216.299 samples/sec
+epoch 8, total_step 136420, total loss is 14.72 , inference loss is 7.64, weight deacy loss is 7.08, training accuracy is 0.375000, time 232.436 samples/sec
+epoch 8, total_step 136440, total loss is 12.42 , inference loss is 5.34, weight deacy loss is 7.08, training accuracy is 0.593750, time 230.280 samples/sec
+epoch 8, total_step 136460, total loss is 15.96 , inference loss is 8.88, weight deacy loss is 7.08, training accuracy is 0.312500, time 218.138 samples/sec
+epoch 8, total_step 136480, total loss is 14.37 , inference loss is 7.29, weight deacy loss is 7.08, training accuracy is 0.250000, time 217.243 samples/sec
+epoch 8, total_step 136500, total loss is 16.12 , inference loss is 9.04, weight deacy loss is 7.08, training accuracy is 0.281250, time 3.511 samples/sec
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+epoch 8, total_step 136540, total loss is 16.72 , inference loss is 9.64, weight deacy loss is 7.08, training accuracy is 0.218750, time 299.592 samples/sec
+epoch 8, total_step 136560, total loss is 18.24 , inference loss is 11.17, weight deacy loss is 7.08, training accuracy is 0.281250, time 277.824 samples/sec
+epoch 8, total_step 136580, total loss is 13.91 , inference loss is 6.84, weight deacy loss is 7.08, training accuracy is 0.343750, time 299.550 samples/sec
+epoch 8, total_step 136600, total loss is 17.78 , inference loss is 10.71, weight deacy loss is 7.08, training accuracy is 0.437500, time 298.834 samples/sec
+epoch 8, total_step 136620, total loss is 15.20 , inference loss is 8.12, weight deacy loss is 7.07, training accuracy is 0.281250, time 300.109 samples/sec
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+epoch 8, total_step 136900, total loss is 17.15 , inference loss is 10.08, weight deacy loss is 7.07, training accuracy is 0.281250, time 210.066 samples/sec
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+epoch 8, total_step 137020, total loss is 21.31 , inference loss is 14.24, weight deacy loss is 7.07, training accuracy is 0.250000, time 223.570 samples/sec
+epoch 8, total_step 137040, total loss is 17.26 , inference loss is 10.20, weight deacy loss is 7.07, training accuracy is 0.281250, time 210.029 samples/sec
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+epoch 8, total_step 137620, total loss is 15.58 , inference loss is 8.53, weight deacy loss is 7.05, training accuracy is 0.281250, time 298.819 samples/sec
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+epoch 8, total_step 137660, total loss is 13.80 , inference loss is 6.75, weight deacy loss is 7.05, training accuracy is 0.437500, time 222.530 samples/sec
+epoch 8, total_step 137680, total loss is 17.80 , inference loss is 10.75, weight deacy loss is 7.05, training accuracy is 0.218750, time 175.909 samples/sec
+epoch 8, total_step 137700, total loss is 17.78 , inference loss is 10.73, weight deacy loss is 7.05, training accuracy is 0.281250, time 3.570 samples/sec
+epoch 8, total_step 137720, total loss is 14.94 , inference loss is 7.89, weight deacy loss is 7.05, training accuracy is 0.375000, time 299.916 samples/sec
+epoch 8, total_step 137740, total loss is 21.17 , inference loss is 14.12, weight deacy loss is 7.05, training accuracy is 0.156250, time 298.662 samples/sec
+epoch 8, total_step 137760, total loss is 17.74 , inference loss is 10.69, weight deacy loss is 7.05, training accuracy is 0.250000, time 299.473 samples/sec
+epoch 8, total_step 137780, total loss is 16.27 , inference loss is 9.22, weight deacy loss is 7.05, training accuracy is 0.156250, time 298.746 samples/sec
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+epoch 8, total_step 137820, total loss is 23.26 , inference loss is 16.21, weight deacy loss is 7.05, training accuracy is 0.406250, time 298.353 samples/sec2022-12-01 09:20:03.005269: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:145] Filling up shuffle buffer (this may take a while): 7416 of 10000
+2022-12-01 09:20:06.544714: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:195] Shuffle buffer filled.
+
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+epoch 8, total_step 137860, total loss is 13.77 , inference loss is 6.72, weight deacy loss is 7.05, training accuracy is 0.468750, time 298.754 samples/sec
+epoch 8, total_step 137880, total loss is 18.65 , inference loss is 11.60, weight deacy loss is 7.05, training accuracy is 0.406250, time 202.256 samples/sec
+epoch 8, total_step 137900, total loss is 16.23 , inference loss is 9.18, weight deacy loss is 7.05, training accuracy is 0.250000, time 171.017 samples/sec
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+epoch 8, total_step 137960, total loss is 17.51 , inference loss is 10.47, weight deacy loss is 7.05, training accuracy is 0.187500, time 177.047 samples/sec
+End of epoch 8
+epoch 9, total_step 137980, total loss is 29.03 , inference loss is 21.98, weight deacy loss is 7.04, training accuracy is 0.000000, time 203.906 samples/sec
+testing verification..
+(12000, 512)
+infer time 34.38512400000001
+best_threshold_index 110 0.9842592592592593
+best_threshold_index 110 0.9831481481481481
+best_threshold_index 110 0.9837037037037037
+best_threshold_index 110 0.985
+best_threshold_index 110 0.9848148148148148
+best_threshold_index 110 0.9824074074074074
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+best_threshold_index 110 0.9838888888888889
+best_threshold_index 110 0.982037037037037
+best_threshold_index 110 0.9825925925925926
+[lfw][138000]XNorm: 18.740737
+[lfw][138000]Accuracy-Flip: 0.98367+-0.00921
+test accuracy is:  0.9836666666666668
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+epoch 9, total_step 139920, total loss is 11.18 , inference loss is 4.16, weight deacy loss is 7.02, training accuracy is 0.593750, time 216.864 samples/sec
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+epoch 9, total_step 139980, total loss is 12.55 , inference loss is 5.53, weight deacy loss is 7.02, training accuracy is 0.562500, time 226.075 samples/sec
+testing verification..
+(12000, 512)
+infer time 26.396229000000012
+best_threshold_index 120 0.987037037037037
+best_threshold_index 119 0.9868518518518519
+best_threshold_index 112 0.9859259259259259
+best_threshold_index 116 0.9874074074074074
+best_threshold_index 120 0.9864814814814815
+best_threshold_index 120 0.9855555555555555
+best_threshold_index 120 0.9868518518518519
+best_threshold_index 120 0.9855555555555555
+best_threshold_index 120 0.9861111111111112
+best_threshold_index 120 0.9861111111111112
+[lfw][140000]XNorm: 22.054561
+[lfw][140000]Accuracy-Flip: 0.98483+-0.00598
+test accuracy is:  0.9848333333333332
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+epoch 9, total_step 141980, total loss is 20.78 , inference loss is 13.80, weight deacy loss is 6.99, training accuracy is 0.343750, time 218.022 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.718047999999996
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+[lfw][142000]XNorm: 21.868196
+[lfw][142000]Accuracy-Flip: 0.98533+-0.00562
+test accuracy is:  0.9853333333333334
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+testing verification..
+(12000, 512)
+infer time 24.473917999999973
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+[lfw][144000]XNorm: 22.618802
+[lfw][144000]Accuracy-Flip: 0.98717+-0.00637
+test accuracy is:  0.9871666666666667
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+testing verification..
+(12000, 512)
+infer time 25.14554600000001
+best_threshold_index 114 0.9898148148148148
+best_threshold_index 114 0.9877777777777778
+best_threshold_index 114 0.9887037037037038
+best_threshold_index 114 0.99
+best_threshold_index 114 0.9892592592592593
+best_threshold_index 114 0.9875925925925926
+best_threshold_index 114 0.9887037037037038
+best_threshold_index 114 0.9877777777777778
+best_threshold_index 114 0.9877777777777778
+best_threshold_index 114 0.9875925925925926
+[lfw][146000]XNorm: 21.526729
+[lfw][146000]Accuracy-Flip: 0.98850+-0.00797
+test accuracy is:  0.9884999999999999
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+epoch 9, total_step 147980, total loss is 19.46 , inference loss is 12.57, weight deacy loss is 6.89, training accuracy is 0.437500, time 207.850 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.863634000000022
+best_threshold_index 107 0.9879629629629629
+best_threshold_index 107 0.9864814814814815
+best_threshold_index 107 0.9862962962962963
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+best_threshold_index 107 0.9853703703703703
+best_threshold_index 107 0.9857407407407407
+[lfw][148000]XNorm: 22.830338
+[lfw][148000]Accuracy-Flip: 0.98600+-0.00831
+test accuracy is:  0.986
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+testing verification..
+(12000, 512)
+infer time 24.16458600000002
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+best_threshold_index 113 0.987037037037037
+best_threshold_index 113 0.9874074074074074
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+best_threshold_index 113 0.9861111111111112
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+[lfw][150000]XNorm: 21.880269
+[lfw][150000]Accuracy-Flip: 0.98700+-0.00488
+test accuracy is:  0.9869999999999999
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+testing verification..
+(12000, 512)
+infer time 24.629655999999976
+best_threshold_index 109 0.9874074074074074
+best_threshold_index 109 0.9855555555555555
+best_threshold_index 109 0.9861111111111112
+best_threshold_index 109 0.9877777777777778
+best_threshold_index 109 0.9868518518518519
+best_threshold_index 109 0.9853703703703703
+best_threshold_index 109 0.9866666666666667
+best_threshold_index 112 0.9853703703703703
+best_threshold_index 109 0.9853703703703703
+best_threshold_index 109 0.9853703703703703
+[lfw][152000]XNorm: 22.079617
+[lfw][152000]Accuracy-Flip: 0.98583+-0.00779
+test accuracy is:  0.9858333333333335
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+epoch 9, total_step 152880, total loss is 18.48 , inference loss is 11.67, weight deacy loss is 6.80, training accuracy is 0.187500, time 217.727 samples/sec
+epoch 9, total_step 152900, total loss is 16.90 , inference loss is 10.10, weight deacy loss is 6.80, training accuracy is 0.187500, time 218.700 samples/sec
+epoch 9, total_step 152920, total loss is 15.16 , inference loss is 8.36, weight deacy loss is 6.80, training accuracy is 0.250000, time 215.263 samples/sec
+epoch 9, total_step 152940, total loss is 16.08 , inference loss is 9.28, weight deacy loss is 6.80, training accuracy is 0.281250, time 212.045 samples/sec
+epoch 9, total_step 152960, total loss is 16.92 , inference loss is 10.12, weight deacy loss is 6.80, training accuracy is 0.218750, time 219.920 samples/sec
+epoch 9, total_step 152980, total loss is 18.20 , inference loss is 11.39, weight deacy loss is 6.80, training accuracy is 0.312500, time 215.998 samples/sec
+epoch 9, total_step 153000, total loss is 19.81 , inference loss is 13.01, weight deacy loss is 6.80, training accuracy is 0.250000, time 11.584 samples/sec
+epoch 9, total_step 153020, total loss is 14.56 , inference loss is 7.76, weight deacy loss is 6.80, training accuracy is 0.250000, time 227.316 samples/sec2022-12-01 10:27:12.292107: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:145] Filling up shuffle buffer (this may take a while): 7784 of 10000
+2022-12-01 10:27:15.149566: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:195] Shuffle buffer filled.
+
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+epoch 9, total_step 153200, total loss is 15.74 , inference loss is 8.94, weight deacy loss is 6.80, training accuracy is 0.375000, time 200.171 samples/sec
+epoch 9, total_step 153220, total loss is 19.47 , inference loss is 12.68, weight deacy loss is 6.80, training accuracy is 0.375000, time 176.732 samples/sec
+epoch 9, total_step 153240, total loss is 19.74 , inference loss is 12.94, weight deacy loss is 6.80, training accuracy is 0.312500, time 180.248 samples/sec
+epoch 9, total_step 153260, total loss is 16.28 , inference loss is 9.49, weight deacy loss is 6.80, training accuracy is 0.156250, time 177.821 samples/sec
+epoch 9, total_step 153280, total loss is 15.97 , inference loss is 9.17, weight deacy loss is 6.80, training accuracy is 0.250000, time 184.281 samples/sec
+epoch 9, total_step 153300, total loss is 12.10 , inference loss is 5.30, weight deacy loss is 6.80, training accuracy is 0.343750, time 11.376 samples/sec
+End of epoch 9
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+epoch 10, total_step 153340, total loss is 10.50 , inference loss is 3.71, weight deacy loss is 6.80, training accuracy is 0.625000, time 219.020 samples/sec
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+epoch 10, total_step 153380, total loss is 12.14 , inference loss is 5.35, weight deacy loss is 6.80, training accuracy is 0.718750, time 215.978 samples/sec
+epoch 10, total_step 153400, total loss is 10.91 , inference loss is 4.12, weight deacy loss is 6.79, training accuracy is 0.656250, time 213.360 samples/sec
+epoch 10, total_step 153420, total loss is 12.58 , inference loss is 5.79, weight deacy loss is 6.79, training accuracy is 0.500000, time 212.346 samples/sec
+epoch 10, total_step 153440, total loss is 10.77 , inference loss is 3.97, weight deacy loss is 6.79, training accuracy is 0.625000, time 224.372 samples/sec
+epoch 10, total_step 153460, total loss is 12.05 , inference loss is 5.25, weight deacy loss is 6.79, training accuracy is 0.656250, time 215.193 samples/sec
+epoch 10, total_step 153480, total loss is 12.24 , inference loss is 5.45, weight deacy loss is 6.79, training accuracy is 0.593750, time 219.273 samples/sec
+epoch 10, total_step 153500, total loss is 11.38 , inference loss is 4.59, weight deacy loss is 6.79, training accuracy is 0.625000, time 221.109 samples/sec
+epoch 10, total_step 153520, total loss is 15.32 , inference loss is 8.53, weight deacy loss is 6.79, training accuracy is 0.562500, time 299.129 samples/sec
+epoch 10, total_step 153540, total loss is 10.04 , inference loss is 3.25, weight deacy loss is 6.79, training accuracy is 0.625000, time 215.592 samples/sec
+epoch 10, total_step 153560, total loss is 10.39 , inference loss is 3.60, weight deacy loss is 6.79, training accuracy is 0.687500, time 215.253 samples/sec
+epoch 10, total_step 153580, total loss is 11.71 , inference loss is 4.92, weight deacy loss is 6.79, training accuracy is 0.468750, time 214.827 samples/sec
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+epoch 10, total_step 153680, total loss is 18.94 , inference loss is 12.15, weight deacy loss is 6.79, training accuracy is 0.500000, time 181.248 samples/sec
+epoch 10, total_step 153700, total loss is 15.09 , inference loss is 8.30, weight deacy loss is 6.79, training accuracy is 0.562500, time 180.752 samples/sec
+epoch 10, total_step 153720, total loss is 11.87 , inference loss is 5.08, weight deacy loss is 6.79, training accuracy is 0.625000, time 176.359 samples/sec
+epoch 10, total_step 153740, total loss is 15.24 , inference loss is 8.45, weight deacy loss is 6.79, training accuracy is 0.562500, time 298.964 samples/sec
+epoch 10, total_step 153760, total loss is 11.62 , inference loss is 4.83, weight deacy loss is 6.79, training accuracy is 0.625000, time 181.243 samples/sec
+epoch 10, total_step 153780, total loss is 14.49 , inference loss is 7.70, weight deacy loss is 6.79, training accuracy is 0.562500, time 180.174 samples/sec
+epoch 10, total_step 153800, total loss is 12.18 , inference loss is 5.40, weight deacy loss is 6.79, training accuracy is 0.687500, time 180.207 samples/sec
+epoch 10, total_step 153820, total loss is 11.96 , inference loss is 5.17, weight deacy loss is 6.79, training accuracy is 0.593750, time 173.001 samples/sec
+epoch 10, total_step 153840, total loss is 14.52 , inference loss is 7.73, weight deacy loss is 6.79, training accuracy is 0.500000, time 170.070 samples/sec
+epoch 10, total_step 153860, total loss is 9.74 , inference loss is 2.95, weight deacy loss is 6.79, training accuracy is 0.718750, time 167.060 samples/sec
+epoch 10, total_step 153880, total loss is 12.09 , inference loss is 5.31, weight deacy loss is 6.79, training accuracy is 0.562500, time 180.719 samples/sec
+epoch 10, total_step 153900, total loss is 16.07 , inference loss is 9.28, weight deacy loss is 6.79, training accuracy is 0.500000, time 13.084 samples/sec
+epoch 10, total_step 153920, total loss is 11.65 , inference loss is 4.86, weight deacy loss is 6.79, training accuracy is 0.562500, time 214.460 samples/sec
+epoch 10, total_step 153940, total loss is 14.22 , inference loss is 7.43, weight deacy loss is 6.79, training accuracy is 0.593750, time 213.038 samples/sec
+epoch 10, total_step 153960, total loss is 12.04 , inference loss is 5.25, weight deacy loss is 6.79, training accuracy is 0.500000, time 211.351 samples/sec
+epoch 10, total_step 153980, total loss is 13.17 , inference loss is 6.38, weight deacy loss is 6.79, training accuracy is 0.625000, time 218.626 samples/sec
+testing verification..
+(12000, 512)
+infer time 23.73838199999999
+best_threshold_index 113 0.9844444444444445
+best_threshold_index 113 0.9848148148148148
+best_threshold_index 113 0.9829629629629629
+best_threshold_index 113 0.9844444444444445
+best_threshold_index 113 0.9851851851851852
+best_threshold_index 113 0.9837037037037037
+best_threshold_index 113 0.9842592592592593
+best_threshold_index 113 0.985
+best_threshold_index 113 0.9833333333333333
+best_threshold_index 113 0.9835185185185186
+[lfw][154000]XNorm: 17.860944
+[lfw][154000]Accuracy-Flip: 0.98417+-0.00642
+test accuracy is:  0.9841666666666666
+epoch 10, total_step 154000, total loss is 15.33 , inference loss is 8.54, weight deacy loss is 6.79, training accuracy is 0.562500, time 15.731 samples/sec
+epoch 10, total_step 154020, total loss is 14.55 , inference loss is 7.76, weight deacy loss is 6.79, training accuracy is 0.468750, time 216.483 samples/sec
+epoch 10, total_step 154040, total loss is 13.54 , inference loss is 6.75, weight deacy loss is 6.79, training accuracy is 0.687500, time 225.094 samples/sec
+epoch 10, total_step 154060, total loss is 14.22 , inference loss is 7.43, weight deacy loss is 6.79, training accuracy is 0.562500, time 298.719 samples/sec
+epoch 10, total_step 154080, total loss is 18.53 , inference loss is 11.74, weight deacy loss is 6.79, training accuracy is 0.500000, time 203.204 samples/sec
+epoch 10, total_step 154100, total loss is 12.95 , inference loss is 6.16, weight deacy loss is 6.79, training accuracy is 0.437500, time 210.012 samples/sec
+epoch 10, total_step 154120, total loss is 14.67 , inference loss is 7.88, weight deacy loss is 6.79, training accuracy is 0.468750, time 209.098 samples/sec
+epoch 10, total_step 154140, total loss is 18.58 , inference loss is 11.79, weight deacy loss is 6.79, training accuracy is 0.406250, time 214.229 samples/sec
+epoch 10, total_step 154160, total loss is 13.66 , inference loss is 6.88, weight deacy loss is 6.79, training accuracy is 0.687500, time 209.389 samples/sec
+epoch 10, total_step 154180, total loss is 14.42 , inference loss is 7.64, weight deacy loss is 6.79, training accuracy is 0.593750, time 212.464 samples/sec
+epoch 10, total_step 154200, total loss is 17.22 , inference loss is 10.43, weight deacy loss is 6.79, training accuracy is 0.437500, time 12.241 samples/sec
+epoch 10, total_step 154220, total loss is 12.87 , inference loss is 6.08, weight deacy loss is 6.79, training accuracy is 0.593750, time 296.274 samples/sec
+epoch 10, total_step 154240, total loss is 16.30 , inference loss is 9.51, weight deacy loss is 6.79, training accuracy is 0.468750, time 208.980 samples/sec
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+epoch 10, total_step 154280, total loss is 10.82 , inference loss is 4.03, weight deacy loss is 6.79, training accuracy is 0.562500, time 208.086 samples/sec
+epoch 10, total_step 154300, total loss is 11.81 , inference loss is 5.03, weight deacy loss is 6.79, training accuracy is 0.593750, time 220.267 samples/sec
+epoch 10, total_step 154320, total loss is 11.81 , inference loss is 5.02, weight deacy loss is 6.79, training accuracy is 0.625000, time 213.495 samples/sec
+epoch 10, total_step 154340, total loss is 14.63 , inference loss is 7.84, weight deacy loss is 6.79, training accuracy is 0.500000, time 211.371 samples/sec
+epoch 10, total_step 154360, total loss is 15.02 , inference loss is 8.23, weight deacy loss is 6.79, training accuracy is 0.562500, time 211.065 samples/sec
+epoch 10, total_step 154380, total loss is 14.33 , inference loss is 7.55, weight deacy loss is 6.79, training accuracy is 0.625000, time 205.360 samples/sec
+epoch 10, total_step 154400, total loss is 15.22 , inference loss is 8.43, weight deacy loss is 6.79, training accuracy is 0.468750, time 215.455 samples/sec
+epoch 10, total_step 154420, total loss is 13.67 , inference loss is 6.88, weight deacy loss is 6.79, training accuracy is 0.562500, time 214.802 samples/sec
+epoch 10, total_step 154440, total loss is 16.02 , inference loss is 9.23, weight deacy loss is 6.79, training accuracy is 0.437500, time 204.053 samples/sec
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+epoch 10, total_step 155980, total loss is 11.79 , inference loss is 5.02, weight deacy loss is 6.77, training accuracy is 0.593750, time 213.416 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.236431999999965
+best_threshold_index 118 0.9883333333333333
+best_threshold_index 118 0.9887037037037038
+best_threshold_index 118 0.9883333333333333
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+best_threshold_index 120 0.9885185185185185
+best_threshold_index 118 0.9875925925925926
+best_threshold_index 118 0.9874074074074074
+[lfw][156000]XNorm: 21.152376
+[lfw][156000]Accuracy-Flip: 0.98800+-0.00542
+test accuracy is:  0.9879999999999999
+epoch 10, total_step 156000, total loss is 11.53 , inference loss is 4.76, weight deacy loss is 6.77, training accuracy is 0.468750, time 16.354 samples/sec
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+testing verification..
+(12000, 512)
+infer time 25.096416000000033
+best_threshold_index 118 0.9883333333333333
+best_threshold_index 120 0.9877777777777778
+best_threshold_index 118 0.9885185185185185
+best_threshold_index 118 0.9888888888888889
+best_threshold_index 120 0.9898148148148148
+best_threshold_index 117 0.9875925925925926
+best_threshold_index 117 0.9875925925925926
+best_threshold_index 118 0.9872222222222222
+best_threshold_index 118 0.987037037037037
+best_threshold_index 118 0.9877777777777778
+[lfw][158000]XNorm: 21.707070
+[lfw][158000]Accuracy-Flip: 0.98717+-0.00703
+test accuracy is:  0.9871666666666666
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+epoch 10, total_step 159160, total loss is 13.58 , inference loss is 6.84, weight deacy loss is 6.74, training accuracy is 0.593750, time 213.185 samples/sec
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+epoch 10, total_step 159200, total loss is 11.79 , inference loss is 5.05, weight deacy loss is 6.74, training accuracy is 0.468750, time 215.396 samples/sec
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+epoch 10, total_step 159240, total loss is 14.36 , inference loss is 7.63, weight deacy loss is 6.74, training accuracy is 0.468750, time 212.282 samples/sec
+epoch 10, total_step 159260, total loss is 13.91 , inference loss is 7.17, weight deacy loss is 6.74, training accuracy is 0.500000, time 218.088 samples/sec
+epoch 10, total_step 159280, total loss is 12.96 , inference loss is 6.23, weight deacy loss is 6.74, training accuracy is 0.500000, time 213.750 samples/sec
+epoch 10, total_step 159300, total loss is 14.62 , inference loss is 7.88, weight deacy loss is 6.74, training accuracy is 0.375000, time 11.502 samples/sec
+epoch 10, total_step 159320, total loss is 15.84 , inference loss is 9.11, weight deacy loss is 6.74, training accuracy is 0.375000, time 212.860 samples/sec
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+epoch 10, total_step 159880, total loss is 15.79 , inference loss is 9.06, weight deacy loss is 6.73, training accuracy is 0.375000, time 214.325 samples/sec
+epoch 10, total_step 159900, total loss is 12.08 , inference loss is 5.35, weight deacy loss is 6.73, training accuracy is 0.500000, time 5.289 samples/sec
+epoch 10, total_step 159920, total loss is 15.34 , inference loss is 8.61, weight deacy loss is 6.73, training accuracy is 0.531250, time 299.200 samples/sec
+epoch 10, total_step 159940, total loss is 17.40 , inference loss is 10.67, weight deacy loss is 6.73, training accuracy is 0.437500, time 298.812 samples/sec
+epoch 10, total_step 159960, total loss is 15.50 , inference loss is 8.77, weight deacy loss is 6.73, training accuracy is 0.312500, time 297.999 samples/sec
+epoch 10, total_step 159980, total loss is 16.22 , inference loss is 9.49, weight deacy loss is 6.73, training accuracy is 0.406250, time 211.463 samples/sec
+testing verification..
+(12000, 512)
+infer time 25.572397
+best_threshold_index 115 0.9901851851851852
+best_threshold_index 115 0.9890740740740741
+best_threshold_index 115 0.9894444444444445
+best_threshold_index 115 0.9905555555555555
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+best_threshold_index 115 0.9890740740740741
+best_threshold_index 115 0.9898148148148148
+best_threshold_index 115 0.9892592592592593
+best_threshold_index 115 0.9887037037037038
+best_threshold_index 115 0.9892592592592593
+[lfw][160000]XNorm: 21.640497
+[lfw][160000]Accuracy-Flip: 0.98950+-0.00478
+test accuracy is:  0.9894999999999999
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+epoch 10, total_step 160020, total loss is 20.90 , inference loss is 14.18, weight deacy loss is 6.73, training accuracy is 0.281250, time 214.792 samples/sec
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+epoch 10, total_step 161500, total loss is 18.04 , inference loss is 11.33, weight deacy loss is 6.71, training accuracy is 0.187500, time 216.625 samples/sec
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+epoch 10, total_step 161920, total loss is 15.94 , inference loss is 9.23, weight deacy loss is 6.70, training accuracy is 0.375000, time 217.814 samples/sec
+epoch 10, total_step 161940, total loss is 17.90 , inference loss is 11.19, weight deacy loss is 6.70, training accuracy is 0.468750, time 299.752 samples/sec
+epoch 10, total_step 161960, total loss is 18.92 , inference loss is 12.22, weight deacy loss is 6.70, training accuracy is 0.312500, time 214.532 samples/sec
+epoch 10, total_step 161980, total loss is 13.14 , inference loss is 6.43, weight deacy loss is 6.70, training accuracy is 0.531250, time 214.213 samples/sec
+testing verification..
+(12000, 512)
+infer time 25.742124999999998
+best_threshold_index 114 0.9879629629629629
+best_threshold_index 112 0.9864814814814815
+best_threshold_index 112 0.9874074074074074
+best_threshold_index 112 0.9883333333333333
+best_threshold_index 112 0.9875925925925926
+best_threshold_index 112 0.9861111111111112
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+best_threshold_index 114 0.9866666666666667
+best_threshold_index 112 0.9861111111111112
+best_threshold_index 112 0.9862962962962963
+[lfw][162000]XNorm: 22.605432
+[lfw][162000]Accuracy-Flip: 0.98633+-0.00710
+test accuracy is:  0.9863333333333333
+epoch 10, total_step 162000, total loss is 16.78 , inference loss is 10.07, weight deacy loss is 6.70, training accuracy is 0.281250, time 16.141 samples/sec
+epoch 10, total_step 162020, total loss is 16.17 , inference loss is 9.47, weight deacy loss is 6.70, training accuracy is 0.312500, time 216.101 samples/sec
+epoch 10, total_step 162040, total loss is 18.87 , inference loss is 12.17, weight deacy loss is 6.70, training accuracy is 0.343750, time 211.532 samples/sec
+epoch 10, total_step 162060, total loss is 9.62 , inference loss is 2.92, weight deacy loss is 6.70, training accuracy is 0.718750, time 213.664 samples/sec
+epoch 10, total_step 162080, total loss is 10.75 , inference loss is 4.05, weight deacy loss is 6.70, training accuracy is 0.500000, time 214.208 samples/sec
+epoch 10, total_step 162100, total loss is 17.96 , inference loss is 11.26, weight deacy loss is 6.70, training accuracy is 0.437500, time 213.851 samples/sec
+epoch 10, total_step 162120, total loss is 17.30 , inference loss is 10.60, weight deacy loss is 6.70, training accuracy is 0.406250, time 217.095 samples/sec
+epoch 10, total_step 162140, total loss is 12.98 , inference loss is 6.28, weight deacy loss is 6.70, training accuracy is 0.531250, time 211.734 samples/sec
+epoch 10, total_step 162160, total loss is 18.92 , inference loss is 12.21, weight deacy loss is 6.70, training accuracy is 0.343750, time 210.950 samples/sec
+epoch 10, total_step 162180, total loss is 14.93 , inference loss is 8.23, weight deacy loss is 6.70, training accuracy is 0.375000, time 219.016 samples/sec
+epoch 10, total_step 162200, total loss is 13.39 , inference loss is 6.69, weight deacy loss is 6.70, training accuracy is 0.500000, time 208.216 samples/sec
+epoch 10, total_step 162220, total loss is 18.34 , inference loss is 11.64, weight deacy loss is 6.70, training accuracy is 0.343750, time 184.238 samples/sec
+epoch 10, total_step 162240, total loss is 14.35 , inference loss is 7.65, weight deacy loss is 6.70, training accuracy is 0.468750, time 213.911 samples/sec
+epoch 10, total_step 162260, total loss is 19.08 , inference loss is 12.38, weight deacy loss is 6.70, training accuracy is 0.250000, time 206.040 samples/sec
+epoch 10, total_step 162280, total loss is 11.74 , inference loss is 5.04, weight deacy loss is 6.70, training accuracy is 0.406250, time 298.375 samples/sec
+epoch 10, total_step 162300, total loss is 15.85 , inference loss is 9.15, weight deacy loss is 6.70, training accuracy is 0.406250, time 11.750 samples/sec
+epoch 10, total_step 162320, total loss is 13.88 , inference loss is 7.19, weight deacy loss is 6.70, training accuracy is 0.531250, time 219.740 samples/sec
+epoch 10, total_step 162340, total loss is 15.60 , inference loss is 8.90, weight deacy loss is 6.70, training accuracy is 0.343750, time 209.348 samples/sec
+epoch 10, total_step 162360, total loss is 15.86 , inference loss is 9.16, weight deacy loss is 6.70, training accuracy is 0.531250, time 211.017 samples/sec
+epoch 10, total_step 162380, total loss is 15.56 , inference loss is 8.86, weight deacy loss is 6.70, training accuracy is 0.437500, time 209.426 samples/sec
+epoch 10, total_step 162400, total loss is 16.88 , inference loss is 10.18, weight deacy loss is 6.70, training accuracy is 0.406250, time 215.444 samples/sec
+epoch 10, total_step 162420, total loss is 12.15 , inference loss is 5.45, weight deacy loss is 6.70, training accuracy is 0.468750, time 215.995 samples/sec
+epoch 10, total_step 162440, total loss is 16.44 , inference loss is 9.74, weight deacy loss is 6.70, training accuracy is 0.437500, time 209.156 samples/sec
+epoch 10, total_step 162460, total loss is 15.75 , inference loss is 9.05, weight deacy loss is 6.70, training accuracy is 0.468750, time 215.048 samples/sec
+epoch 10, total_step 162480, total loss is 20.62 , inference loss is 13.93, weight deacy loss is 6.70, training accuracy is 0.375000, time 215.086 samples/sec
+epoch 10, total_step 162500, total loss is 14.19 , inference loss is 7.49, weight deacy loss is 6.70, training accuracy is 0.437500, time 204.435 samples/sec
+epoch 10, total_step 162520, total loss is 13.88 , inference loss is 7.18, weight deacy loss is 6.70, training accuracy is 0.500000, time 217.363 samples/sec
+epoch 10, total_step 162540, total loss is 17.79 , inference loss is 11.10, weight deacy loss is 6.70, training accuracy is 0.406250, time 300.421 samples/sec
+epoch 10, total_step 162560, total loss is 14.76 , inference loss is 8.06, weight deacy loss is 6.70, training accuracy is 0.531250, time 228.821 samples/sec
+epoch 10, total_step 162580, total loss is 18.70 , inference loss is 12.00, weight deacy loss is 6.70, training accuracy is 0.437500, time 219.150 samples/sec
+epoch 10, total_step 162600, total loss is 18.29 , inference loss is 11.60, weight deacy loss is 6.70, training accuracy is 0.437500, time 12.990 samples/sec
+epoch 10, total_step 162620, total loss is 14.13 , inference loss is 7.44, weight deacy loss is 6.70, training accuracy is 0.531250, time 222.417 samples/sec
+epoch 10, total_step 162640, total loss is 16.19 , inference loss is 9.49, weight deacy loss is 6.70, training accuracy is 0.406250, time 216.188 samples/sec
+epoch 10, total_step 162660, total loss is 17.14 , inference loss is 10.44, weight deacy loss is 6.70, training accuracy is 0.281250, time 212.161 samples/sec
+epoch 10, total_step 162680, total loss is 13.01 , inference loss is 6.32, weight deacy loss is 6.70, training accuracy is 0.500000, time 216.818 samples/sec
+epoch 10, total_step 162700, total loss is 10.85 , inference loss is 4.15, weight deacy loss is 6.69, training accuracy is 0.656250, time 217.944 samples/sec
+epoch 10, total_step 162720, total loss is 12.80 , inference loss is 6.10, weight deacy loss is 6.69, training accuracy is 0.406250, time 216.484 samples/sec
+epoch 10, total_step 162740, total loss is 14.93 , inference loss is 8.23, weight deacy loss is 6.69, training accuracy is 0.468750, time 217.533 samples/sec
+epoch 10, total_step 162760, total loss is 16.05 , inference loss is 9.35, weight deacy loss is 6.69, training accuracy is 0.468750, time 220.250 samples/sec
+epoch 10, total_step 162780, total loss is 13.17 , inference loss is 6.47, weight deacy loss is 6.69, training accuracy is 0.531250, time 217.386 samples/sec
+epoch 10, total_step 162800, total loss is 12.60 , inference loss is 5.90, weight deacy loss is 6.69, training accuracy is 0.437500, time 213.723 samples/sec
+epoch 10, total_step 162820, total loss is 14.81 , inference loss is 8.11, weight deacy loss is 6.69, training accuracy is 0.468750, time 220.071 samples/sec
+epoch 10, total_step 162840, total loss is 13.16 , inference loss is 6.47, weight deacy loss is 6.69, training accuracy is 0.468750, time 231.532 samples/sec
+epoch 10, total_step 162860, total loss is 14.43 , inference loss is 7.73, weight deacy loss is 6.69, training accuracy is 0.468750, time 300.921 samples/sec
+epoch 10, total_step 162880, total loss is 13.66 , inference loss is 6.97, weight deacy loss is 6.69, training accuracy is 0.406250, time 301.322 samples/sec
+epoch 10, total_step 162900, total loss is 17.68 , inference loss is 10.99, weight deacy loss is 6.69, training accuracy is 0.468750, time 13.279 samples/sec
+epoch 10, total_step 162920, total loss is 17.46 , inference loss is 10.77, weight deacy loss is 6.69, training accuracy is 0.343750, time 218.004 samples/sec
+epoch 10, total_step 162940, total loss is 19.12 , inference loss is 12.42, weight deacy loss is 6.69, training accuracy is 0.343750, time 299.002 samples/sec
+epoch 10, total_step 162960, total loss is 14.45 , inference loss is 7.76, weight deacy loss is 6.69, training accuracy is 0.500000, time 228.821 samples/sec
+epoch 10, total_step 162980, total loss is 11.64 , inference loss is 4.95, weight deacy loss is 6.69, training accuracy is 0.593750, time 217.860 samples/sec
+epoch 10, total_step 163000, total loss is 12.63 , inference loss is 5.94, weight deacy loss is 6.69, training accuracy is 0.562500, time 220.592 samples/sec
+epoch 10, total_step 163020, total loss is 14.41 , inference loss is 7.72, weight deacy loss is 6.69, training accuracy is 0.468750, time 218.808 samples/sec
+epoch 10, total_step 163040, total loss is 14.55 , inference loss is 7.86, weight deacy loss is 6.69, training accuracy is 0.531250, time 207.851 samples/sec
+epoch 10, total_step 163060, total loss is 13.63 , inference loss is 6.94, weight deacy loss is 6.69, training accuracy is 0.562500, time 221.857 samples/sec
+epoch 10, total_step 163080, total loss is 10.13 , inference loss is 3.44, weight deacy loss is 6.69, training accuracy is 0.531250, time 219.843 samples/sec
+epoch 10, total_step 163100, total loss is 11.56 , inference loss is 4.87, weight deacy loss is 6.69, training accuracy is 0.531250, time 215.583 samples/sec
+epoch 10, total_step 163120, total loss is 14.31 , inference loss is 7.62, weight deacy loss is 6.69, training accuracy is 0.531250, time 220.858 samples/sec
+epoch 10, total_step 163140, total loss is 14.92 , inference loss is 8.23, weight deacy loss is 6.69, training accuracy is 0.437500, time 219.747 samples/sec
+epoch 10, total_step 163160, total loss is 17.73 , inference loss is 11.04, weight deacy loss is 6.69, training accuracy is 0.281250, time 194.221 samples/sec
+epoch 10, total_step 163180, total loss is 15.06 , inference loss is 8.37, weight deacy loss is 6.69, training accuracy is 0.312500, time 201.666 samples/sec
+epoch 10, total_step 163200, total loss is 12.27 , inference loss is 5.58, weight deacy loss is 6.69, training accuracy is 0.531250, time 12.658 samples/sec
+epoch 10, total_step 163220, total loss is 12.31 , inference loss is 5.62, weight deacy loss is 6.69, training accuracy is 0.562500, time 217.052 samples/sec
+epoch 10, total_step 163240, total loss is 16.41 , inference loss is 9.72, weight deacy loss is 6.69, training accuracy is 0.468750, time 214.429 samples/sec
+epoch 10, total_step 163260, total loss is 15.88 , inference loss is 9.19, weight deacy loss is 6.69, training accuracy is 0.312500, time 214.070 samples/sec
+epoch 10, total_step 163280, total loss is 13.38 , inference loss is 6.69, weight deacy loss is 6.69, training accuracy is 0.406250, time 215.130 samples/sec
+epoch 10, total_step 163300, total loss is 16.07 , inference loss is 9.38, weight deacy loss is 6.69, training accuracy is 0.500000, time 214.987 samples/sec
+epoch 10, total_step 163320, total loss is 10.16 , inference loss is 3.47, weight deacy loss is 6.69, training accuracy is 0.593750, time 211.760 samples/sec
+epoch 10, total_step 163340, total loss is 12.38 , inference loss is 5.69, weight deacy loss is 6.69, training accuracy is 0.406250, time 211.654 samples/sec
+epoch 10, total_step 163360, total loss is 11.13 , inference loss is 4.45, weight deacy loss is 6.69, training accuracy is 0.625000, time 218.607 samples/sec
+epoch 10, total_step 163380, total loss is 11.99 , inference loss is 5.30, weight deacy loss is 6.69, training accuracy is 0.500000, time 207.202 samples/sec
+epoch 10, total_step 163400, total loss is 12.75 , inference loss is 6.06, weight deacy loss is 6.69, training accuracy is 0.343750, time 212.056 samples/sec
+epoch 10, total_step 163420, total loss is 12.76 , inference loss is 6.08, weight deacy loss is 6.69, training accuracy is 0.468750, time 207.897 samples/sec
+epoch 10, total_step 163440, total loss is 12.29 , inference loss is 5.61, weight deacy loss is 6.69, training accuracy is 0.500000, time 212.450 samples/sec
+epoch 10, total_step 163460, total loss is 15.95 , inference loss is 9.26, weight deacy loss is 6.69, training accuracy is 0.500000, time 230.090 samples/sec
+epoch 10, total_step 163480, total loss is 14.19 , inference loss is 7.51, weight deacy loss is 6.69, training accuracy is 0.375000, time 210.424 samples/sec
+epoch 10, total_step 163500, total loss is 15.48 , inference loss is 8.80, weight deacy loss is 6.69, training accuracy is 0.250000, time 12.424 samples/sec
+epoch 10, total_step 163520, total loss is 15.69 , inference loss is 9.00, weight deacy loss is 6.68, training accuracy is 0.406250, time 209.918 samples/sec
+epoch 10, total_step 163540, total loss is 12.94 , inference loss is 6.25, weight deacy loss is 6.68, training accuracy is 0.500000, time 207.193 samples/sec
+epoch 10, total_step 163560, total loss is 12.07 , inference loss is 5.39, weight deacy loss is 6.68, training accuracy is 0.468750, time 222.850 samples/sec
+epoch 10, total_step 163580, total loss is 14.56 , inference loss is 7.88, weight deacy loss is 6.68, training accuracy is 0.468750, time 233.832 samples/sec
+epoch 10, total_step 163600, total loss is 13.13 , inference loss is 6.45, weight deacy loss is 6.68, training accuracy is 0.593750, time 226.185 samples/sec
+epoch 10, total_step 163620, total loss is 15.84 , inference loss is 9.16, weight deacy loss is 6.68, training accuracy is 0.468750, time 216.266 samples/sec
+epoch 10, total_step 163640, total loss is 14.93 , inference loss is 8.24, weight deacy loss is 6.68, training accuracy is 0.500000, time 214.703 samples/sec
+epoch 10, total_step 163660, total loss is 19.51 , inference loss is 12.82, weight deacy loss is 6.68, training accuracy is 0.281250, time 211.446 samples/sec
+epoch 10, total_step 163680, total loss is 11.76 , inference loss is 5.08, weight deacy loss is 6.68, training accuracy is 0.625000, time 212.669 samples/sec
+epoch 10, total_step 163700, total loss is 13.35 , inference loss is 6.67, weight deacy loss is 6.68, training accuracy is 0.593750, time 217.434 samples/sec
+epoch 10, total_step 163720, total loss is 13.94 , inference loss is 7.26, weight deacy loss is 6.68, training accuracy is 0.468750, time 200.606 samples/sec
+epoch 10, total_step 163740, total loss is 18.38 , inference loss is 11.69, weight deacy loss is 6.68, training accuracy is 0.343750, time 209.110 samples/sec
+epoch 10, total_step 163760, total loss is 16.88 , inference loss is 10.20, weight deacy loss is 6.68, training accuracy is 0.312500, time 216.126 samples/sec
+epoch 10, total_step 163780, total loss is 12.04 , inference loss is 5.36, weight deacy loss is 6.68, training accuracy is 0.500000, time 298.195 samples/sec
+epoch 10, total_step 163800, total loss is 11.77 , inference loss is 5.09, weight deacy loss is 6.68, training accuracy is 0.593750, time 12.579 samples/sec
+epoch 10, total_step 163820, total loss is 12.11 , inference loss is 5.42, weight deacy loss is 6.68, training accuracy is 0.593750, time 214.543 samples/sec
+epoch 10, total_step 163840, total loss is 9.95 , inference loss is 3.27, weight deacy loss is 6.68, training accuracy is 0.593750, time 213.848 samples/sec
+epoch 10, total_step 163860, total loss is 13.35 , inference loss is 6.67, weight deacy loss is 6.68, training accuracy is 0.437500, time 215.407 samples/sec
+epoch 10, total_step 163880, total loss is 13.46 , inference loss is 6.78, weight deacy loss is 6.68, training accuracy is 0.500000, time 218.233 samples/sec
+epoch 10, total_step 163900, total loss is 12.32 , inference loss is 5.64, weight deacy loss is 6.68, training accuracy is 0.500000, time 217.887 samples/sec
+epoch 10, total_step 163920, total loss is 10.49 , inference loss is 3.81, weight deacy loss is 6.68, training accuracy is 0.593750, time 212.871 samples/sec
+epoch 10, total_step 163940, total loss is 12.72 , inference loss is 6.04, weight deacy loss is 6.68, training accuracy is 0.531250, time 212.049 samples/sec
+epoch 10, total_step 163960, total loss is 14.77 , inference loss is 8.09, weight deacy loss is 6.68, training accuracy is 0.468750, time 211.949 samples/sec
+epoch 10, total_step 163980, total loss is 15.21 , inference loss is 8.53, weight deacy loss is 6.68, training accuracy is 0.500000, time 225.426 samples/sec
+testing verification..
+(12000, 512)
+infer time 25.088353000000037
+best_threshold_index 111 0.9866666666666667
+best_threshold_index 111 0.9857407407407407
+best_threshold_index 111 0.9862962962962963
+best_threshold_index 108 0.9864814814814815
+best_threshold_index 111 0.9866666666666667
+best_threshold_index 111 0.985
+best_threshold_index 111 0.9861111111111112
+best_threshold_index 111 0.9857407407407407
+best_threshold_index 111 0.9844444444444445
+best_threshold_index 111 0.9851851851851852
+[lfw][164000]XNorm: 21.339537
+[lfw][164000]Accuracy-Flip: 0.98550+-0.00683
+test accuracy is:  0.9855
+epoch 10, total_step 164000, total loss is 10.57 , inference loss is 3.89, weight deacy loss is 6.68, training accuracy is 0.531250, time 15.878 samples/sec
+epoch 10, total_step 164020, total loss is 11.70 , inference loss is 5.03, weight deacy loss is 6.68, training accuracy is 0.406250, time 212.744 samples/sec
+epoch 10, total_step 164040, total loss is 18.26 , inference loss is 11.58, weight deacy loss is 6.68, training accuracy is 0.312500, time 214.105 samples/sec
+epoch 10, total_step 164060, total loss is 14.88 , inference loss is 8.20, weight deacy loss is 6.68, training accuracy is 0.500000, time 221.216 samples/sec
+epoch 10, total_step 164080, total loss is 19.93 , inference loss is 13.25, weight deacy loss is 6.68, training accuracy is 0.500000, time 219.825 samples/sec
+epoch 10, total_step 164100, total loss is 12.78 , inference loss is 6.10, weight deacy loss is 6.68, training accuracy is 0.468750, time 12.509 samples/sec
+epoch 10, total_step 164120, total loss is 15.59 , inference loss is 8.91, weight deacy loss is 6.68, training accuracy is 0.375000, time 217.595 samples/sec
+epoch 10, total_step 164140, total loss is 12.93 , inference loss is 6.25, weight deacy loss is 6.68, training accuracy is 0.406250, time 216.415 samples/sec
+epoch 10, total_step 164160, total loss is 15.77 , inference loss is 9.09, weight deacy loss is 6.68, training accuracy is 0.406250, time 216.545 samples/sec
+epoch 10, total_step 164180, total loss is 13.97 , inference loss is 7.29, weight deacy loss is 6.68, training accuracy is 0.531250, time 214.443 samples/sec
+epoch 10, total_step 164200, total loss is 12.83 , inference loss is 6.15, weight deacy loss is 6.68, training accuracy is 0.500000, time 213.885 samples/sec
+epoch 10, total_step 164220, total loss is 16.58 , inference loss is 9.90, weight deacy loss is 6.68, training accuracy is 0.343750, time 213.375 samples/sec
+epoch 10, total_step 164240, total loss is 14.37 , inference loss is 7.70, weight deacy loss is 6.68, training accuracy is 0.375000, time 212.790 samples/sec
+epoch 10, total_step 164260, total loss is 12.98 , inference loss is 6.31, weight deacy loss is 6.68, training accuracy is 0.437500, time 223.597 samples/sec
+epoch 10, total_step 164280, total loss is 12.34 , inference loss is 5.67, weight deacy loss is 6.68, training accuracy is 0.531250, time 221.063 samples/sec
+epoch 10, total_step 164300, total loss is 15.62 , inference loss is 8.95, weight deacy loss is 6.68, training accuracy is 0.437500, time 237.216 samples/sec
+epoch 10, total_step 164320, total loss is 17.71 , inference loss is 11.03, weight deacy loss is 6.68, training accuracy is 0.406250, time 208.637 samples/sec
+epoch 10, total_step 164340, total loss is 18.68 , inference loss is 12.01, weight deacy loss is 6.68, training accuracy is 0.281250, time 222.000 samples/sec
+epoch 10, total_step 164360, total loss is 12.39 , inference loss is 5.71, weight deacy loss is 6.67, training accuracy is 0.406250, time 223.025 samples/sec
+epoch 10, total_step 164380, total loss is 13.13 , inference loss is 6.45, weight deacy loss is 6.67, training accuracy is 0.593750, time 246.661 samples/sec
+epoch 10, total_step 164400, total loss is 14.27 , inference loss is 7.60, weight deacy loss is 6.67, training accuracy is 0.375000, time 12.065 samples/sec
+epoch 10, total_step 164420, total loss is 15.44 , inference loss is 8.77, weight deacy loss is 6.67, training accuracy is 0.468750, time 215.053 samples/sec
+epoch 10, total_step 164440, total loss is 12.23 , inference loss is 5.55, weight deacy loss is 6.67, training accuracy is 0.375000, time 299.271 samples/sec
+epoch 10, total_step 164460, total loss is 13.75 , inference loss is 7.08, weight deacy loss is 6.67, training accuracy is 0.531250, time 214.573 samples/sec
+epoch 10, total_step 164480, total loss is 16.15 , inference loss is 9.47, weight deacy loss is 6.67, training accuracy is 0.437500, time 216.753 samples/sec
+epoch 10, total_step 164500, total loss is 14.29 , inference loss is 7.62, weight deacy loss is 6.67, training accuracy is 0.406250, time 225.439 samples/sec
+epoch 10, total_step 164520, total loss is 14.28 , inference loss is 7.61, weight deacy loss is 6.67, training accuracy is 0.281250, time 217.760 samples/sec
+epoch 10, total_step 164540, total loss is 15.19 , inference loss is 8.52, weight deacy loss is 6.67, training accuracy is 0.468750, time 215.237 samples/sec
+epoch 10, total_step 164560, total loss is 10.84 , inference loss is 4.17, weight deacy loss is 6.67, training accuracy is 0.531250, time 214.454 samples/sec
+epoch 10, total_step 164580, total loss is 13.68 , inference loss is 7.01, weight deacy loss is 6.67, training accuracy is 0.468750, time 220.706 samples/sec
+epoch 10, total_step 164600, total loss is 12.29 , inference loss is 5.62, weight deacy loss is 6.67, training accuracy is 0.468750, time 296.519 samples/sec
+epoch 10, total_step 164620, total loss is 15.07 , inference loss is 8.40, weight deacy loss is 6.67, training accuracy is 0.343750, time 299.158 samples/sec
+epoch 10, total_step 164640, total loss is 14.49 , inference loss is 7.82, weight deacy loss is 6.67, training accuracy is 0.437500, time 211.817 samples/sec
+epoch 10, total_step 164660, total loss is 12.71 , inference loss is 6.04, weight deacy loss is 6.67, training accuracy is 0.437500, time 215.891 samples/sec
+epoch 10, total_step 164680, total loss is 14.77 , inference loss is 8.10, weight deacy loss is 6.67, training accuracy is 0.437500, time 221.258 samples/sec
+epoch 10, total_step 164700, total loss is 15.46 , inference loss is 8.78, weight deacy loss is 6.67, training accuracy is 0.531250, time 13.323 samples/sec
+epoch 10, total_step 164720, total loss is 16.23 , inference loss is 9.56, weight deacy loss is 6.67, training accuracy is 0.375000, time 213.369 samples/sec
+epoch 10, total_step 164740, total loss is 12.00 , inference loss is 5.33, weight deacy loss is 6.67, training accuracy is 0.468750, time 228.357 samples/sec
+epoch 10, total_step 164760, total loss is 12.84 , inference loss is 6.17, weight deacy loss is 6.67, training accuracy is 0.437500, time 208.414 samples/sec
+epoch 10, total_step 164780, total loss is 14.40 , inference loss is 7.73, weight deacy loss is 6.67, training accuracy is 0.343750, time 205.239 samples/sec
+epoch 10, total_step 164800, total loss is 16.16 , inference loss is 9.49, weight deacy loss is 6.67, training accuracy is 0.312500, time 275.906 samples/sec
+epoch 10, total_step 164820, total loss is 18.41 , inference loss is 11.74, weight deacy loss is 6.67, training accuracy is 0.312500, time 209.902 samples/sec
+epoch 10, total_step 164840, total loss is 16.27 , inference loss is 9.60, weight deacy loss is 6.67, training accuracy is 0.343750, time 224.999 samples/sec
+epoch 10, total_step 164860, total loss is 16.42 , inference loss is 9.75, weight deacy loss is 6.67, training accuracy is 0.281250, time 212.809 samples/sec
+epoch 10, total_step 164880, total loss is 15.27 , inference loss is 8.60, weight deacy loss is 6.67, training accuracy is 0.343750, time 221.702 samples/sec
+epoch 10, total_step 164900, total loss is 14.52 , inference loss is 7.86, weight deacy loss is 6.67, training accuracy is 0.437500, time 214.596 samples/sec
+epoch 10, total_step 164920, total loss is 15.30 , inference loss is 8.63, weight deacy loss is 6.67, training accuracy is 0.312500, time 219.632 samples/sec
+epoch 10, total_step 164940, total loss is 15.29 , inference loss is 8.62, weight deacy loss is 6.67, training accuracy is 0.437500, time 219.775 samples/sec
+epoch 10, total_step 164960, total loss is 16.87 , inference loss is 10.20, weight deacy loss is 6.67, training accuracy is 0.218750, time 217.202 samples/sec
+epoch 10, total_step 164980, total loss is 13.82 , inference loss is 7.15, weight deacy loss is 6.67, training accuracy is 0.406250, time 212.031 samples/sec
+epoch 10, total_step 165000, total loss is 11.68 , inference loss is 5.01, weight deacy loss is 6.67, training accuracy is 0.531250, time 12.693 samples/sec
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+epoch 10, total_step 165040, total loss is 16.52 , inference loss is 9.85, weight deacy loss is 6.67, training accuracy is 0.468750, time 214.795 samples/sec
+epoch 10, total_step 165060, total loss is 18.02 , inference loss is 11.35, weight deacy loss is 6.67, training accuracy is 0.343750, time 299.659 samples/sec
+epoch 10, total_step 165080, total loss is 16.30 , inference loss is 9.63, weight deacy loss is 6.67, training accuracy is 0.343750, time 223.795 samples/sec
+epoch 10, total_step 165100, total loss is 18.01 , inference loss is 11.34, weight deacy loss is 6.67, training accuracy is 0.468750, time 223.248 samples/sec
+epoch 10, total_step 165120, total loss is 15.75 , inference loss is 9.09, weight deacy loss is 6.67, training accuracy is 0.437500, time 220.236 samples/sec
+epoch 10, total_step 165140, total loss is 14.68 , inference loss is 8.01, weight deacy loss is 6.66, training accuracy is 0.343750, time 212.953 samples/sec
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+epoch 10, total_step 165200, total loss is 14.99 , inference loss is 8.33, weight deacy loss is 6.66, training accuracy is 0.406250, time 222.875 samples/sec
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+epoch 10, total_step 165240, total loss is 12.13 , inference loss is 5.46, weight deacy loss is 6.66, training accuracy is 0.468750, time 214.634 samples/sec
+epoch 10, total_step 165260, total loss is 15.97 , inference loss is 9.31, weight deacy loss is 6.66, training accuracy is 0.375000, time 213.402 samples/sec
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+epoch 10, total_step 165780, total loss is 11.04 , inference loss is 4.39, weight deacy loss is 6.66, training accuracy is 0.562500, time 212.983 samples/sec
+epoch 10, total_step 165800, total loss is 16.78 , inference loss is 10.13, weight deacy loss is 6.66, training accuracy is 0.375000, time 217.498 samples/sec
+epoch 10, total_step 165820, total loss is 14.18 , inference loss is 7.52, weight deacy loss is 6.66, training accuracy is 0.375000, time 197.570 samples/sec
+epoch 10, total_step 165840, total loss is 14.66 , inference loss is 8.00, weight deacy loss is 6.66, training accuracy is 0.468750, time 203.397 samples/sec
+epoch 10, total_step 165860, total loss is 13.31 , inference loss is 6.65, weight deacy loss is 6.66, training accuracy is 0.281250, time 209.082 samples/sec
+epoch 10, total_step 165880, total loss is 13.48 , inference loss is 6.82, weight deacy loss is 6.66, training accuracy is 0.250000, time 206.145 samples/sec
+epoch 10, total_step 165900, total loss is 16.71 , inference loss is 10.06, weight deacy loss is 6.66, training accuracy is 0.281250, time 13.361 samples/sec
+epoch 10, total_step 165920, total loss is 18.76 , inference loss is 12.10, weight deacy loss is 6.65, training accuracy is 0.281250, time 214.872 samples/sec
+epoch 10, total_step 165940, total loss is 12.77 , inference loss is 6.12, weight deacy loss is 6.65, training accuracy is 0.343750, time 219.485 samples/sec
+epoch 10, total_step 165960, total loss is 14.54 , inference loss is 7.88, weight deacy loss is 6.65, training accuracy is 0.406250, time 221.739 samples/sec
+epoch 10, total_step 165980, total loss is 16.38 , inference loss is 9.73, weight deacy loss is 6.65, training accuracy is 0.343750, time 222.082 samples/sec
+testing verification..
+(12000, 512)
+infer time 23.568762000000017
+best_threshold_index 110 0.9840740740740741
+best_threshold_index 110 0.9833333333333333
+best_threshold_index 110 0.9838888888888889
+best_threshold_index 110 0.9848148148148148
+best_threshold_index 110 0.9844444444444445
+best_threshold_index 110 0.9831481481481481
+best_threshold_index 110 0.9844444444444445
+best_threshold_index 110 0.9835185185185186
+best_threshold_index 110 0.9822222222222222
+best_threshold_index 110 0.9827777777777778
+[lfw][166000]XNorm: 23.164252
+[lfw][166000]Accuracy-Flip: 0.98367+-0.00698
+test accuracy is:  0.9836666666666668
+epoch 10, total_step 166000, total loss is 16.22 , inference loss is 9.57, weight deacy loss is 6.65, training accuracy is 0.250000, time 16.454 samples/sec
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+epoch 10, total_step 166140, total loss is 18.75 , inference loss is 12.10, weight deacy loss is 6.65, training accuracy is 0.281250, time 209.311 samples/sec
+epoch 10, total_step 166160, total loss is 14.83 , inference loss is 8.18, weight deacy loss is 6.65, training accuracy is 0.468750, time 181.227 samples/sec
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+epoch 10, total_step 167900, total loss is 16.33 , inference loss is 9.70, weight deacy loss is 6.63, training accuracy is 0.250000, time 220.016 samples/sec
+epoch 10, total_step 167920, total loss is 18.72 , inference loss is 12.09, weight deacy loss is 6.63, training accuracy is 0.250000, time 216.698 samples/sec
+epoch 10, total_step 167940, total loss is 19.76 , inference loss is 13.13, weight deacy loss is 6.63, training accuracy is 0.250000, time 230.195 samples/sec
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+epoch 10, total_step 167980, total loss is 20.06 , inference loss is 13.43, weight deacy loss is 6.63, training accuracy is 0.406250, time 224.789 samples/sec
+testing verification..
+(12000, 512)
+infer time 25.377277000000028
+best_threshold_index 107 0.9840740740740741
+best_threshold_index 105 0.9827777777777778
+best_threshold_index 105 0.9840740740740741
+best_threshold_index 109 0.9855555555555555
+best_threshold_index 107 0.9835185185185186
+best_threshold_index 107 0.9824074074074074
+best_threshold_index 107 0.9840740740740741
+best_threshold_index 105 0.9829629629629629
+best_threshold_index 107 0.9816666666666667
+best_threshold_index 107 0.9829629629629629
+[lfw][168000]XNorm: 23.014036
+[lfw][168000]Accuracy-Flip: 0.98217+-0.00931
+test accuracy is:  0.9821666666666667
+epoch 10, total_step 168000, total loss is 19.10 , inference loss is 12.47, weight deacy loss is 6.63, training accuracy is 0.343750, time 16.866 samples/sec2022-12-01 11:29:29.108516: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:145] Filling up shuffle buffer (this may take a while): 6939 of 10000
+2022-12-01 11:29:33.585448: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:195] Shuffle buffer filled.
+
+epoch 10, total_step 168020, total loss is 14.40 , inference loss is 7.78, weight deacy loss is 6.63, training accuracy is 0.343750, time 213.768 samples/sec
+epoch 10, total_step 168040, total loss is 14.75 , inference loss is 8.13, weight deacy loss is 6.63, training accuracy is 0.375000, time 225.806 samples/sec
+epoch 10, total_step 168060, total loss is 16.01 , inference loss is 9.38, weight deacy loss is 6.63, training accuracy is 0.250000, time 215.661 samples/sec
+epoch 10, total_step 168080, total loss is 16.00 , inference loss is 9.38, weight deacy loss is 6.63, training accuracy is 0.312500, time 213.749 samples/sec
+epoch 10, total_step 168100, total loss is 15.33 , inference loss is 8.70, weight deacy loss is 6.63, training accuracy is 0.406250, time 215.840 samples/sec
+epoch 10, total_step 168120, total loss is 17.07 , inference loss is 10.45, weight deacy loss is 6.63, training accuracy is 0.375000, time 222.788 samples/sec
+epoch 10, total_step 168140, total loss is 13.86 , inference loss is 7.23, weight deacy loss is 6.63, training accuracy is 0.437500, time 297.948 samples/sec
+epoch 10, total_step 168160, total loss is 18.48 , inference loss is 11.86, weight deacy loss is 6.62, training accuracy is 0.343750, time 215.734 samples/sec
+epoch 10, total_step 168180, total loss is 19.92 , inference loss is 13.30, weight deacy loss is 6.62, training accuracy is 0.250000, time 212.883 samples/sec
+epoch 10, total_step 168200, total loss is 17.65 , inference loss is 11.03, weight deacy loss is 6.62, training accuracy is 0.312500, time 222.168 samples/sec
+epoch 10, total_step 168220, total loss is 14.26 , inference loss is 7.64, weight deacy loss is 6.62, training accuracy is 0.468750, time 216.120 samples/sec
+epoch 10, total_step 168240, total loss is 18.88 , inference loss is 12.26, weight deacy loss is 6.62, training accuracy is 0.250000, time 215.052 samples/sec
+epoch 10, total_step 168260, total loss is 17.16 , inference loss is 10.54, weight deacy loss is 6.62, training accuracy is 0.312500, time 298.613 samples/sec
+epoch 10, total_step 168280, total loss is 14.01 , inference loss is 7.39, weight deacy loss is 6.62, training accuracy is 0.437500, time 218.935 samples/sec
+epoch 10, total_step 168300, total loss is 15.70 , inference loss is 9.08, weight deacy loss is 6.62, training accuracy is 0.343750, time 12.668 samples/sec
+epoch 10, total_step 168320, total loss is 15.45 , inference loss is 8.83, weight deacy loss is 6.62, training accuracy is 0.187500, time 216.124 samples/sec
+epoch 10, total_step 168340, total loss is 17.07 , inference loss is 10.45, weight deacy loss is 6.62, training accuracy is 0.312500, time 187.895 samples/sec
+epoch 10, total_step 168360, total loss is 13.44 , inference loss is 6.82, weight deacy loss is 6.62, training accuracy is 0.468750, time 183.063 samples/sec
+epoch 10, total_step 168380, total loss is 20.09 , inference loss is 13.46, weight deacy loss is 6.62, training accuracy is 0.187500, time 203.905 samples/sec
+epoch 10, total_step 168400, total loss is 17.08 , inference loss is 10.46, weight deacy loss is 6.62, training accuracy is 0.343750, time 203.846 samples/sec
+epoch 10, total_step 168420, total loss is 11.09 , inference loss is 4.47, weight deacy loss is 6.62, training accuracy is 0.531250, time 225.281 samples/sec
+epoch 10, total_step 168440, total loss is 10.82 , inference loss is 4.20, weight deacy loss is 6.62, training accuracy is 0.375000, time 206.667 samples/sec
+epoch 10, total_step 168460, total loss is 14.48 , inference loss is 7.85, weight deacy loss is 6.62, training accuracy is 0.312500, time 203.961 samples/sec
+epoch 10, total_step 168480, total loss is 16.71 , inference loss is 10.09, weight deacy loss is 6.62, training accuracy is 0.125000, time 181.332 samples/sec
+epoch 10, total_step 168500, total loss is 14.41 , inference loss is 7.78, weight deacy loss is 6.62, training accuracy is 0.312500, time 206.322 samples/sec
+epoch 10, total_step 168520, total loss is 14.86 , inference loss is 8.24, weight deacy loss is 6.62, training accuracy is 0.500000, time 211.448 samples/sec
+epoch 10, total_step 168540, total loss is 15.95 , inference loss is 9.33, weight deacy loss is 6.62, training accuracy is 0.343750, time 219.373 samples/sec
+epoch 10, total_step 168560, total loss is 16.64 , inference loss is 10.02, weight deacy loss is 6.62, training accuracy is 0.281250, time 181.639 samples/sec
+epoch 10, total_step 168580, total loss is 16.26 , inference loss is 9.64, weight deacy loss is 6.62, training accuracy is 0.343750, time 298.100 samples/sec
+epoch 10, total_step 168600, total loss is 14.49 , inference loss is 7.87, weight deacy loss is 6.62, training accuracy is 0.406250, time 13.878 samples/sec
+epoch 10, total_step 168620, total loss is 15.72 , inference loss is 9.10, weight deacy loss is 6.62, training accuracy is 0.250000, time 227.177 samples/sec
+epoch 10, total_step 168640, total loss is 13.53 , inference loss is 6.91, weight deacy loss is 6.62, training accuracy is 0.375000, time 197.613 samples/sec
+End of epoch 10
+epoch 11, total_step 168660, total loss is 11.06 , inference loss is 4.45, weight deacy loss is 6.62, training accuracy is 0.593750, time 218.967 samples/sec
+epoch 11, total_step 168680, total loss is 9.74 , inference loss is 3.12, weight deacy loss is 6.62, training accuracy is 0.656250, time 225.902 samples/sec
+epoch 11, total_step 168700, total loss is 11.27 , inference loss is 4.65, weight deacy loss is 6.62, training accuracy is 0.718750, time 230.515 samples/sec
+epoch 11, total_step 168720, total loss is 10.99 , inference loss is 4.37, weight deacy loss is 6.62, training accuracy is 0.625000, time 222.531 samples/sec
+epoch 11, total_step 168740, total loss is 9.80 , inference loss is 3.18, weight deacy loss is 6.62, training accuracy is 0.718750, time 227.462 samples/sec
+epoch 11, total_step 168760, total loss is 12.39 , inference loss is 5.78, weight deacy loss is 6.62, training accuracy is 0.687500, time 201.689 samples/sec
+epoch 11, total_step 168780, total loss is 10.24 , inference loss is 3.63, weight deacy loss is 6.62, training accuracy is 0.812500, time 202.851 samples/sec
+epoch 11, total_step 168800, total loss is 9.93 , inference loss is 3.32, weight deacy loss is 6.62, training accuracy is 0.656250, time 245.856 samples/sec
+epoch 11, total_step 168820, total loss is 9.85 , inference loss is 3.24, weight deacy loss is 6.61, training accuracy is 0.625000, time 206.155 samples/sec
+epoch 11, total_step 168840, total loss is 12.53 , inference loss is 5.92, weight deacy loss is 6.61, training accuracy is 0.687500, time 202.499 samples/sec
+epoch 11, total_step 168860, total loss is 12.98 , inference loss is 6.37, weight deacy loss is 6.61, training accuracy is 0.750000, time 224.125 samples/sec
+epoch 11, total_step 168880, total loss is 9.13 , inference loss is 2.51, weight deacy loss is 6.61, training accuracy is 0.656250, time 249.387 samples/sec
+epoch 11, total_step 168900, total loss is 11.35 , inference loss is 4.74, weight deacy loss is 6.61, training accuracy is 0.593750, time 13.530 samples/sec
+epoch 11, total_step 168920, total loss is 11.55 , inference loss is 4.94, weight deacy loss is 6.61, training accuracy is 0.625000, time 225.062 samples/sec
+epoch 11, total_step 168940, total loss is 13.68 , inference loss is 7.07, weight deacy loss is 6.61, training accuracy is 0.625000, time 227.424 samples/sec
+epoch 11, total_step 168960, total loss is 8.26 , inference loss is 1.65, weight deacy loss is 6.61, training accuracy is 0.812500, time 214.481 samples/sec
+epoch 11, total_step 168980, total loss is 10.79 , inference loss is 4.18, weight deacy loss is 6.61, training accuracy is 0.593750, time 225.427 samples/sec
+epoch 11, total_step 169000, total loss is 13.29 , inference loss is 6.68, weight deacy loss is 6.61, training accuracy is 0.625000, time 246.164 samples/sec
+epoch 11, total_step 169020, total loss is 9.87 , inference loss is 3.26, weight deacy loss is 6.61, training accuracy is 0.625000, time 298.319 samples/sec
+epoch 11, total_step 169040, total loss is 9.21 , inference loss is 2.60, weight deacy loss is 6.61, training accuracy is 0.625000, time 232.915 samples/sec
+epoch 11, total_step 169060, total loss is 12.81 , inference loss is 6.20, weight deacy loss is 6.61, training accuracy is 0.593750, time 224.108 samples/sec
+epoch 11, total_step 169080, total loss is 12.14 , inference loss is 5.53, weight deacy loss is 6.61, training accuracy is 0.593750, time 183.836 samples/sec
+epoch 11, total_step 169100, total loss is 13.25 , inference loss is 6.63, weight deacy loss is 6.61, training accuracy is 0.593750, time 231.201 samples/sec
+epoch 11, total_step 169120, total loss is 15.84 , inference loss is 9.23, weight deacy loss is 6.61, training accuracy is 0.437500, time 218.530 samples/sec
+epoch 11, total_step 169140, total loss is 10.38 , inference loss is 3.77, weight deacy loss is 6.61, training accuracy is 0.718750, time 199.913 samples/sec
+epoch 11, total_step 169160, total loss is 10.93 , inference loss is 4.31, weight deacy loss is 6.61, training accuracy is 0.531250, time 219.536 samples/sec
+epoch 11, total_step 169180, total loss is 14.66 , inference loss is 8.04, weight deacy loss is 6.61, training accuracy is 0.593750, time 227.313 samples/sec
+epoch 11, total_step 169200, total loss is 16.46 , inference loss is 9.85, weight deacy loss is 6.61, training accuracy is 0.531250, time 13.040 samples/sec
+epoch 11, total_step 169220, total loss is 8.77 , inference loss is 2.16, weight deacy loss is 6.61, training accuracy is 0.750000, time 229.091 samples/sec
+epoch 11, total_step 169240, total loss is 18.40 , inference loss is 11.79, weight deacy loss is 6.61, training accuracy is 0.531250, time 223.699 samples/sec
+epoch 11, total_step 169260, total loss is 8.25 , inference loss is 1.64, weight deacy loss is 6.61, training accuracy is 0.812500, time 214.415 samples/sec
+epoch 11, total_step 169280, total loss is 11.55 , inference loss is 4.93, weight deacy loss is 6.61, training accuracy is 0.562500, time 300.658 samples/sec
+epoch 11, total_step 169300, total loss is 16.26 , inference loss is 9.64, weight deacy loss is 6.61, training accuracy is 0.656250, time 220.694 samples/sec
+epoch 11, total_step 169320, total loss is 13.23 , inference loss is 6.62, weight deacy loss is 6.61, training accuracy is 0.593750, time 225.327 samples/sec
+epoch 11, total_step 169340, total loss is 12.92 , inference loss is 6.30, weight deacy loss is 6.61, training accuracy is 0.687500, time 225.828 samples/sec
+epoch 11, total_step 169360, total loss is 11.66 , inference loss is 5.05, weight deacy loss is 6.61, training accuracy is 0.656250, time 227.155 samples/sec
+epoch 11, total_step 169380, total loss is 13.93 , inference loss is 7.31, weight deacy loss is 6.61, training accuracy is 0.593750, time 226.509 samples/sec
+epoch 11, total_step 169400, total loss is 11.75 , inference loss is 5.14, weight deacy loss is 6.61, training accuracy is 0.562500, time 225.421 samples/sec
+epoch 11, total_step 169420, total loss is 10.56 , inference loss is 3.95, weight deacy loss is 6.61, training accuracy is 0.656250, time 214.634 samples/sec
+epoch 11, total_step 169440, total loss is 13.41 , inference loss is 6.80, weight deacy loss is 6.61, training accuracy is 0.500000, time 227.744 samples/sec
+epoch 11, total_step 169460, total loss is 13.60 , inference loss is 6.99, weight deacy loss is 6.61, training accuracy is 0.468750, time 226.746 samples/sec
+epoch 11, total_step 169480, total loss is 9.17 , inference loss is 2.56, weight deacy loss is 6.61, training accuracy is 0.750000, time 226.746 samples/sec
+epoch 11, total_step 169500, total loss is 11.00 , inference loss is 4.38, weight deacy loss is 6.61, training accuracy is 0.656250, time 12.049 samples/sec
+epoch 11, total_step 169520, total loss is 13.04 , inference loss is 6.43, weight deacy loss is 6.61, training accuracy is 0.593750, time 219.348 samples/sec
+epoch 11, total_step 169540, total loss is 15.99 , inference loss is 9.37, weight deacy loss is 6.61, training accuracy is 0.500000, time 296.595 samples/sec
+epoch 11, total_step 169560, total loss is 10.97 , inference loss is 4.35, weight deacy loss is 6.61, training accuracy is 0.593750, time 237.775 samples/sec
+epoch 11, total_step 169580, total loss is 13.89 , inference loss is 7.28, weight deacy loss is 6.61, training accuracy is 0.562500, time 225.074 samples/sec
+epoch 11, total_step 169600, total loss is 11.89 , inference loss is 5.27, weight deacy loss is 6.61, training accuracy is 0.656250, time 225.994 samples/sec
+epoch 11, total_step 169620, total loss is 14.81 , inference loss is 8.19, weight deacy loss is 6.61, training accuracy is 0.468750, time 208.140 samples/sec
+epoch 11, total_step 169640, total loss is 12.61 , inference loss is 6.00, weight deacy loss is 6.61, training accuracy is 0.625000, time 239.937 samples/sec
+epoch 11, total_step 169660, total loss is 12.37 , inference loss is 5.76, weight deacy loss is 6.61, training accuracy is 0.593750, time 221.938 samples/sec
+epoch 11, total_step 169680, total loss is 15.23 , inference loss is 8.62, weight deacy loss is 6.61, training accuracy is 0.437500, time 221.184 samples/sec
+epoch 11, total_step 169700, total loss is 22.95 , inference loss is 16.34, weight deacy loss is 6.61, training accuracy is 0.437500, time 223.142 samples/sec
+epoch 11, total_step 169720, total loss is 14.93 , inference loss is 8.31, weight deacy loss is 6.61, training accuracy is 0.625000, time 225.109 samples/sec
+epoch 11, total_step 169740, total loss is 13.98 , inference loss is 7.37, weight deacy loss is 6.61, training accuracy is 0.562500, time 217.927 samples/sec
+epoch 11, total_step 169760, total loss is 13.40 , inference loss is 6.79, weight deacy loss is 6.61, training accuracy is 0.531250, time 222.802 samples/sec
+epoch 11, total_step 169780, total loss is 9.98 , inference loss is 3.37, weight deacy loss is 6.61, training accuracy is 0.562500, time 222.511 samples/sec
+epoch 11, total_step 169800, total loss is 13.83 , inference loss is 7.22, weight deacy loss is 6.61, training accuracy is 0.593750, time 12.593 samples/sec
+epoch 11, total_step 169820, total loss is 14.84 , inference loss is 8.22, weight deacy loss is 6.61, training accuracy is 0.468750, time 226.671 samples/sec
+epoch 11, total_step 169840, total loss is 10.84 , inference loss is 4.23, weight deacy loss is 6.61, training accuracy is 0.625000, time 219.762 samples/sec
+epoch 11, total_step 169860, total loss is 12.38 , inference loss is 5.77, weight deacy loss is 6.61, training accuracy is 0.656250, time 225.712 samples/sec
+epoch 11, total_step 169880, total loss is 12.45 , inference loss is 5.83, weight deacy loss is 6.61, training accuracy is 0.593750, time 228.021 samples/sec
+epoch 11, total_step 169900, total loss is 10.58 , inference loss is 3.97, weight deacy loss is 6.61, training accuracy is 0.593750, time 235.604 samples/sec
+epoch 11, total_step 169920, total loss is 12.81 , inference loss is 6.19, weight deacy loss is 6.61, training accuracy is 0.531250, time 212.333 samples/sec
+epoch 11, total_step 169940, total loss is 10.94 , inference loss is 4.33, weight deacy loss is 6.61, training accuracy is 0.531250, time 220.981 samples/sec
+epoch 11, total_step 169960, total loss is 13.31 , inference loss is 6.69, weight deacy loss is 6.62, training accuracy is 0.562500, time 216.133 samples/sec
+epoch 11, total_step 169980, total loss is 14.76 , inference loss is 8.15, weight deacy loss is 6.62, training accuracy is 0.531250, time 235.228 samples/sec
+testing verification..
+(12000, 512)
+infer time 21.827897000000025
+best_threshold_index 114 0.9842592592592593
+best_threshold_index 114 0.9835185185185186
+best_threshold_index 114 0.9840740740740741
+best_threshold_index 114 0.9844444444444445
+best_threshold_index 120 0.9840740740740741
+best_threshold_index 120 0.9835185185185186
+best_threshold_index 115 0.9846296296296296
+best_threshold_index 114 0.9844444444444445
+best_threshold_index 114 0.9825925925925926
+best_threshold_index 114 0.9835185185185186
+[lfw][170000]XNorm: 22.310656
+[lfw][170000]Accuracy-Flip: 0.98283+-0.00527
+test accuracy is:  0.9828333333333333
+epoch 11, total_step 170000, total loss is 10.97 , inference loss is 4.35, weight deacy loss is 6.62, training accuracy is 0.625000, time 16.808 samples/sec
+epoch 11, total_step 170020, total loss is 11.94 , inference loss is 5.33, weight deacy loss is 6.62, training accuracy is 0.625000, time 226.283 samples/sec
+epoch 11, total_step 170040, total loss is 16.19 , inference loss is 9.58, weight deacy loss is 6.62, training accuracy is 0.562500, time 226.022 samples/sec
+epoch 11, total_step 170060, total loss is 12.56 , inference loss is 5.95, weight deacy loss is 6.62, training accuracy is 0.625000, time 299.865 samples/sec
+epoch 11, total_step 170080, total loss is 14.03 , inference loss is 7.41, weight deacy loss is 6.62, training accuracy is 0.531250, time 218.022 samples/sec
+epoch 11, total_step 170100, total loss is 14.02 , inference loss is 7.40, weight deacy loss is 6.62, training accuracy is 0.593750, time 13.190 samples/sec
+epoch 11, total_step 170120, total loss is 15.35 , inference loss is 8.73, weight deacy loss is 6.62, training accuracy is 0.468750, time 222.246 samples/sec
+epoch 11, total_step 170140, total loss is 13.27 , inference loss is 6.66, weight deacy loss is 6.62, training accuracy is 0.468750, time 220.598 samples/sec
+epoch 11, total_step 170160, total loss is 16.46 , inference loss is 9.84, weight deacy loss is 6.62, training accuracy is 0.406250, time 222.286 samples/sec
+epoch 11, total_step 170180, total loss is 11.74 , inference loss is 5.12, weight deacy loss is 6.62, training accuracy is 0.500000, time 224.882 samples/sec
+epoch 11, total_step 170200, total loss is 13.17 , inference loss is 6.55, weight deacy loss is 6.62, training accuracy is 0.500000, time 222.308 samples/sec
+epoch 11, total_step 170220, total loss is 11.53 , inference loss is 4.92, weight deacy loss is 6.62, training accuracy is 0.562500, time 221.441 samples/sec
+epoch 11, total_step 170240, total loss is 16.62 , inference loss is 10.00, weight deacy loss is 6.62, training accuracy is 0.343750, time 227.615 samples/sec
+epoch 11, total_step 170260, total loss is 13.98 , inference loss is 7.37, weight deacy loss is 6.62, training accuracy is 0.500000, time 226.279 samples/sec
+epoch 11, total_step 170280, total loss is 14.11 , inference loss is 7.50, weight deacy loss is 6.62, training accuracy is 0.406250, time 226.464 samples/sec
+epoch 11, total_step 170300, total loss is 12.35 , inference loss is 5.74, weight deacy loss is 6.62, training accuracy is 0.500000, time 228.813 samples/sec
+epoch 11, total_step 170320, total loss is 12.85 , inference loss is 6.24, weight deacy loss is 6.62, training accuracy is 0.593750, time 298.203 samples/sec
+epoch 11, total_step 170340, total loss is 15.96 , inference loss is 9.34, weight deacy loss is 6.62, training accuracy is 0.531250, time 216.049 samples/sec
+epoch 11, total_step 170360, total loss is 16.11 , inference loss is 9.50, weight deacy loss is 6.62, training accuracy is 0.468750, time 225.045 samples/sec
+epoch 11, total_step 170380, total loss is 16.38 , inference loss is 9.77, weight deacy loss is 6.62, training accuracy is 0.343750, time 204.649 samples/sec
+epoch 11, total_step 170400, total loss is 12.92 , inference loss is 6.30, weight deacy loss is 6.62, training accuracy is 0.562500, time 12.392 samples/sec
+epoch 11, total_step 170420, total loss is 16.17 , inference loss is 9.55, weight deacy loss is 6.62, training accuracy is 0.312500, time 225.699 samples/sec
+epoch 11, total_step 170440, total loss is 16.56 , inference loss is 9.94, weight deacy loss is 6.62, training accuracy is 0.468750, time 235.966 samples/sec
+epoch 11, total_step 170460, total loss is 14.75 , inference loss is 8.14, weight deacy loss is 6.62, training accuracy is 0.375000, time 226.906 samples/sec
+epoch 11, total_step 170480, total loss is 12.80 , inference loss is 6.19, weight deacy loss is 6.62, training accuracy is 0.500000, time 219.071 samples/sec
+epoch 11, total_step 170500, total loss is 13.09 , inference loss is 6.47, weight deacy loss is 6.62, training accuracy is 0.562500, time 205.226 samples/sec
+epoch 11, total_step 170520, total loss is 11.91 , inference loss is 5.30, weight deacy loss is 6.62, training accuracy is 0.718750, time 227.305 samples/sec
+epoch 11, total_step 170540, total loss is 13.69 , inference loss is 7.07, weight deacy loss is 6.62, training accuracy is 0.531250, time 221.401 samples/sec
+epoch 11, total_step 170560, total loss is 12.50 , inference loss is 5.88, weight deacy loss is 6.62, training accuracy is 0.500000, time 228.059 samples/sec
+epoch 11, total_step 170580, total loss is 11.89 , inference loss is 5.28, weight deacy loss is 6.62, training accuracy is 0.593750, time 298.340 samples/sec
+epoch 11, total_step 170600, total loss is 11.83 , inference loss is 5.21, weight deacy loss is 6.62, training accuracy is 0.500000, time 219.679 samples/sec
+epoch 11, total_step 170620, total loss is 9.83 , inference loss is 3.21, weight deacy loss is 6.62, training accuracy is 0.718750, time 227.283 samples/sec
+epoch 11, total_step 170640, total loss is 11.50 , inference loss is 4.89, weight deacy loss is 6.62, training accuracy is 0.593750, time 228.623 samples/sec
+epoch 11, total_step 170660, total loss is 13.81 , inference loss is 7.19, weight deacy loss is 6.62, training accuracy is 0.531250, time 219.095 samples/sec
+epoch 11, total_step 170680, total loss is 17.45 , inference loss is 10.84, weight deacy loss is 6.62, training accuracy is 0.375000, time 231.284 samples/sec
+epoch 11, total_step 170700, total loss is 12.70 , inference loss is 6.08, weight deacy loss is 6.62, training accuracy is 0.625000, time 12.681 samples/sec
+epoch 11, total_step 170720, total loss is 14.63 , inference loss is 8.01, weight deacy loss is 6.61, training accuracy is 0.656250, time 218.344 samples/sec
+epoch 11, total_step 170740, total loss is 11.71 , inference loss is 5.10, weight deacy loss is 6.61, training accuracy is 0.718750, time 216.088 samples/sec
+epoch 11, total_step 170760, total loss is 14.31 , inference loss is 7.70, weight deacy loss is 6.61, training accuracy is 0.500000, time 225.543 samples/sec
+epoch 11, total_step 170780, total loss is 10.64 , inference loss is 4.03, weight deacy loss is 6.61, training accuracy is 0.593750, time 226.465 samples/sec
+epoch 11, total_step 170800, total loss is 13.20 , inference loss is 6.59, weight deacy loss is 6.61, training accuracy is 0.593750, time 223.556 samples/sec
+epoch 11, total_step 170820, total loss is 9.84 , inference loss is 3.22, weight deacy loss is 6.61, training accuracy is 0.656250, time 222.587 samples/sec
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+epoch 11, total_step 170860, total loss is 11.31 , inference loss is 4.69, weight deacy loss is 6.61, training accuracy is 0.593750, time 219.913 samples/sec
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+testing verification..
+(12000, 512)
+infer time 24.403120999999967
+best_threshold_index 120 0.9888888888888889
+best_threshold_index 119 0.9881481481481481
+best_threshold_index 120 0.9885185185185185
+best_threshold_index 120 0.9894444444444445
+best_threshold_index 120 0.9888888888888889
+best_threshold_index 120 0.9879629629629629
+best_threshold_index 120 0.9888888888888889
+best_threshold_index 120 0.9877777777777778
+best_threshold_index 120 0.9874074074074074
+best_threshold_index 119 0.9874074074074074
+[lfw][172000]XNorm: 21.122836
+[lfw][172000]Accuracy-Flip: 0.98800+-0.00572
+test accuracy is:  0.9879999999999999
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+epoch 11, total_step 173920, total loss is 13.48 , inference loss is 6.88, weight deacy loss is 6.59, training accuracy is 0.531250, time 298.980 samples/sec
+epoch 11, total_step 173940, total loss is 14.75 , inference loss is 8.15, weight deacy loss is 6.59, training accuracy is 0.375000, time 227.607 samples/sec
+epoch 11, total_step 173960, total loss is 12.35 , inference loss is 5.75, weight deacy loss is 6.59, training accuracy is 0.468750, time 226.779 samples/sec
+epoch 11, total_step 173980, total loss is 16.49 , inference loss is 9.90, weight deacy loss is 6.59, training accuracy is 0.375000, time 223.632 samples/sec
+testing verification..
+(12000, 512)
+infer time 26.010637000000006
+best_threshold_index 117 0.9879629629629629
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+best_threshold_index 117 0.9883333333333333
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+best_threshold_index 117 0.9868518518518519
+best_threshold_index 117 0.987037037037037
+[lfw][174000]XNorm: 21.142689
+[lfw][174000]Accuracy-Flip: 0.98650+-0.00608
+test accuracy is:  0.9865
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+testing verification..
+(12000, 512)
+infer time 25.279884000000003
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+[lfw][176000]XNorm: 22.814379
+[lfw][176000]Accuracy-Flip: 0.98750+-0.00473
+test accuracy is:  0.9875000000000002
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+testing verification..
+(12000, 512)
+infer time 24.323046999999956
+best_threshold_index 116 0.9887037037037038
+best_threshold_index 116 0.9877777777777778
+best_threshold_index 114 0.9881481481481481
+best_threshold_index 116 0.9887037037037038
+best_threshold_index 116 0.9879629629629629
+best_threshold_index 118 0.9875925925925926
+best_threshold_index 114 0.9883333333333333
+best_threshold_index 114 0.9875925925925926
+best_threshold_index 116 0.9866666666666667
+best_threshold_index 116 0.9872222222222222
+[lfw][178000]XNorm: 22.369717
+[lfw][178000]Accuracy-Flip: 0.98683+-0.00560
+test accuracy is:  0.9868333333333335
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+epoch 11, total_step 179980, total loss is 11.45 , inference loss is 4.91, weight deacy loss is 6.54, training accuracy is 0.406250, time 222.443 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.669421000000003
+best_threshold_index 112 0.9848148148148148
+best_threshold_index 112 0.9835185185185186
+best_threshold_index 110 0.9835185185185186
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+best_threshold_index 111 0.9829629629629629
+[lfw][180000]XNorm: 23.515961
+[lfw][180000]Accuracy-Flip: 0.98200+-0.00678
+test accuracy is:  0.982
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+testing verification..
+(12000, 512)
+infer time 25.112255
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+[lfw][182000]XNorm: 21.552021
+[lfw][182000]Accuracy-Flip: 0.98317+-0.00845
+test accuracy is:  0.9831666666666665
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+epoch 11, total_step 182640, total loss is 17.33 , inference loss is 10.81, weight deacy loss is 6.51, training accuracy is 0.156250, time 237.516 samples/sec
+epoch 11, total_step 182660, total loss is 15.93 , inference loss is 9.41, weight deacy loss is 6.51, training accuracy is 0.312500, time 224.705 samples/sec
+epoch 11, total_step 182680, total loss is 15.88 , inference loss is 9.37, weight deacy loss is 6.51, training accuracy is 0.375000, time 226.840 samples/sec
+epoch 11, total_step 182700, total loss is 13.44 , inference loss is 6.93, weight deacy loss is 6.51, training accuracy is 0.500000, time 12.750 samples/sec
+epoch 11, total_step 182720, total loss is 16.18 , inference loss is 9.66, weight deacy loss is 6.51, training accuracy is 0.281250, time 205.453 samples/sec
+epoch 11, total_step 182740, total loss is 15.53 , inference loss is 9.01, weight deacy loss is 6.51, training accuracy is 0.437500, time 206.769 samples/sec
+epoch 11, total_step 182760, total loss is 16.57 , inference loss is 10.06, weight deacy loss is 6.51, training accuracy is 0.312500, time 219.968 samples/sec
+epoch 11, total_step 182780, total loss is 16.38 , inference loss is 9.86, weight deacy loss is 6.51, training accuracy is 0.312500, time 224.462 samples/sec
+epoch 11, total_step 182800, total loss is 12.29 , inference loss is 5.78, weight deacy loss is 6.51, training accuracy is 0.437500, time 225.022 samples/sec
+epoch 11, total_step 182820, total loss is 15.37 , inference loss is 8.86, weight deacy loss is 6.51, training accuracy is 0.375000, time 225.759 samples/sec
+epoch 11, total_step 182840, total loss is 15.13 , inference loss is 8.62, weight deacy loss is 6.51, training accuracy is 0.250000, time 217.208 samples/sec
+epoch 11, total_step 182860, total loss is 17.42 , inference loss is 10.91, weight deacy loss is 6.51, training accuracy is 0.437500, time 224.697 samples/sec
+epoch 11, total_step 182880, total loss is 21.52 , inference loss is 15.01, weight deacy loss is 6.51, training accuracy is 0.312500, time 236.525 samples/sec
+epoch 11, total_step 182900, total loss is 16.14 , inference loss is 9.63, weight deacy loss is 6.51, training accuracy is 0.406250, time 219.521 samples/sec
+epoch 11, total_step 182920, total loss is 14.82 , inference loss is 8.31, weight deacy loss is 6.51, training accuracy is 0.343750, time 223.430 samples/sec
+epoch 11, total_step 182940, total loss is 11.88 , inference loss is 5.37, weight deacy loss is 6.51, training accuracy is 0.437500, time 223.928 samples/sec
+epoch 11, total_step 182960, total loss is 18.51 , inference loss is 12.00, weight deacy loss is 6.51, training accuracy is 0.218750, time 216.438 samples/sec
+epoch 11, total_step 182980, total loss is 12.33 , inference loss is 5.82, weight deacy loss is 6.51, training accuracy is 0.312500, time 223.956 samples/sec
+epoch 11, total_step 183000, total loss is 13.04 , inference loss is 6.53, weight deacy loss is 6.51, training accuracy is 0.375000, time 13.239 samples/sec
+epoch 11, total_step 183020, total loss is 13.50 , inference loss is 6.99, weight deacy loss is 6.51, training accuracy is 0.312500, time 226.638 samples/sec
+epoch 11, total_step 183040, total loss is 14.04 , inference loss is 7.53, weight deacy loss is 6.51, training accuracy is 0.593750, time 224.859 samples/sec
+epoch 11, total_step 183060, total loss is 16.86 , inference loss is 10.35, weight deacy loss is 6.51, training accuracy is 0.375000, time 242.065 samples/sec
+epoch 11, total_step 183080, total loss is 11.86 , inference loss is 5.35, weight deacy loss is 6.51, training accuracy is 0.375000, time 222.469 samples/sec2022-12-01 12:30:33.409700: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:145] Filling up shuffle buffer (this may take a while): 7416 of 10000
+2022-12-01 12:30:36.875612: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:195] Shuffle buffer filled.
+
+epoch 11, total_step 183100, total loss is 16.46 , inference loss is 9.95, weight deacy loss is 6.51, training accuracy is 0.437500, time 216.934 samples/sec
+epoch 11, total_step 183120, total loss is 13.78 , inference loss is 7.27, weight deacy loss is 6.51, training accuracy is 0.406250, time 218.228 samples/sec
+epoch 11, total_step 183140, total loss is 14.60 , inference loss is 8.09, weight deacy loss is 6.51, training accuracy is 0.343750, time 222.241 samples/sec
+epoch 11, total_step 183160, total loss is 16.55 , inference loss is 10.04, weight deacy loss is 6.51, training accuracy is 0.437500, time 224.116 samples/sec
+epoch 11, total_step 183180, total loss is 18.63 , inference loss is 12.12, weight deacy loss is 6.51, training accuracy is 0.312500, time 226.758 samples/sec
+epoch 11, total_step 183200, total loss is 12.96 , inference loss is 6.45, weight deacy loss is 6.51, training accuracy is 0.406250, time 213.769 samples/sec
+epoch 11, total_step 183220, total loss is 18.54 , inference loss is 12.03, weight deacy loss is 6.51, training accuracy is 0.281250, time 216.231 samples/sec
+epoch 11, total_step 183240, total loss is 15.64 , inference loss is 9.14, weight deacy loss is 6.51, training accuracy is 0.437500, time 218.073 samples/sec
+epoch 11, total_step 183260, total loss is 19.45 , inference loss is 12.94, weight deacy loss is 6.51, training accuracy is 0.218750, time 230.436 samples/sec
+epoch 11, total_step 183280, total loss is 15.21 , inference loss is 8.70, weight deacy loss is 6.51, training accuracy is 0.281250, time 230.871 samples/sec
+epoch 11, total_step 183300, total loss is 11.62 , inference loss is 5.11, weight deacy loss is 6.51, training accuracy is 0.468750, time 13.592 samples/sec
+epoch 11, total_step 183320, total loss is 15.92 , inference loss is 9.42, weight deacy loss is 6.51, training accuracy is 0.343750, time 226.253 samples/sec
+epoch 11, total_step 183340, total loss is 14.75 , inference loss is 8.25, weight deacy loss is 6.51, training accuracy is 0.312500, time 231.664 samples/sec
+epoch 11, total_step 183360, total loss is 13.96 , inference loss is 7.45, weight deacy loss is 6.51, training accuracy is 0.312500, time 225.498 samples/sec
+epoch 11, total_step 183380, total loss is 14.96 , inference loss is 8.45, weight deacy loss is 6.51, training accuracy is 0.343750, time 223.812 samples/sec
+epoch 11, total_step 183400, total loss is 13.00 , inference loss is 6.50, weight deacy loss is 6.51, training accuracy is 0.281250, time 226.775 samples/sec
+epoch 11, total_step 183420, total loss is 17.87 , inference loss is 11.37, weight deacy loss is 6.51, training accuracy is 0.312500, time 227.195 samples/sec
+epoch 11, total_step 183440, total loss is 14.30 , inference loss is 7.79, weight deacy loss is 6.51, training accuracy is 0.312500, time 224.712 samples/sec
+epoch 11, total_step 183460, total loss is 13.58 , inference loss is 7.08, weight deacy loss is 6.51, training accuracy is 0.375000, time 222.457 samples/sec
+epoch 11, total_step 183480, total loss is 14.68 , inference loss is 8.17, weight deacy loss is 6.50, training accuracy is 0.281250, time 210.355 samples/sec
+epoch 11, total_step 183500, total loss is 15.42 , inference loss is 8.91, weight deacy loss is 6.50, training accuracy is 0.437500, time 224.817 samples/sec
+epoch 11, total_step 183520, total loss is 12.88 , inference loss is 6.38, weight deacy loss is 6.50, training accuracy is 0.468750, time 226.142 samples/sec
+epoch 11, total_step 183540, total loss is 20.38 , inference loss is 13.88, weight deacy loss is 6.50, training accuracy is 0.281250, time 216.703 samples/sec
+epoch 11, total_step 183560, total loss is 14.31 , inference loss is 7.80, weight deacy loss is 6.50, training accuracy is 0.312500, time 220.946 samples/sec
+epoch 11, total_step 183580, total loss is 13.22 , inference loss is 6.71, weight deacy loss is 6.50, training accuracy is 0.312500, time 298.907 samples/sec
+epoch 11, total_step 183600, total loss is 13.99 , inference loss is 7.49, weight deacy loss is 6.50, training accuracy is 0.375000, time 12.624 samples/sec
+epoch 11, total_step 183620, total loss is 16.19 , inference loss is 9.69, weight deacy loss is 6.50, training accuracy is 0.281250, time 225.812 samples/sec
+epoch 11, total_step 183640, total loss is 14.61 , inference loss is 8.10, weight deacy loss is 6.50, training accuracy is 0.250000, time 221.799 samples/sec
+epoch 11, total_step 183660, total loss is 18.05 , inference loss is 11.55, weight deacy loss is 6.50, training accuracy is 0.281250, time 208.178 samples/sec
+epoch 11, total_step 183680, total loss is 16.88 , inference loss is 10.37, weight deacy loss is 6.50, training accuracy is 0.281250, time 213.346 samples/sec
+epoch 11, total_step 183700, total loss is 16.65 , inference loss is 10.15, weight deacy loss is 6.50, training accuracy is 0.343750, time 175.724 samples/sec
+epoch 11, total_step 183720, total loss is 17.34 , inference loss is 10.84, weight deacy loss is 6.50, training accuracy is 0.375000, time 203.620 samples/sec
+epoch 11, total_step 183740, total loss is 18.84 , inference loss is 12.34, weight deacy loss is 6.50, training accuracy is 0.187500, time 206.791 samples/sec
+epoch 11, total_step 183760, total loss is 14.06 , inference loss is 7.55, weight deacy loss is 6.50, training accuracy is 0.406250, time 204.731 samples/sec
+epoch 11, total_step 183780, total loss is 17.28 , inference loss is 10.78, weight deacy loss is 6.50, training accuracy is 0.375000, time 198.318 samples/sec
+epoch 11, total_step 183800, total loss is 14.77 , inference loss is 8.27, weight deacy loss is 6.50, training accuracy is 0.437500, time 226.324 samples/sec
+epoch 11, total_step 183820, total loss is 11.00 , inference loss is 4.50, weight deacy loss is 6.50, training accuracy is 0.468750, time 191.520 samples/sec
+epoch 11, total_step 183840, total loss is 15.25 , inference loss is 8.75, weight deacy loss is 6.50, training accuracy is 0.406250, time 198.039 samples/sec
+epoch 11, total_step 183860, total loss is 16.11 , inference loss is 9.61, weight deacy loss is 6.50, training accuracy is 0.406250, time 176.148 samples/sec
+epoch 11, total_step 183880, total loss is 12.78 , inference loss is 6.28, weight deacy loss is 6.50, training accuracy is 0.375000, time 184.104 samples/sec
+epoch 11, total_step 183900, total loss is 15.51 , inference loss is 9.01, weight deacy loss is 6.50, training accuracy is 0.375000, time 13.334 samples/sec
+epoch 11, total_step 183920, total loss is 13.77 , inference loss is 7.27, weight deacy loss is 6.50, training accuracy is 0.250000, time 227.426 samples/sec
+epoch 11, total_step 183940, total loss is 16.50 , inference loss is 10.00, weight deacy loss is 6.50, training accuracy is 0.343750, time 201.327 samples/sec
+epoch 11, total_step 183960, total loss is 18.75 , inference loss is 12.25, weight deacy loss is 6.50, training accuracy is 0.250000, time 215.562 samples/sec
+End of epoch 11
+epoch 12, total_step 183980, total loss is 21.01 , inference loss is 14.51, weight deacy loss is 6.50, training accuracy is 0.062500, time 213.428 samples/sec
+testing verification..
+(12000, 512)
+infer time 22.228492000000028
+best_threshold_index 89 0.9814814814814815
+best_threshold_index 89 0.980925925925926
+best_threshold_index 89 0.9805555555555555
+best_threshold_index 89 0.9831481481481481
+best_threshold_index 88 0.9807407407407407
+best_threshold_index 88 0.9798148148148148
+best_threshold_index 88 0.9814814814814815
+best_threshold_index 89 0.9803703703703703
+best_threshold_index 89 0.9801851851851852
+best_threshold_index 89 0.9798148148148148
+[lfw][184000]XNorm: 18.610883
+[lfw][184000]Accuracy-Flip: 0.98017+-0.00848
+test accuracy is:  0.9801666666666667
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+epoch 12, total_step 184060, total loss is 9.96 , inference loss is 3.46, weight deacy loss is 6.50, training accuracy is 0.750000, time 214.439 samples/sec
+epoch 12, total_step 184080, total loss is 11.17 , inference loss is 4.67, weight deacy loss is 6.50, training accuracy is 0.718750, time 298.032 samples/sec
+epoch 12, total_step 184100, total loss is 8.47 , inference loss is 1.97, weight deacy loss is 6.50, training accuracy is 0.750000, time 212.758 samples/sec
+epoch 12, total_step 184120, total loss is 10.45 , inference loss is 3.95, weight deacy loss is 6.50, training accuracy is 0.781250, time 207.182 samples/sec
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+epoch 12, total_step 184240, total loss is 12.20 , inference loss is 5.70, weight deacy loss is 6.50, training accuracy is 0.656250, time 211.880 samples/sec
+epoch 12, total_step 184260, total loss is 11.85 , inference loss is 5.35, weight deacy loss is 6.50, training accuracy is 0.687500, time 215.128 samples/sec
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+epoch 12, total_step 184320, total loss is 9.88 , inference loss is 3.39, weight deacy loss is 6.50, training accuracy is 0.593750, time 221.724 samples/sec
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+epoch 12, total_step 184400, total loss is 11.12 , inference loss is 4.63, weight deacy loss is 6.50, training accuracy is 0.593750, time 209.016 samples/sec
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+epoch 12, total_step 185980, total loss is 11.76 , inference loss is 5.26, weight deacy loss is 6.51, training accuracy is 0.500000, time 217.643 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.169746999999983
+best_threshold_index 120 0.9875925925925926
+best_threshold_index 124 0.9875925925925926
+best_threshold_index 120 0.987037037037037
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+[lfw][186000]XNorm: 22.228304
+[lfw][186000]Accuracy-Flip: 0.98567+-0.00442
+test accuracy is:  0.9856666666666666
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+testing verification..
+(12000, 512)
+infer time 25.977706999999988
+best_threshold_index 120 0.9892592592592593
+best_threshold_index 120 0.9892592592592593
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+best_threshold_index 122 0.9881481481481481
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+[lfw][188000]XNorm: 19.732608
+[lfw][188000]Accuracy-Flip: 0.98833+-0.00587
+test accuracy is:  0.9883333333333333
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+testing verification..
+(12000, 512)
+infer time 24.381966999999996
+best_threshold_index 118 0.9890740740740741
+best_threshold_index 118 0.9885185185185185
+best_threshold_index 118 0.9890740740740741
+best_threshold_index 119 0.9896296296296296
+best_threshold_index 118 0.9888888888888889
+best_threshold_index 123 0.9877777777777778
+best_threshold_index 118 0.9885185185185185
+best_threshold_index 118 0.9879629629629629
+best_threshold_index 118 0.9875925925925926
+best_threshold_index 118 0.9883333333333333
+[lfw][190000]XNorm: 21.369249
+[lfw][190000]Accuracy-Flip: 0.98783+-0.00517
+test accuracy is:  0.9878333333333333
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+testing verification..
+(12000, 512)
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+testing verification..
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+testing verification..
+(12000, 512)
+infer time 26.41791999999998
+best_threshold_index 113 0.9855555555555555
+best_threshold_index 108 0.9848148148148148
+best_threshold_index 109 0.9853703703703703
+best_threshold_index 113 0.987037037037037
+best_threshold_index 109 0.9851851851851852
+best_threshold_index 113 0.9851851851851852
+best_threshold_index 109 0.9857407407407407
+best_threshold_index 106 0.9848148148148148
+best_threshold_index 109 0.9835185185185186
+best_threshold_index 113 0.9846296296296296
+[lfw][196000]XNorm: 22.314521
+[lfw][196000]Accuracy-Flip: 0.98283+-0.00753
+test accuracy is:  0.9828333333333333
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+epoch 12, total_step 197480, total loss is 18.56 , inference loss is 12.12, weight deacy loss is 6.44, training accuracy is 0.281250, time 211.616 samples/sec
+epoch 12, total_step 197500, total loss is 17.70 , inference loss is 11.26, weight deacy loss is 6.44, training accuracy is 0.375000, time 299.623 samples/sec
+epoch 12, total_step 197520, total loss is 16.80 , inference loss is 10.36, weight deacy loss is 6.44, training accuracy is 0.406250, time 215.894 samples/sec
+epoch 12, total_step 197540, total loss is 11.91 , inference loss is 5.48, weight deacy loss is 6.44, training accuracy is 0.406250, time 217.949 samples/sec
+epoch 12, total_step 197560, total loss is 12.73 , inference loss is 6.29, weight deacy loss is 6.44, training accuracy is 0.343750, time 223.931 samples/sec
+epoch 12, total_step 197580, total loss is 13.77 , inference loss is 7.33, weight deacy loss is 6.44, training accuracy is 0.468750, time 221.110 samples/sec
+epoch 12, total_step 197600, total loss is 13.11 , inference loss is 6.67, weight deacy loss is 6.44, training accuracy is 0.468750, time 221.568 samples/sec
+epoch 12, total_step 197620, total loss is 15.36 , inference loss is 8.92, weight deacy loss is 6.44, training accuracy is 0.406250, time 214.603 samples/sec
+epoch 12, total_step 197640, total loss is 16.30 , inference loss is 9.86, weight deacy loss is 6.44, training accuracy is 0.406250, time 219.472 samples/sec
+epoch 12, total_step 197660, total loss is 14.33 , inference loss is 7.90, weight deacy loss is 6.44, training accuracy is 0.375000, time 214.257 samples/sec
+epoch 12, total_step 197680, total loss is 13.09 , inference loss is 6.65, weight deacy loss is 6.44, training accuracy is 0.375000, time 215.489 samples/sec
+epoch 12, total_step 197700, total loss is 12.80 , inference loss is 6.36, weight deacy loss is 6.44, training accuracy is 0.281250, time 12.194 samples/sec
+epoch 12, total_step 197720, total loss is 17.07 , inference loss is 10.64, weight deacy loss is 6.44, training accuracy is 0.218750, time 220.977 samples/sec
+epoch 12, total_step 197740, total loss is 17.99 , inference loss is 11.55, weight deacy loss is 6.44, training accuracy is 0.343750, time 204.731 samples/sec
+epoch 12, total_step 197760, total loss is 17.52 , inference loss is 11.09, weight deacy loss is 6.44, training accuracy is 0.281250, time 297.470 samples/sec
+epoch 12, total_step 197780, total loss is 11.98 , inference loss is 5.55, weight deacy loss is 6.44, training accuracy is 0.375000, time 216.711 samples/sec
+epoch 12, total_step 197800, total loss is 14.06 , inference loss is 7.62, weight deacy loss is 6.44, training accuracy is 0.437500, time 224.351 samples/sec
+epoch 12, total_step 197820, total loss is 15.69 , inference loss is 9.26, weight deacy loss is 6.44, training accuracy is 0.375000, time 218.860 samples/sec
+epoch 12, total_step 197840, total loss is 16.20 , inference loss is 9.76, weight deacy loss is 6.44, training accuracy is 0.437500, time 217.325 samples/sec
+epoch 12, total_step 197860, total loss is 14.88 , inference loss is 8.45, weight deacy loss is 6.44, training accuracy is 0.312500, time 220.677 samples/sec
+epoch 12, total_step 197880, total loss is 20.63 , inference loss is 14.19, weight deacy loss is 6.44, training accuracy is 0.312500, time 223.372 samples/sec
+epoch 12, total_step 197900, total loss is 12.95 , inference loss is 6.51, weight deacy loss is 6.44, training accuracy is 0.406250, time 215.136 samples/sec
+epoch 12, total_step 197920, total loss is 14.99 , inference loss is 8.56, weight deacy loss is 6.44, training accuracy is 0.343750, time 207.790 samples/sec
+epoch 12, total_step 197940, total loss is 14.97 , inference loss is 8.53, weight deacy loss is 6.44, training accuracy is 0.343750, time 211.973 samples/sec
+epoch 12, total_step 197960, total loss is 12.01 , inference loss is 5.58, weight deacy loss is 6.44, training accuracy is 0.468750, time 219.438 samples/sec
+epoch 12, total_step 197980, total loss is 20.09 , inference loss is 13.65, weight deacy loss is 6.44, training accuracy is 0.218750, time 208.891 samples/sec
+testing verification..
+(12000, 512)
+infer time 25.52494500000003
+best_threshold_index 114 0.9857407407407407
+best_threshold_index 114 0.9848148148148148
+best_threshold_index 114 0.9844444444444445
+best_threshold_index 108 0.9855555555555555
+best_threshold_index 111 0.9848148148148148
+best_threshold_index 114 0.9840740740740741
+best_threshold_index 114 0.9842592592592593
+best_threshold_index 114 0.9846296296296296
+best_threshold_index 114 0.9829629629629629
+best_threshold_index 111 0.9840740740740741
+[lfw][198000]XNorm: 23.293579
+[lfw][198000]Accuracy-Flip: 0.98283+-0.00850
+test accuracy is:  0.9828333333333333
+epoch 12, total_step 198000, total loss is 16.61 , inference loss is 10.18, weight deacy loss is 6.44, training accuracy is 0.375000, time 18.494 samples/sec
+epoch 12, total_step 198020, total loss is 14.85 , inference loss is 8.42, weight deacy loss is 6.44, training accuracy is 0.312500, time 212.345 samples/sec
+epoch 12, total_step 198040, total loss is 15.68 , inference loss is 9.25, weight deacy loss is 6.43, training accuracy is 0.218750, time 216.835 samples/sec
+epoch 12, total_step 198060, total loss is 12.05 , inference loss is 5.62, weight deacy loss is 6.43, training accuracy is 0.593750, time 213.996 samples/sec
+epoch 12, total_step 198080, total loss is 15.21 , inference loss is 8.77, weight deacy loss is 6.43, training accuracy is 0.312500, time 224.995 samples/sec
+epoch 12, total_step 198100, total loss is 17.51 , inference loss is 11.08, weight deacy loss is 6.43, training accuracy is 0.406250, time 222.717 samples/sec
+epoch 12, total_step 198120, total loss is 17.38 , inference loss is 10.95, weight deacy loss is 6.43, training accuracy is 0.218750, time 219.779 samples/sec
+epoch 12, total_step 198140, total loss is 12.82 , inference loss is 6.39, weight deacy loss is 6.43, training accuracy is 0.375000, time 299.072 samples/sec
+epoch 12, total_step 198160, total loss is 14.60 , inference loss is 8.16, weight deacy loss is 6.43, training accuracy is 0.312500, time 216.008 samples/sec
+epoch 12, total_step 198180, total loss is 14.97 , inference loss is 8.54, weight deacy loss is 6.43, training accuracy is 0.375000, time 219.076 samples/sec
+epoch 12, total_step 198200, total loss is 11.64 , inference loss is 5.20, weight deacy loss is 6.43, training accuracy is 0.343750, time 216.389 samples/sec
+epoch 12, total_step 198220, total loss is 15.79 , inference loss is 9.36, weight deacy loss is 6.43, training accuracy is 0.218750, time 225.193 samples/sec
+epoch 12, total_step 198240, total loss is 14.41 , inference loss is 7.98, weight deacy loss is 6.43, training accuracy is 0.468750, time 215.800 samples/sec
+epoch 12, total_step 198260, total loss is 14.16 , inference loss is 7.73, weight deacy loss is 6.43, training accuracy is 0.187500, time 211.931 samples/sec
+epoch 12, total_step 198280, total loss is 13.74 , inference loss is 7.31, weight deacy loss is 6.43, training accuracy is 0.406250, time 222.235 samples/sec
+epoch 12, total_step 198300, total loss is 13.59 , inference loss is 7.15, weight deacy loss is 6.43, training accuracy is 0.375000, time 12.354 samples/sec
+epoch 12, total_step 198320, total loss is 10.74 , inference loss is 4.31, weight deacy loss is 6.43, training accuracy is 0.406250, time 214.880 samples/sec
+epoch 12, total_step 198340, total loss is 19.76 , inference loss is 13.32, weight deacy loss is 6.43, training accuracy is 0.312500, time 216.867 samples/sec
+epoch 12, total_step 198360, total loss is 23.46 , inference loss is 17.03, weight deacy loss is 6.43, training accuracy is 0.250000, time 220.203 samples/sec
+epoch 12, total_step 198380, total loss is 20.39 , inference loss is 13.96, weight deacy loss is 6.43, training accuracy is 0.312500, time 212.947 samples/sec
+epoch 12, total_step 198400, total loss is 17.35 , inference loss is 10.91, weight deacy loss is 6.43, training accuracy is 0.406250, time 224.438 samples/sec
+epoch 12, total_step 198420, total loss is 18.82 , inference loss is 12.39, weight deacy loss is 6.43, training accuracy is 0.250000, time 221.525 samples/sec
+epoch 12, total_step 198440, total loss is 11.84 , inference loss is 5.40, weight deacy loss is 6.43, training accuracy is 0.406250, time 208.068 samples/sec
+epoch 12, total_step 198460, total loss is 13.29 , inference loss is 6.86, weight deacy loss is 6.43, training accuracy is 0.375000, time 209.586 samples/sec
+epoch 12, total_step 198480, total loss is 11.41 , inference loss is 4.98, weight deacy loss is 6.43, training accuracy is 0.437500, time 211.307 samples/sec
+epoch 12, total_step 198500, total loss is 11.04 , inference loss is 4.61, weight deacy loss is 6.43, training accuracy is 0.437500, time 216.620 samples/sec
+epoch 12, total_step 198520, total loss is 15.55 , inference loss is 9.11, weight deacy loss is 6.43, training accuracy is 0.406250, time 213.515 samples/sec
+epoch 12, total_step 198540, total loss is 14.46 , inference loss is 8.03, weight deacy loss is 6.43, training accuracy is 0.343750, time 214.133 samples/sec
+epoch 12, total_step 198560, total loss is 18.26 , inference loss is 11.83, weight deacy loss is 6.43, training accuracy is 0.343750, time 213.286 samples/sec
+epoch 12, total_step 198580, total loss is 13.74 , inference loss is 7.31, weight deacy loss is 6.43, training accuracy is 0.437500, time 299.692 samples/sec
+epoch 12, total_step 198600, total loss is 16.29 , inference loss is 9.86, weight deacy loss is 6.43, training accuracy is 0.218750, time 12.370 samples/sec
+epoch 12, total_step 198620, total loss is 13.01 , inference loss is 6.58, weight deacy loss is 6.43, training accuracy is 0.281250, time 218.554 samples/sec
+epoch 12, total_step 198640, total loss is 14.54 , inference loss is 8.11, weight deacy loss is 6.43, training accuracy is 0.312500, time 220.611 samples/sec
+epoch 12, total_step 198660, total loss is 14.96 , inference loss is 8.53, weight deacy loss is 6.43, training accuracy is 0.406250, time 217.236 samples/sec
+epoch 12, total_step 198680, total loss is 16.71 , inference loss is 10.28, weight deacy loss is 6.43, training accuracy is 0.250000, time 217.425 samples/sec
+epoch 12, total_step 198700, total loss is 17.77 , inference loss is 11.34, weight deacy loss is 6.43, training accuracy is 0.218750, time 215.050 samples/sec
+epoch 12, total_step 198720, total loss is 14.60 , inference loss is 8.17, weight deacy loss is 6.43, training accuracy is 0.406250, time 215.475 samples/sec
+epoch 12, total_step 198740, total loss is 15.22 , inference loss is 8.79, weight deacy loss is 6.43, training accuracy is 0.312500, time 215.712 samples/sec
+epoch 12, total_step 198760, total loss is 16.05 , inference loss is 9.62, weight deacy loss is 6.43, training accuracy is 0.375000, time 212.793 samples/sec
+epoch 12, total_step 198780, total loss is 11.62 , inference loss is 5.19, weight deacy loss is 6.43, training accuracy is 0.500000, time 223.160 samples/sec
+epoch 12, total_step 198800, total loss is 14.13 , inference loss is 7.70, weight deacy loss is 6.43, training accuracy is 0.281250, time 216.443 samples/sec
+epoch 12, total_step 198820, total loss is 12.85 , inference loss is 6.42, weight deacy loss is 6.43, training accuracy is 0.437500, time 225.367 samples/sec
+epoch 12, total_step 198840, total loss is 11.84 , inference loss is 5.41, weight deacy loss is 6.43, training accuracy is 0.531250, time 296.139 samples/sec
+epoch 12, total_step 198860, total loss is 12.49 , inference loss is 6.06, weight deacy loss is 6.43, training accuracy is 0.375000, time 215.217 samples/sec
+epoch 12, total_step 198880, total loss is 12.88 , inference loss is 6.45, weight deacy loss is 6.43, training accuracy is 0.375000, time 218.571 samples/sec
+epoch 12, total_step 198900, total loss is 20.65 , inference loss is 14.23, weight deacy loss is 6.43, training accuracy is 0.156250, time 13.002 samples/sec
+epoch 12, total_step 198920, total loss is 15.80 , inference loss is 9.37, weight deacy loss is 6.43, training accuracy is 0.281250, time 217.439 samples/sec
+epoch 12, total_step 198940, total loss is 15.21 , inference loss is 8.78, weight deacy loss is 6.43, training accuracy is 0.312500, time 219.400 samples/sec
+epoch 12, total_step 198960, total loss is 15.84 , inference loss is 9.42, weight deacy loss is 6.43, training accuracy is 0.187500, time 218.290 samples/sec
+epoch 12, total_step 198980, total loss is 14.37 , inference loss is 7.94, weight deacy loss is 6.43, training accuracy is 0.406250, time 213.373 samples/sec
+epoch 12, total_step 199000, total loss is 15.19 , inference loss is 8.77, weight deacy loss is 6.43, training accuracy is 0.437500, time 201.090 samples/sec
+epoch 12, total_step 199020, total loss is 11.92 , inference loss is 5.50, weight deacy loss is 6.43, training accuracy is 0.406250, time 198.634 samples/sec
+epoch 12, total_step 199040, total loss is 12.28 , inference loss is 5.86, weight deacy loss is 6.43, training accuracy is 0.343750, time 219.651 samples/sec
+epoch 12, total_step 199060, total loss is 15.45 , inference loss is 9.02, weight deacy loss is 6.43, training accuracy is 0.218750, time 182.907 samples/sec
+epoch 12, total_step 199080, total loss is 14.29 , inference loss is 7.86, weight deacy loss is 6.43, training accuracy is 0.281250, time 200.381 samples/sec2022-12-01 13:31:35.585015: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:145] Filling up shuffle buffer (this may take a while): 6820 of 10000
+2022-12-01 13:31:40.179242: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:195] Shuffle buffer filled.
+
+epoch 12, total_step 199100, total loss is 13.33 , inference loss is 6.91, weight deacy loss is 6.43, training accuracy is 0.312500, time 205.865 samples/sec
+epoch 12, total_step 199120, total loss is 16.22 , inference loss is 9.80, weight deacy loss is 6.43, training accuracy is 0.312500, time 175.513 samples/sec
+epoch 12, total_step 199140, total loss is 11.72 , inference loss is 5.29, weight deacy loss is 6.43, training accuracy is 0.468750, time 299.469 samples/sec
+epoch 12, total_step 199160, total loss is 14.53 , inference loss is 8.10, weight deacy loss is 6.43, training accuracy is 0.281250, time 224.454 samples/sec
+epoch 12, total_step 199180, total loss is 13.73 , inference loss is 7.30, weight deacy loss is 6.43, training accuracy is 0.375000, time 181.891 samples/sec
+epoch 12, total_step 199200, total loss is 14.01 , inference loss is 7.59, weight deacy loss is 6.43, training accuracy is 0.281250, time 12.160 samples/sec
+epoch 12, total_step 199220, total loss is 17.49 , inference loss is 11.06, weight deacy loss is 6.43, training accuracy is 0.343750, time 203.023 samples/sec
+epoch 12, total_step 199240, total loss is 14.99 , inference loss is 8.56, weight deacy loss is 6.43, training accuracy is 0.406250, time 220.045 samples/sec
+epoch 12, total_step 199260, total loss is 13.96 , inference loss is 7.54, weight deacy loss is 6.43, training accuracy is 0.437500, time 222.502 samples/sec
+epoch 12, total_step 199280, total loss is 15.92 , inference loss is 9.49, weight deacy loss is 6.42, training accuracy is 0.437500, time 177.379 samples/sec
+epoch 12, total_step 199300, total loss is 14.09 , inference loss is 7.67, weight deacy loss is 6.42, training accuracy is 0.375000, time 193.820 samples/sec
+End of epoch 12
+epoch 13, total_step 199320, total loss is 13.67 , inference loss is 7.25, weight deacy loss is 6.42, training accuracy is 0.500000, time 232.519 samples/sec
+epoch 13, total_step 199340, total loss is 9.19 , inference loss is 2.76, weight deacy loss is 6.42, training accuracy is 0.750000, time 297.225 samples/sec
+epoch 13, total_step 199360, total loss is 10.55 , inference loss is 4.12, weight deacy loss is 6.42, training accuracy is 0.750000, time 227.530 samples/sec
+epoch 13, total_step 199380, total loss is 9.99 , inference loss is 3.57, weight deacy loss is 6.42, training accuracy is 0.593750, time 228.101 samples/sec
+epoch 13, total_step 199400, total loss is 8.80 , inference loss is 2.38, weight deacy loss is 6.42, training accuracy is 0.781250, time 228.327 samples/sec
+epoch 13, total_step 199420, total loss is 8.67 , inference loss is 2.24, weight deacy loss is 6.42, training accuracy is 0.906250, time 221.201 samples/sec
+epoch 13, total_step 199440, total loss is 12.71 , inference loss is 6.29, weight deacy loss is 6.42, training accuracy is 0.718750, time 215.895 samples/sec
+epoch 13, total_step 199460, total loss is 11.04 , inference loss is 4.61, weight deacy loss is 6.42, training accuracy is 0.718750, time 222.991 samples/sec
+epoch 13, total_step 199480, total loss is 12.26 , inference loss is 5.83, weight deacy loss is 6.42, training accuracy is 0.656250, time 228.868 samples/sec
+epoch 13, total_step 199500, total loss is 10.18 , inference loss is 3.76, weight deacy loss is 6.42, training accuracy is 0.781250, time 11.966 samples/sec
+epoch 13, total_step 199520, total loss is 12.81 , inference loss is 6.39, weight deacy loss is 6.42, training accuracy is 0.656250, time 214.625 samples/sec
+epoch 13, total_step 199540, total loss is 11.93 , inference loss is 5.51, weight deacy loss is 6.42, training accuracy is 0.656250, time 210.720 samples/sec
+epoch 13, total_step 199560, total loss is 10.98 , inference loss is 4.56, weight deacy loss is 6.42, training accuracy is 0.625000, time 200.751 samples/sec
+epoch 13, total_step 199580, total loss is 10.75 , inference loss is 4.33, weight deacy loss is 6.42, training accuracy is 0.718750, time 211.992 samples/sec
+epoch 13, total_step 199600, total loss is 10.23 , inference loss is 3.81, weight deacy loss is 6.42, training accuracy is 0.687500, time 299.176 samples/sec
+epoch 13, total_step 199620, total loss is 10.14 , inference loss is 3.71, weight deacy loss is 6.42, training accuracy is 0.593750, time 237.664 samples/sec
+epoch 13, total_step 199640, total loss is 11.98 , inference loss is 5.56, weight deacy loss is 6.42, training accuracy is 0.718750, time 219.565 samples/sec
+epoch 13, total_step 199660, total loss is 10.66 , inference loss is 4.24, weight deacy loss is 6.42, training accuracy is 0.625000, time 213.441 samples/sec
+epoch 13, total_step 199680, total loss is 13.00 , inference loss is 6.58, weight deacy loss is 6.42, training accuracy is 0.656250, time 235.040 samples/sec
+epoch 13, total_step 199700, total loss is 7.87 , inference loss is 1.45, weight deacy loss is 6.42, training accuracy is 0.812500, time 217.770 samples/sec
+epoch 13, total_step 199720, total loss is 9.48 , inference loss is 3.05, weight deacy loss is 6.42, training accuracy is 0.656250, time 217.590 samples/sec
+epoch 13, total_step 199740, total loss is 9.34 , inference loss is 2.92, weight deacy loss is 6.42, training accuracy is 0.750000, time 210.013 samples/sec
+epoch 13, total_step 199760, total loss is 13.08 , inference loss is 6.66, weight deacy loss is 6.42, training accuracy is 0.593750, time 231.086 samples/sec
+epoch 13, total_step 199780, total loss is 10.40 , inference loss is 3.97, weight deacy loss is 6.42, training accuracy is 0.812500, time 221.137 samples/sec
+epoch 13, total_step 199800, total loss is 10.99 , inference loss is 4.57, weight deacy loss is 6.42, training accuracy is 0.625000, time 12.220 samples/sec
+epoch 13, total_step 199820, total loss is 13.26 , inference loss is 6.84, weight deacy loss is 6.42, training accuracy is 0.437500, time 220.581 samples/sec
+epoch 13, total_step 199840, total loss is 11.51 , inference loss is 5.09, weight deacy loss is 6.42, training accuracy is 0.562500, time 224.214 samples/sec
+epoch 13, total_step 199860, total loss is 17.30 , inference loss is 10.88, weight deacy loss is 6.42, training accuracy is 0.531250, time 295.701 samples/sec
+epoch 13, total_step 199880, total loss is 12.78 , inference loss is 6.36, weight deacy loss is 6.42, training accuracy is 0.687500, time 227.899 samples/sec
+epoch 13, total_step 199900, total loss is 11.68 , inference loss is 5.26, weight deacy loss is 6.42, training accuracy is 0.562500, time 217.670 samples/sec
+epoch 13, total_step 199920, total loss is 14.47 , inference loss is 8.05, weight deacy loss is 6.42, training accuracy is 0.656250, time 242.318 samples/sec
+epoch 13, total_step 199940, total loss is 18.01 , inference loss is 11.59, weight deacy loss is 6.42, training accuracy is 0.500000, time 223.695 samples/sec
+epoch 13, total_step 199960, total loss is 10.25 , inference loss is 3.83, weight deacy loss is 6.42, training accuracy is 0.718750, time 218.436 samples/sec
+epoch 13, total_step 199980, total loss is 10.40 , inference loss is 3.97, weight deacy loss is 6.42, training accuracy is 0.656250, time 214.329 samples/sec
+testing verification..
+(12000, 512)
+infer time 21.83346500000001
+best_threshold_index 115 0.9844444444444445
+best_threshold_index 115 0.9835185185185186
+best_threshold_index 115 0.9838888888888889
+best_threshold_index 115 0.985
+best_threshold_index 115 0.9846296296296296
+best_threshold_index 117 0.9829629629629629
+best_threshold_index 115 0.9844444444444445
+best_threshold_index 115 0.9840740740740741
+best_threshold_index 117 0.9822222222222222
+best_threshold_index 117 0.9837037037037037
+[lfw][200000]XNorm: 18.833636
+[lfw][200000]Accuracy-Flip: 0.98333+-0.00711
+test accuracy is:  0.9833333333333332
+epoch 13, total_step 200000, total loss is 14.51 , inference loss is 8.09, weight deacy loss is 6.42, training accuracy is 0.687500, time 18.194 samples/sec
+epoch 13, total_step 200020, total loss is 11.84 , inference loss is 5.42, weight deacy loss is 6.42, training accuracy is 0.687500, time 214.685 samples/sec
+epoch 13, total_step 200040, total loss is 12.46 , inference loss is 6.03, weight deacy loss is 6.42, training accuracy is 0.750000, time 179.323 samples/sec
+epoch 13, total_step 200060, total loss is 12.07 , inference loss is 5.64, weight deacy loss is 6.42, training accuracy is 0.625000, time 226.743 samples/sec
+epoch 13, total_step 200080, total loss is 11.03 , inference loss is 4.61, weight deacy loss is 6.43, training accuracy is 0.593750, time 232.849 samples/sec
+epoch 13, total_step 200100, total loss is 12.53 , inference loss is 6.11, weight deacy loss is 6.43, training accuracy is 0.500000, time 12.594 samples/sec
+epoch 13, total_step 200120, total loss is 11.76 , inference loss is 5.33, weight deacy loss is 6.43, training accuracy is 0.562500, time 222.611 samples/sec
+epoch 13, total_step 200140, total loss is 12.41 , inference loss is 5.98, weight deacy loss is 6.43, training accuracy is 0.562500, time 217.103 samples/sec
+epoch 13, total_step 200160, total loss is 11.87 , inference loss is 5.44, weight deacy loss is 6.43, training accuracy is 0.656250, time 230.886 samples/sec
+epoch 13, total_step 200180, total loss is 12.60 , inference loss is 6.18, weight deacy loss is 6.43, training accuracy is 0.656250, time 217.836 samples/sec
+epoch 13, total_step 200200, total loss is 10.01 , inference loss is 3.58, weight deacy loss is 6.43, training accuracy is 0.625000, time 221.980 samples/sec
+epoch 13, total_step 200220, total loss is 15.12 , inference loss is 8.69, weight deacy loss is 6.43, training accuracy is 0.593750, time 217.716 samples/sec
+epoch 13, total_step 200240, total loss is 10.90 , inference loss is 4.47, weight deacy loss is 6.43, training accuracy is 0.625000, time 223.421 samples/sec
+epoch 13, total_step 200260, total loss is 14.57 , inference loss is 8.14, weight deacy loss is 6.43, training accuracy is 0.562500, time 224.785 samples/sec
+epoch 13, total_step 200280, total loss is 11.94 , inference loss is 5.51, weight deacy loss is 6.43, training accuracy is 0.656250, time 225.982 samples/sec
+epoch 13, total_step 200300, total loss is 13.24 , inference loss is 6.81, weight deacy loss is 6.43, training accuracy is 0.468750, time 215.250 samples/sec
+epoch 13, total_step 200320, total loss is 11.75 , inference loss is 5.32, weight deacy loss is 6.43, training accuracy is 0.656250, time 228.983 samples/sec
+epoch 13, total_step 200340, total loss is 12.65 , inference loss is 6.23, weight deacy loss is 6.43, training accuracy is 0.531250, time 239.436 samples/sec
+epoch 13, total_step 200360, total loss is 11.64 , inference loss is 5.21, weight deacy loss is 6.43, training accuracy is 0.687500, time 213.765 samples/sec
+epoch 13, total_step 200380, total loss is 15.32 , inference loss is 8.89, weight deacy loss is 6.43, training accuracy is 0.687500, time 299.062 samples/sec
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+testing verification..
+(12000, 512)
+infer time 25.354742999999992
+best_threshold_index 117 0.9851851851851852
+best_threshold_index 117 0.9846296296296296
+best_threshold_index 116 0.9837037037037037
+best_threshold_index 115 0.9851851851851852
+best_threshold_index 115 0.9842592592592593
+best_threshold_index 117 0.9833333333333333
+best_threshold_index 117 0.9848148148148148
+best_threshold_index 117 0.9831481481481481
+best_threshold_index 117 0.9837037037037037
+best_threshold_index 117 0.9837037037037037
+[lfw][202000]XNorm: 20.752503
+[lfw][202000]Accuracy-Flip: 0.98333+-0.00695
+test accuracy is:  0.9833333333333334
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+epoch 13, total_step 203660, total loss is 9.99 , inference loss is 3.57, weight deacy loss is 6.43, training accuracy is 0.656250, time 298.204 samples/sec
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+epoch 13, total_step 203880, total loss is 15.41 , inference loss is 8.98, weight deacy loss is 6.43, training accuracy is 0.562500, time 220.408 samples/sec
+epoch 13, total_step 203900, total loss is 10.24 , inference loss is 3.81, weight deacy loss is 6.43, training accuracy is 0.562500, time 228.195 samples/sec
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+epoch 13, total_step 203980, total loss is 14.25 , inference loss is 7.82, weight deacy loss is 6.43, training accuracy is 0.500000, time 217.206 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.903029000000025
+best_threshold_index 117 0.9892592592592593
+best_threshold_index 120 0.9885185185185185
+best_threshold_index 117 0.9887037037037038
+best_threshold_index 117 0.9888888888888889
+best_threshold_index 117 0.9888888888888889
+best_threshold_index 117 0.9877777777777778
+best_threshold_index 117 0.9888888888888889
+best_threshold_index 117 0.9874074074074074
+best_threshold_index 117 0.9872222222222222
+best_threshold_index 117 0.9881481481481481
+[lfw][204000]XNorm: 21.583302
+[lfw][204000]Accuracy-Flip: 0.98783+-0.00601
+test accuracy is:  0.9878333333333333
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+epoch 13, total_step 204020, total loss is 13.17 , inference loss is 6.74, weight deacy loss is 6.43, training accuracy is 0.656250, time 218.288 samples/sec
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+epoch 13, total_step 204420, total loss is 12.11 , inference loss is 5.68, weight deacy loss is 6.43, training accuracy is 0.406250, time 226.849 samples/sec
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+epoch 13, total_step 205980, total loss is 13.26 , inference loss is 6.84, weight deacy loss is 6.42, training accuracy is 0.500000, time 216.045 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.786638000000018
+best_threshold_index 110 0.9861111111111112
+best_threshold_index 111 0.9866666666666667
+best_threshold_index 111 0.9853703703703703
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+best_threshold_index 122 0.9862962962962963
+best_threshold_index 111 0.9848148148148148
+best_threshold_index 111 0.9857407407407407
+best_threshold_index 111 0.985
+best_threshold_index 111 0.9846296296296296
+best_threshold_index 111 0.985
+[lfw][206000]XNorm: 22.059153
+[lfw][206000]Accuracy-Flip: 0.98383+-0.00969
+test accuracy is:  0.9838333333333333
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+testing verification..
+(12000, 512)
+infer time 24.040967999999975
+best_threshold_index 112 0.9874074074074074
+best_threshold_index 115 0.9879629629629629
+best_threshold_index 116 0.9877777777777778
+best_threshold_index 112 0.9885185185185185
+best_threshold_index 116 0.9881481481481481
+best_threshold_index 117 0.9868518518518519
+best_threshold_index 115 0.9877777777777778
+best_threshold_index 115 0.9868518518518519
+best_threshold_index 115 0.9862962962962963
+best_threshold_index 115 0.9866666666666667
+[lfw][208000]XNorm: 21.910983
+[lfw][208000]Accuracy-Flip: 0.98583+-0.00629
+test accuracy is:  0.9858333333333335
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+epoch 13, total_step 209980, total loss is 11.93 , inference loss is 5.52, weight deacy loss is 6.40, training accuracy is 0.531250, time 233.374 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.71724600000003
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+best_threshold_index 110 0.9862962962962963
+[lfw][210000]XNorm: 21.479118
+[lfw][210000]Accuracy-Flip: 0.98517+-0.00621
+test accuracy is:  0.9851666666666665
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+testing verification..
+(12000, 512)
+infer time 25.775781000000006
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+[lfw][212000]XNorm: 22.116135
+[lfw][212000]Accuracy-Flip: 0.98417+-0.00688
+test accuracy is:  0.9841666666666666
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+epoch 13, total_step 212180, total loss is 18.00 , inference loss is 11.61, weight deacy loss is 6.40, training accuracy is 0.437500, time 218.323 samples/sec
+epoch 13, total_step 212200, total loss is 14.13 , inference loss is 7.74, weight deacy loss is 6.40, training accuracy is 0.312500, time 213.846 samples/sec
+epoch 13, total_step 212220, total loss is 13.89 , inference loss is 7.50, weight deacy loss is 6.40, training accuracy is 0.375000, time 229.278 samples/sec
+epoch 13, total_step 212240, total loss is 14.10 , inference loss is 7.70, weight deacy loss is 6.40, training accuracy is 0.593750, time 209.858 samples/sec
+epoch 13, total_step 212260, total loss is 14.12 , inference loss is 7.72, weight deacy loss is 6.40, training accuracy is 0.343750, time 222.787 samples/sec
+epoch 13, total_step 212280, total loss is 17.78 , inference loss is 11.39, weight deacy loss is 6.40, training accuracy is 0.406250, time 213.699 samples/sec
+epoch 13, total_step 212300, total loss is 13.91 , inference loss is 7.52, weight deacy loss is 6.40, training accuracy is 0.312500, time 217.943 samples/sec
+epoch 13, total_step 212320, total loss is 15.34 , inference loss is 8.95, weight deacy loss is 6.39, training accuracy is 0.343750, time 297.422 samples/sec
+epoch 13, total_step 212340, total loss is 13.71 , inference loss is 7.32, weight deacy loss is 6.39, training accuracy is 0.406250, time 298.453 samples/sec
+epoch 13, total_step 212360, total loss is 15.90 , inference loss is 9.50, weight deacy loss is 6.39, training accuracy is 0.312500, time 222.828 samples/sec
+epoch 13, total_step 212380, total loss is 15.70 , inference loss is 9.31, weight deacy loss is 6.39, training accuracy is 0.312500, time 214.383 samples/sec
+epoch 13, total_step 212400, total loss is 13.81 , inference loss is 7.41, weight deacy loss is 6.39, training accuracy is 0.500000, time 11.811 samples/sec
+epoch 13, total_step 212420, total loss is 13.35 , inference loss is 6.95, weight deacy loss is 6.39, training accuracy is 0.406250, time 211.581 samples/sec
+epoch 13, total_step 212440, total loss is 15.63 , inference loss is 9.24, weight deacy loss is 6.39, training accuracy is 0.562500, time 232.103 samples/sec
+epoch 13, total_step 212460, total loss is 13.22 , inference loss is 6.83, weight deacy loss is 6.39, training accuracy is 0.406250, time 243.997 samples/sec
+epoch 13, total_step 212480, total loss is 9.90 , inference loss is 3.50, weight deacy loss is 6.39, training accuracy is 0.531250, time 219.647 samples/sec
+epoch 13, total_step 212500, total loss is 17.20 , inference loss is 10.81, weight deacy loss is 6.39, training accuracy is 0.281250, time 295.885 samples/sec
+epoch 13, total_step 212520, total loss is 12.62 , inference loss is 6.23, weight deacy loss is 6.39, training accuracy is 0.406250, time 223.374 samples/sec
+epoch 13, total_step 212540, total loss is 16.30 , inference loss is 9.91, weight deacy loss is 6.39, training accuracy is 0.343750, time 225.713 samples/sec
+epoch 13, total_step 212560, total loss is 12.63 , inference loss is 6.24, weight deacy loss is 6.39, training accuracy is 0.406250, time 228.665 samples/sec
+epoch 13, total_step 212580, total loss is 12.42 , inference loss is 6.03, weight deacy loss is 6.39, training accuracy is 0.531250, time 225.084 samples/sec
+epoch 13, total_step 212600, total loss is 13.66 , inference loss is 7.26, weight deacy loss is 6.39, training accuracy is 0.375000, time 229.553 samples/sec
+epoch 13, total_step 212620, total loss is 13.99 , inference loss is 7.60, weight deacy loss is 6.39, training accuracy is 0.375000, time 227.552 samples/sec
+epoch 13, total_step 212640, total loss is 10.68 , inference loss is 4.29, weight deacy loss is 6.39, training accuracy is 0.468750, time 221.326 samples/sec
+epoch 13, total_step 212660, total loss is 13.29 , inference loss is 6.90, weight deacy loss is 6.39, training accuracy is 0.406250, time 224.593 samples/sec
+epoch 13, total_step 212680, total loss is 11.30 , inference loss is 4.90, weight deacy loss is 6.39, training accuracy is 0.500000, time 225.110 samples/sec
+epoch 13, total_step 212700, total loss is 18.06 , inference loss is 11.67, weight deacy loss is 6.39, training accuracy is 0.406250, time 12.652 samples/sec
+epoch 13, total_step 212720, total loss is 12.43 , inference loss is 6.04, weight deacy loss is 6.39, training accuracy is 0.562500, time 226.261 samples/sec
+epoch 13, total_step 212740, total loss is 17.74 , inference loss is 11.34, weight deacy loss is 6.39, training accuracy is 0.437500, time 231.110 samples/sec
+epoch 13, total_step 212760, total loss is 11.88 , inference loss is 5.49, weight deacy loss is 6.39, training accuracy is 0.531250, time 298.311 samples/sec
+epoch 13, total_step 212780, total loss is 12.34 , inference loss is 5.94, weight deacy loss is 6.39, training accuracy is 0.468750, time 219.757 samples/sec
+epoch 13, total_step 212800, total loss is 12.64 , inference loss is 6.25, weight deacy loss is 6.39, training accuracy is 0.468750, time 224.427 samples/sec
+epoch 13, total_step 212820, total loss is 11.44 , inference loss is 5.05, weight deacy loss is 6.39, training accuracy is 0.468750, time 223.665 samples/sec
+epoch 13, total_step 212840, total loss is 16.27 , inference loss is 9.87, weight deacy loss is 6.39, training accuracy is 0.375000, time 225.407 samples/sec
+epoch 13, total_step 212860, total loss is 16.81 , inference loss is 10.42, weight deacy loss is 6.39, training accuracy is 0.406250, time 210.386 samples/sec
+epoch 13, total_step 212880, total loss is 11.99 , inference loss is 5.60, weight deacy loss is 6.39, training accuracy is 0.468750, time 218.878 samples/sec
+epoch 13, total_step 212900, total loss is 11.38 , inference loss is 4.98, weight deacy loss is 6.39, training accuracy is 0.468750, time 215.609 samples/sec
+epoch 13, total_step 212920, total loss is 18.89 , inference loss is 12.50, weight deacy loss is 6.39, training accuracy is 0.281250, time 218.722 samples/sec
+epoch 13, total_step 212940, total loss is 10.71 , inference loss is 4.32, weight deacy loss is 6.39, training accuracy is 0.437500, time 249.260 samples/sec
+epoch 13, total_step 212960, total loss is 16.22 , inference loss is 9.83, weight deacy loss is 6.39, training accuracy is 0.281250, time 223.637 samples/sec
+epoch 13, total_step 212980, total loss is 12.41 , inference loss is 6.02, weight deacy loss is 6.39, training accuracy is 0.437500, time 225.639 samples/sec
+epoch 13, total_step 213000, total loss is 16.94 , inference loss is 10.55, weight deacy loss is 6.39, training accuracy is 0.312500, time 12.587 samples/sec
+epoch 13, total_step 213020, total loss is 13.79 , inference loss is 7.40, weight deacy loss is 6.39, training accuracy is 0.281250, time 197.349 samples/sec
+epoch 13, total_step 213040, total loss is 17.71 , inference loss is 11.32, weight deacy loss is 6.39, training accuracy is 0.250000, time 239.222 samples/sec
+epoch 13, total_step 213060, total loss is 15.08 , inference loss is 8.69, weight deacy loss is 6.39, training accuracy is 0.343750, time 225.387 samples/sec
+epoch 13, total_step 213080, total loss is 16.35 , inference loss is 9.96, weight deacy loss is 6.39, training accuracy is 0.312500, time 215.679 samples/sec
+epoch 13, total_step 213100, total loss is 14.39 , inference loss is 8.00, weight deacy loss is 6.39, training accuracy is 0.281250, time 246.818 samples/sec
+epoch 13, total_step 213120, total loss is 12.03 , inference loss is 5.64, weight deacy loss is 6.39, training accuracy is 0.468750, time 219.554 samples/sec
+epoch 13, total_step 213140, total loss is 14.68 , inference loss is 8.29, weight deacy loss is 6.39, training accuracy is 0.406250, time 211.379 samples/sec
+epoch 13, total_step 213160, total loss is 15.34 , inference loss is 8.95, weight deacy loss is 6.39, training accuracy is 0.500000, time 224.708 samples/sec
+epoch 13, total_step 213180, total loss is 18.98 , inference loss is 12.59, weight deacy loss is 6.39, training accuracy is 0.250000, time 213.506 samples/sec
+epoch 13, total_step 213200, total loss is 16.00 , inference loss is 9.61, weight deacy loss is 6.39, training accuracy is 0.437500, time 220.850 samples/sec
+epoch 13, total_step 213220, total loss is 17.49 , inference loss is 11.10, weight deacy loss is 6.39, training accuracy is 0.281250, time 222.743 samples/sec
+epoch 13, total_step 213240, total loss is 15.40 , inference loss is 9.01, weight deacy loss is 6.39, training accuracy is 0.406250, time 218.476 samples/sec
+epoch 13, total_step 213260, total loss is 16.87 , inference loss is 10.48, weight deacy loss is 6.39, training accuracy is 0.281250, time 227.041 samples/sec
+epoch 13, total_step 213280, total loss is 15.67 , inference loss is 9.28, weight deacy loss is 6.39, training accuracy is 0.218750, time 228.715 samples/sec
+epoch 13, total_step 213300, total loss is 18.76 , inference loss is 12.37, weight deacy loss is 6.39, training accuracy is 0.281250, time 12.051 samples/sec
+epoch 13, total_step 213320, total loss is 13.79 , inference loss is 7.40, weight deacy loss is 6.39, training accuracy is 0.375000, time 224.435 samples/sec
+epoch 13, total_step 213340, total loss is 14.20 , inference loss is 7.82, weight deacy loss is 6.39, training accuracy is 0.218750, time 214.533 samples/sec
+epoch 13, total_step 213360, total loss is 11.87 , inference loss is 5.48, weight deacy loss is 6.39, training accuracy is 0.406250, time 228.327 samples/sec
+epoch 13, total_step 213380, total loss is 13.16 , inference loss is 6.77, weight deacy loss is 6.39, training accuracy is 0.437500, time 229.945 samples/sec
+epoch 13, total_step 213400, total loss is 13.41 , inference loss is 7.02, weight deacy loss is 6.39, training accuracy is 0.468750, time 229.848 samples/sec
+epoch 13, total_step 213420, total loss is 13.57 , inference loss is 7.18, weight deacy loss is 6.39, training accuracy is 0.375000, time 220.746 samples/sec
+epoch 13, total_step 213440, total loss is 14.52 , inference loss is 8.13, weight deacy loss is 6.39, training accuracy is 0.437500, time 225.872 samples/sec
+epoch 13, total_step 213460, total loss is 12.48 , inference loss is 6.09, weight deacy loss is 6.39, training accuracy is 0.375000, time 224.756 samples/sec
+epoch 13, total_step 213480, total loss is 14.68 , inference loss is 8.29, weight deacy loss is 6.39, training accuracy is 0.312500, time 224.862 samples/sec
+epoch 13, total_step 213500, total loss is 13.48 , inference loss is 7.09, weight deacy loss is 6.39, training accuracy is 0.343750, time 224.385 samples/sec
+epoch 13, total_step 213520, total loss is 15.18 , inference loss is 8.79, weight deacy loss is 6.39, training accuracy is 0.281250, time 224.948 samples/sec
+epoch 13, total_step 213540, total loss is 13.98 , inference loss is 7.59, weight deacy loss is 6.39, training accuracy is 0.406250, time 221.003 samples/sec
+epoch 13, total_step 213560, total loss is 17.34 , inference loss is 10.95, weight deacy loss is 6.39, training accuracy is 0.468750, time 212.941 samples/sec
+epoch 13, total_step 213580, total loss is 13.85 , inference loss is 7.46, weight deacy loss is 6.39, training accuracy is 0.437500, time 296.723 samples/sec
+epoch 13, total_step 213600, total loss is 15.81 , inference loss is 9.42, weight deacy loss is 6.39, training accuracy is 0.312500, time 12.344 samples/sec
+epoch 13, total_step 213620, total loss is 14.72 , inference loss is 8.33, weight deacy loss is 6.39, training accuracy is 0.312500, time 224.002 samples/sec
+epoch 13, total_step 213640, total loss is 12.26 , inference loss is 5.88, weight deacy loss is 6.39, training accuracy is 0.437500, time 226.172 samples/sec
+epoch 13, total_step 213660, total loss is 14.20 , inference loss is 7.81, weight deacy loss is 6.39, training accuracy is 0.281250, time 176.317 samples/sec
+epoch 13, total_step 213680, total loss is 15.90 , inference loss is 9.51, weight deacy loss is 6.39, training accuracy is 0.406250, time 229.418 samples/sec
+epoch 13, total_step 213700, total loss is 15.35 , inference loss is 8.96, weight deacy loss is 6.39, training accuracy is 0.375000, time 227.125 samples/sec
+epoch 13, total_step 213720, total loss is 11.87 , inference loss is 5.48, weight deacy loss is 6.39, training accuracy is 0.406250, time 219.989 samples/sec
+epoch 13, total_step 213740, total loss is 11.07 , inference loss is 4.68, weight deacy loss is 6.39, training accuracy is 0.531250, time 227.871 samples/sec
+epoch 13, total_step 213760, total loss is 12.36 , inference loss is 5.97, weight deacy loss is 6.39, training accuracy is 0.406250, time 221.299 samples/sec
+epoch 13, total_step 213780, total loss is 13.14 , inference loss is 6.75, weight deacy loss is 6.39, training accuracy is 0.375000, time 226.174 samples/sec
+epoch 13, total_step 213800, total loss is 14.01 , inference loss is 7.62, weight deacy loss is 6.39, training accuracy is 0.406250, time 219.870 samples/sec
+epoch 13, total_step 213820, total loss is 16.76 , inference loss is 10.37, weight deacy loss is 6.39, training accuracy is 0.343750, time 227.168 samples/sec
+epoch 13, total_step 213840, total loss is 12.51 , inference loss is 6.12, weight deacy loss is 6.39, training accuracy is 0.468750, time 297.189 samples/sec
+epoch 13, total_step 213860, total loss is 15.70 , inference loss is 9.32, weight deacy loss is 6.39, training accuracy is 0.312500, time 226.874 samples/sec
+epoch 13, total_step 213880, total loss is 16.75 , inference loss is 10.37, weight deacy loss is 6.39, training accuracy is 0.281250, time 224.513 samples/sec
+epoch 13, total_step 213900, total loss is 13.95 , inference loss is 7.57, weight deacy loss is 6.39, training accuracy is 0.406250, time 12.156 samples/sec
+epoch 13, total_step 213920, total loss is 13.61 , inference loss is 7.22, weight deacy loss is 6.39, training accuracy is 0.437500, time 227.156 samples/sec
+epoch 13, total_step 213940, total loss is 17.87 , inference loss is 11.49, weight deacy loss is 6.39, training accuracy is 0.343750, time 216.035 samples/sec
+epoch 13, total_step 213960, total loss is 14.89 , inference loss is 8.51, weight deacy loss is 6.39, training accuracy is 0.437500, time 225.672 samples/sec
+epoch 13, total_step 213980, total loss is 10.55 , inference loss is 4.17, weight deacy loss is 6.39, training accuracy is 0.500000, time 231.193 samples/sec
+testing verification..
+(12000, 512)
+infer time 23.572648000000008
+best_threshold_index 105 0.9842592592592593
+best_threshold_index 105 0.9835185185185186
+best_threshold_index 103 0.9837037037037037
+best_threshold_index 105 0.9851851851851852
+best_threshold_index 105 0.9837037037037037
+best_threshold_index 106 0.9825925925925926
+best_threshold_index 105 0.9833333333333333
+best_threshold_index 105 0.9829629629629629
+best_threshold_index 105 0.9816666666666667
+best_threshold_index 105 0.9829629629629629
+[lfw][214000]XNorm: 22.200127
+[lfw][214000]Accuracy-Flip: 0.98233+-0.00817
+test accuracy is:  0.9823333333333333
+epoch 13, total_step 214000, total loss is 15.45 , inference loss is 9.06, weight deacy loss is 6.39, training accuracy is 0.437500, time 16.159 samples/sec
+epoch 13, total_step 214020, total loss is 15.62 , inference loss is 9.24, weight deacy loss is 6.39, training accuracy is 0.250000, time 221.592 samples/sec
+epoch 13, total_step 214040, total loss is 14.46 , inference loss is 8.07, weight deacy loss is 6.39, training accuracy is 0.406250, time 300.617 samples/sec
+epoch 13, total_step 214060, total loss is 13.25 , inference loss is 6.87, weight deacy loss is 6.39, training accuracy is 0.406250, time 298.675 samples/sec
+epoch 13, total_step 214080, total loss is 14.19 , inference loss is 7.80, weight deacy loss is 6.39, training accuracy is 0.375000, time 215.995 samples/sec2022-12-01 14:33:40.717779: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:145] Filling up shuffle buffer (this may take a while): 6916 of 10000
+2022-12-01 14:33:45.113660: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:195] Shuffle buffer filled.
+
+epoch 13, total_step 214100, total loss is 12.77 , inference loss is 6.38, weight deacy loss is 6.39, training accuracy is 0.343750, time 217.955 samples/sec
+epoch 13, total_step 214120, total loss is 12.62 , inference loss is 6.24, weight deacy loss is 6.38, training accuracy is 0.406250, time 215.664 samples/sec
+epoch 13, total_step 214140, total loss is 15.07 , inference loss is 8.69, weight deacy loss is 6.38, training accuracy is 0.312500, time 233.458 samples/sec
+epoch 13, total_step 214160, total loss is 14.63 , inference loss is 8.25, weight deacy loss is 6.38, training accuracy is 0.375000, time 227.392 samples/sec
+epoch 13, total_step 214180, total loss is 18.06 , inference loss is 11.67, weight deacy loss is 6.38, training accuracy is 0.250000, time 213.405 samples/sec
+epoch 13, total_step 214200, total loss is 13.26 , inference loss is 6.88, weight deacy loss is 6.38, training accuracy is 0.375000, time 13.115 samples/sec
+epoch 13, total_step 214220, total loss is 13.87 , inference loss is 7.49, weight deacy loss is 6.38, training accuracy is 0.312500, time 235.784 samples/sec
+epoch 13, total_step 214240, total loss is 15.77 , inference loss is 9.38, weight deacy loss is 6.38, training accuracy is 0.281250, time 214.820 samples/sec
+epoch 13, total_step 214260, total loss is 15.27 , inference loss is 8.89, weight deacy loss is 6.38, training accuracy is 0.406250, time 219.136 samples/sec
+epoch 13, total_step 214280, total loss is 14.59 , inference loss is 8.21, weight deacy loss is 6.38, training accuracy is 0.250000, time 226.059 samples/sec
+epoch 13, total_step 214300, total loss is 18.64 , inference loss is 12.26, weight deacy loss is 6.38, training accuracy is 0.250000, time 231.461 samples/sec
+epoch 13, total_step 214320, total loss is 14.66 , inference loss is 8.28, weight deacy loss is 6.38, training accuracy is 0.281250, time 296.379 samples/sec
+epoch 13, total_step 214340, total loss is 12.95 , inference loss is 6.57, weight deacy loss is 6.38, training accuracy is 0.531250, time 202.525 samples/sec
+epoch 13, total_step 214360, total loss is 17.38 , inference loss is 10.99, weight deacy loss is 6.38, training accuracy is 0.281250, time 208.633 samples/sec
+epoch 13, total_step 214380, total loss is 11.02 , inference loss is 4.64, weight deacy loss is 6.38, training accuracy is 0.531250, time 222.972 samples/sec
+epoch 13, total_step 214400, total loss is 12.16 , inference loss is 5.78, weight deacy loss is 6.38, training accuracy is 0.437500, time 194.116 samples/sec
+epoch 13, total_step 214420, total loss is 12.50 , inference loss is 6.12, weight deacy loss is 6.38, training accuracy is 0.500000, time 209.373 samples/sec
+epoch 13, total_step 214440, total loss is 19.14 , inference loss is 12.75, weight deacy loss is 6.38, training accuracy is 0.406250, time 193.340 samples/sec
+epoch 13, total_step 214460, total loss is 11.99 , inference loss is 5.61, weight deacy loss is 6.38, training accuracy is 0.500000, time 202.342 samples/sec
+epoch 13, total_step 214480, total loss is 16.35 , inference loss is 9.97, weight deacy loss is 6.38, training accuracy is 0.312500, time 213.495 samples/sec
+epoch 13, total_step 214500, total loss is 14.22 , inference loss is 7.83, weight deacy loss is 6.38, training accuracy is 0.375000, time 12.872 samples/sec
+epoch 13, total_step 214520, total loss is 14.79 , inference loss is 8.40, weight deacy loss is 6.38, training accuracy is 0.468750, time 228.093 samples/sec
+epoch 13, total_step 214540, total loss is 14.45 , inference loss is 8.07, weight deacy loss is 6.38, training accuracy is 0.500000, time 195.571 samples/sec
+epoch 13, total_step 214560, total loss is 18.89 , inference loss is 12.51, weight deacy loss is 6.38, training accuracy is 0.250000, time 201.427 samples/sec
+epoch 13, total_step 214580, total loss is 14.23 , inference loss is 7.85, weight deacy loss is 6.38, training accuracy is 0.406250, time 200.238 samples/sec
+epoch 13, total_step 214600, total loss is 18.77 , inference loss is 12.39, weight deacy loss is 6.38, training accuracy is 0.312500, time 192.323 samples/sec
+epoch 13, total_step 214620, total loss is 10.92 , inference loss is 4.54, weight deacy loss is 6.38, training accuracy is 0.531250, time 205.844 samples/sec
+End of epoch 13
+epoch 14, total_step 214640, total loss is 20.68 , inference loss is 14.29, weight deacy loss is 6.38, training accuracy is 0.093750, time 216.634 samples/sec
+epoch 14, total_step 214660, total loss is 10.21 , inference loss is 3.83, weight deacy loss is 6.38, training accuracy is 0.656250, time 219.879 samples/sec
+epoch 14, total_step 214680, total loss is 9.34 , inference loss is 2.95, weight deacy loss is 6.38, training accuracy is 0.718750, time 216.656 samples/sec
+epoch 14, total_step 214700, total loss is 9.99 , inference loss is 3.61, weight deacy loss is 6.38, training accuracy is 0.593750, time 237.944 samples/sec
+epoch 14, total_step 214720, total loss is 12.57 , inference loss is 6.19, weight deacy loss is 6.38, training accuracy is 0.656250, time 222.630 samples/sec
+epoch 14, total_step 214740, total loss is 8.70 , inference loss is 2.32, weight deacy loss is 6.38, training accuracy is 0.718750, time 222.137 samples/sec
+epoch 14, total_step 214760, total loss is 10.90 , inference loss is 4.52, weight deacy loss is 6.38, training accuracy is 0.781250, time 227.458 samples/sec
+epoch 14, total_step 214780, total loss is 8.47 , inference loss is 2.09, weight deacy loss is 6.38, training accuracy is 0.937500, time 218.040 samples/sec
+epoch 14, total_step 214800, total loss is 11.21 , inference loss is 4.83, weight deacy loss is 6.38, training accuracy is 0.718750, time 11.972 samples/sec
+epoch 14, total_step 214820, total loss is 12.89 , inference loss is 6.51, weight deacy loss is 6.38, training accuracy is 0.687500, time 298.102 samples/sec
+epoch 14, total_step 214840, total loss is 9.72 , inference loss is 3.34, weight deacy loss is 6.38, training accuracy is 0.625000, time 217.047 samples/sec
+epoch 14, total_step 214860, total loss is 10.41 , inference loss is 4.03, weight deacy loss is 6.38, training accuracy is 0.656250, time 223.988 samples/sec
+epoch 14, total_step 214880, total loss is 8.17 , inference loss is 1.79, weight deacy loss is 6.38, training accuracy is 0.812500, time 216.892 samples/sec
+epoch 14, total_step 214900, total loss is 9.61 , inference loss is 3.23, weight deacy loss is 6.38, training accuracy is 0.750000, time 193.590 samples/sec
+epoch 14, total_step 214920, total loss is 11.44 , inference loss is 5.06, weight deacy loss is 6.38, training accuracy is 0.750000, time 231.807 samples/sec
+epoch 14, total_step 214940, total loss is 10.30 , inference loss is 3.92, weight deacy loss is 6.38, training accuracy is 0.562500, time 219.621 samples/sec
+epoch 14, total_step 214960, total loss is 12.87 , inference loss is 6.49, weight deacy loss is 6.38, training accuracy is 0.625000, time 220.122 samples/sec
+epoch 14, total_step 214980, total loss is 13.74 , inference loss is 7.36, weight deacy loss is 6.38, training accuracy is 0.593750, time 219.011 samples/sec
+epoch 14, total_step 215000, total loss is 11.40 , inference loss is 5.02, weight deacy loss is 6.38, training accuracy is 0.656250, time 225.629 samples/sec
+epoch 14, total_step 215020, total loss is 13.34 , inference loss is 6.96, weight deacy loss is 6.38, training accuracy is 0.718750, time 219.805 samples/sec
+epoch 14, total_step 215040, total loss is 13.22 , inference loss is 6.84, weight deacy loss is 6.38, training accuracy is 0.687500, time 224.119 samples/sec
+epoch 14, total_step 215060, total loss is 9.25 , inference loss is 2.87, weight deacy loss is 6.38, training accuracy is 0.781250, time 225.630 samples/sec
+epoch 14, total_step 215080, total loss is 8.80 , inference loss is 2.42, weight deacy loss is 6.38, training accuracy is 0.718750, time 223.778 samples/sec
+epoch 14, total_step 215100, total loss is 10.92 , inference loss is 4.54, weight deacy loss is 6.38, training accuracy is 0.718750, time 12.562 samples/sec
+epoch 14, total_step 215120, total loss is 11.64 , inference loss is 5.26, weight deacy loss is 6.38, training accuracy is 0.625000, time 222.386 samples/sec
+epoch 14, total_step 215140, total loss is 12.30 , inference loss is 5.92, weight deacy loss is 6.38, training accuracy is 0.656250, time 245.917 samples/sec
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+epoch 14, total_step 215220, total loss is 12.74 , inference loss is 6.35, weight deacy loss is 6.38, training accuracy is 0.593750, time 238.854 samples/sec
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+epoch 14, total_step 215900, total loss is 13.99 , inference loss is 7.60, weight deacy loss is 6.39, training accuracy is 0.562500, time 223.989 samples/sec
+epoch 14, total_step 215920, total loss is 15.01 , inference loss is 8.62, weight deacy loss is 6.39, training accuracy is 0.531250, time 298.313 samples/sec
+epoch 14, total_step 215940, total loss is 11.70 , inference loss is 5.31, weight deacy loss is 6.39, training accuracy is 0.593750, time 297.323 samples/sec
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+epoch 14, total_step 215980, total loss is 11.50 , inference loss is 5.10, weight deacy loss is 6.39, training accuracy is 0.656250, time 217.374 samples/sec
+testing verification..
+(12000, 512)
+infer time 36.69806999999997
+best_threshold_index 122 0.9853703703703703
+best_threshold_index 121 0.985
+best_threshold_index 122 0.9840740740740741
+best_threshold_index 122 0.985
+best_threshold_index 121 0.9851851851851852
+best_threshold_index 122 0.9840740740740741
+best_threshold_index 122 0.985
+best_threshold_index 121 0.9851851851851852
+best_threshold_index 122 0.9835185185185186
+best_threshold_index 122 0.9842592592592593
+[lfw][216000]XNorm: 19.926864
+[lfw][216000]Accuracy-Flip: 0.98417+-0.00579
+test accuracy is:  0.9841666666666666
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+epoch 14, total_step 216020, total loss is 13.46 , inference loss is 7.07, weight deacy loss is 6.39, training accuracy is 0.437500, time 227.250 samples/sec
+epoch 14, total_step 216040, total loss is 15.46 , inference loss is 9.07, weight deacy loss is 6.39, training accuracy is 0.468750, time 297.869 samples/sec
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+epoch 14, total_step 216300, total loss is 11.03 , inference loss is 4.64, weight deacy loss is 6.39, training accuracy is 0.750000, time 12.337 samples/sec
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+epoch 14, total_step 217980, total loss is 10.76 , inference loss is 4.37, weight deacy loss is 6.39, training accuracy is 0.687500, time 225.605 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.87530799999998
+best_threshold_index 115 0.9838888888888889
+best_threshold_index 113 0.9840740740740741
+best_threshold_index 114 0.9837037037037037
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+best_threshold_index 116 0.9838888888888889
+best_threshold_index 121 0.9822222222222222
+best_threshold_index 121 0.9831481481481481
+[lfw][218000]XNorm: 21.484166
+[lfw][218000]Accuracy-Flip: 0.98117+-0.00654
+test accuracy is:  0.9811666666666665
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+epoch 14, total_step 219980, total loss is 13.05 , inference loss is 6.66, weight deacy loss is 6.39, training accuracy is 0.468750, time 226.753 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.759050999999996
+best_threshold_index 123 0.9885185185185185
+best_threshold_index 123 0.9883333333333333
+best_threshold_index 122 0.9875925925925926
+best_threshold_index 118 0.9887037037037038
+best_threshold_index 123 0.9885185185185185
+best_threshold_index 123 0.9874074074074074
+best_threshold_index 118 0.9875925925925926
+best_threshold_index 122 0.9879629629629629
+best_threshold_index 123 0.9868518518518519
+best_threshold_index 123 0.987037037037037
+[lfw][220000]XNorm: 19.728749
+[lfw][220000]Accuracy-Flip: 0.98667+-0.00601
+test accuracy is:  0.9866666666666666
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+testing verification..
+(12000, 512)
+infer time 24.328447000000022
+best_threshold_index 121 0.99
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+best_threshold_index 121 0.9894444444444445
+[lfw][222000]XNorm: 22.846084
+[lfw][222000]Accuracy-Flip: 0.98967+-0.00433
+test accuracy is:  0.9896666666666667
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+testing verification..
+(12000, 512)
+infer time 24.396721000000007
+best_threshold_index 116 0.9872222222222222
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+best_threshold_index 116 0.9866666666666667
+best_threshold_index 108 0.9868518518518519
+best_threshold_index 108 0.9862962962962963
+best_threshold_index 116 0.9853703703703703
+best_threshold_index 116 0.9864814814814815
+[lfw][224000]XNorm: 23.009132
+[lfw][224000]Accuracy-Flip: 0.98400+-0.00528
+test accuracy is:  0.984
+epoch 14, total_step 224000, total loss is 12.64 , inference loss is 6.25, weight deacy loss is 6.38, training accuracy is 0.625000, time 16.727 samples/sec
+epoch 14, total_step 224020, total loss is 9.88 , inference loss is 3.49, weight deacy loss is 6.38, training accuracy is 0.593750, time 211.410 samples/sec
+epoch 14, total_step 224040, total loss is 12.98 , inference loss is 6.60, weight deacy loss is 6.38, training accuracy is 0.531250, time 219.294 samples/sec
+epoch 14, total_step 224060, total loss is 11.38 , inference loss is 4.99, weight deacy loss is 6.38, training accuracy is 0.468750, time 226.838 samples/sec
+epoch 14, total_step 224080, total loss is 10.49 , inference loss is 4.10, weight deacy loss is 6.38, training accuracy is 0.500000, time 225.797 samples/sec
+epoch 14, total_step 224100, total loss is 12.48 , inference loss is 6.10, weight deacy loss is 6.38, training accuracy is 0.562500, time 12.385 samples/sec
+epoch 14, total_step 224120, total loss is 13.24 , inference loss is 6.86, weight deacy loss is 6.38, training accuracy is 0.468750, time 244.200 samples/sec
+epoch 14, total_step 224140, total loss is 12.09 , inference loss is 5.70, weight deacy loss is 6.38, training accuracy is 0.500000, time 220.839 samples/sec
+epoch 14, total_step 224160, total loss is 12.16 , inference loss is 5.77, weight deacy loss is 6.38, training accuracy is 0.500000, time 222.421 samples/sec
+epoch 14, total_step 224180, total loss is 10.92 , inference loss is 4.54, weight deacy loss is 6.38, training accuracy is 0.562500, time 199.509 samples/sec
+epoch 14, total_step 224200, total loss is 14.66 , inference loss is 8.27, weight deacy loss is 6.38, training accuracy is 0.531250, time 297.797 samples/sec
+epoch 14, total_step 224220, total loss is 12.80 , inference loss is 6.42, weight deacy loss is 6.38, training accuracy is 0.500000, time 217.125 samples/sec
+epoch 14, total_step 224240, total loss is 11.28 , inference loss is 4.90, weight deacy loss is 6.38, training accuracy is 0.625000, time 220.565 samples/sec
+epoch 14, total_step 224260, total loss is 15.78 , inference loss is 9.40, weight deacy loss is 6.38, training accuracy is 0.468750, time 217.164 samples/sec
+epoch 14, total_step 224280, total loss is 14.89 , inference loss is 8.51, weight deacy loss is 6.38, training accuracy is 0.468750, time 229.179 samples/sec
+epoch 14, total_step 224300, total loss is 10.68 , inference loss is 4.30, weight deacy loss is 6.38, training accuracy is 0.593750, time 181.218 samples/sec
+epoch 14, total_step 224320, total loss is 13.42 , inference loss is 7.03, weight deacy loss is 6.38, training accuracy is 0.437500, time 220.543 samples/sec
+epoch 14, total_step 224340, total loss is 12.74 , inference loss is 6.36, weight deacy loss is 6.38, training accuracy is 0.531250, time 222.807 samples/sec
+epoch 14, total_step 224360, total loss is 9.45 , inference loss is 3.07, weight deacy loss is 6.38, training accuracy is 0.625000, time 223.029 samples/sec
+epoch 14, total_step 224380, total loss is 12.78 , inference loss is 6.39, weight deacy loss is 6.38, training accuracy is 0.500000, time 225.445 samples/sec
+epoch 14, total_step 224400, total loss is 13.12 , inference loss is 6.74, weight deacy loss is 6.38, training accuracy is 0.625000, time 12.271 samples/sec
+epoch 14, total_step 224420, total loss is 12.45 , inference loss is 6.06, weight deacy loss is 6.38, training accuracy is 0.531250, time 216.014 samples/sec
+epoch 14, total_step 224440, total loss is 12.28 , inference loss is 5.90, weight deacy loss is 6.38, training accuracy is 0.593750, time 222.938 samples/sec
+epoch 14, total_step 224460, total loss is 13.35 , inference loss is 6.97, weight deacy loss is 6.38, training accuracy is 0.437500, time 299.000 samples/sec
+epoch 14, total_step 224480, total loss is 13.28 , inference loss is 6.90, weight deacy loss is 6.38, training accuracy is 0.500000, time 223.876 samples/sec
+epoch 14, total_step 224500, total loss is 13.60 , inference loss is 7.21, weight deacy loss is 6.38, training accuracy is 0.500000, time 218.534 samples/sec
+epoch 14, total_step 224520, total loss is 11.62 , inference loss is 5.23, weight deacy loss is 6.38, training accuracy is 0.562500, time 229.386 samples/sec
+epoch 14, total_step 224540, total loss is 11.06 , inference loss is 4.68, weight deacy loss is 6.38, training accuracy is 0.593750, time 215.245 samples/sec
+epoch 14, total_step 224560, total loss is 10.22 , inference loss is 3.84, weight deacy loss is 6.38, training accuracy is 0.531250, time 216.655 samples/sec
+epoch 14, total_step 224580, total loss is 17.31 , inference loss is 10.93, weight deacy loss is 6.38, training accuracy is 0.468750, time 214.146 samples/sec
+epoch 14, total_step 224600, total loss is 10.88 , inference loss is 4.50, weight deacy loss is 6.38, training accuracy is 0.468750, time 227.584 samples/sec
+epoch 14, total_step 224620, total loss is 13.12 , inference loss is 6.74, weight deacy loss is 6.38, training accuracy is 0.500000, time 296.246 samples/sec
+epoch 14, total_step 224640, total loss is 12.40 , inference loss is 6.01, weight deacy loss is 6.38, training accuracy is 0.593750, time 211.458 samples/sec
+epoch 14, total_step 224660, total loss is 11.41 , inference loss is 5.03, weight deacy loss is 6.38, training accuracy is 0.531250, time 219.208 samples/sec
+epoch 14, total_step 224680, total loss is 12.14 , inference loss is 5.76, weight deacy loss is 6.38, training accuracy is 0.500000, time 227.668 samples/sec
+epoch 14, total_step 224700, total loss is 13.07 , inference loss is 6.69, weight deacy loss is 6.38, training accuracy is 0.406250, time 12.684 samples/sec
+epoch 14, total_step 224720, total loss is 15.38 , inference loss is 9.00, weight deacy loss is 6.38, training accuracy is 0.343750, time 220.148 samples/sec
+epoch 14, total_step 224740, total loss is 10.20 , inference loss is 3.82, weight deacy loss is 6.38, training accuracy is 0.656250, time 225.498 samples/sec
+epoch 14, total_step 224760, total loss is 9.40 , inference loss is 3.02, weight deacy loss is 6.38, training accuracy is 0.562500, time 229.561 samples/sec
+epoch 14, total_step 224780, total loss is 11.10 , inference loss is 4.72, weight deacy loss is 6.38, training accuracy is 0.500000, time 227.235 samples/sec
+epoch 14, total_step 224800, total loss is 12.08 , inference loss is 5.70, weight deacy loss is 6.38, training accuracy is 0.625000, time 228.197 samples/sec
+epoch 14, total_step 224820, total loss is 13.48 , inference loss is 7.10, weight deacy loss is 6.38, training accuracy is 0.500000, time 225.570 samples/sec
+epoch 14, total_step 224840, total loss is 12.88 , inference loss is 6.50, weight deacy loss is 6.38, training accuracy is 0.468750, time 225.286 samples/sec
+epoch 14, total_step 224860, total loss is 9.72 , inference loss is 3.34, weight deacy loss is 6.38, training accuracy is 0.531250, time 235.036 samples/sec
+epoch 14, total_step 224880, total loss is 12.05 , inference loss is 5.67, weight deacy loss is 6.38, training accuracy is 0.625000, time 220.154 samples/sec
+epoch 14, total_step 224900, total loss is 13.56 , inference loss is 7.18, weight deacy loss is 6.38, training accuracy is 0.593750, time 201.598 samples/sec
+epoch 14, total_step 224920, total loss is 12.44 , inference loss is 6.06, weight deacy loss is 6.38, training accuracy is 0.437500, time 240.205 samples/sec
+epoch 14, total_step 224940, total loss is 13.04 , inference loss is 6.65, weight deacy loss is 6.38, training accuracy is 0.437500, time 227.827 samples/sec
+epoch 14, total_step 224960, total loss is 12.26 , inference loss is 5.88, weight deacy loss is 6.38, training accuracy is 0.437500, time 222.509 samples/sec
+epoch 14, total_step 224980, total loss is 11.48 , inference loss is 5.10, weight deacy loss is 6.38, training accuracy is 0.468750, time 221.415 samples/sec
+epoch 14, total_step 225000, total loss is 12.99 , inference loss is 6.61, weight deacy loss is 6.38, training accuracy is 0.500000, time 12.833 samples/sec
+epoch 14, total_step 225020, total loss is 12.83 , inference loss is 6.45, weight deacy loss is 6.38, training accuracy is 0.500000, time 222.713 samples/sec
+epoch 14, total_step 225040, total loss is 12.85 , inference loss is 6.47, weight deacy loss is 6.38, training accuracy is 0.625000, time 236.508 samples/sec
+epoch 14, total_step 225060, total loss is 13.47 , inference loss is 7.09, weight deacy loss is 6.38, training accuracy is 0.531250, time 215.542 samples/sec
+epoch 14, total_step 225080, total loss is 13.36 , inference loss is 6.98, weight deacy loss is 6.38, training accuracy is 0.531250, time 220.131 samples/sec
+epoch 14, total_step 225100, total loss is 12.90 , inference loss is 6.51, weight deacy loss is 6.38, training accuracy is 0.375000, time 213.983 samples/sec
+epoch 14, total_step 225120, total loss is 10.20 , inference loss is 3.82, weight deacy loss is 6.38, training accuracy is 0.593750, time 229.173 samples/sec
+epoch 14, total_step 225140, total loss is 10.76 , inference loss is 4.38, weight deacy loss is 6.38, training accuracy is 0.625000, time 178.879 samples/sec
+epoch 14, total_step 225160, total loss is 13.53 , inference loss is 7.14, weight deacy loss is 6.38, training accuracy is 0.437500, time 211.445 samples/sec
+epoch 14, total_step 225180, total loss is 11.48 , inference loss is 5.09, weight deacy loss is 6.38, training accuracy is 0.531250, time 224.934 samples/sec
+epoch 14, total_step 225200, total loss is 15.20 , inference loss is 8.81, weight deacy loss is 6.38, training accuracy is 0.531250, time 219.357 samples/sec
+epoch 14, total_step 225220, total loss is 13.11 , inference loss is 6.73, weight deacy loss is 6.38, training accuracy is 0.500000, time 198.950 samples/sec
+epoch 14, total_step 225240, total loss is 9.53 , inference loss is 3.14, weight deacy loss is 6.38, training accuracy is 0.562500, time 221.954 samples/sec
+epoch 14, total_step 225260, total loss is 12.51 , inference loss is 6.13, weight deacy loss is 6.38, training accuracy is 0.531250, time 214.537 samples/sec
+epoch 14, total_step 225280, total loss is 12.96 , inference loss is 6.58, weight deacy loss is 6.38, training accuracy is 0.625000, time 179.909 samples/sec
+epoch 14, total_step 225300, total loss is 13.94 , inference loss is 7.56, weight deacy loss is 6.38, training accuracy is 0.406250, time 12.985 samples/sec
+epoch 14, total_step 225320, total loss is 13.45 , inference loss is 7.07, weight deacy loss is 6.38, training accuracy is 0.500000, time 184.821 samples/sec
+epoch 14, total_step 225340, total loss is 16.79 , inference loss is 10.41, weight deacy loss is 6.38, training accuracy is 0.468750, time 225.349 samples/sec
+epoch 14, total_step 225360, total loss is 11.27 , inference loss is 4.89, weight deacy loss is 6.38, training accuracy is 0.500000, time 196.229 samples/sec
+epoch 14, total_step 225380, total loss is 10.35 , inference loss is 3.97, weight deacy loss is 6.38, training accuracy is 0.687500, time 214.817 samples/sec
+epoch 14, total_step 225400, total loss is 16.18 , inference loss is 9.80, weight deacy loss is 6.38, training accuracy is 0.468750, time 295.990 samples/sec
+epoch 14, total_step 225420, total loss is 11.38 , inference loss is 5.00, weight deacy loss is 6.38, training accuracy is 0.625000, time 221.985 samples/sec
+epoch 14, total_step 225440, total loss is 13.28 , inference loss is 6.90, weight deacy loss is 6.38, training accuracy is 0.406250, time 178.028 samples/sec
+epoch 14, total_step 225460, total loss is 11.25 , inference loss is 4.86, weight deacy loss is 6.38, training accuracy is 0.500000, time 218.989 samples/sec
+epoch 14, total_step 225480, total loss is 13.35 , inference loss is 6.97, weight deacy loss is 6.38, training accuracy is 0.562500, time 198.411 samples/sec
+epoch 14, total_step 225500, total loss is 9.64 , inference loss is 3.26, weight deacy loss is 6.38, training accuracy is 0.687500, time 241.412 samples/sec
+epoch 14, total_step 225520, total loss is 11.13 , inference loss is 4.75, weight deacy loss is 6.38, training accuracy is 0.531250, time 190.286 samples/sec
+epoch 14, total_step 225540, total loss is 15.20 , inference loss is 8.82, weight deacy loss is 6.38, training accuracy is 0.406250, time 201.492 samples/sec
+epoch 14, total_step 225560, total loss is 12.78 , inference loss is 6.40, weight deacy loss is 6.38, training accuracy is 0.562500, time 209.842 samples/sec
+epoch 14, total_step 225580, total loss is 12.48 , inference loss is 6.10, weight deacy loss is 6.38, training accuracy is 0.437500, time 228.079 samples/sec
+epoch 14, total_step 225600, total loss is 10.71 , inference loss is 4.33, weight deacy loss is 6.38, training accuracy is 0.437500, time 12.224 samples/sec
+epoch 14, total_step 225620, total loss is 12.96 , inference loss is 6.58, weight deacy loss is 6.38, training accuracy is 0.500000, time 220.500 samples/sec
+epoch 14, total_step 225640, total loss is 14.84 , inference loss is 8.46, weight deacy loss is 6.38, training accuracy is 0.500000, time 212.901 samples/sec
+epoch 14, total_step 225660, total loss is 13.35 , inference loss is 6.97, weight deacy loss is 6.38, training accuracy is 0.437500, time 229.625 samples/sec
+epoch 14, total_step 225680, total loss is 18.10 , inference loss is 11.72, weight deacy loss is 6.38, training accuracy is 0.375000, time 224.230 samples/sec
+epoch 14, total_step 225700, total loss is 14.54 , inference loss is 8.16, weight deacy loss is 6.38, training accuracy is 0.500000, time 228.178 samples/sec
+epoch 14, total_step 225720, total loss is 12.38 , inference loss is 6.00, weight deacy loss is 6.38, training accuracy is 0.500000, time 216.386 samples/sec
+epoch 14, total_step 225740, total loss is 13.94 , inference loss is 7.56, weight deacy loss is 6.38, training accuracy is 0.562500, time 218.351 samples/sec
+epoch 14, total_step 225760, total loss is 11.02 , inference loss is 4.64, weight deacy loss is 6.38, training accuracy is 0.562500, time 237.999 samples/sec
+epoch 14, total_step 225780, total loss is 14.32 , inference loss is 7.93, weight deacy loss is 6.38, training accuracy is 0.531250, time 199.952 samples/sec
+epoch 14, total_step 225800, total loss is 12.03 , inference loss is 5.65, weight deacy loss is 6.38, training accuracy is 0.562500, time 233.066 samples/sec
+epoch 14, total_step 225820, total loss is 16.96 , inference loss is 10.58, weight deacy loss is 6.38, training accuracy is 0.375000, time 218.581 samples/sec
+epoch 14, total_step 225840, total loss is 13.99 , inference loss is 7.61, weight deacy loss is 6.38, training accuracy is 0.531250, time 220.285 samples/sec
+epoch 14, total_step 225860, total loss is 12.38 , inference loss is 6.00, weight deacy loss is 6.38, training accuracy is 0.375000, time 216.723 samples/sec
+epoch 14, total_step 225880, total loss is 15.59 , inference loss is 9.21, weight deacy loss is 6.38, training accuracy is 0.468750, time 242.917 samples/sec
+epoch 14, total_step 225900, total loss is 12.16 , inference loss is 5.78, weight deacy loss is 6.38, training accuracy is 0.468750, time 12.330 samples/sec
+epoch 14, total_step 225920, total loss is 13.78 , inference loss is 7.40, weight deacy loss is 6.38, training accuracy is 0.406250, time 175.359 samples/sec
+epoch 14, total_step 225940, total loss is 13.90 , inference loss is 7.52, weight deacy loss is 6.38, training accuracy is 0.500000, time 225.025 samples/sec
+epoch 14, total_step 225960, total loss is 13.95 , inference loss is 7.57, weight deacy loss is 6.38, training accuracy is 0.531250, time 220.414 samples/sec
+epoch 14, total_step 225980, total loss is 13.66 , inference loss is 7.28, weight deacy loss is 6.38, training accuracy is 0.500000, time 217.229 samples/sec
+testing verification..
+(12000, 512)
+infer time 26.001047000000014
+best_threshold_index 109 0.9853703703703703
+best_threshold_index 109 0.9846296296296296
+best_threshold_index 109 0.9848148148148148
+best_threshold_index 109 0.9855555555555555
+best_threshold_index 109 0.9853703703703703
+best_threshold_index 109 0.9838888888888889
+best_threshold_index 105 0.985
+best_threshold_index 109 0.9844444444444445
+best_threshold_index 105 0.9833333333333333
+best_threshold_index 105 0.9844444444444445
+[lfw][226000]XNorm: 19.447554
+[lfw][226000]Accuracy-Flip: 0.98400+-0.00569
+test accuracy is:  0.984
+epoch 14, total_step 226000, total loss is 14.32 , inference loss is 7.94, weight deacy loss is 6.38, training accuracy is 0.375000, time 16.249 samples/sec
+epoch 14, total_step 226020, total loss is 10.58 , inference loss is 4.20, weight deacy loss is 6.38, training accuracy is 0.656250, time 224.257 samples/sec
+epoch 14, total_step 226040, total loss is 10.93 , inference loss is 4.55, weight deacy loss is 6.38, training accuracy is 0.500000, time 220.867 samples/sec
+epoch 14, total_step 226060, total loss is 15.12 , inference loss is 8.74, weight deacy loss is 6.38, training accuracy is 0.406250, time 225.089 samples/sec
+epoch 14, total_step 226080, total loss is 15.15 , inference loss is 8.77, weight deacy loss is 6.38, training accuracy is 0.500000, time 211.902 samples/sec
+epoch 14, total_step 226100, total loss is 16.15 , inference loss is 9.77, weight deacy loss is 6.38, training accuracy is 0.343750, time 229.876 samples/sec
+epoch 14, total_step 226120, total loss is 9.97 , inference loss is 3.59, weight deacy loss is 6.38, training accuracy is 0.531250, time 220.660 samples/sec
+epoch 14, total_step 226140, total loss is 12.51 , inference loss is 6.13, weight deacy loss is 6.38, training accuracy is 0.625000, time 221.202 samples/sec
+epoch 14, total_step 226160, total loss is 11.91 , inference loss is 5.54, weight deacy loss is 6.38, training accuracy is 0.406250, time 226.164 samples/sec
+epoch 14, total_step 226180, total loss is 16.43 , inference loss is 10.05, weight deacy loss is 6.38, training accuracy is 0.437500, time 221.572 samples/sec
+epoch 14, total_step 226200, total loss is 12.20 , inference loss is 5.82, weight deacy loss is 6.38, training accuracy is 0.531250, time 12.962 samples/sec
+epoch 14, total_step 226220, total loss is 14.94 , inference loss is 8.56, weight deacy loss is 6.38, training accuracy is 0.281250, time 212.740 samples/sec
+epoch 14, total_step 226240, total loss is 17.87 , inference loss is 11.49, weight deacy loss is 6.38, training accuracy is 0.250000, time 218.538 samples/sec
+epoch 14, total_step 226260, total loss is 10.57 , inference loss is 4.19, weight deacy loss is 6.38, training accuracy is 0.625000, time 214.971 samples/sec
+epoch 14, total_step 226280, total loss is 15.62 , inference loss is 9.24, weight deacy loss is 6.38, training accuracy is 0.500000, time 212.639 samples/sec
+epoch 14, total_step 226300, total loss is 14.96 , inference loss is 8.58, weight deacy loss is 6.38, training accuracy is 0.500000, time 208.578 samples/sec
+epoch 14, total_step 226320, total loss is 16.68 , inference loss is 10.30, weight deacy loss is 6.38, training accuracy is 0.375000, time 241.497 samples/sec
+epoch 14, total_step 226340, total loss is 16.04 , inference loss is 9.66, weight deacy loss is 6.38, training accuracy is 0.437500, time 298.327 samples/sec
+epoch 14, total_step 226360, total loss is 12.39 , inference loss is 6.01, weight deacy loss is 6.38, training accuracy is 0.531250, time 297.109 samples/sec
+epoch 14, total_step 226380, total loss is 12.74 , inference loss is 6.36, weight deacy loss is 6.38, training accuracy is 0.500000, time 226.127 samples/sec
+epoch 14, total_step 226400, total loss is 13.80 , inference loss is 7.43, weight deacy loss is 6.38, training accuracy is 0.406250, time 228.358 samples/sec
+epoch 14, total_step 226420, total loss is 14.10 , inference loss is 7.72, weight deacy loss is 6.38, training accuracy is 0.406250, time 243.657 samples/sec
+epoch 14, total_step 226440, total loss is 14.54 , inference loss is 8.16, weight deacy loss is 6.38, training accuracy is 0.531250, time 219.023 samples/sec
+epoch 14, total_step 226460, total loss is 14.17 , inference loss is 7.80, weight deacy loss is 6.38, training accuracy is 0.375000, time 224.249 samples/sec
+epoch 14, total_step 226480, total loss is 11.66 , inference loss is 5.29, weight deacy loss is 6.38, training accuracy is 0.468750, time 299.200 samples/sec
+epoch 14, total_step 226500, total loss is 14.71 , inference loss is 8.34, weight deacy loss is 6.38, training accuracy is 0.468750, time 12.378 samples/sec
+epoch 14, total_step 226520, total loss is 12.80 , inference loss is 6.42, weight deacy loss is 6.38, training accuracy is 0.562500, time 226.177 samples/sec
+epoch 14, total_step 226540, total loss is 16.02 , inference loss is 9.64, weight deacy loss is 6.38, training accuracy is 0.406250, time 220.214 samples/sec
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+epoch 14, total_step 226580, total loss is 13.26 , inference loss is 6.88, weight deacy loss is 6.38, training accuracy is 0.437500, time 226.090 samples/sec
+epoch 14, total_step 226600, total loss is 9.53 , inference loss is 3.15, weight deacy loss is 6.38, training accuracy is 0.687500, time 224.013 samples/sec
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+epoch 14, total_step 226660, total loss is 15.39 , inference loss is 9.01, weight deacy loss is 6.38, training accuracy is 0.406250, time 227.266 samples/sec
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+epoch 14, total_step 226720, total loss is 15.46 , inference loss is 9.08, weight deacy loss is 6.38, training accuracy is 0.375000, time 227.625 samples/sec
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+epoch 14, total_step 226880, total loss is 12.83 , inference loss is 6.46, weight deacy loss is 6.38, training accuracy is 0.343750, time 226.806 samples/sec
+epoch 14, total_step 226900, total loss is 13.32 , inference loss is 6.94, weight deacy loss is 6.38, training accuracy is 0.562500, time 228.002 samples/sec
+epoch 14, total_step 226920, total loss is 14.17 , inference loss is 7.79, weight deacy loss is 6.38, training accuracy is 0.406250, time 223.415 samples/sec
+epoch 14, total_step 226940, total loss is 15.24 , inference loss is 8.87, weight deacy loss is 6.38, training accuracy is 0.250000, time 222.975 samples/sec
+epoch 14, total_step 226960, total loss is 12.77 , inference loss is 6.39, weight deacy loss is 6.38, training accuracy is 0.437500, time 224.026 samples/sec
+epoch 14, total_step 226980, total loss is 16.64 , inference loss is 10.27, weight deacy loss is 6.38, training accuracy is 0.250000, time 219.845 samples/sec
+epoch 14, total_step 227000, total loss is 13.76 , inference loss is 7.38, weight deacy loss is 6.38, training accuracy is 0.375000, time 299.372 samples/sec
+epoch 14, total_step 227020, total loss is 15.14 , inference loss is 8.77, weight deacy loss is 6.38, training accuracy is 0.375000, time 223.746 samples/sec
+epoch 14, total_step 227040, total loss is 18.65 , inference loss is 12.28, weight deacy loss is 6.38, training accuracy is 0.406250, time 221.716 samples/sec
+epoch 14, total_step 227060, total loss is 12.77 , inference loss is 6.39, weight deacy loss is 6.38, training accuracy is 0.375000, time 223.152 samples/sec
+epoch 14, total_step 227080, total loss is 12.78 , inference loss is 6.41, weight deacy loss is 6.38, training accuracy is 0.500000, time 213.991 samples/sec
+epoch 14, total_step 227100, total loss is 15.02 , inference loss is 8.64, weight deacy loss is 6.38, training accuracy is 0.375000, time 12.137 samples/sec
+epoch 14, total_step 227120, total loss is 14.95 , inference loss is 8.57, weight deacy loss is 6.38, training accuracy is 0.468750, time 227.684 samples/sec
+epoch 14, total_step 227140, total loss is 13.28 , inference loss is 6.90, weight deacy loss is 6.38, training accuracy is 0.312500, time 205.716 samples/sec
+epoch 14, total_step 227160, total loss is 13.67 , inference loss is 7.29, weight deacy loss is 6.38, training accuracy is 0.500000, time 222.494 samples/sec
+epoch 14, total_step 227180, total loss is 11.96 , inference loss is 5.58, weight deacy loss is 6.38, training accuracy is 0.468750, time 215.366 samples/sec
+epoch 14, total_step 227200, total loss is 16.52 , inference loss is 10.15, weight deacy loss is 6.38, training accuracy is 0.437500, time 215.322 samples/sec
+epoch 14, total_step 227220, total loss is 12.96 , inference loss is 6.58, weight deacy loss is 6.38, training accuracy is 0.562500, time 221.000 samples/sec
+epoch 14, total_step 227240, total loss is 14.43 , inference loss is 8.05, weight deacy loss is 6.38, training accuracy is 0.281250, time 223.977 samples/sec
+epoch 14, total_step 227260, total loss is 11.63 , inference loss is 5.26, weight deacy loss is 6.38, training accuracy is 0.562500, time 295.416 samples/sec
+epoch 14, total_step 227280, total loss is 15.28 , inference loss is 8.91, weight deacy loss is 6.38, training accuracy is 0.406250, time 226.221 samples/sec
+epoch 14, total_step 227300, total loss is 12.63 , inference loss is 6.25, weight deacy loss is 6.38, training accuracy is 0.562500, time 221.044 samples/sec
+epoch 14, total_step 227320, total loss is 16.30 , inference loss is 9.92, weight deacy loss is 6.38, training accuracy is 0.437500, time 220.566 samples/sec
+epoch 14, total_step 227340, total loss is 13.44 , inference loss is 7.07, weight deacy loss is 6.38, training accuracy is 0.406250, time 225.702 samples/sec
+epoch 14, total_step 227360, total loss is 11.72 , inference loss is 5.34, weight deacy loss is 6.38, training accuracy is 0.625000, time 231.805 samples/sec
+epoch 14, total_step 227380, total loss is 14.40 , inference loss is 8.02, weight deacy loss is 6.38, training accuracy is 0.406250, time 222.893 samples/sec
+epoch 14, total_step 227400, total loss is 16.75 , inference loss is 10.37, weight deacy loss is 6.38, training accuracy is 0.437500, time 12.468 samples/sec
+epoch 14, total_step 227420, total loss is 13.04 , inference loss is 6.66, weight deacy loss is 6.38, training accuracy is 0.531250, time 229.902 samples/sec
+epoch 14, total_step 227440, total loss is 9.89 , inference loss is 3.52, weight deacy loss is 6.38, training accuracy is 0.562500, time 225.307 samples/sec
+epoch 14, total_step 227460, total loss is 15.18 , inference loss is 8.81, weight deacy loss is 6.38, training accuracy is 0.343750, time 227.921 samples/sec
+epoch 14, total_step 227480, total loss is 17.45 , inference loss is 11.08, weight deacy loss is 6.38, training accuracy is 0.281250, time 220.375 samples/sec
+epoch 14, total_step 227500, total loss is 17.07 , inference loss is 10.69, weight deacy loss is 6.38, training accuracy is 0.187500, time 232.128 samples/sec
+epoch 14, total_step 227520, total loss is 13.16 , inference loss is 6.78, weight deacy loss is 6.38, training accuracy is 0.406250, time 297.464 samples/sec
+epoch 14, total_step 227540, total loss is 15.30 , inference loss is 8.93, weight deacy loss is 6.37, training accuracy is 0.468750, time 219.469 samples/sec
+epoch 14, total_step 227560, total loss is 16.97 , inference loss is 10.60, weight deacy loss is 6.37, training accuracy is 0.281250, time 213.494 samples/sec
+epoch 14, total_step 227580, total loss is 15.18 , inference loss is 8.81, weight deacy loss is 6.37, training accuracy is 0.468750, time 228.544 samples/sec
+epoch 14, total_step 227600, total loss is 15.05 , inference loss is 8.67, weight deacy loss is 6.37, training accuracy is 0.343750, time 228.124 samples/sec
+epoch 14, total_step 227620, total loss is 13.41 , inference loss is 7.03, weight deacy loss is 6.37, training accuracy is 0.406250, time 217.386 samples/sec
+epoch 14, total_step 227640, total loss is 12.59 , inference loss is 6.22, weight deacy loss is 6.37, training accuracy is 0.343750, time 226.054 samples/sec
+epoch 14, total_step 227660, total loss is 14.54 , inference loss is 8.16, weight deacy loss is 6.37, training accuracy is 0.437500, time 219.743 samples/sec
+epoch 14, total_step 227680, total loss is 12.92 , inference loss is 6.54, weight deacy loss is 6.37, training accuracy is 0.531250, time 223.935 samples/sec
+epoch 14, total_step 227700, total loss is 18.42 , inference loss is 12.04, weight deacy loss is 6.37, training accuracy is 0.281250, time 12.629 samples/sec
+epoch 14, total_step 227720, total loss is 12.89 , inference loss is 6.51, weight deacy loss is 6.37, training accuracy is 0.437500, time 223.123 samples/sec
+epoch 14, total_step 227740, total loss is 14.05 , inference loss is 7.68, weight deacy loss is 6.37, training accuracy is 0.375000, time 222.637 samples/sec
+epoch 14, total_step 227760, total loss is 10.16 , inference loss is 3.78, weight deacy loss is 6.37, training accuracy is 0.468750, time 223.296 samples/sec
+epoch 14, total_step 227780, total loss is 14.58 , inference loss is 8.21, weight deacy loss is 6.37, training accuracy is 0.406250, time 282.682 samples/sec
+epoch 14, total_step 227800, total loss is 13.03 , inference loss is 6.66, weight deacy loss is 6.37, training accuracy is 0.437500, time 225.737 samples/sec
+epoch 14, total_step 227820, total loss is 16.41 , inference loss is 10.04, weight deacy loss is 6.37, training accuracy is 0.343750, time 225.838 samples/sec
+epoch 14, total_step 227840, total loss is 14.30 , inference loss is 7.92, weight deacy loss is 6.37, training accuracy is 0.437500, time 227.720 samples/sec
+epoch 14, total_step 227860, total loss is 15.86 , inference loss is 9.49, weight deacy loss is 6.37, training accuracy is 0.437500, time 220.180 samples/sec
+epoch 14, total_step 227880, total loss is 11.68 , inference loss is 5.30, weight deacy loss is 6.37, training accuracy is 0.468750, time 226.747 samples/sec
+epoch 14, total_step 227900, total loss is 17.16 , inference loss is 10.79, weight deacy loss is 6.37, training accuracy is 0.343750, time 224.973 samples/sec
+epoch 14, total_step 227920, total loss is 17.01 , inference loss is 10.64, weight deacy loss is 6.37, training accuracy is 0.312500, time 246.234 samples/sec
+epoch 14, total_step 227940, total loss is 17.96 , inference loss is 11.59, weight deacy loss is 6.37, training accuracy is 0.343750, time 228.995 samples/sec
+epoch 14, total_step 227960, total loss is 16.16 , inference loss is 9.79, weight deacy loss is 6.37, training accuracy is 0.375000, time 224.445 samples/sec
+epoch 14, total_step 227980, total loss is 16.14 , inference loss is 9.77, weight deacy loss is 6.37, training accuracy is 0.406250, time 222.773 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.74069200000001
+best_threshold_index 108 0.9862962962962963
+best_threshold_index 110 0.9855555555555555
+best_threshold_index 108 0.9848148148148148
+best_threshold_index 107 0.9862962962962963
+best_threshold_index 108 0.9857407407407407
+best_threshold_index 108 0.9848148148148148
+best_threshold_index 110 0.985
+best_threshold_index 110 0.9844444444444445
+best_threshold_index 110 0.9837037037037037
+best_threshold_index 110 0.9842592592592593
+[lfw][228000]XNorm: 22.208574
+[lfw][228000]Accuracy-Flip: 0.98383+-0.00792
+test accuracy is:  0.9838333333333334
+epoch 14, total_step 228000, total loss is 13.11 , inference loss is 6.73, weight deacy loss is 6.37, training accuracy is 0.500000, time 16.087 samples/sec
+epoch 14, total_step 228020, total loss is 10.75 , inference loss is 4.37, weight deacy loss is 6.37, training accuracy is 0.562500, time 212.435 samples/sec
+epoch 14, total_step 228040, total loss is 13.90 , inference loss is 7.53, weight deacy loss is 6.37, training accuracy is 0.406250, time 219.023 samples/sec
+epoch 14, total_step 228060, total loss is 12.06 , inference loss is 5.68, weight deacy loss is 6.37, training accuracy is 0.437500, time 234.263 samples/sec
+epoch 14, total_step 228080, total loss is 9.80 , inference loss is 3.43, weight deacy loss is 6.37, training accuracy is 0.562500, time 227.032 samples/sec
+epoch 14, total_step 228100, total loss is 10.81 , inference loss is 4.44, weight deacy loss is 6.37, training accuracy is 0.500000, time 297.663 samples/sec
+epoch 14, total_step 228120, total loss is 19.10 , inference loss is 12.72, weight deacy loss is 6.37, training accuracy is 0.343750, time 297.468 samples/sec
+epoch 14, total_step 228140, total loss is 17.40 , inference loss is 11.03, weight deacy loss is 6.37, training accuracy is 0.437500, time 213.108 samples/sec
+epoch 14, total_step 228160, total loss is 12.87 , inference loss is 6.50, weight deacy loss is 6.37, training accuracy is 0.500000, time 215.131 samples/sec
+epoch 14, total_step 228180, total loss is 12.55 , inference loss is 6.17, weight deacy loss is 6.37, training accuracy is 0.406250, time 225.803 samples/sec
+epoch 14, total_step 228200, total loss is 16.13 , inference loss is 9.75, weight deacy loss is 6.37, training accuracy is 0.250000, time 220.004 samples/sec
+epoch 14, total_step 228220, total loss is 13.69 , inference loss is 7.31, weight deacy loss is 6.37, training accuracy is 0.343750, time 220.681 samples/sec
+epoch 14, total_step 228240, total loss is 16.25 , inference loss is 9.88, weight deacy loss is 6.37, training accuracy is 0.375000, time 219.765 samples/sec
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+epoch 14, total_step 228300, total loss is 13.29 , inference loss is 6.92, weight deacy loss is 6.37, training accuracy is 0.437500, time 12.766 samples/sec
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+epoch 14, total_step 228360, total loss is 15.38 , inference loss is 9.01, weight deacy loss is 6.37, training accuracy is 0.218750, time 221.639 samples/sec
+epoch 14, total_step 228380, total loss is 10.40 , inference loss is 4.03, weight deacy loss is 6.37, training accuracy is 0.437500, time 228.954 samples/sec
+epoch 14, total_step 228400, total loss is 15.29 , inference loss is 8.92, weight deacy loss is 6.37, training accuracy is 0.312500, time 237.680 samples/sec
+epoch 14, total_step 228420, total loss is 11.28 , inference loss is 4.91, weight deacy loss is 6.37, training accuracy is 0.406250, time 222.992 samples/sec
+epoch 14, total_step 228440, total loss is 13.22 , inference loss is 6.85, weight deacy loss is 6.37, training accuracy is 0.312500, time 227.526 samples/sec
+epoch 14, total_step 228460, total loss is 15.79 , inference loss is 9.42, weight deacy loss is 6.37, training accuracy is 0.312500, time 210.193 samples/sec
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+epoch 14, total_step 228500, total loss is 14.74 , inference loss is 8.37, weight deacy loss is 6.37, training accuracy is 0.406250, time 206.532 samples/sec
+epoch 14, total_step 228520, total loss is 17.73 , inference loss is 11.36, weight deacy loss is 6.37, training accuracy is 0.250000, time 201.268 samples/sec
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+epoch 14, total_step 228560, total loss is 17.51 , inference loss is 11.13, weight deacy loss is 6.37, training accuracy is 0.312500, time 217.618 samples/sec
+epoch 14, total_step 228580, total loss is 16.42 , inference loss is 10.05, weight deacy loss is 6.37, training accuracy is 0.406250, time 218.904 samples/sec
+epoch 14, total_step 228600, total loss is 11.73 , inference loss is 5.36, weight deacy loss is 6.37, training accuracy is 0.531250, time 13.370 samples/sec
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+epoch 14, total_step 228640, total loss is 15.69 , inference loss is 9.32, weight deacy loss is 6.37, training accuracy is 0.281250, time 224.484 samples/sec
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+epoch 14, total_step 228700, total loss is 15.44 , inference loss is 9.07, weight deacy loss is 6.37, training accuracy is 0.218750, time 225.580 samples/sec
+epoch 14, total_step 228720, total loss is 15.97 , inference loss is 9.60, weight deacy loss is 6.37, training accuracy is 0.281250, time 235.241 samples/sec
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+epoch 14, total_step 228780, total loss is 14.75 , inference loss is 8.38, weight deacy loss is 6.37, training accuracy is 0.406250, time 225.543 samples/sec
+epoch 14, total_step 228800, total loss is 14.64 , inference loss is 8.27, weight deacy loss is 6.37, training accuracy is 0.375000, time 226.453 samples/sec
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+epoch 14, total_step 228840, total loss is 13.00 , inference loss is 6.63, weight deacy loss is 6.37, training accuracy is 0.343750, time 215.694 samples/sec
+epoch 14, total_step 228860, total loss is 19.27 , inference loss is 12.90, weight deacy loss is 6.37, training accuracy is 0.218750, time 295.502 samples/sec
+epoch 14, total_step 228880, total loss is 14.09 , inference loss is 7.72, weight deacy loss is 6.37, training accuracy is 0.468750, time 225.714 samples/sec
+epoch 14, total_step 228900, total loss is 10.97 , inference loss is 4.60, weight deacy loss is 6.37, training accuracy is 0.437500, time 12.047 samples/sec
+epoch 14, total_step 228920, total loss is 13.41 , inference loss is 7.04, weight deacy loss is 6.37, training accuracy is 0.406250, time 223.771 samples/sec
+epoch 14, total_step 228940, total loss is 14.70 , inference loss is 8.33, weight deacy loss is 6.37, training accuracy is 0.500000, time 226.406 samples/sec
+epoch 14, total_step 228960, total loss is 13.01 , inference loss is 6.64, weight deacy loss is 6.37, training accuracy is 0.312500, time 218.776 samples/sec
+epoch 14, total_step 228980, total loss is 18.23 , inference loss is 11.86, weight deacy loss is 6.37, training accuracy is 0.406250, time 233.994 samples/sec
+epoch 14, total_step 229000, total loss is 14.24 , inference loss is 7.87, weight deacy loss is 6.37, training accuracy is 0.500000, time 235.860 samples/sec
+epoch 14, total_step 229020, total loss is 14.37 , inference loss is 8.00, weight deacy loss is 6.37, training accuracy is 0.375000, time 216.794 samples/sec
+epoch 14, total_step 229040, total loss is 16.86 , inference loss is 10.49, weight deacy loss is 6.37, training accuracy is 0.375000, time 197.160 samples/sec
+epoch 14, total_step 229060, total loss is 12.91 , inference loss is 6.54, weight deacy loss is 6.37, training accuracy is 0.468750, time 226.875 samples/sec
+epoch 14, total_step 229080, total loss is 18.26 , inference loss is 11.89, weight deacy loss is 6.37, training accuracy is 0.312500, time 214.341 samples/sec
+epoch 14, total_step 229100, total loss is 15.60 , inference loss is 9.24, weight deacy loss is 6.37, training accuracy is 0.281250, time 181.601 samples/sec
+epoch 14, total_step 229120, total loss is 10.65 , inference loss is 4.28, weight deacy loss is 6.37, training accuracy is 0.625000, time 297.879 samples/sec
+epoch 14, total_step 229140, total loss is 18.20 , inference loss is 11.83, weight deacy loss is 6.37, training accuracy is 0.312500, time 219.219 samples/sec
+epoch 14, total_step 229160, total loss is 16.02 , inference loss is 9.65, weight deacy loss is 6.37, training accuracy is 0.312500, time 224.126 samples/sec
+epoch 14, total_step 229180, total loss is 14.34 , inference loss is 7.97, weight deacy loss is 6.37, training accuracy is 0.500000, time 226.868 samples/sec
+epoch 14, total_step 229200, total loss is 13.89 , inference loss is 7.52, weight deacy loss is 6.37, training accuracy is 0.500000, time 12.080 samples/sec
+epoch 14, total_step 229220, total loss is 14.44 , inference loss is 8.07, weight deacy loss is 6.37, training accuracy is 0.375000, time 223.356 samples/sec2022-12-01 15:34:55.858746: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:145] Filling up shuffle buffer (this may take a while): 7057 of 10000
+2022-12-01 15:35:00.004269: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:195] Shuffle buffer filled.
+
+epoch 14, total_step 229240, total loss is 13.86 , inference loss is 7.50, weight deacy loss is 6.37, training accuracy is 0.437500, time 223.880 samples/sec
+epoch 14, total_step 229260, total loss is 16.76 , inference loss is 10.39, weight deacy loss is 6.37, training accuracy is 0.281250, time 232.216 samples/sec
+epoch 14, total_step 229280, total loss is 13.19 , inference loss is 6.82, weight deacy loss is 6.37, training accuracy is 0.312500, time 225.629 samples/sec
+epoch 14, total_step 229300, total loss is 13.73 , inference loss is 7.36, weight deacy loss is 6.37, training accuracy is 0.250000, time 204.136 samples/sec
+epoch 14, total_step 229320, total loss is 16.45 , inference loss is 10.08, weight deacy loss is 6.37, training accuracy is 0.437500, time 218.662 samples/sec
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+epoch 14, total_step 229440, total loss is 12.67 , inference loss is 6.30, weight deacy loss is 6.37, training accuracy is 0.312500, time 215.555 samples/sec
+epoch 14, total_step 229460, total loss is 13.94 , inference loss is 7.57, weight deacy loss is 6.37, training accuracy is 0.406250, time 218.282 samples/sec
+epoch 14, total_step 229480, total loss is 14.48 , inference loss is 8.11, weight deacy loss is 6.37, training accuracy is 0.375000, time 225.792 samples/sec
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+epoch 14, total_step 229540, total loss is 16.11 , inference loss is 9.75, weight deacy loss is 6.37, training accuracy is 0.312500, time 228.530 samples/sec
+epoch 14, total_step 229560, total loss is 15.83 , inference loss is 9.47, weight deacy loss is 6.37, training accuracy is 0.375000, time 227.412 samples/sec
+epoch 14, total_step 229580, total loss is 14.80 , inference loss is 8.43, weight deacy loss is 6.37, training accuracy is 0.281250, time 228.103 samples/sec
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+epoch 14, total_step 229880, total loss is 16.57 , inference loss is 10.21, weight deacy loss is 6.37, training accuracy is 0.468750, time 222.074 samples/sec
+epoch 14, total_step 229900, total loss is 15.61 , inference loss is 9.25, weight deacy loss is 6.37, training accuracy is 0.343750, time 198.496 samples/sec
+epoch 14, total_step 229920, total loss is 14.13 , inference loss is 7.76, weight deacy loss is 6.37, training accuracy is 0.406250, time 191.893 samples/sec
+epoch 14, total_step 229940, total loss is 15.72 , inference loss is 9.35, weight deacy loss is 6.37, training accuracy is 0.437500, time 208.579 samples/sec
+epoch 14, total_step 229960, total loss is 15.66 , inference loss is 9.30, weight deacy loss is 6.37, training accuracy is 0.406250, time 297.621 samples/sec
+End of epoch 14
+epoch 15, total_step 229980, total loss is 10.87 , inference loss is 4.50, weight deacy loss is 6.37, training accuracy is 0.531250, time 214.297 samples/sec
+testing verification..
+(12000, 512)
+infer time 25.085755000000006
+best_threshold_index 96 0.9833333333333333
+best_threshold_index 96 0.9829629629629629
+best_threshold_index 96 0.9827777777777778
+best_threshold_index 96 0.9842592592592593
+best_threshold_index 96 0.9824074074074074
+best_threshold_index 96 0.9824074074074074
+best_threshold_index 96 0.9827777777777778
+best_threshold_index 96 0.9835185185185186
+best_threshold_index 96 0.982037037037037
+best_threshold_index 101 0.982037037037037
+[lfw][230000]XNorm: 22.228475
+[lfw][230000]Accuracy-Flip: 0.98233+-0.00564
+test accuracy is:  0.9823333333333333
+epoch 15, total_step 230000, total loss is 14.02 , inference loss is 7.65, weight deacy loss is 6.37, training accuracy is 0.687500, time 16.028 samples/sec
+epoch 15, total_step 230020, total loss is 9.34 , inference loss is 2.98, weight deacy loss is 6.37, training accuracy is 0.750000, time 221.082 samples/sec
+epoch 15, total_step 230040, total loss is 11.13 , inference loss is 4.77, weight deacy loss is 6.37, training accuracy is 0.812500, time 216.669 samples/sec
+epoch 15, total_step 230060, total loss is 9.37 , inference loss is 3.01, weight deacy loss is 6.37, training accuracy is 0.750000, time 219.809 samples/sec
+epoch 15, total_step 230080, total loss is 11.33 , inference loss is 4.97, weight deacy loss is 6.36, training accuracy is 0.687500, time 225.123 samples/sec
+epoch 15, total_step 230100, total loss is 9.22 , inference loss is 2.86, weight deacy loss is 6.36, training accuracy is 0.781250, time 13.020 samples/sec
+epoch 15, total_step 230120, total loss is 9.79 , inference loss is 3.43, weight deacy loss is 6.36, training accuracy is 0.812500, time 221.568 samples/sec
+epoch 15, total_step 230140, total loss is 13.12 , inference loss is 6.75, weight deacy loss is 6.36, training accuracy is 0.687500, time 224.660 samples/sec
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+epoch 15, total_step 230240, total loss is 11.48 , inference loss is 5.11, weight deacy loss is 6.36, training accuracy is 0.750000, time 199.610 samples/sec
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+epoch 15, total_step 230380, total loss is 9.02 , inference loss is 2.65, weight deacy loss is 6.36, training accuracy is 0.718750, time 224.347 samples/sec
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+epoch 15, total_step 231980, total loss is 11.91 , inference loss is 5.53, weight deacy loss is 6.38, training accuracy is 0.562500, time 196.528 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.690452000000025
+best_threshold_index 121 0.9855555555555555
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+best_threshold_index 116 0.9837037037037037
+[lfw][232000]XNorm: 21.724717
+[lfw][232000]Accuracy-Flip: 0.98267+-0.00588
+test accuracy is:  0.9826666666666666
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+testing verification..
+(12000, 512)
+infer time 24.275073000000035
+best_threshold_index 122 0.9890740740740741
+best_threshold_index 122 0.9879629629629629
+best_threshold_index 122 0.9888888888888889
+best_threshold_index 119 0.9890740740740741
+best_threshold_index 122 0.9883333333333333
+best_threshold_index 122 0.9881481481481481
+best_threshold_index 119 0.9875925925925926
+best_threshold_index 122 0.9879629629629629
+best_threshold_index 122 0.9868518518518519
+best_threshold_index 122 0.9877777777777778
+[lfw][234000]XNorm: 19.795196
+[lfw][234000]Accuracy-Flip: 0.98750+-0.00634
+test accuracy is:  0.9875
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+epoch 15, total_step 235980, total loss is 9.89 , inference loss is 3.50, weight deacy loss is 6.38, training accuracy is 0.625000, time 299.068 samples/sec
+testing verification..
+(12000, 512)
+infer time 25.494266999999994
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+[lfw][236000]XNorm: 21.149444
+[lfw][236000]Accuracy-Flip: 0.98683+-0.00621
+test accuracy is:  0.9868333333333335
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+testing verification..
+(12000, 512)
+infer time 25.269894000000022
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+[lfw][238000]XNorm: 21.477620
+[lfw][238000]Accuracy-Flip: 0.98800+-0.00670
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+testing verification..
+(12000, 512)
+infer time 23.888999999999985
+best_threshold_index 121 0.9866666666666667
+best_threshold_index 118 0.9866666666666667
+best_threshold_index 121 0.9855555555555555
+best_threshold_index 118 0.9866666666666667
+best_threshold_index 116 0.9859259259259259
+best_threshold_index 121 0.9853703703703703
+best_threshold_index 116 0.9862962962962963
+best_threshold_index 118 0.9859259259259259
+best_threshold_index 121 0.9848148148148148
+best_threshold_index 118 0.9853703703703703
+[lfw][240000]XNorm: 22.500482
+[lfw][240000]Accuracy-Flip: 0.98467+-0.00567
+test accuracy is:  0.9846666666666666
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+epoch 15, total_step 241900, total loss is 11.50 , inference loss is 5.12, weight deacy loss is 6.38, training accuracy is 0.562500, time 221.726 samples/sec
+epoch 15, total_step 241920, total loss is 10.80 , inference loss is 4.42, weight deacy loss is 6.38, training accuracy is 0.531250, time 296.782 samples/sec
+epoch 15, total_step 241940, total loss is 13.67 , inference loss is 7.29, weight deacy loss is 6.38, training accuracy is 0.312500, time 212.257 samples/sec
+epoch 15, total_step 241960, total loss is 17.31 , inference loss is 10.93, weight deacy loss is 6.38, training accuracy is 0.375000, time 222.787 samples/sec
+epoch 15, total_step 241980, total loss is 12.63 , inference loss is 6.25, weight deacy loss is 6.38, training accuracy is 0.406250, time 220.373 samples/sec
+testing verification..
+(12000, 512)
+infer time 23.56913900000002
+best_threshold_index 109 0.9855555555555555
+best_threshold_index 109 0.985
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+[lfw][242000]XNorm: 21.562455
+[lfw][242000]Accuracy-Flip: 0.98467+-0.00662
+test accuracy is:  0.9846666666666666
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+epoch 15, total_step 242020, total loss is 17.97 , inference loss is 11.59, weight deacy loss is 6.38, training accuracy is 0.375000, time 217.762 samples/sec
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+epoch 15, total_step 243980, total loss is 13.78 , inference loss is 7.41, weight deacy loss is 6.37, training accuracy is 0.375000, time 213.146 samples/sec
+testing verification..
+(12000, 512)
+infer time 23.440799000000002
+best_threshold_index 109 0.9857407407407407
+best_threshold_index 111 0.985
+best_threshold_index 111 0.9846296296296296
+best_threshold_index 109 0.987037037037037
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+best_threshold_index 111 0.9842592592592593
+best_threshold_index 111 0.9851851851851852
+best_threshold_index 111 0.9842592592592593
+best_threshold_index 111 0.9835185185185186
+best_threshold_index 111 0.9846296296296296
+[lfw][244000]XNorm: 20.884777
+[lfw][244000]Accuracy-Flip: 0.98450+-0.00910
+test accuracy is:  0.9845
+epoch 15, total_step 244000, total loss is 17.65 , inference loss is 11.28, weight deacy loss is 6.37, training accuracy is 0.343750, time 8.998 samples/sec
+epoch 15, total_step 244020, total loss is 16.53 , inference loss is 10.16, weight deacy loss is 6.37, training accuracy is 0.281250, time 207.327 samples/sec
+epoch 15, total_step 244040, total loss is 19.29 , inference loss is 12.92, weight deacy loss is 6.37, training accuracy is 0.281250, time 215.592 samples/sec
+epoch 15, total_step 244060, total loss is 12.97 , inference loss is 6.59, weight deacy loss is 6.37, training accuracy is 0.406250, time 203.922 samples/sec
+epoch 15, total_step 244080, total loss is 19.34 , inference loss is 12.97, weight deacy loss is 6.37, training accuracy is 0.218750, time 214.079 samples/sec
+epoch 15, total_step 244100, total loss is 15.02 , inference loss is 8.65, weight deacy loss is 6.37, training accuracy is 0.468750, time 214.017 samples/sec
+epoch 15, total_step 244120, total loss is 15.78 , inference loss is 9.41, weight deacy loss is 6.37, training accuracy is 0.281250, time 212.674 samples/sec
+epoch 15, total_step 244140, total loss is 17.45 , inference loss is 11.08, weight deacy loss is 6.37, training accuracy is 0.312500, time 202.365 samples/sec
+epoch 15, total_step 244160, total loss is 11.24 , inference loss is 4.87, weight deacy loss is 6.37, training accuracy is 0.468750, time 216.027 samples/sec
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+epoch 15, total_step 244200, total loss is 14.56 , inference loss is 8.19, weight deacy loss is 6.37, training accuracy is 0.437500, time 8.946 samples/sec
+epoch 15, total_step 244220, total loss is 16.10 , inference loss is 9.73, weight deacy loss is 6.37, training accuracy is 0.406250, time 239.831 samples/sec
+epoch 15, total_step 244240, total loss is 13.71 , inference loss is 7.34, weight deacy loss is 6.37, training accuracy is 0.250000, time 212.250 samples/sec
+epoch 15, total_step 244260, total loss is 14.58 , inference loss is 8.21, weight deacy loss is 6.37, training accuracy is 0.406250, time 215.825 samples/sec2022-12-01 16:37:21.565021: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:145] Filling up shuffle buffer (this may take a while): 7192 of 10000
+2022-12-01 16:37:25.400989: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:195] Shuffle buffer filled.
+
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+epoch 15, total_step 244600, total loss is 15.97 , inference loss is 9.60, weight deacy loss is 6.37, training accuracy is 0.500000, time 199.561 samples/sec
+epoch 15, total_step 244620, total loss is 14.97 , inference loss is 8.60, weight deacy loss is 6.37, training accuracy is 0.437500, time 219.019 samples/sec
+epoch 15, total_step 244640, total loss is 16.87 , inference loss is 10.50, weight deacy loss is 6.37, training accuracy is 0.281250, time 216.155 samples/sec
+epoch 15, total_step 244660, total loss is 14.59 , inference loss is 8.22, weight deacy loss is 6.37, training accuracy is 0.375000, time 215.947 samples/sec
+epoch 15, total_step 244680, total loss is 10.00 , inference loss is 3.63, weight deacy loss is 6.37, training accuracy is 0.468750, time 220.962 samples/sec
+epoch 15, total_step 244700, total loss is 10.58 , inference loss is 4.21, weight deacy loss is 6.37, training accuracy is 0.562500, time 225.391 samples/sec
+epoch 15, total_step 244720, total loss is 11.76 , inference loss is 5.39, weight deacy loss is 6.37, training accuracy is 0.343750, time 237.316 samples/sec
+epoch 15, total_step 244740, total loss is 17.62 , inference loss is 11.25, weight deacy loss is 6.37, training accuracy is 0.312500, time 299.655 samples/sec
+epoch 15, total_step 244760, total loss is 16.06 , inference loss is 9.69, weight deacy loss is 6.37, training accuracy is 0.343750, time 226.205 samples/sec
+epoch 15, total_step 244780, total loss is 14.34 , inference loss is 7.97, weight deacy loss is 6.37, training accuracy is 0.406250, time 210.743 samples/sec
+epoch 15, total_step 244800, total loss is 14.45 , inference loss is 8.08, weight deacy loss is 6.37, training accuracy is 0.312500, time 13.250 samples/sec
+epoch 15, total_step 244820, total loss is 13.68 , inference loss is 7.32, weight deacy loss is 6.37, training accuracy is 0.468750, time 214.665 samples/sec
+epoch 15, total_step 244840, total loss is 13.68 , inference loss is 7.31, weight deacy loss is 6.37, training accuracy is 0.343750, time 210.200 samples/sec
+epoch 15, total_step 244860, total loss is 15.41 , inference loss is 9.04, weight deacy loss is 6.37, training accuracy is 0.437500, time 219.349 samples/sec
+epoch 15, total_step 244880, total loss is 10.97 , inference loss is 4.60, weight deacy loss is 6.37, training accuracy is 0.562500, time 213.221 samples/sec
+epoch 15, total_step 244900, total loss is 15.77 , inference loss is 9.40, weight deacy loss is 6.37, training accuracy is 0.218750, time 235.134 samples/sec
+epoch 15, total_step 244920, total loss is 16.21 , inference loss is 9.84, weight deacy loss is 6.37, training accuracy is 0.312500, time 215.408 samples/sec
+epoch 15, total_step 244940, total loss is 12.30 , inference loss is 5.93, weight deacy loss is 6.37, training accuracy is 0.375000, time 221.896 samples/sec
+epoch 15, total_step 244960, total loss is 17.24 , inference loss is 10.87, weight deacy loss is 6.37, training accuracy is 0.281250, time 218.775 samples/sec
+epoch 15, total_step 244980, total loss is 13.97 , inference loss is 7.60, weight deacy loss is 6.37, training accuracy is 0.375000, time 211.503 samples/sec
+epoch 15, total_step 245000, total loss is 15.68 , inference loss is 9.31, weight deacy loss is 6.37, training accuracy is 0.437500, time 211.626 samples/sec
+epoch 15, total_step 245020, total loss is 16.75 , inference loss is 10.38, weight deacy loss is 6.37, training accuracy is 0.281250, time 296.817 samples/sec
+epoch 15, total_step 245040, total loss is 11.64 , inference loss is 5.27, weight deacy loss is 6.37, training accuracy is 0.406250, time 193.597 samples/sec
+epoch 15, total_step 245060, total loss is 15.82 , inference loss is 9.45, weight deacy loss is 6.37, training accuracy is 0.250000, time 225.909 samples/sec
+epoch 15, total_step 245080, total loss is 10.27 , inference loss is 3.90, weight deacy loss is 6.37, training accuracy is 0.437500, time 193.147 samples/sec
+epoch 15, total_step 245100, total loss is 12.22 , inference loss is 5.85, weight deacy loss is 6.37, training accuracy is 0.437500, time 12.977 samples/sec
+epoch 15, total_step 245120, total loss is 12.94 , inference loss is 6.57, weight deacy loss is 6.37, training accuracy is 0.406250, time 201.107 samples/sec
+epoch 15, total_step 245140, total loss is 11.38 , inference loss is 5.01, weight deacy loss is 6.37, training accuracy is 0.562500, time 175.544 samples/sec
+epoch 15, total_step 245160, total loss is 13.19 , inference loss is 6.82, weight deacy loss is 6.37, training accuracy is 0.437500, time 200.213 samples/sec
+epoch 15, total_step 245180, total loss is 14.88 , inference loss is 8.51, weight deacy loss is 6.37, training accuracy is 0.375000, time 198.433 samples/sec
+epoch 15, total_step 245200, total loss is 15.06 , inference loss is 8.69, weight deacy loss is 6.37, training accuracy is 0.250000, time 202.932 samples/sec
+epoch 15, total_step 245220, total loss is 12.83 , inference loss is 6.46, weight deacy loss is 6.37, training accuracy is 0.312500, time 179.370 samples/sec
+epoch 15, total_step 245240, total loss is 15.50 , inference loss is 9.13, weight deacy loss is 6.37, training accuracy is 0.406250, time 202.106 samples/sec
+epoch 15, total_step 245260, total loss is 15.20 , inference loss is 8.83, weight deacy loss is 6.37, training accuracy is 0.531250, time 218.858 samples/sec
+epoch 15, total_step 245280, total loss is 11.82 , inference loss is 5.45, weight deacy loss is 6.37, training accuracy is 0.625000, time 192.609 samples/sec
+End of epoch 15
+epoch 16, total_step 245300, total loss is 19.98 , inference loss is 13.62, weight deacy loss is 6.37, training accuracy is 0.093750, time 218.770 samples/sec
+epoch 16, total_step 245320, total loss is 12.23 , inference loss is 5.86, weight deacy loss is 6.37, training accuracy is 0.593750, time 214.747 samples/sec
+epoch 16, total_step 245340, total loss is 13.35 , inference loss is 6.99, weight deacy loss is 6.37, training accuracy is 0.656250, time 214.087 samples/sec
+epoch 16, total_step 245360, total loss is 9.28 , inference loss is 2.91, weight deacy loss is 6.37, training accuracy is 0.812500, time 225.427 samples/sec
+epoch 16, total_step 245380, total loss is 9.67 , inference loss is 3.30, weight deacy loss is 6.37, training accuracy is 0.875000, time 214.978 samples/sec
+epoch 16, total_step 245400, total loss is 11.56 , inference loss is 5.19, weight deacy loss is 6.37, training accuracy is 0.718750, time 13.568 samples/sec
+epoch 16, total_step 245420, total loss is 10.01 , inference loss is 3.64, weight deacy loss is 6.37, training accuracy is 0.812500, time 214.327 samples/sec
+epoch 16, total_step 245440, total loss is 10.20 , inference loss is 3.84, weight deacy loss is 6.37, training accuracy is 0.843750, time 225.385 samples/sec
+epoch 16, total_step 245460, total loss is 8.37 , inference loss is 2.00, weight deacy loss is 6.37, training accuracy is 0.812500, time 218.193 samples/sec
+epoch 16, total_step 245480, total loss is 9.53 , inference loss is 3.17, weight deacy loss is 6.37, training accuracy is 0.812500, time 205.157 samples/sec
+epoch 16, total_step 245500, total loss is 11.79 , inference loss is 5.42, weight deacy loss is 6.37, training accuracy is 0.687500, time 216.138 samples/sec
+epoch 16, total_step 245520, total loss is 9.48 , inference loss is 3.12, weight deacy loss is 6.37, training accuracy is 0.718750, time 298.128 samples/sec
+epoch 16, total_step 245540, total loss is 9.81 , inference loss is 3.45, weight deacy loss is 6.37, training accuracy is 0.687500, time 298.037 samples/sec
+epoch 16, total_step 245560, total loss is 8.90 , inference loss is 2.53, weight deacy loss is 6.37, training accuracy is 0.750000, time 215.577 samples/sec
+epoch 16, total_step 245580, total loss is 13.92 , inference loss is 7.56, weight deacy loss is 6.37, training accuracy is 0.531250, time 213.096 samples/sec
+epoch 16, total_step 245600, total loss is 11.38 , inference loss is 5.01, weight deacy loss is 6.37, training accuracy is 0.593750, time 217.479 samples/sec
+epoch 16, total_step 245620, total loss is 8.96 , inference loss is 2.59, weight deacy loss is 6.37, training accuracy is 0.781250, time 214.949 samples/sec
+epoch 16, total_step 245640, total loss is 11.70 , inference loss is 5.33, weight deacy loss is 6.37, training accuracy is 0.781250, time 216.774 samples/sec
+epoch 16, total_step 245660, total loss is 7.52 , inference loss is 1.15, weight deacy loss is 6.37, training accuracy is 0.875000, time 213.471 samples/sec
+epoch 16, total_step 245680, total loss is 10.32 , inference loss is 3.95, weight deacy loss is 6.37, training accuracy is 0.718750, time 210.970 samples/sec
+epoch 16, total_step 245700, total loss is 9.75 , inference loss is 3.38, weight deacy loss is 6.37, training accuracy is 0.625000, time 13.597 samples/sec
+epoch 16, total_step 245720, total loss is 11.37 , inference loss is 5.00, weight deacy loss is 6.37, training accuracy is 0.656250, time 215.752 samples/sec
+epoch 16, total_step 245740, total loss is 9.99 , inference loss is 3.62, weight deacy loss is 6.37, training accuracy is 0.875000, time 217.511 samples/sec
+epoch 16, total_step 245760, total loss is 11.96 , inference loss is 5.59, weight deacy loss is 6.37, training accuracy is 0.593750, time 229.309 samples/sec
+epoch 16, total_step 245780, total loss is 11.25 , inference loss is 4.88, weight deacy loss is 6.37, training accuracy is 0.656250, time 216.179 samples/sec
+epoch 16, total_step 245800, total loss is 13.90 , inference loss is 7.53, weight deacy loss is 6.37, training accuracy is 0.656250, time 224.548 samples/sec
+epoch 16, total_step 245820, total loss is 12.15 , inference loss is 5.78, weight deacy loss is 6.37, training accuracy is 0.625000, time 213.905 samples/sec
+epoch 16, total_step 245840, total loss is 8.60 , inference loss is 2.23, weight deacy loss is 6.37, training accuracy is 0.781250, time 226.903 samples/sec
+epoch 16, total_step 245860, total loss is 11.30 , inference loss is 4.94, weight deacy loss is 6.37, training accuracy is 0.656250, time 223.536 samples/sec
+epoch 16, total_step 245880, total loss is 8.57 , inference loss is 2.20, weight deacy loss is 6.37, training accuracy is 0.781250, time 232.095 samples/sec
+epoch 16, total_step 245900, total loss is 14.59 , inference loss is 8.22, weight deacy loss is 6.37, training accuracy is 0.500000, time 218.629 samples/sec
+epoch 16, total_step 245920, total loss is 11.11 , inference loss is 4.75, weight deacy loss is 6.37, training accuracy is 0.625000, time 213.784 samples/sec
+epoch 16, total_step 245940, total loss is 9.10 , inference loss is 2.73, weight deacy loss is 6.37, training accuracy is 0.625000, time 219.661 samples/sec
+epoch 16, total_step 245960, total loss is 11.55 , inference loss is 5.18, weight deacy loss is 6.37, training accuracy is 0.687500, time 225.252 samples/sec
+epoch 16, total_step 245980, total loss is 10.15 , inference loss is 3.78, weight deacy loss is 6.37, training accuracy is 0.593750, time 298.558 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.718526999999963
+best_threshold_index 115 0.9829629629629629
+best_threshold_index 112 0.9827777777777778
+best_threshold_index 115 0.982037037037037
+best_threshold_index 109 0.9837037037037037
+best_threshold_index 112 0.9818518518518519
+best_threshold_index 115 0.9818518518518519
+best_threshold_index 112 0.9827777777777778
+best_threshold_index 115 0.9837037037037037
+best_threshold_index 112 0.9814814814814815
+best_threshold_index 112 0.982037037037037
+[lfw][246000]XNorm: 18.760091
+[lfw][246000]Accuracy-Flip: 0.98017+-0.00762
+test accuracy is:  0.9801666666666666
+epoch 16, total_step 246000, total loss is 12.24 , inference loss is 5.87, weight deacy loss is 6.37, training accuracy is 0.718750, time 16.318 samples/sec
+epoch 16, total_step 246020, total loss is 12.87 , inference loss is 6.50, weight deacy loss is 6.37, training accuracy is 0.562500, time 220.655 samples/sec
+epoch 16, total_step 246040, total loss is 10.34 , inference loss is 3.97, weight deacy loss is 6.37, training accuracy is 0.687500, time 224.515 samples/sec
+epoch 16, total_step 246060, total loss is 10.69 , inference loss is 4.32, weight deacy loss is 6.37, training accuracy is 0.718750, time 204.710 samples/sec
+epoch 16, total_step 246080, total loss is 13.11 , inference loss is 6.74, weight deacy loss is 6.37, training accuracy is 0.625000, time 215.688 samples/sec
+epoch 16, total_step 246100, total loss is 12.90 , inference loss is 6.53, weight deacy loss is 6.37, training accuracy is 0.687500, time 222.557 samples/sec
+epoch 16, total_step 246120, total loss is 12.00 , inference loss is 5.63, weight deacy loss is 6.37, training accuracy is 0.687500, time 230.181 samples/sec
+epoch 16, total_step 246140, total loss is 12.15 , inference loss is 5.77, weight deacy loss is 6.37, training accuracy is 0.656250, time 215.096 samples/sec
+epoch 16, total_step 246160, total loss is 8.04 , inference loss is 1.67, weight deacy loss is 6.37, training accuracy is 0.750000, time 215.151 samples/sec
+epoch 16, total_step 246180, total loss is 13.07 , inference loss is 6.70, weight deacy loss is 6.37, training accuracy is 0.562500, time 218.708 samples/sec
+epoch 16, total_step 246200, total loss is 10.30 , inference loss is 3.93, weight deacy loss is 6.37, training accuracy is 0.718750, time 210.077 samples/sec
+epoch 16, total_step 246220, total loss is 11.64 , inference loss is 5.27, weight deacy loss is 6.37, training accuracy is 0.562500, time 221.541 samples/sec
+epoch 16, total_step 246240, total loss is 11.98 , inference loss is 5.61, weight deacy loss is 6.37, training accuracy is 0.656250, time 216.804 samples/sec
+epoch 16, total_step 246260, total loss is 9.60 , inference loss is 3.23, weight deacy loss is 6.37, training accuracy is 0.781250, time 226.696 samples/sec
+epoch 16, total_step 246280, total loss is 8.50 , inference loss is 2.12, weight deacy loss is 6.37, training accuracy is 0.781250, time 216.120 samples/sec
+epoch 16, total_step 246300, total loss is 11.03 , inference loss is 4.65, weight deacy loss is 6.38, training accuracy is 0.750000, time 12.265 samples/sec
+epoch 16, total_step 246320, total loss is 8.56 , inference loss is 2.19, weight deacy loss is 6.38, training accuracy is 0.718750, time 300.372 samples/sec
+epoch 16, total_step 246340, total loss is 10.58 , inference loss is 4.21, weight deacy loss is 6.38, training accuracy is 0.781250, time 231.951 samples/sec
+epoch 16, total_step 246360, total loss is 9.30 , inference loss is 2.92, weight deacy loss is 6.38, training accuracy is 0.656250, time 218.554 samples/sec
+epoch 16, total_step 246380, total loss is 10.44 , inference loss is 4.07, weight deacy loss is 6.38, training accuracy is 0.593750, time 215.741 samples/sec
+epoch 16, total_step 246400, total loss is 9.34 , inference loss is 2.96, weight deacy loss is 6.38, training accuracy is 0.656250, time 223.242 samples/sec
+epoch 16, total_step 246420, total loss is 10.26 , inference loss is 3.89, weight deacy loss is 6.38, training accuracy is 0.656250, time 223.940 samples/sec
+epoch 16, total_step 246440, total loss is 11.02 , inference loss is 4.64, weight deacy loss is 6.38, training accuracy is 0.625000, time 231.626 samples/sec
+epoch 16, total_step 246460, total loss is 11.83 , inference loss is 5.45, weight deacy loss is 6.38, training accuracy is 0.562500, time 225.293 samples/sec
+epoch 16, total_step 246480, total loss is 16.89 , inference loss is 10.52, weight deacy loss is 6.38, training accuracy is 0.562500, time 220.133 samples/sec
+epoch 16, total_step 246500, total loss is 12.23 , inference loss is 5.85, weight deacy loss is 6.38, training accuracy is 0.593750, time 228.118 samples/sec
+epoch 16, total_step 246520, total loss is 13.66 , inference loss is 7.28, weight deacy loss is 6.38, training accuracy is 0.500000, time 219.962 samples/sec
+epoch 16, total_step 246540, total loss is 12.63 , inference loss is 6.25, weight deacy loss is 6.38, training accuracy is 0.500000, time 224.288 samples/sec
+epoch 16, total_step 246560, total loss is 11.70 , inference loss is 5.32, weight deacy loss is 6.38, training accuracy is 0.593750, time 221.734 samples/sec
+epoch 16, total_step 246580, total loss is 12.26 , inference loss is 5.88, weight deacy loss is 6.38, training accuracy is 0.656250, time 215.684 samples/sec
+epoch 16, total_step 246600, total loss is 14.78 , inference loss is 8.40, weight deacy loss is 6.38, training accuracy is 0.562500, time 12.781 samples/sec
+epoch 16, total_step 246620, total loss is 13.41 , inference loss is 7.03, weight deacy loss is 6.38, training accuracy is 0.562500, time 222.448 samples/sec
+epoch 16, total_step 246640, total loss is 11.43 , inference loss is 5.05, weight deacy loss is 6.38, training accuracy is 0.750000, time 215.809 samples/sec
+epoch 16, total_step 246660, total loss is 9.87 , inference loss is 3.49, weight deacy loss is 6.38, training accuracy is 0.562500, time 219.925 samples/sec
+epoch 16, total_step 246680, total loss is 7.95 , inference loss is 1.56, weight deacy loss is 6.38, training accuracy is 0.718750, time 211.669 samples/sec
+epoch 16, total_step 246700, total loss is 10.69 , inference loss is 4.31, weight deacy loss is 6.38, training accuracy is 0.656250, time 218.200 samples/sec
+epoch 16, total_step 246720, total loss is 11.72 , inference loss is 5.34, weight deacy loss is 6.38, training accuracy is 0.625000, time 211.577 samples/sec
+epoch 16, total_step 246740, total loss is 15.78 , inference loss is 9.40, weight deacy loss is 6.38, training accuracy is 0.531250, time 221.878 samples/sec
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+epoch 16, total_step 246800, total loss is 10.07 , inference loss is 3.68, weight deacy loss is 6.38, training accuracy is 0.718750, time 226.930 samples/sec
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+epoch 16, total_step 246840, total loss is 17.44 , inference loss is 11.05, weight deacy loss is 6.38, training accuracy is 0.468750, time 207.500 samples/sec
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+epoch 16, total_step 246880, total loss is 12.76 , inference loss is 6.38, weight deacy loss is 6.38, training accuracy is 0.562500, time 217.865 samples/sec
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+epoch 16, total_step 246980, total loss is 16.47 , inference loss is 10.08, weight deacy loss is 6.39, training accuracy is 0.437500, time 218.500 samples/sec
+epoch 16, total_step 247000, total loss is 10.82 , inference loss is 4.43, weight deacy loss is 6.39, training accuracy is 0.500000, time 217.917 samples/sec
+epoch 16, total_step 247020, total loss is 12.91 , inference loss is 6.53, weight deacy loss is 6.39, training accuracy is 0.593750, time 217.033 samples/sec
+epoch 16, total_step 247040, total loss is 11.40 , inference loss is 5.01, weight deacy loss is 6.39, training accuracy is 0.593750, time 223.293 samples/sec
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+epoch 16, total_step 247280, total loss is 10.08 , inference loss is 3.69, weight deacy loss is 6.39, training accuracy is 0.843750, time 214.100 samples/sec
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+epoch 16, total_step 247540, total loss is 10.21 , inference loss is 3.82, weight deacy loss is 6.39, training accuracy is 0.656250, time 295.319 samples/sec
+epoch 16, total_step 247560, total loss is 10.99 , inference loss is 4.60, weight deacy loss is 6.39, training accuracy is 0.625000, time 216.298 samples/sec
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+epoch 16, total_step 247600, total loss is 14.92 , inference loss is 8.53, weight deacy loss is 6.39, training accuracy is 0.468750, time 227.382 samples/sec
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+epoch 16, total_step 247760, total loss is 11.06 , inference loss is 4.67, weight deacy loss is 6.39, training accuracy is 0.781250, time 214.043 samples/sec
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+epoch 16, total_step 247820, total loss is 12.01 , inference loss is 5.62, weight deacy loss is 6.39, training accuracy is 0.687500, time 201.957 samples/sec
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+epoch 16, total_step 247900, total loss is 8.46 , inference loss is 2.07, weight deacy loss is 6.39, training accuracy is 0.750000, time 223.511 samples/sec
+epoch 16, total_step 247920, total loss is 9.44 , inference loss is 3.06, weight deacy loss is 6.39, training accuracy is 0.687500, time 230.785 samples/sec
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+epoch 16, total_step 247980, total loss is 12.86 , inference loss is 6.48, weight deacy loss is 6.39, training accuracy is 0.656250, time 217.710 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.705411999999995
+best_threshold_index 121 0.9868518518518519
+best_threshold_index 121 0.9855555555555555
+best_threshold_index 123 0.9855555555555555
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+best_threshold_index 121 0.9851851851851852
+best_threshold_index 121 0.985
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+best_threshold_index 121 0.9851851851851852
+[lfw][248000]XNorm: 18.430484
+[lfw][248000]Accuracy-Flip: 0.98500+-0.00537
+test accuracy is:  0.985
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+epoch 16, total_step 248020, total loss is 10.54 , inference loss is 4.15, weight deacy loss is 6.39, training accuracy is 0.656250, time 226.317 samples/sec
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+epoch 16, total_step 248300, total loss is 9.20 , inference loss is 2.81, weight deacy loss is 6.39, training accuracy is 0.750000, time 220.922 samples/sec
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+epoch 16, total_step 248340, total loss is 10.22 , inference loss is 3.84, weight deacy loss is 6.39, training accuracy is 0.593750, time 210.343 samples/sec
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+testing verification..
+(12000, 512)
+infer time 24.864779
+best_threshold_index 122 0.9877777777777778
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+best_threshold_index 126 0.9868518518518519
+best_threshold_index 123 0.9866666666666667
+[lfw][250000]XNorm: 22.257239
+[lfw][250000]Accuracy-Flip: 0.98667+-0.00483
+test accuracy is:  0.9866666666666667
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+testing verification..
+(12000, 512)
+infer time 24.156855000000014
+best_threshold_index 115 0.9874074074074074
+best_threshold_index 115 0.9875925925925926
+best_threshold_index 114 0.9868518518518519
+best_threshold_index 115 0.9885185185185185
+best_threshold_index 115 0.9874074074074074
+best_threshold_index 115 0.9866666666666667
+best_threshold_index 115 0.9877777777777778
+best_threshold_index 115 0.9868518518518519
+best_threshold_index 115 0.9861111111111112
+best_threshold_index 115 0.9864814814814815
+[lfw][252000]XNorm: 22.372356
+[lfw][252000]Accuracy-Flip: 0.98683+-0.00598
+test accuracy is:  0.9868333333333335
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+epoch 16, total_step 253500, total loss is 10.35 , inference loss is 3.97, weight deacy loss is 6.39, training accuracy is 0.437500, time 12.585 samples/sec
+epoch 16, total_step 253520, total loss is 11.74 , inference loss is 5.36, weight deacy loss is 6.39, training accuracy is 0.531250, time 217.511 samples/sec
+epoch 16, total_step 253540, total loss is 15.44 , inference loss is 9.06, weight deacy loss is 6.39, training accuracy is 0.500000, time 213.768 samples/sec
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+epoch 16, total_step 253580, total loss is 12.69 , inference loss is 6.30, weight deacy loss is 6.39, training accuracy is 0.468750, time 210.214 samples/sec
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+epoch 16, total_step 253760, total loss is 11.89 , inference loss is 5.50, weight deacy loss is 6.39, training accuracy is 0.500000, time 205.127 samples/sec
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+epoch 16, total_step 253820, total loss is 9.77 , inference loss is 3.38, weight deacy loss is 6.39, training accuracy is 0.656250, time 299.581 samples/sec
+epoch 16, total_step 253840, total loss is 12.69 , inference loss is 6.31, weight deacy loss is 6.39, training accuracy is 0.656250, time 224.834 samples/sec
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+epoch 16, total_step 253880, total loss is 15.79 , inference loss is 9.41, weight deacy loss is 6.39, training accuracy is 0.500000, time 212.125 samples/sec
+epoch 16, total_step 253900, total loss is 12.97 , inference loss is 6.58, weight deacy loss is 6.39, training accuracy is 0.593750, time 223.238 samples/sec
+epoch 16, total_step 253920, total loss is 12.55 , inference loss is 6.17, weight deacy loss is 6.39, training accuracy is 0.593750, time 219.842 samples/sec
+epoch 16, total_step 253940, total loss is 10.32 , inference loss is 3.93, weight deacy loss is 6.39, training accuracy is 0.500000, time 222.968 samples/sec
+epoch 16, total_step 253960, total loss is 9.29 , inference loss is 2.90, weight deacy loss is 6.39, training accuracy is 0.656250, time 213.658 samples/sec
+epoch 16, total_step 253980, total loss is 17.18 , inference loss is 10.79, weight deacy loss is 6.39, training accuracy is 0.406250, time 226.469 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.486558000000013
+best_threshold_index 119 0.9887037037037038
+best_threshold_index 119 0.9885185185185185
+best_threshold_index 119 0.9896296296296296
+best_threshold_index 119 0.9901851851851852
+best_threshold_index 119 0.9894444444444445
+best_threshold_index 119 0.9888888888888889
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+best_threshold_index 119 0.9883333333333333
+best_threshold_index 119 0.9881481481481481
+best_threshold_index 119 0.9885185185185185
+[lfw][254000]XNorm: 21.660630
+[lfw][254000]Accuracy-Flip: 0.98900+-0.00578
+test accuracy is:  0.9890000000000001
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+epoch 16, total_step 254020, total loss is 12.14 , inference loss is 5.75, weight deacy loss is 6.39, training accuracy is 0.593750, time 215.881 samples/sec
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+epoch 16, total_step 254300, total loss is 13.91 , inference loss is 7.52, weight deacy loss is 6.39, training accuracy is 0.500000, time 217.704 samples/sec
+epoch 16, total_step 254320, total loss is 11.95 , inference loss is 5.56, weight deacy loss is 6.39, training accuracy is 0.562500, time 216.515 samples/sec
+epoch 16, total_step 254340, total loss is 18.31 , inference loss is 11.92, weight deacy loss is 6.39, training accuracy is 0.437500, time 212.862 samples/sec
+epoch 16, total_step 254360, total loss is 13.95 , inference loss is 7.56, weight deacy loss is 6.39, training accuracy is 0.375000, time 221.269 samples/sec
+epoch 16, total_step 254380, total loss is 13.69 , inference loss is 7.30, weight deacy loss is 6.39, training accuracy is 0.406250, time 215.588 samples/sec
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+epoch 16, total_step 254440, total loss is 12.13 , inference loss is 5.74, weight deacy loss is 6.39, training accuracy is 0.531250, time 211.736 samples/sec
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+epoch 16, total_step 255980, total loss is 11.80 , inference loss is 5.41, weight deacy loss is 6.39, training accuracy is 0.562500, time 208.647 samples/sec
+testing verification..
+(12000, 512)
+infer time 26.01503499999998
+best_threshold_index 115 0.9844444444444445
+best_threshold_index 115 0.9835185185185186
+best_threshold_index 115 0.9831481481481481
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+best_threshold_index 115 0.9824074074074074
+best_threshold_index 115 0.9831481481481481
+[lfw][256000]XNorm: 21.499211
+[lfw][256000]Accuracy-Flip: 0.98317+-0.00693
+test accuracy is:  0.9831666666666667
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+epoch 16, total_step 257820, total loss is 13.57 , inference loss is 7.18, weight deacy loss is 6.39, training accuracy is 0.625000, time 216.213 samples/sec
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+epoch 16, total_step 257880, total loss is 12.64 , inference loss is 6.25, weight deacy loss is 6.39, training accuracy is 0.406250, time 213.529 samples/sec
+epoch 16, total_step 257900, total loss is 12.82 , inference loss is 6.43, weight deacy loss is 6.39, training accuracy is 0.406250, time 210.973 samples/sec
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+epoch 16, total_step 257980, total loss is 12.10 , inference loss is 5.71, weight deacy loss is 6.39, training accuracy is 0.531250, time 300.936 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.371736999999996
+best_threshold_index 110 0.9875925925925926
+best_threshold_index 110 0.9861111111111112
+best_threshold_index 110 0.9864814814814815
+best_threshold_index 110 0.9875925925925926
+best_threshold_index 110 0.987037037037037
+best_threshold_index 110 0.9857407407407407
+best_threshold_index 110 0.9868518518518519
+best_threshold_index 110 0.9862962962962963
+best_threshold_index 110 0.9855555555555555
+best_threshold_index 110 0.9857407407407407
+[lfw][258000]XNorm: 21.391791
+[lfw][258000]Accuracy-Flip: 0.98650+-0.00639
+test accuracy is:  0.9865
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+epoch 16, total_step 259380, total loss is 16.18 , inference loss is 9.80, weight deacy loss is 6.38, training accuracy is 0.500000, time 221.120 samples/sec
+epoch 16, total_step 259400, total loss is 11.67 , inference loss is 5.28, weight deacy loss is 6.38, training accuracy is 0.375000, time 214.579 samples/sec
+epoch 16, total_step 259420, total loss is 16.38 , inference loss is 9.99, weight deacy loss is 6.38, training accuracy is 0.406250, time 223.479 samples/sec
+epoch 16, total_step 259440, total loss is 13.95 , inference loss is 7.57, weight deacy loss is 6.38, training accuracy is 0.375000, time 224.557 samples/sec
+epoch 16, total_step 259460, total loss is 16.45 , inference loss is 10.06, weight deacy loss is 6.38, training accuracy is 0.406250, time 299.229 samples/sec
+epoch 16, total_step 259480, total loss is 12.91 , inference loss is 6.52, weight deacy loss is 6.38, training accuracy is 0.500000, time 301.523 samples/sec
+epoch 16, total_step 259500, total loss is 12.83 , inference loss is 6.44, weight deacy loss is 6.38, training accuracy is 0.531250, time 14.395 samples/sec
+epoch 16, total_step 259520, total loss is 15.89 , inference loss is 9.51, weight deacy loss is 6.38, training accuracy is 0.375000, time 296.744 samples/sec
+epoch 16, total_step 259540, total loss is 18.59 , inference loss is 12.21, weight deacy loss is 6.38, training accuracy is 0.250000, time 213.565 samples/sec
+epoch 16, total_step 259560, total loss is 13.68 , inference loss is 7.30, weight deacy loss is 6.38, training accuracy is 0.500000, time 209.256 samples/sec
+epoch 16, total_step 259580, total loss is 14.88 , inference loss is 8.50, weight deacy loss is 6.38, training accuracy is 0.343750, time 196.988 samples/sec
+epoch 16, total_step 259600, total loss is 15.20 , inference loss is 8.81, weight deacy loss is 6.38, training accuracy is 0.343750, time 187.711 samples/sec
+epoch 16, total_step 259620, total loss is 15.47 , inference loss is 9.09, weight deacy loss is 6.38, training accuracy is 0.375000, time 213.058 samples/sec
+epoch 16, total_step 259640, total loss is 14.82 , inference loss is 8.43, weight deacy loss is 6.38, training accuracy is 0.343750, time 209.955 samples/sec
+epoch 16, total_step 259660, total loss is 14.51 , inference loss is 8.12, weight deacy loss is 6.38, training accuracy is 0.312500, time 200.794 samples/sec
+epoch 16, total_step 259680, total loss is 12.70 , inference loss is 6.32, weight deacy loss is 6.38, training accuracy is 0.437500, time 213.619 samples/sec
+epoch 16, total_step 259700, total loss is 12.09 , inference loss is 5.70, weight deacy loss is 6.38, training accuracy is 0.312500, time 215.414 samples/sec
+epoch 16, total_step 259720, total loss is 14.63 , inference loss is 8.24, weight deacy loss is 6.38, training accuracy is 0.312500, time 298.904 samples/sec
+epoch 16, total_step 259740, total loss is 12.75 , inference loss is 6.37, weight deacy loss is 6.38, training accuracy is 0.531250, time 195.198 samples/sec
+epoch 16, total_step 259760, total loss is 11.93 , inference loss is 5.55, weight deacy loss is 6.38, training accuracy is 0.593750, time 213.383 samples/sec
+epoch 16, total_step 259780, total loss is 15.93 , inference loss is 9.54, weight deacy loss is 6.38, training accuracy is 0.562500, time 202.882 samples/sec
+epoch 16, total_step 259800, total loss is 14.25 , inference loss is 7.87, weight deacy loss is 6.38, training accuracy is 0.468750, time 12.368 samples/sec
+epoch 16, total_step 259820, total loss is 13.14 , inference loss is 6.75, weight deacy loss is 6.38, training accuracy is 0.312500, time 218.310 samples/sec
+epoch 16, total_step 259840, total loss is 13.31 , inference loss is 6.92, weight deacy loss is 6.38, training accuracy is 0.562500, time 231.130 samples/sec
+epoch 16, total_step 259860, total loss is 15.89 , inference loss is 9.50, weight deacy loss is 6.38, training accuracy is 0.250000, time 211.557 samples/sec
+epoch 16, total_step 259880, total loss is 14.77 , inference loss is 8.39, weight deacy loss is 6.38, training accuracy is 0.437500, time 200.470 samples/sec
+epoch 16, total_step 259900, total loss is 10.90 , inference loss is 4.52, weight deacy loss is 6.38, training accuracy is 0.500000, time 216.796 samples/sec
+epoch 16, total_step 259920, total loss is 14.74 , inference loss is 8.35, weight deacy loss is 6.38, training accuracy is 0.375000, time 202.366 samples/sec
+epoch 16, total_step 259940, total loss is 10.79 , inference loss is 4.40, weight deacy loss is 6.38, training accuracy is 0.437500, time 186.583 samples/sec
+epoch 16, total_step 259960, total loss is 12.74 , inference loss is 6.35, weight deacy loss is 6.38, training accuracy is 0.281250, time 214.369 samples/sec
+epoch 16, total_step 259980, total loss is 14.93 , inference loss is 8.54, weight deacy loss is 6.38, training accuracy is 0.468750, time 301.469 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.478475000000024
+best_threshold_index 109 0.9844444444444445
+best_threshold_index 111 0.9835185185185186
+best_threshold_index 109 0.9837037037037037
+best_threshold_index 109 0.9857407407407407
+best_threshold_index 112 0.9846296296296296
+best_threshold_index 111 0.9831481481481481
+best_threshold_index 109 0.9842592592592593
+best_threshold_index 109 0.9831481481481481
+best_threshold_index 109 0.9822222222222222
+best_threshold_index 109 0.9827777777777778
+[lfw][260000]XNorm: 22.419194
+[lfw][260000]Accuracy-Flip: 0.98283+-0.00879
+test accuracy is:  0.9828333333333333
+epoch 16, total_step 260000, total loss is 12.40 , inference loss is 6.01, weight deacy loss is 6.38, training accuracy is 0.375000, time 17.499 samples/sec
+epoch 16, total_step 260020, total loss is 13.91 , inference loss is 7.53, weight deacy loss is 6.38, training accuracy is 0.437500, time 202.251 samples/sec
+epoch 16, total_step 260040, total loss is 14.01 , inference loss is 7.63, weight deacy loss is 6.38, training accuracy is 0.437500, time 224.494 samples/sec
+epoch 16, total_step 260060, total loss is 14.38 , inference loss is 7.99, weight deacy loss is 6.38, training accuracy is 0.281250, time 196.438 samples/sec
+epoch 16, total_step 260080, total loss is 16.66 , inference loss is 10.27, weight deacy loss is 6.38, training accuracy is 0.312500, time 225.019 samples/sec
+epoch 16, total_step 260100, total loss is 14.68 , inference loss is 8.30, weight deacy loss is 6.38, training accuracy is 0.500000, time 11.085 samples/sec
+epoch 16, total_step 260120, total loss is 12.54 , inference loss is 6.16, weight deacy loss is 6.38, training accuracy is 0.562500, time 214.825 samples/sec
+epoch 16, total_step 260140, total loss is 17.03 , inference loss is 10.65, weight deacy loss is 6.38, training accuracy is 0.281250, time 211.543 samples/sec
+epoch 16, total_step 260160, total loss is 11.24 , inference loss is 4.86, weight deacy loss is 6.38, training accuracy is 0.437500, time 211.089 samples/sec
+epoch 16, total_step 260180, total loss is 10.93 , inference loss is 4.54, weight deacy loss is 6.38, training accuracy is 0.437500, time 224.356 samples/sec
+epoch 16, total_step 260200, total loss is 13.09 , inference loss is 6.71, weight deacy loss is 6.38, training accuracy is 0.468750, time 225.688 samples/sec
+epoch 16, total_step 260220, total loss is 13.02 , inference loss is 6.64, weight deacy loss is 6.38, training accuracy is 0.437500, time 223.076 samples/sec
+epoch 16, total_step 260240, total loss is 12.10 , inference loss is 5.71, weight deacy loss is 6.38, training accuracy is 0.531250, time 205.503 samples/sec
+epoch 16, total_step 260260, total loss is 12.80 , inference loss is 6.42, weight deacy loss is 6.38, training accuracy is 0.437500, time 202.075 samples/sec
+epoch 16, total_step 260280, total loss is 12.15 , inference loss is 5.77, weight deacy loss is 6.38, training accuracy is 0.437500, time 223.921 samples/sec
+epoch 16, total_step 260300, total loss is 14.81 , inference loss is 8.42, weight deacy loss is 6.38, training accuracy is 0.406250, time 224.555 samples/sec
+epoch 16, total_step 260320, total loss is 14.89 , inference loss is 8.50, weight deacy loss is 6.38, training accuracy is 0.468750, time 208.152 samples/sec
+epoch 16, total_step 260340, total loss is 13.68 , inference loss is 7.30, weight deacy loss is 6.38, training accuracy is 0.406250, time 203.369 samples/sec2022-12-01 17:40:50.126030: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:145] Filling up shuffle buffer (this may take a while): 9407 of 10000
+2022-12-01 17:40:50.710858: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:195] Shuffle buffer filled.
+
+epoch 16, total_step 260360, total loss is 13.35 , inference loss is 6.97, weight deacy loss is 6.38, training accuracy is 0.531250, time 189.725 samples/sec
+epoch 16, total_step 260380, total loss is 17.40 , inference loss is 11.02, weight deacy loss is 6.38, training accuracy is 0.437500, time 193.599 samples/sec
+epoch 16, total_step 260400, total loss is 14.99 , inference loss is 8.61, weight deacy loss is 6.38, training accuracy is 0.468750, time 8.509 samples/sec
+epoch 16, total_step 260420, total loss is 17.41 , inference loss is 11.03, weight deacy loss is 6.38, training accuracy is 0.250000, time 217.457 samples/sec
+epoch 16, total_step 260440, total loss is 12.21 , inference loss is 5.83, weight deacy loss is 6.38, training accuracy is 0.593750, time 187.627 samples/sec
+epoch 16, total_step 260460, total loss is 18.76 , inference loss is 12.37, weight deacy loss is 6.38, training accuracy is 0.468750, time 183.809 samples/sec
+epoch 16, total_step 260480, total loss is 12.22 , inference loss is 5.84, weight deacy loss is 6.38, training accuracy is 0.468750, time 174.690 samples/sec
+epoch 16, total_step 260500, total loss is 14.27 , inference loss is 7.89, weight deacy loss is 6.38, training accuracy is 0.500000, time 188.604 samples/sec
+epoch 16, total_step 260520, total loss is 14.68 , inference loss is 8.30, weight deacy loss is 6.38, training accuracy is 0.500000, time 196.788 samples/sec
+epoch 16, total_step 260540, total loss is 12.34 , inference loss is 5.96, weight deacy loss is 6.38, training accuracy is 0.468750, time 199.209 samples/sec
+epoch 16, total_step 260560, total loss is 16.71 , inference loss is 10.33, weight deacy loss is 6.38, training accuracy is 0.281250, time 200.547 samples/sec
+epoch 16, total_step 260580, total loss is 14.90 , inference loss is 8.51, weight deacy loss is 6.38, training accuracy is 0.437500, time 191.285 samples/sec
+epoch 16, total_step 260600, total loss is 15.31 , inference loss is 8.93, weight deacy loss is 6.38, training accuracy is 0.468750, time 200.939 samples/sec
+epoch 16, total_step 260620, total loss is 11.85 , inference loss is 5.47, weight deacy loss is 6.38, training accuracy is 0.437500, time 191.580 samples/sec
+End of epoch 16
+epoch 17, total_step 260640, total loss is 10.63 , inference loss is 4.25, weight deacy loss is 6.38, training accuracy is 0.531250, time 204.167 samples/sec
+epoch 17, total_step 260660, total loss is 9.39 , inference loss is 3.01, weight deacy loss is 6.38, training accuracy is 0.718750, time 211.810 samples/sec
+epoch 17, total_step 260680, total loss is 8.65 , inference loss is 2.27, weight deacy loss is 6.38, training accuracy is 0.812500, time 200.805 samples/sec
+epoch 17, total_step 260700, total loss is 8.18 , inference loss is 1.80, weight deacy loss is 6.38, training accuracy is 0.843750, time 9.315 samples/sec
+epoch 17, total_step 260720, total loss is 11.20 , inference loss is 4.82, weight deacy loss is 6.38, training accuracy is 0.718750, time 195.659 samples/sec
+epoch 17, total_step 260740, total loss is 8.05 , inference loss is 1.66, weight deacy loss is 6.38, training accuracy is 0.718750, time 207.924 samples/sec
+epoch 17, total_step 260760, total loss is 10.56 , inference loss is 4.18, weight deacy loss is 6.38, training accuracy is 0.843750, time 201.757 samples/sec
+epoch 17, total_step 260780, total loss is 10.40 , inference loss is 4.02, weight deacy loss is 6.38, training accuracy is 0.843750, time 209.306 samples/sec
+epoch 17, total_step 260800, total loss is 11.82 , inference loss is 5.44, weight deacy loss is 6.38, training accuracy is 0.687500, time 204.919 samples/sec
+epoch 17, total_step 260820, total loss is 8.42 , inference loss is 2.04, weight deacy loss is 6.38, training accuracy is 0.843750, time 228.009 samples/sec
+epoch 17, total_step 260840, total loss is 7.40 , inference loss is 1.01, weight deacy loss is 6.38, training accuracy is 0.812500, time 298.919 samples/sec
+epoch 17, total_step 260860, total loss is 14.79 , inference loss is 8.41, weight deacy loss is 6.38, training accuracy is 0.718750, time 299.371 samples/sec
+epoch 17, total_step 260880, total loss is 11.34 , inference loss is 4.96, weight deacy loss is 6.38, training accuracy is 0.687500, time 246.019 samples/sec
+epoch 17, total_step 260900, total loss is 11.77 , inference loss is 5.39, weight deacy loss is 6.38, training accuracy is 0.687500, time 211.508 samples/sec
+epoch 17, total_step 260920, total loss is 11.21 , inference loss is 4.83, weight deacy loss is 6.38, training accuracy is 0.593750, time 215.821 samples/sec
+epoch 17, total_step 260940, total loss is 7.77 , inference loss is 1.39, weight deacy loss is 6.38, training accuracy is 0.875000, time 214.851 samples/sec
+epoch 17, total_step 260960, total loss is 9.84 , inference loss is 3.46, weight deacy loss is 6.38, training accuracy is 0.718750, time 210.655 samples/sec
+epoch 17, total_step 260980, total loss is 16.83 , inference loss is 10.45, weight deacy loss is 6.38, training accuracy is 0.593750, time 211.612 samples/sec
+epoch 17, total_step 261000, total loss is 9.84 , inference loss is 3.46, weight deacy loss is 6.38, training accuracy is 0.625000, time 9.884 samples/sec
+epoch 17, total_step 261020, total loss is 11.26 , inference loss is 4.88, weight deacy loss is 6.38, training accuracy is 0.656250, time 199.470 samples/sec
+epoch 17, total_step 261040, total loss is 13.96 , inference loss is 7.58, weight deacy loss is 6.38, training accuracy is 0.531250, time 201.130 samples/sec
+epoch 17, total_step 261060, total loss is 12.58 , inference loss is 6.20, weight deacy loss is 6.38, training accuracy is 0.750000, time 198.426 samples/sec
+epoch 17, total_step 261080, total loss is 8.19 , inference loss is 1.81, weight deacy loss is 6.38, training accuracy is 0.781250, time 215.362 samples/sec
+epoch 17, total_step 261100, total loss is 9.22 , inference loss is 2.84, weight deacy loss is 6.38, training accuracy is 0.781250, time 198.204 samples/sec
+epoch 17, total_step 261120, total loss is 15.19 , inference loss is 8.80, weight deacy loss is 6.38, training accuracy is 0.531250, time 204.718 samples/sec
+epoch 17, total_step 261140, total loss is 7.88 , inference loss is 1.50, weight deacy loss is 6.38, training accuracy is 0.812500, time 107.549 samples/sec
+epoch 17, total_step 261160, total loss is 10.42 , inference loss is 4.03, weight deacy loss is 6.38, training accuracy is 0.656250, time 207.537 samples/sec
+epoch 17, total_step 261180, total loss is 11.01 , inference loss is 4.63, weight deacy loss is 6.38, training accuracy is 0.718750, time 205.077 samples/sec
+epoch 17, total_step 261200, total loss is 12.56 , inference loss is 6.17, weight deacy loss is 6.38, training accuracy is 0.593750, time 196.763 samples/sec
+epoch 17, total_step 261220, total loss is 10.09 , inference loss is 3.71, weight deacy loss is 6.38, training accuracy is 0.812500, time 222.695 samples/sec
+epoch 17, total_step 261240, total loss is 11.74 , inference loss is 5.35, weight deacy loss is 6.38, training accuracy is 0.656250, time 200.049 samples/sec
+epoch 17, total_step 261260, total loss is 17.60 , inference loss is 11.21, weight deacy loss is 6.38, training accuracy is 0.500000, time 201.017 samples/sec
+epoch 17, total_step 261280, total loss is 15.49 , inference loss is 9.11, weight deacy loss is 6.38, training accuracy is 0.593750, time 210.128 samples/sec
+epoch 17, total_step 261300, total loss is 13.87 , inference loss is 7.48, weight deacy loss is 6.38, training accuracy is 0.687500, time 12.450 samples/sec
+epoch 17, total_step 261320, total loss is 11.46 , inference loss is 5.08, weight deacy loss is 6.39, training accuracy is 0.593750, time 208.962 samples/sec
+epoch 17, total_step 261340, total loss is 9.57 , inference loss is 3.19, weight deacy loss is 6.39, training accuracy is 0.781250, time 224.783 samples/sec
+epoch 17, total_step 261360, total loss is 11.75 , inference loss is 5.36, weight deacy loss is 6.39, training accuracy is 0.687500, time 214.995 samples/sec
+epoch 17, total_step 261380, total loss is 8.35 , inference loss is 1.97, weight deacy loss is 6.39, training accuracy is 0.687500, time 220.433 samples/sec
+epoch 17, total_step 261400, total loss is 11.22 , inference loss is 4.84, weight deacy loss is 6.39, training accuracy is 0.687500, time 217.457 samples/sec
+epoch 17, total_step 261420, total loss is 8.42 , inference loss is 2.03, weight deacy loss is 6.39, training accuracy is 0.750000, time 174.824 samples/sec
+epoch 17, total_step 261440, total loss is 10.74 , inference loss is 4.35, weight deacy loss is 6.39, training accuracy is 0.625000, time 212.929 samples/sec
+epoch 17, total_step 261460, total loss is 11.17 , inference loss is 4.78, weight deacy loss is 6.39, training accuracy is 0.625000, time 201.182 samples/sec
+epoch 17, total_step 261480, total loss is 11.63 , inference loss is 5.24, weight deacy loss is 6.39, training accuracy is 0.687500, time 182.182 samples/sec
+epoch 17, total_step 261500, total loss is 9.63 , inference loss is 3.24, weight deacy loss is 6.39, training accuracy is 0.781250, time 205.422 samples/sec
+epoch 17, total_step 261520, total loss is 9.80 , inference loss is 3.41, weight deacy loss is 6.39, training accuracy is 0.593750, time 206.797 samples/sec
+epoch 17, total_step 261540, total loss is 9.70 , inference loss is 3.31, weight deacy loss is 6.39, training accuracy is 0.750000, time 215.791 samples/sec
+epoch 17, total_step 261560, total loss is 14.44 , inference loss is 8.05, weight deacy loss is 6.39, training accuracy is 0.500000, time 297.321 samples/sec
+epoch 17, total_step 261580, total loss is 12.91 , inference loss is 6.53, weight deacy loss is 6.39, training accuracy is 0.500000, time 210.439 samples/sec
+epoch 17, total_step 261600, total loss is 19.95 , inference loss is 13.56, weight deacy loss is 6.39, training accuracy is 0.406250, time 9.083 samples/sec
+epoch 17, total_step 261620, total loss is 15.41 , inference loss is 9.02, weight deacy loss is 6.39, training accuracy is 0.437500, time 207.487 samples/sec
+epoch 17, total_step 261640, total loss is 10.00 , inference loss is 3.61, weight deacy loss is 6.39, training accuracy is 0.562500, time 212.836 samples/sec
+epoch 17, total_step 261660, total loss is 12.59 , inference loss is 6.20, weight deacy loss is 6.39, training accuracy is 0.562500, time 213.927 samples/sec
+epoch 17, total_step 261680, total loss is 15.31 , inference loss is 8.92, weight deacy loss is 6.39, training accuracy is 0.468750, time 212.273 samples/sec
+epoch 17, total_step 261700, total loss is 11.06 , inference loss is 4.67, weight deacy loss is 6.39, training accuracy is 0.562500, time 219.985 samples/sec
+epoch 17, total_step 261720, total loss is 13.95 , inference loss is 7.56, weight deacy loss is 6.39, training accuracy is 0.562500, time 211.285 samples/sec
+epoch 17, total_step 261740, total loss is 8.27 , inference loss is 1.88, weight deacy loss is 6.39, training accuracy is 0.750000, time 209.373 samples/sec
+epoch 17, total_step 261760, total loss is 14.93 , inference loss is 8.53, weight deacy loss is 6.39, training accuracy is 0.656250, time 218.802 samples/sec
+epoch 17, total_step 261780, total loss is 12.46 , inference loss is 6.06, weight deacy loss is 6.39, training accuracy is 0.593750, time 206.381 samples/sec
+epoch 17, total_step 261800, total loss is 11.43 , inference loss is 5.04, weight deacy loss is 6.39, training accuracy is 0.625000, time 294.857 samples/sec
+epoch 17, total_step 261820, total loss is 15.75 , inference loss is 9.35, weight deacy loss is 6.39, training accuracy is 0.500000, time 300.156 samples/sec
+epoch 17, total_step 261840, total loss is 11.72 , inference loss is 5.33, weight deacy loss is 6.39, training accuracy is 0.593750, time 226.610 samples/sec
+epoch 17, total_step 261860, total loss is 10.44 , inference loss is 4.05, weight deacy loss is 6.39, training accuracy is 0.656250, time 225.562 samples/sec
+epoch 17, total_step 261880, total loss is 9.59 , inference loss is 3.19, weight deacy loss is 6.39, training accuracy is 0.718750, time 207.924 samples/sec
+epoch 17, total_step 261900, total loss is 12.51 , inference loss is 6.12, weight deacy loss is 6.39, training accuracy is 0.687500, time 12.975 samples/sec
+epoch 17, total_step 261920, total loss is 10.95 , inference loss is 4.56, weight deacy loss is 6.40, training accuracy is 0.781250, time 217.025 samples/sec
+epoch 17, total_step 261940, total loss is 10.53 , inference loss is 4.14, weight deacy loss is 6.40, training accuracy is 0.750000, time 212.211 samples/sec
+epoch 17, total_step 261960, total loss is 11.98 , inference loss is 5.58, weight deacy loss is 6.40, training accuracy is 0.718750, time 217.935 samples/sec
+epoch 17, total_step 261980, total loss is 9.14 , inference loss is 2.74, weight deacy loss is 6.40, training accuracy is 0.718750, time 229.705 samples/sec
+testing verification..
+(12000, 512)
+infer time 23.893060999999964
+best_threshold_index 116 0.985
+best_threshold_index 120 0.9846296296296296
+best_threshold_index 116 0.9842592592592593
+best_threshold_index 116 0.9855555555555555
+best_threshold_index 116 0.9846296296296296
+best_threshold_index 116 0.9833333333333333
+best_threshold_index 116 0.9853703703703703
+best_threshold_index 120 0.9846296296296296
+best_threshold_index 120 0.9844444444444445
+best_threshold_index 116 0.9838888888888889
+[lfw][262000]XNorm: 18.990887
+[lfw][262000]Accuracy-Flip: 0.98383+-0.00563
+test accuracy is:  0.9838333333333333
+epoch 17, total_step 262000, total loss is 11.17 , inference loss is 4.77, weight deacy loss is 6.40, training accuracy is 0.656250, time 16.338 samples/sec
+epoch 17, total_step 262020, total loss is 12.38 , inference loss is 5.99, weight deacy loss is 6.40, training accuracy is 0.437500, time 222.134 samples/sec
+epoch 17, total_step 262040, total loss is 15.50 , inference loss is 9.10, weight deacy loss is 6.40, training accuracy is 0.656250, time 220.326 samples/sec
+epoch 17, total_step 262060, total loss is 11.68 , inference loss is 5.28, weight deacy loss is 6.40, training accuracy is 0.687500, time 226.516 samples/sec
+epoch 17, total_step 262080, total loss is 9.92 , inference loss is 3.52, weight deacy loss is 6.40, training accuracy is 0.656250, time 220.688 samples/sec
+epoch 17, total_step 262100, total loss is 10.75 , inference loss is 4.36, weight deacy loss is 6.40, training accuracy is 0.625000, time 232.410 samples/sec
+epoch 17, total_step 262120, total loss is 15.00 , inference loss is 8.60, weight deacy loss is 6.40, training accuracy is 0.625000, time 211.654 samples/sec
+epoch 17, total_step 262140, total loss is 10.91 , inference loss is 4.52, weight deacy loss is 6.40, training accuracy is 0.750000, time 218.973 samples/sec
+epoch 17, total_step 262160, total loss is 9.41 , inference loss is 3.01, weight deacy loss is 6.40, training accuracy is 0.656250, time 197.956 samples/sec
+epoch 17, total_step 262180, total loss is 12.21 , inference loss is 5.82, weight deacy loss is 6.40, training accuracy is 0.562500, time 199.473 samples/sec
+epoch 17, total_step 262200, total loss is 14.20 , inference loss is 7.80, weight deacy loss is 6.40, training accuracy is 0.562500, time 12.630 samples/sec
+epoch 17, total_step 262220, total loss is 9.75 , inference loss is 3.35, weight deacy loss is 6.40, training accuracy is 0.656250, time 226.757 samples/sec
+epoch 17, total_step 262240, total loss is 12.53 , inference loss is 6.13, weight deacy loss is 6.40, training accuracy is 0.687500, time 217.271 samples/sec
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+epoch 17, total_step 262300, total loss is 14.91 , inference loss is 8.51, weight deacy loss is 6.40, training accuracy is 0.593750, time 213.625 samples/sec
+epoch 17, total_step 262320, total loss is 16.35 , inference loss is 9.95, weight deacy loss is 6.40, training accuracy is 0.531250, time 214.029 samples/sec
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+epoch 17, total_step 262420, total loss is 12.39 , inference loss is 5.99, weight deacy loss is 6.40, training accuracy is 0.687500, time 220.204 samples/sec
+epoch 17, total_step 262440, total loss is 12.50 , inference loss is 6.10, weight deacy loss is 6.40, training accuracy is 0.687500, time 237.073 samples/sec
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+epoch 17, total_step 263060, total loss is 10.28 , inference loss is 3.88, weight deacy loss is 6.40, training accuracy is 0.687500, time 213.756 samples/sec
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+epoch 17, total_step 263980, total loss is 9.30 , inference loss is 2.89, weight deacy loss is 6.40, training accuracy is 0.656250, time 203.660 samples/sec
+testing verification..
+(12000, 512)
+infer time 25.984782000000017
+best_threshold_index 124 0.9875925925925926
+best_threshold_index 124 0.9872222222222222
+best_threshold_index 124 0.9881481481481481
+best_threshold_index 123 0.9885185185185185
+best_threshold_index 124 0.9872222222222222
+best_threshold_index 124 0.9874074074074074
+best_threshold_index 124 0.9887037037037038
+best_threshold_index 124 0.987037037037037
+best_threshold_index 124 0.9864814814814815
+best_threshold_index 124 0.9866666666666667
+[lfw][264000]XNorm: 22.130629
+[lfw][264000]Accuracy-Flip: 0.98717+-0.00691
+test accuracy is:  0.9871666666666664
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+epoch 17, total_step 264240, total loss is 14.01 , inference loss is 7.61, weight deacy loss is 6.40, training accuracy is 0.625000, time 220.570 samples/sec
+epoch 17, total_step 264260, total loss is 10.75 , inference loss is 4.34, weight deacy loss is 6.40, training accuracy is 0.468750, time 220.592 samples/sec
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+epoch 17, total_step 264400, total loss is 14.63 , inference loss is 8.23, weight deacy loss is 6.40, training accuracy is 0.625000, time 216.632 samples/sec
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+epoch 17, total_step 265900, total loss is 17.89 , inference loss is 11.48, weight deacy loss is 6.41, training accuracy is 0.375000, time 210.198 samples/sec
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+epoch 17, total_step 265940, total loss is 10.32 , inference loss is 3.92, weight deacy loss is 6.41, training accuracy is 0.656250, time 215.310 samples/sec
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+epoch 17, total_step 265980, total loss is 7.61 , inference loss is 1.20, weight deacy loss is 6.41, training accuracy is 0.750000, time 208.286 samples/sec
+testing verification..
+(12000, 512)
+infer time 25.241230000000016
+best_threshold_index 127 0.9887037037037038
+best_threshold_index 127 0.9883333333333333
+best_threshold_index 127 0.9885185185185185
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+best_threshold_index 121 0.9874074074074074
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+best_threshold_index 127 0.9872222222222222
+[lfw][266000]XNorm: 20.713881
+[lfw][266000]Accuracy-Flip: 0.98667+-0.00506
+test accuracy is:  0.9866666666666667
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+epoch 17, total_step 266020, total loss is 8.34 , inference loss is 1.93, weight deacy loss is 6.41, training accuracy is 0.750000, time 223.940 samples/sec
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+epoch 17, total_step 266280, total loss is 12.13 , inference loss is 5.72, weight deacy loss is 6.41, training accuracy is 0.625000, time 299.935 samples/sec
+epoch 17, total_step 266300, total loss is 15.65 , inference loss is 9.24, weight deacy loss is 6.41, training accuracy is 0.437500, time 297.847 samples/sec
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+epoch 17, total_step 266380, total loss is 12.43 , inference loss is 6.02, weight deacy loss is 6.41, training accuracy is 0.562500, time 214.968 samples/sec
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+epoch 17, total_step 267980, total loss is 14.51 , inference loss is 8.11, weight deacy loss is 6.41, training accuracy is 0.562500, time 221.993 samples/sec
+testing verification..
+(12000, 512)
+infer time 25.45081800000001
+best_threshold_index 119 0.9888888888888889
+best_threshold_index 121 0.9887037037037038
+best_threshold_index 119 0.9881481481481481
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+best_threshold_index 119 0.9877777777777778
+best_threshold_index 119 0.9875925925925926
+[lfw][268000]XNorm: 21.725108
+[lfw][268000]Accuracy-Flip: 0.98800+-0.00499
+test accuracy is:  0.9879999999999999
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+epoch 17, total_step 268180, total loss is 14.08 , inference loss is 7.67, weight deacy loss is 6.41, training accuracy is 0.500000, time 206.805 samples/sec
+epoch 17, total_step 268200, total loss is 15.75 , inference loss is 9.35, weight deacy loss is 6.41, training accuracy is 0.531250, time 8.933 samples/sec
+epoch 17, total_step 268220, total loss is 11.97 , inference loss is 5.56, weight deacy loss is 6.41, training accuracy is 0.531250, time 205.206 samples/sec
+epoch 17, total_step 268240, total loss is 13.77 , inference loss is 7.36, weight deacy loss is 6.41, training accuracy is 0.531250, time 205.677 samples/sec
+epoch 17, total_step 268260, total loss is 8.58 , inference loss is 2.18, weight deacy loss is 6.41, training accuracy is 0.781250, time 196.189 samples/sec
+epoch 17, total_step 268280, total loss is 13.67 , inference loss is 7.27, weight deacy loss is 6.41, training accuracy is 0.468750, time 226.941 samples/sec
+epoch 17, total_step 268300, total loss is 12.60 , inference loss is 6.20, weight deacy loss is 6.41, training accuracy is 0.625000, time 198.172 samples/sec
+epoch 17, total_step 268320, total loss is 9.08 , inference loss is 2.67, weight deacy loss is 6.40, training accuracy is 0.812500, time 217.022 samples/sec
+epoch 17, total_step 268340, total loss is 12.81 , inference loss is 6.40, weight deacy loss is 6.40, training accuracy is 0.500000, time 300.231 samples/sec
+epoch 17, total_step 268360, total loss is 11.30 , inference loss is 4.90, weight deacy loss is 6.40, training accuracy is 0.593750, time 209.222 samples/sec
+epoch 17, total_step 268380, total loss is 12.30 , inference loss is 5.89, weight deacy loss is 6.40, training accuracy is 0.500000, time 217.700 samples/sec
+epoch 17, total_step 268400, total loss is 11.77 , inference loss is 5.36, weight deacy loss is 6.40, training accuracy is 0.687500, time 208.580 samples/sec
+epoch 17, total_step 268420, total loss is 9.76 , inference loss is 3.35, weight deacy loss is 6.40, training accuracy is 0.718750, time 217.825 samples/sec
+epoch 17, total_step 268440, total loss is 12.58 , inference loss is 6.17, weight deacy loss is 6.40, training accuracy is 0.593750, time 209.867 samples/sec
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+epoch 17, total_step 268480, total loss is 12.87 , inference loss is 6.46, weight deacy loss is 6.40, training accuracy is 0.562500, time 192.007 samples/sec
+epoch 17, total_step 268500, total loss is 12.63 , inference loss is 6.22, weight deacy loss is 6.40, training accuracy is 0.468750, time 8.526 samples/sec
+epoch 17, total_step 268520, total loss is 12.44 , inference loss is 6.03, weight deacy loss is 6.40, training accuracy is 0.468750, time 206.125 samples/sec
+epoch 17, total_step 268540, total loss is 11.23 , inference loss is 4.83, weight deacy loss is 6.40, training accuracy is 0.656250, time 218.525 samples/sec
+epoch 17, total_step 268560, total loss is 11.08 , inference loss is 4.68, weight deacy loss is 6.40, training accuracy is 0.656250, time 300.222 samples/sec
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+epoch 17, total_step 268640, total loss is 16.00 , inference loss is 9.60, weight deacy loss is 6.40, training accuracy is 0.531250, time 210.745 samples/sec
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+epoch 17, total_step 268700, total loss is 12.51 , inference loss is 6.11, weight deacy loss is 6.40, training accuracy is 0.562500, time 208.276 samples/sec
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+epoch 17, total_step 268840, total loss is 10.74 , inference loss is 4.33, weight deacy loss is 6.40, training accuracy is 0.531250, time 300.425 samples/sec
+epoch 17, total_step 268860, total loss is 12.27 , inference loss is 5.86, weight deacy loss is 6.40, training accuracy is 0.531250, time 198.069 samples/sec
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+epoch 17, total_step 268900, total loss is 12.82 , inference loss is 6.42, weight deacy loss is 6.40, training accuracy is 0.500000, time 197.355 samples/sec
+epoch 17, total_step 268920, total loss is 11.18 , inference loss is 4.78, weight deacy loss is 6.40, training accuracy is 0.437500, time 198.389 samples/sec
+epoch 17, total_step 268940, total loss is 12.47 , inference loss is 6.06, weight deacy loss is 6.41, training accuracy is 0.656250, time 212.880 samples/sec
+epoch 17, total_step 268960, total loss is 15.70 , inference loss is 9.29, weight deacy loss is 6.41, training accuracy is 0.500000, time 209.853 samples/sec
+epoch 17, total_step 268980, total loss is 11.82 , inference loss is 5.41, weight deacy loss is 6.41, training accuracy is 0.562500, time 208.660 samples/sec
+epoch 17, total_step 269000, total loss is 13.61 , inference loss is 7.20, weight deacy loss is 6.41, training accuracy is 0.437500, time 199.949 samples/sec
+epoch 17, total_step 269020, total loss is 15.41 , inference loss is 9.00, weight deacy loss is 6.41, training accuracy is 0.468750, time 204.719 samples/sec
+epoch 17, total_step 269040, total loss is 15.95 , inference loss is 9.55, weight deacy loss is 6.41, training accuracy is 0.531250, time 208.911 samples/sec
+epoch 17, total_step 269060, total loss is 11.79 , inference loss is 5.38, weight deacy loss is 6.41, training accuracy is 0.562500, time 213.898 samples/sec
+epoch 17, total_step 269080, total loss is 15.81 , inference loss is 9.41, weight deacy loss is 6.41, training accuracy is 0.406250, time 219.999 samples/sec
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+epoch 17, total_step 269140, total loss is 14.41 , inference loss is 8.01, weight deacy loss is 6.41, training accuracy is 0.468750, time 229.269 samples/sec
+epoch 17, total_step 269160, total loss is 17.94 , inference loss is 11.54, weight deacy loss is 6.41, training accuracy is 0.500000, time 195.384 samples/sec
+epoch 17, total_step 269180, total loss is 10.89 , inference loss is 4.49, weight deacy loss is 6.41, training accuracy is 0.656250, time 222.928 samples/sec
+epoch 17, total_step 269200, total loss is 16.80 , inference loss is 10.39, weight deacy loss is 6.41, training accuracy is 0.375000, time 205.419 samples/sec
+epoch 17, total_step 269220, total loss is 9.84 , inference loss is 3.44, weight deacy loss is 6.41, training accuracy is 0.531250, time 207.997 samples/sec
+epoch 17, total_step 269240, total loss is 12.35 , inference loss is 5.95, weight deacy loss is 6.41, training accuracy is 0.562500, time 228.792 samples/sec
+epoch 17, total_step 269260, total loss is 13.82 , inference loss is 7.41, weight deacy loss is 6.41, training accuracy is 0.468750, time 222.511 samples/sec
+epoch 17, total_step 269280, total loss is 11.54 , inference loss is 5.14, weight deacy loss is 6.41, training accuracy is 0.593750, time 212.805 samples/sec
+epoch 17, total_step 269300, total loss is 10.69 , inference loss is 4.28, weight deacy loss is 6.41, training accuracy is 0.625000, time 215.408 samples/sec
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+epoch 17, total_step 269340, total loss is 9.96 , inference loss is 3.55, weight deacy loss is 6.41, training accuracy is 0.656250, time 213.985 samples/sec
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+epoch 17, total_step 269460, total loss is 10.56 , inference loss is 4.15, weight deacy loss is 6.41, training accuracy is 0.593750, time 196.676 samples/sec
+epoch 17, total_step 269480, total loss is 13.24 , inference loss is 6.84, weight deacy loss is 6.41, training accuracy is 0.437500, time 226.017 samples/sec
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+epoch 17, total_step 269540, total loss is 16.65 , inference loss is 10.25, weight deacy loss is 6.41, training accuracy is 0.343750, time 231.545 samples/sec
+epoch 17, total_step 269560, total loss is 15.14 , inference loss is 8.73, weight deacy loss is 6.41, training accuracy is 0.531250, time 198.135 samples/sec
+epoch 17, total_step 269580, total loss is 16.32 , inference loss is 9.91, weight deacy loss is 6.41, training accuracy is 0.437500, time 205.795 samples/sec
+epoch 17, total_step 269600, total loss is 11.12 , inference loss is 4.71, weight deacy loss is 6.41, training accuracy is 0.500000, time 202.184 samples/sec
+epoch 17, total_step 269620, total loss is 11.76 , inference loss is 5.35, weight deacy loss is 6.41, training accuracy is 0.593750, time 227.494 samples/sec
+epoch 17, total_step 269640, total loss is 11.83 , inference loss is 5.43, weight deacy loss is 6.41, training accuracy is 0.500000, time 296.419 samples/sec
+epoch 17, total_step 269660, total loss is 14.47 , inference loss is 8.06, weight deacy loss is 6.41, training accuracy is 0.531250, time 206.822 samples/sec
+epoch 17, total_step 269680, total loss is 11.37 , inference loss is 4.96, weight deacy loss is 6.41, training accuracy is 0.593750, time 213.925 samples/sec
+epoch 17, total_step 269700, total loss is 11.50 , inference loss is 5.09, weight deacy loss is 6.41, training accuracy is 0.531250, time 9.751 samples/sec
+epoch 17, total_step 269720, total loss is 12.22 , inference loss is 5.81, weight deacy loss is 6.41, training accuracy is 0.625000, time 191.470 samples/sec
+epoch 17, total_step 269740, total loss is 12.64 , inference loss is 6.23, weight deacy loss is 6.41, training accuracy is 0.468750, time 217.939 samples/sec
+epoch 17, total_step 269760, total loss is 9.72 , inference loss is 3.32, weight deacy loss is 6.41, training accuracy is 0.656250, time 217.251 samples/sec
+epoch 17, total_step 269780, total loss is 12.45 , inference loss is 6.04, weight deacy loss is 6.41, training accuracy is 0.500000, time 198.276 samples/sec
+epoch 17, total_step 269800, total loss is 14.49 , inference loss is 8.08, weight deacy loss is 6.41, training accuracy is 0.500000, time 192.801 samples/sec
+epoch 17, total_step 269820, total loss is 13.67 , inference loss is 7.27, weight deacy loss is 6.41, training accuracy is 0.468750, time 214.179 samples/sec
+epoch 17, total_step 269840, total loss is 12.36 , inference loss is 5.95, weight deacy loss is 6.41, training accuracy is 0.625000, time 176.376 samples/sec
+epoch 17, total_step 269860, total loss is 11.11 , inference loss is 4.71, weight deacy loss is 6.41, training accuracy is 0.625000, time 299.088 samples/sec
+epoch 17, total_step 269880, total loss is 11.88 , inference loss is 5.47, weight deacy loss is 6.41, training accuracy is 0.468750, time 215.031 samples/sec
+epoch 17, total_step 269900, total loss is 11.57 , inference loss is 5.16, weight deacy loss is 6.41, training accuracy is 0.531250, time 195.653 samples/sec
+epoch 17, total_step 269920, total loss is 14.00 , inference loss is 7.59, weight deacy loss is 6.41, training accuracy is 0.562500, time 224.330 samples/sec
+epoch 17, total_step 269940, total loss is 12.47 , inference loss is 6.06, weight deacy loss is 6.41, training accuracy is 0.500000, time 215.524 samples/sec
+epoch 17, total_step 269960, total loss is 13.75 , inference loss is 7.34, weight deacy loss is 6.41, training accuracy is 0.531250, time 210.910 samples/sec
+epoch 17, total_step 269980, total loss is 11.95 , inference loss is 5.54, weight deacy loss is 6.41, training accuracy is 0.531250, time 223.487 samples/sec
+testing verification..
+(12000, 512)
+infer time 23.555210999999996
+best_threshold_index 115 0.9877777777777778
+best_threshold_index 115 0.9879629629629629
+best_threshold_index 115 0.9881481481481481
+best_threshold_index 115 0.9883333333333333
+best_threshold_index 115 0.9883333333333333
+best_threshold_index 115 0.9874074074074074
+best_threshold_index 115 0.9883333333333333
+best_threshold_index 115 0.987037037037037
+best_threshold_index 115 0.9874074074074074
+best_threshold_index 115 0.9875925925925926
+[lfw][270000]XNorm: 19.484533
+[lfw][270000]Accuracy-Flip: 0.98783+-0.00395
+test accuracy is:  0.9878333333333333
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+epoch 17, total_step 270020, total loss is 13.94 , inference loss is 7.53, weight deacy loss is 6.41, training accuracy is 0.437500, time 211.472 samples/sec
+epoch 17, total_step 270040, total loss is 10.42 , inference loss is 4.01, weight deacy loss is 6.41, training accuracy is 0.625000, time 229.847 samples/sec
+epoch 17, total_step 270060, total loss is 13.30 , inference loss is 6.90, weight deacy loss is 6.41, training accuracy is 0.593750, time 225.106 samples/sec
+epoch 17, total_step 270080, total loss is 11.83 , inference loss is 5.42, weight deacy loss is 6.41, training accuracy is 0.531250, time 220.969 samples/sec
+epoch 17, total_step 270100, total loss is 12.61 , inference loss is 6.20, weight deacy loss is 6.41, training accuracy is 0.500000, time 202.783 samples/sec
+epoch 17, total_step 270120, total loss is 15.87 , inference loss is 9.46, weight deacy loss is 6.41, training accuracy is 0.531250, time 212.740 samples/sec
+epoch 17, total_step 270140, total loss is 11.17 , inference loss is 4.76, weight deacy loss is 6.41, training accuracy is 0.468750, time 211.579 samples/sec
+epoch 17, total_step 270160, total loss is 15.21 , inference loss is 8.80, weight deacy loss is 6.41, training accuracy is 0.312500, time 215.587 samples/sec
+epoch 17, total_step 270180, total loss is 12.82 , inference loss is 6.41, weight deacy loss is 6.41, training accuracy is 0.531250, time 218.943 samples/sec
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+epoch 17, total_step 270220, total loss is 13.86 , inference loss is 7.45, weight deacy loss is 6.41, training accuracy is 0.562500, time 299.498 samples/sec
+epoch 17, total_step 270240, total loss is 12.12 , inference loss is 5.71, weight deacy loss is 6.41, training accuracy is 0.500000, time 213.816 samples/sec
+epoch 17, total_step 270260, total loss is 16.80 , inference loss is 10.39, weight deacy loss is 6.41, training accuracy is 0.437500, time 230.988 samples/sec
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+epoch 17, total_step 270300, total loss is 13.55 , inference loss is 7.14, weight deacy loss is 6.41, training accuracy is 0.500000, time 12.047 samples/sec
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+epoch 17, total_step 270380, total loss is 11.20 , inference loss is 4.79, weight deacy loss is 6.41, training accuracy is 0.750000, time 211.690 samples/sec
+epoch 17, total_step 270400, total loss is 13.35 , inference loss is 6.94, weight deacy loss is 6.41, training accuracy is 0.593750, time 193.549 samples/sec
+epoch 17, total_step 270420, total loss is 10.22 , inference loss is 3.81, weight deacy loss is 6.41, training accuracy is 0.687500, time 194.624 samples/sec
+epoch 17, total_step 270440, total loss is 12.25 , inference loss is 5.84, weight deacy loss is 6.41, training accuracy is 0.562500, time 192.695 samples/sec
+epoch 17, total_step 270460, total loss is 12.22 , inference loss is 5.81, weight deacy loss is 6.41, training accuracy is 0.656250, time 298.108 samples/sec
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+epoch 17, total_step 270520, total loss is 13.51 , inference loss is 7.10, weight deacy loss is 6.41, training accuracy is 0.468750, time 214.414 samples/sec
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+epoch 17, total_step 270880, total loss is 10.19 , inference loss is 3.78, weight deacy loss is 6.41, training accuracy is 0.562500, time 218.800 samples/sec
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+epoch 17, total_step 271000, total loss is 9.89 , inference loss is 3.48, weight deacy loss is 6.41, training accuracy is 0.687500, time 211.517 samples/sec
+epoch 17, total_step 271020, total loss is 11.82 , inference loss is 5.41, weight deacy loss is 6.41, training accuracy is 0.625000, time 215.201 samples/sec
+epoch 17, total_step 271040, total loss is 10.91 , inference loss is 4.50, weight deacy loss is 6.41, training accuracy is 0.593750, time 214.466 samples/sec
+epoch 17, total_step 271060, total loss is 8.82 , inference loss is 2.41, weight deacy loss is 6.41, training accuracy is 0.812500, time 212.551 samples/sec
+epoch 17, total_step 271080, total loss is 10.37 , inference loss is 3.96, weight deacy loss is 6.41, training accuracy is 0.593750, time 216.521 samples/sec
+epoch 17, total_step 271100, total loss is 11.61 , inference loss is 5.20, weight deacy loss is 6.41, training accuracy is 0.656250, time 214.014 samples/sec
+epoch 17, total_step 271120, total loss is 11.56 , inference loss is 5.15, weight deacy loss is 6.41, training accuracy is 0.656250, time 298.815 samples/sec
+epoch 17, total_step 271140, total loss is 16.94 , inference loss is 10.54, weight deacy loss is 6.41, training accuracy is 0.343750, time 297.091 samples/sec
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+epoch 17, total_step 271180, total loss is 13.96 , inference loss is 7.55, weight deacy loss is 6.41, training accuracy is 0.500000, time 222.048 samples/sec
+epoch 17, total_step 271200, total loss is 14.42 , inference loss is 8.02, weight deacy loss is 6.41, training accuracy is 0.468750, time 12.494 samples/sec
+epoch 17, total_step 271220, total loss is 14.99 , inference loss is 8.58, weight deacy loss is 6.41, training accuracy is 0.437500, time 226.486 samples/sec
+epoch 17, total_step 271240, total loss is 13.01 , inference loss is 6.60, weight deacy loss is 6.41, training accuracy is 0.468750, time 225.413 samples/sec
+epoch 17, total_step 271260, total loss is 13.99 , inference loss is 7.59, weight deacy loss is 6.41, training accuracy is 0.437500, time 220.795 samples/sec
+epoch 17, total_step 271280, total loss is 10.80 , inference loss is 4.39, weight deacy loss is 6.41, training accuracy is 0.593750, time 213.985 samples/sec
+epoch 17, total_step 271300, total loss is 14.20 , inference loss is 7.79, weight deacy loss is 6.41, training accuracy is 0.406250, time 216.270 samples/sec
+epoch 17, total_step 271320, total loss is 12.59 , inference loss is 6.18, weight deacy loss is 6.41, training accuracy is 0.562500, time 213.695 samples/sec
+epoch 17, total_step 271340, total loss is 12.24 , inference loss is 5.84, weight deacy loss is 6.41, training accuracy is 0.468750, time 210.275 samples/sec
+epoch 17, total_step 271360, total loss is 9.25 , inference loss is 2.84, weight deacy loss is 6.41, training accuracy is 0.625000, time 211.864 samples/sec
+epoch 17, total_step 271380, total loss is 11.29 , inference loss is 4.88, weight deacy loss is 6.41, training accuracy is 0.531250, time 216.399 samples/sec
+epoch 17, total_step 271400, total loss is 17.65 , inference loss is 11.24, weight deacy loss is 6.41, training accuracy is 0.437500, time 217.905 samples/sec
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+epoch 17, total_step 271480, total loss is 12.41 , inference loss is 6.00, weight deacy loss is 6.41, training accuracy is 0.562500, time 217.121 samples/sec
+epoch 17, total_step 271500, total loss is 11.43 , inference loss is 5.02, weight deacy loss is 6.41, training accuracy is 0.656250, time 12.025 samples/sec
+epoch 17, total_step 271520, total loss is 15.02 , inference loss is 8.62, weight deacy loss is 6.41, training accuracy is 0.468750, time 225.059 samples/sec
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+epoch 17, total_step 271560, total loss is 12.00 , inference loss is 5.59, weight deacy loss is 6.41, training accuracy is 0.562500, time 221.417 samples/sec
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+epoch 17, total_step 271640, total loss is 12.73 , inference loss is 6.32, weight deacy loss is 6.41, training accuracy is 0.500000, time 224.835 samples/sec
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+epoch 17, total_step 271680, total loss is 13.48 , inference loss is 7.08, weight deacy loss is 6.41, training accuracy is 0.375000, time 296.820 samples/sec
+epoch 17, total_step 271700, total loss is 12.37 , inference loss is 5.96, weight deacy loss is 6.41, training accuracy is 0.531250, time 218.430 samples/sec
+epoch 17, total_step 271720, total loss is 15.57 , inference loss is 9.17, weight deacy loss is 6.41, training accuracy is 0.437500, time 219.029 samples/sec
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+epoch 17, total_step 271760, total loss is 11.04 , inference loss is 4.63, weight deacy loss is 6.41, training accuracy is 0.500000, time 219.695 samples/sec
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+epoch 17, total_step 271800, total loss is 13.42 , inference loss is 7.01, weight deacy loss is 6.41, training accuracy is 0.468750, time 12.497 samples/sec
+epoch 17, total_step 271820, total loss is 12.71 , inference loss is 6.30, weight deacy loss is 6.41, training accuracy is 0.500000, time 219.601 samples/sec
+epoch 17, total_step 271840, total loss is 12.06 , inference loss is 5.65, weight deacy loss is 6.41, training accuracy is 0.593750, time 222.965 samples/sec
+epoch 17, total_step 271860, total loss is 16.03 , inference loss is 9.62, weight deacy loss is 6.41, training accuracy is 0.375000, time 222.024 samples/sec
+epoch 17, total_step 271880, total loss is 10.02 , inference loss is 3.61, weight deacy loss is 6.41, training accuracy is 0.562500, time 225.570 samples/sec
+epoch 17, total_step 271900, total loss is 10.57 , inference loss is 4.17, weight deacy loss is 6.41, training accuracy is 0.593750, time 232.975 samples/sec
+epoch 17, total_step 271920, total loss is 12.81 , inference loss is 6.40, weight deacy loss is 6.41, training accuracy is 0.562500, time 236.602 samples/sec
+epoch 17, total_step 271940, total loss is 10.04 , inference loss is 3.63, weight deacy loss is 6.41, training accuracy is 0.687500, time 295.424 samples/sec
+epoch 17, total_step 271960, total loss is 13.03 , inference loss is 6.62, weight deacy loss is 6.41, training accuracy is 0.500000, time 209.528 samples/sec
+epoch 17, total_step 271980, total loss is 15.22 , inference loss is 8.81, weight deacy loss is 6.41, training accuracy is 0.437500, time 215.946 samples/sec
+testing verification..
+(12000, 512)
+infer time 26.032024999999994
+best_threshold_index 114 0.9842592592592593
+best_threshold_index 116 0.9835185185185186
+best_threshold_index 116 0.9838888888888889
+best_threshold_index 116 0.9857407407407407
+best_threshold_index 116 0.985
+best_threshold_index 116 0.9825925925925926
+best_threshold_index 116 0.9842592592592593
+best_threshold_index 116 0.9848148148148148
+best_threshold_index 116 0.9833333333333333
+best_threshold_index 116 0.9842592592592593
+[lfw][272000]XNorm: 21.595662
+[lfw][272000]Accuracy-Flip: 0.98383+-0.00778
+test accuracy is:  0.9838333333333333
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+epoch 17, total_step 272020, total loss is 14.46 , inference loss is 8.05, weight deacy loss is 6.41, training accuracy is 0.437500, time 297.629 samples/sec
+epoch 17, total_step 272040, total loss is 13.39 , inference loss is 6.99, weight deacy loss is 6.41, training accuracy is 0.593750, time 212.519 samples/sec
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+epoch 17, total_step 272100, total loss is 10.92 , inference loss is 4.51, weight deacy loss is 6.41, training accuracy is 0.687500, time 12.977 samples/sec
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+epoch 17, total_step 272160, total loss is 13.20 , inference loss is 6.79, weight deacy loss is 6.41, training accuracy is 0.500000, time 219.725 samples/sec
+epoch 17, total_step 272180, total loss is 12.98 , inference loss is 6.57, weight deacy loss is 6.41, training accuracy is 0.468750, time 216.732 samples/sec
+epoch 17, total_step 272200, total loss is 12.21 , inference loss is 5.81, weight deacy loss is 6.41, training accuracy is 0.406250, time 225.580 samples/sec
+epoch 17, total_step 272220, total loss is 10.53 , inference loss is 4.12, weight deacy loss is 6.41, training accuracy is 0.593750, time 233.470 samples/sec
+epoch 17, total_step 272240, total loss is 9.91 , inference loss is 3.50, weight deacy loss is 6.41, training accuracy is 0.562500, time 205.055 samples/sec
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+epoch 17, total_step 272280, total loss is 14.14 , inference loss is 7.73, weight deacy loss is 6.41, training accuracy is 0.343750, time 298.701 samples/sec
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+epoch 17, total_step 272340, total loss is 13.31 , inference loss is 6.90, weight deacy loss is 6.41, training accuracy is 0.406250, time 212.212 samples/sec
+epoch 17, total_step 272360, total loss is 11.45 , inference loss is 5.04, weight deacy loss is 6.41, training accuracy is 0.625000, time 212.623 samples/sec
+epoch 17, total_step 272380, total loss is 17.52 , inference loss is 11.11, weight deacy loss is 6.41, training accuracy is 0.343750, time 212.693 samples/sec
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+epoch 17, total_step 272420, total loss is 12.30 , inference loss is 5.89, weight deacy loss is 6.41, training accuracy is 0.500000, time 217.746 samples/sec
+epoch 17, total_step 272440, total loss is 16.22 , inference loss is 9.82, weight deacy loss is 6.41, training accuracy is 0.562500, time 215.044 samples/sec
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+epoch 17, total_step 273100, total loss is 12.21 , inference loss is 5.80, weight deacy loss is 6.41, training accuracy is 0.375000, time 213.037 samples/sec
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+epoch 17, total_step 273920, total loss is 14.09 , inference loss is 7.68, weight deacy loss is 6.41, training accuracy is 0.531250, time 232.015 samples/sec
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+epoch 17, total_step 273980, total loss is 15.30 , inference loss is 8.89, weight deacy loss is 6.41, training accuracy is 0.375000, time 297.431 samples/sec
+testing verification..
+(12000, 512)
+infer time 25.828824999999973
+best_threshold_index 112 0.9835185185185186
+best_threshold_index 112 0.9822222222222222
+best_threshold_index 113 0.9833333333333333
+best_threshold_index 112 0.9848148148148148
+best_threshold_index 112 0.9837037037037037
+best_threshold_index 112 0.9818518518518519
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+best_threshold_index 112 0.9822222222222222
+best_threshold_index 112 0.980925925925926
+best_threshold_index 112 0.9816666666666667
+[lfw][274000]XNorm: 21.992819
+[lfw][274000]Accuracy-Flip: 0.98233+-0.00995
+test accuracy is:  0.9823333333333334
+epoch 17, total_step 274000, total loss is 18.28 , inference loss is 11.87, weight deacy loss is 6.41, training accuracy is 0.218750, time 15.366 samples/sec
+epoch 17, total_step 274020, total loss is 14.58 , inference loss is 8.18, weight deacy loss is 6.41, training accuracy is 0.500000, time 198.537 samples/sec
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+epoch 17, total_step 274080, total loss is 14.87 , inference loss is 8.46, weight deacy loss is 6.41, training accuracy is 0.468750, time 296.618 samples/sec
+epoch 17, total_step 274100, total loss is 12.58 , inference loss is 6.17, weight deacy loss is 6.41, training accuracy is 0.562500, time 205.962 samples/sec
+epoch 17, total_step 274120, total loss is 11.62 , inference loss is 5.21, weight deacy loss is 6.41, training accuracy is 0.375000, time 231.597 samples/sec
+epoch 17, total_step 274140, total loss is 11.26 , inference loss is 4.85, weight deacy loss is 6.41, training accuracy is 0.500000, time 201.562 samples/sec
+epoch 17, total_step 274160, total loss is 12.69 , inference loss is 6.28, weight deacy loss is 6.41, training accuracy is 0.437500, time 205.176 samples/sec
+epoch 17, total_step 274180, total loss is 10.09 , inference loss is 3.68, weight deacy loss is 6.41, training accuracy is 0.625000, time 218.180 samples/sec
+epoch 17, total_step 274200, total loss is 12.97 , inference loss is 6.56, weight deacy loss is 6.41, training accuracy is 0.500000, time 12.189 samples/sec
+epoch 17, total_step 274220, total loss is 12.48 , inference loss is 6.08, weight deacy loss is 6.41, training accuracy is 0.437500, time 231.197 samples/sec
+epoch 17, total_step 274240, total loss is 12.58 , inference loss is 6.17, weight deacy loss is 6.41, training accuracy is 0.468750, time 211.000 samples/sec
+epoch 17, total_step 274260, total loss is 15.34 , inference loss is 8.93, weight deacy loss is 6.41, training accuracy is 0.500000, time 223.857 samples/sec
+epoch 17, total_step 274280, total loss is 11.88 , inference loss is 5.47, weight deacy loss is 6.41, training accuracy is 0.531250, time 208.301 samples/sec
+epoch 17, total_step 274300, total loss is 13.74 , inference loss is 7.34, weight deacy loss is 6.41, training accuracy is 0.500000, time 210.270 samples/sec
+epoch 17, total_step 274320, total loss is 10.19 , inference loss is 3.78, weight deacy loss is 6.41, training accuracy is 0.500000, time 224.632 samples/sec
+epoch 17, total_step 274340, total loss is 13.83 , inference loss is 7.42, weight deacy loss is 6.41, training accuracy is 0.343750, time 297.138 samples/sec
+epoch 17, total_step 274360, total loss is 13.45 , inference loss is 7.04, weight deacy loss is 6.41, training accuracy is 0.406250, time 239.837 samples/sec
+epoch 17, total_step 274380, total loss is 16.05 , inference loss is 9.64, weight deacy loss is 6.41, training accuracy is 0.437500, time 227.027 samples/sec
+epoch 17, total_step 274400, total loss is 14.95 , inference loss is 8.54, weight deacy loss is 6.41, training accuracy is 0.312500, time 214.877 samples/sec
+epoch 17, total_step 274420, total loss is 13.39 , inference loss is 6.99, weight deacy loss is 6.41, training accuracy is 0.625000, time 220.169 samples/sec
+epoch 17, total_step 274440, total loss is 14.12 , inference loss is 7.71, weight deacy loss is 6.41, training accuracy is 0.468750, time 233.045 samples/sec
+epoch 17, total_step 274460, total loss is 14.25 , inference loss is 7.84, weight deacy loss is 6.41, training accuracy is 0.593750, time 214.804 samples/sec
+epoch 17, total_step 274480, total loss is 15.12 , inference loss is 8.71, weight deacy loss is 6.41, training accuracy is 0.437500, time 225.885 samples/sec
+epoch 17, total_step 274500, total loss is 11.16 , inference loss is 4.76, weight deacy loss is 6.41, training accuracy is 0.500000, time 8.672 samples/sec
+epoch 17, total_step 274520, total loss is 11.09 , inference loss is 4.68, weight deacy loss is 6.41, training accuracy is 0.500000, time 294.748 samples/sec
+epoch 17, total_step 274540, total loss is 10.75 , inference loss is 4.34, weight deacy loss is 6.41, training accuracy is 0.625000, time 301.912 samples/sec
+epoch 17, total_step 274560, total loss is 14.85 , inference loss is 8.45, weight deacy loss is 6.41, training accuracy is 0.406250, time 300.485 samples/sec
+epoch 17, total_step 274580, total loss is 13.22 , inference loss is 6.81, weight deacy loss is 6.41, training accuracy is 0.593750, time 300.293 samples/sec
+epoch 17, total_step 274600, total loss is 19.01 , inference loss is 12.60, weight deacy loss is 6.41, training accuracy is 0.312500, time 298.307 samples/sec
+epoch 17, total_step 274620, total loss is 14.70 , inference loss is 8.29, weight deacy loss is 6.41, training accuracy is 0.406250, time 296.641 samples/sec
+epoch 17, total_step 274640, total loss is 17.56 , inference loss is 11.15, weight deacy loss is 6.41, training accuracy is 0.406250, time 300.038 samples/sec
+epoch 17, total_step 274660, total loss is 15.41 , inference loss is 9.00, weight deacy loss is 6.41, training accuracy is 0.406250, time 203.781 samples/sec
+epoch 17, total_step 274680, total loss is 13.02 , inference loss is 6.62, weight deacy loss is 6.41, training accuracy is 0.500000, time 205.533 samples/sec
+epoch 17, total_step 274700, total loss is 14.04 , inference loss is 7.63, weight deacy loss is 6.41, training accuracy is 0.375000, time 207.993 samples/sec
+epoch 17, total_step 274720, total loss is 11.94 , inference loss is 5.53, weight deacy loss is 6.41, training accuracy is 0.562500, time 215.741 samples/sec
+epoch 17, total_step 274740, total loss is 13.98 , inference loss is 7.57, weight deacy loss is 6.41, training accuracy is 0.437500, time 228.019 samples/sec
+epoch 17, total_step 274760, total loss is 13.79 , inference loss is 7.38, weight deacy loss is 6.41, training accuracy is 0.437500, time 210.133 samples/sec
+epoch 17, total_step 274780, total loss is 18.26 , inference loss is 11.85, weight deacy loss is 6.41, training accuracy is 0.406250, time 199.479 samples/sec
+epoch 17, total_step 274800, total loss is 12.71 , inference loss is 6.31, weight deacy loss is 6.41, training accuracy is 0.437500, time 8.794 samples/sec
+epoch 17, total_step 274820, total loss is 14.69 , inference loss is 8.29, weight deacy loss is 6.41, training accuracy is 0.312500, time 216.478 samples/sec
+epoch 17, total_step 274840, total loss is 10.66 , inference loss is 4.26, weight deacy loss is 6.41, training accuracy is 0.468750, time 226.121 samples/sec
+epoch 17, total_step 274860, total loss is 13.57 , inference loss is 7.17, weight deacy loss is 6.41, training accuracy is 0.343750, time 238.111 samples/sec
+epoch 17, total_step 274880, total loss is 15.80 , inference loss is 9.39, weight deacy loss is 6.41, training accuracy is 0.343750, time 226.467 samples/sec
+epoch 17, total_step 274900, total loss is 14.17 , inference loss is 7.76, weight deacy loss is 6.41, training accuracy is 0.343750, time 212.404 samples/sec
+epoch 17, total_step 274920, total loss is 12.17 , inference loss is 5.76, weight deacy loss is 6.41, training accuracy is 0.468750, time 204.310 samples/sec
+epoch 17, total_step 274940, total loss is 17.02 , inference loss is 10.61, weight deacy loss is 6.41, training accuracy is 0.312500, time 195.676 samples/sec
+epoch 17, total_step 274960, total loss is 18.97 , inference loss is 12.57, weight deacy loss is 6.41, training accuracy is 0.343750, time 196.500 samples/sec
+epoch 17, total_step 274980, total loss is 14.80 , inference loss is 8.40, weight deacy loss is 6.41, training accuracy is 0.375000, time 196.603 samples/sec
+epoch 17, total_step 275000, total loss is 12.07 , inference loss is 5.66, weight deacy loss is 6.41, training accuracy is 0.562500, time 217.078 samples/sec
+epoch 17, total_step 275020, total loss is 11.47 , inference loss is 5.06, weight deacy loss is 6.41, training accuracy is 0.500000, time 197.962 samples/sec
+epoch 17, total_step 275040, total loss is 17.46 , inference loss is 11.05, weight deacy loss is 6.41, training accuracy is 0.375000, time 217.647 samples/sec
+epoch 17, total_step 275060, total loss is 15.03 , inference loss is 8.63, weight deacy loss is 6.41, training accuracy is 0.343750, time 207.403 samples/sec
+epoch 17, total_step 275080, total loss is 15.40 , inference loss is 9.00, weight deacy loss is 6.41, training accuracy is 0.312500, time 296.145 samples/sec
+epoch 17, total_step 275100, total loss is 14.28 , inference loss is 7.87, weight deacy loss is 6.41, training accuracy is 0.437500, time 8.874 samples/sec
+epoch 17, total_step 275120, total loss is 14.29 , inference loss is 7.88, weight deacy loss is 6.41, training accuracy is 0.406250, time 202.044 samples/sec
+epoch 17, total_step 275140, total loss is 14.98 , inference loss is 8.57, weight deacy loss is 6.41, training accuracy is 0.281250, time 221.008 samples/sec
+epoch 17, total_step 275160, total loss is 10.28 , inference loss is 3.87, weight deacy loss is 6.41, training accuracy is 0.531250, time 197.711 samples/sec
+epoch 17, total_step 275180, total loss is 18.24 , inference loss is 11.83, weight deacy loss is 6.41, training accuracy is 0.406250, time 215.375 samples/sec
+epoch 17, total_step 275200, total loss is 13.51 , inference loss is 7.10, weight deacy loss is 6.41, training accuracy is 0.375000, time 204.397 samples/sec
+epoch 17, total_step 275220, total loss is 14.44 , inference loss is 8.03, weight deacy loss is 6.41, training accuracy is 0.468750, time 207.973 samples/sec
+epoch 17, total_step 275240, total loss is 12.29 , inference loss is 5.89, weight deacy loss is 6.41, training accuracy is 0.562500, time 196.305 samples/sec
+epoch 17, total_step 275260, total loss is 12.14 , inference loss is 5.73, weight deacy loss is 6.41, training accuracy is 0.468750, time 209.378 samples/sec
+epoch 17, total_step 275280, total loss is 16.68 , inference loss is 10.28, weight deacy loss is 6.41, training accuracy is 0.500000, time 208.924 samples/sec
+epoch 17, total_step 275300, total loss is 15.79 , inference loss is 9.39, weight deacy loss is 6.41, training accuracy is 0.406250, time 233.342 samples/sec
+epoch 17, total_step 275320, total loss is 20.25 , inference loss is 13.84, weight deacy loss is 6.41, training accuracy is 0.281250, time 296.943 samples/sec
+epoch 17, total_step 275340, total loss is 15.18 , inference loss is 8.78, weight deacy loss is 6.41, training accuracy is 0.375000, time 224.406 samples/sec
+epoch 17, total_step 275360, total loss is 13.20 , inference loss is 6.79, weight deacy loss is 6.41, training accuracy is 0.406250, time 211.038 samples/sec
+epoch 17, total_step 275380, total loss is 11.64 , inference loss is 5.23, weight deacy loss is 6.41, training accuracy is 0.531250, time 221.561 samples/sec
+epoch 17, total_step 275400, total loss is 14.59 , inference loss is 8.18, weight deacy loss is 6.41, training accuracy is 0.437500, time 11.993 samples/sec
+epoch 17, total_step 275420, total loss is 13.16 , inference loss is 6.75, weight deacy loss is 6.41, training accuracy is 0.562500, time 223.903 samples/sec
+epoch 17, total_step 275440, total loss is 14.20 , inference loss is 7.79, weight deacy loss is 6.41, training accuracy is 0.375000, time 214.068 samples/sec
+epoch 17, total_step 275460, total loss is 16.49 , inference loss is 10.08, weight deacy loss is 6.41, training accuracy is 0.343750, time 217.216 samples/sec
+epoch 17, total_step 275480, total loss is 13.11 , inference loss is 6.70, weight deacy loss is 6.41, training accuracy is 0.531250, time 224.583 samples/sec2022-12-01 18:43:17.498392: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:145] Filling up shuffle buffer (this may take a while): 7067 of 10000
+2022-12-01 18:43:21.693740: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:195] Shuffle buffer filled.
+
+epoch 17, total_step 275500, total loss is 13.60 , inference loss is 7.20, weight deacy loss is 6.41, training accuracy is 0.468750, time 223.593 samples/sec
+epoch 17, total_step 275520, total loss is 16.31 , inference loss is 9.90, weight deacy loss is 6.41, training accuracy is 0.375000, time 218.644 samples/sec
+epoch 17, total_step 275540, total loss is 13.79 , inference loss is 7.38, weight deacy loss is 6.41, training accuracy is 0.406250, time 217.986 samples/sec
+epoch 17, total_step 275560, total loss is 15.17 , inference loss is 8.76, weight deacy loss is 6.41, training accuracy is 0.312500, time 226.460 samples/sec
+epoch 17, total_step 275580, total loss is 13.53 , inference loss is 7.12, weight deacy loss is 6.41, training accuracy is 0.531250, time 298.809 samples/sec
+epoch 17, total_step 275600, total loss is 13.75 , inference loss is 7.34, weight deacy loss is 6.41, training accuracy is 0.468750, time 215.070 samples/sec
+epoch 17, total_step 275620, total loss is 15.20 , inference loss is 8.79, weight deacy loss is 6.41, training accuracy is 0.343750, time 216.674 samples/sec
+epoch 17, total_step 275640, total loss is 13.59 , inference loss is 7.18, weight deacy loss is 6.41, training accuracy is 0.343750, time 217.216 samples/sec
+epoch 17, total_step 275660, total loss is 12.82 , inference loss is 6.41, weight deacy loss is 6.41, training accuracy is 0.468750, time 193.356 samples/sec
+epoch 17, total_step 275680, total loss is 15.00 , inference loss is 8.59, weight deacy loss is 6.41, training accuracy is 0.312500, time 211.853 samples/sec
+epoch 17, total_step 275700, total loss is 11.75 , inference loss is 5.35, weight deacy loss is 6.41, training accuracy is 0.312500, time 12.357 samples/sec
+epoch 17, total_step 275720, total loss is 19.44 , inference loss is 13.04, weight deacy loss is 6.41, training accuracy is 0.218750, time 194.302 samples/sec
+epoch 17, total_step 275740, total loss is 14.12 , inference loss is 7.71, weight deacy loss is 6.41, training accuracy is 0.500000, time 192.601 samples/sec
+epoch 17, total_step 275760, total loss is 16.94 , inference loss is 10.53, weight deacy loss is 6.41, training accuracy is 0.406250, time 225.100 samples/sec
+epoch 17, total_step 275780, total loss is 13.40 , inference loss is 7.00, weight deacy loss is 6.41, training accuracy is 0.531250, time 175.248 samples/sec
+epoch 17, total_step 275800, total loss is 13.82 , inference loss is 7.41, weight deacy loss is 6.41, training accuracy is 0.281250, time 191.875 samples/sec
+epoch 17, total_step 275820, total loss is 12.08 , inference loss is 5.67, weight deacy loss is 6.41, training accuracy is 0.437500, time 226.345 samples/sec
+epoch 17, total_step 275840, total loss is 11.45 , inference loss is 5.05, weight deacy loss is 6.41, training accuracy is 0.562500, time 176.902 samples/sec
+epoch 17, total_step 275860, total loss is 16.50 , inference loss is 10.10, weight deacy loss is 6.41, training accuracy is 0.406250, time 196.485 samples/sec
+epoch 17, total_step 275880, total loss is 9.35 , inference loss is 2.94, weight deacy loss is 6.41, training accuracy is 0.593750, time 195.323 samples/sec
+epoch 17, total_step 275900, total loss is 18.77 , inference loss is 12.37, weight deacy loss is 6.41, training accuracy is 0.343750, time 299.098 samples/sec
+epoch 17, total_step 275920, total loss is 11.67 , inference loss is 5.26, weight deacy loss is 6.41, training accuracy is 0.437500, time 198.397 samples/sec
+epoch 17, total_step 275940, total loss is 16.40 , inference loss is 10.00, weight deacy loss is 6.41, training accuracy is 0.250000, time 209.909 samples/sec
+End of epoch 17
+epoch 18, total_step 275960, total loss is 22.53 , inference loss is 16.13, weight deacy loss is 6.41, training accuracy is 0.000000, time 210.295 samples/sec
+epoch 18, total_step 275980, total loss is 13.51 , inference loss is 7.10, weight deacy loss is 6.41, training accuracy is 0.593750, time 226.382 samples/sec
+testing verification..
+(12000, 512)
+infer time 25.229265999999985
+best_threshold_index 100 0.9835185185185186
+best_threshold_index 100 0.9827777777777778
+best_threshold_index 99 0.9827777777777778
+best_threshold_index 99 0.9840740740740741
+best_threshold_index 100 0.9827777777777778
+best_threshold_index 100 0.9822222222222222
+best_threshold_index 100 0.9833333333333333
+best_threshold_index 99 0.9829629629629629
+best_threshold_index 100 0.9822222222222222
+best_threshold_index 100 0.9818518518518519
+[lfw][276000]XNorm: 19.663508
+[lfw][276000]Accuracy-Flip: 0.98217+-0.00606
+test accuracy is:  0.9821666666666667
+epoch 18, total_step 276000, total loss is 9.61 , inference loss is 3.20, weight deacy loss is 6.41, training accuracy is 0.718750, time 15.802 samples/sec
+epoch 18, total_step 276020, total loss is 10.45 , inference loss is 4.04, weight deacy loss is 6.41, training accuracy is 0.687500, time 218.228 samples/sec
+epoch 18, total_step 276040, total loss is 8.75 , inference loss is 2.35, weight deacy loss is 6.41, training accuracy is 0.843750, time 219.747 samples/sec
+epoch 18, total_step 276060, total loss is 11.88 , inference loss is 5.47, weight deacy loss is 6.41, training accuracy is 0.687500, time 219.772 samples/sec
+epoch 18, total_step 276080, total loss is 6.60 , inference loss is 0.20, weight deacy loss is 6.41, training accuracy is 0.968750, time 226.989 samples/sec
+epoch 18, total_step 276100, total loss is 9.91 , inference loss is 3.51, weight deacy loss is 6.41, training accuracy is 0.750000, time 237.130 samples/sec
+epoch 18, total_step 276120, total loss is 7.56 , inference loss is 1.16, weight deacy loss is 6.41, training accuracy is 0.812500, time 218.091 samples/sec
+epoch 18, total_step 276140, total loss is 8.55 , inference loss is 2.15, weight deacy loss is 6.40, training accuracy is 0.875000, time 219.087 samples/sec
+epoch 18, total_step 276160, total loss is 14.10 , inference loss is 7.70, weight deacy loss is 6.40, training accuracy is 0.781250, time 222.922 samples/sec
+epoch 18, total_step 276180, total loss is 9.39 , inference loss is 2.98, weight deacy loss is 6.40, training accuracy is 0.812500, time 293.989 samples/sec
+epoch 18, total_step 276200, total loss is 8.86 , inference loss is 2.46, weight deacy loss is 6.40, training accuracy is 0.812500, time 210.064 samples/sec
+epoch 18, total_step 276220, total loss is 11.01 , inference loss is 4.61, weight deacy loss is 6.40, training accuracy is 0.718750, time 208.941 samples/sec
+epoch 18, total_step 276240, total loss is 11.65 , inference loss is 5.25, weight deacy loss is 6.40, training accuracy is 0.750000, time 222.893 samples/sec
+epoch 18, total_step 276260, total loss is 10.44 , inference loss is 4.03, weight deacy loss is 6.40, training accuracy is 0.750000, time 213.000 samples/sec
+epoch 18, total_step 276280, total loss is 12.10 , inference loss is 5.70, weight deacy loss is 6.40, training accuracy is 0.687500, time 213.691 samples/sec
+epoch 18, total_step 276300, total loss is 14.68 , inference loss is 8.28, weight deacy loss is 6.40, training accuracy is 0.593750, time 12.758 samples/sec
+epoch 18, total_step 276320, total loss is 9.19 , inference loss is 2.79, weight deacy loss is 6.40, training accuracy is 0.750000, time 299.136 samples/sec
+epoch 18, total_step 276340, total loss is 8.84 , inference loss is 2.44, weight deacy loss is 6.41, training accuracy is 0.812500, time 222.370 samples/sec
+epoch 18, total_step 276360, total loss is 12.72 , inference loss is 6.32, weight deacy loss is 6.41, training accuracy is 0.750000, time 219.946 samples/sec
+epoch 18, total_step 276380, total loss is 8.94 , inference loss is 2.54, weight deacy loss is 6.41, training accuracy is 0.781250, time 225.432 samples/sec
+epoch 18, total_step 276400, total loss is 10.48 , inference loss is 4.07, weight deacy loss is 6.41, training accuracy is 0.843750, time 223.431 samples/sec
+epoch 18, total_step 276420, total loss is 10.51 , inference loss is 4.11, weight deacy loss is 6.41, training accuracy is 0.593750, time 219.787 samples/sec
+epoch 18, total_step 276440, total loss is 12.47 , inference loss is 6.06, weight deacy loss is 6.41, training accuracy is 0.750000, time 240.632 samples/sec
+epoch 18, total_step 276460, total loss is 8.23 , inference loss is 1.82, weight deacy loss is 6.41, training accuracy is 0.718750, time 223.107 samples/sec
+epoch 18, total_step 276480, total loss is 6.99 , inference loss is 0.59, weight deacy loss is 6.41, training accuracy is 0.812500, time 231.606 samples/sec
+epoch 18, total_step 276500, total loss is 15.19 , inference loss is 8.78, weight deacy loss is 6.41, training accuracy is 0.625000, time 216.295 samples/sec
+epoch 18, total_step 276520, total loss is 8.14 , inference loss is 1.73, weight deacy loss is 6.41, training accuracy is 0.750000, time 216.971 samples/sec
+epoch 18, total_step 276540, total loss is 11.72 , inference loss is 5.31, weight deacy loss is 6.41, training accuracy is 0.750000, time 213.466 samples/sec
+epoch 18, total_step 276560, total loss is 13.50 , inference loss is 7.09, weight deacy loss is 6.41, training accuracy is 0.625000, time 222.628 samples/sec
+epoch 18, total_step 276580, total loss is 9.33 , inference loss is 2.92, weight deacy loss is 6.41, training accuracy is 0.718750, time 222.946 samples/sec
+epoch 18, total_step 276600, total loss is 9.29 , inference loss is 2.88, weight deacy loss is 6.41, training accuracy is 0.750000, time 12.616 samples/sec
+epoch 18, total_step 276620, total loss is 12.82 , inference loss is 6.41, weight deacy loss is 6.41, training accuracy is 0.625000, time 223.931 samples/sec
+epoch 18, total_step 276640, total loss is 10.36 , inference loss is 3.95, weight deacy loss is 6.41, training accuracy is 0.875000, time 221.052 samples/sec
+epoch 18, total_step 276660, total loss is 12.23 , inference loss is 5.82, weight deacy loss is 6.41, training accuracy is 0.656250, time 212.067 samples/sec
+epoch 18, total_step 276680, total loss is 12.58 , inference loss is 6.17, weight deacy loss is 6.41, training accuracy is 0.687500, time 224.348 samples/sec
+epoch 18, total_step 276700, total loss is 8.47 , inference loss is 2.06, weight deacy loss is 6.41, training accuracy is 0.843750, time 218.945 samples/sec
+epoch 18, total_step 276720, total loss is 12.40 , inference loss is 5.99, weight deacy loss is 6.41, training accuracy is 0.750000, time 223.272 samples/sec
+epoch 18, total_step 276740, total loss is 9.43 , inference loss is 3.02, weight deacy loss is 6.41, training accuracy is 0.812500, time 225.949 samples/sec
+epoch 18, total_step 276760, total loss is 11.37 , inference loss is 4.96, weight deacy loss is 6.41, training accuracy is 0.562500, time 220.156 samples/sec
+epoch 18, total_step 276780, total loss is 11.87 , inference loss is 5.46, weight deacy loss is 6.41, training accuracy is 0.718750, time 224.036 samples/sec
+epoch 18, total_step 276800, total loss is 12.53 , inference loss is 6.12, weight deacy loss is 6.41, training accuracy is 0.687500, time 223.456 samples/sec
+epoch 18, total_step 276820, total loss is 12.94 , inference loss is 6.53, weight deacy loss is 6.41, training accuracy is 0.437500, time 219.342 samples/sec
+epoch 18, total_step 276840, total loss is 10.08 , inference loss is 3.67, weight deacy loss is 6.41, training accuracy is 0.718750, time 224.163 samples/sec
+epoch 18, total_step 276860, total loss is 10.19 , inference loss is 3.78, weight deacy loss is 6.41, training accuracy is 0.625000, time 228.523 samples/sec
+epoch 18, total_step 276880, total loss is 10.65 , inference loss is 4.23, weight deacy loss is 6.41, training accuracy is 0.687500, time 224.958 samples/sec
+epoch 18, total_step 276900, total loss is 12.24 , inference loss is 5.82, weight deacy loss is 6.41, training accuracy is 0.625000, time 11.665 samples/sec
+epoch 18, total_step 276920, total loss is 12.09 , inference loss is 5.68, weight deacy loss is 6.41, training accuracy is 0.562500, time 229.428 samples/sec
+epoch 18, total_step 276940, total loss is 8.63 , inference loss is 2.22, weight deacy loss is 6.41, training accuracy is 0.843750, time 224.625 samples/sec
+epoch 18, total_step 276960, total loss is 11.84 , inference loss is 5.43, weight deacy loss is 6.41, training accuracy is 0.531250, time 226.805 samples/sec
+epoch 18, total_step 276980, total loss is 14.24 , inference loss is 7.83, weight deacy loss is 6.41, training accuracy is 0.562500, time 227.463 samples/sec
+epoch 18, total_step 277000, total loss is 9.33 , inference loss is 2.92, weight deacy loss is 6.42, training accuracy is 0.656250, time 225.319 samples/sec
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+epoch 18, total_step 277040, total loss is 13.03 , inference loss is 6.61, weight deacy loss is 6.42, training accuracy is 0.625000, time 223.495 samples/sec
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+epoch 18, total_step 277180, total loss is 8.82 , inference loss is 2.40, weight deacy loss is 6.42, training accuracy is 0.562500, time 229.986 samples/sec
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+epoch 18, total_step 277220, total loss is 11.79 , inference loss is 5.37, weight deacy loss is 6.42, training accuracy is 0.562500, time 223.385 samples/sec
+epoch 18, total_step 277240, total loss is 12.80 , inference loss is 6.39, weight deacy loss is 6.42, training accuracy is 0.625000, time 225.372 samples/sec
+epoch 18, total_step 277260, total loss is 12.69 , inference loss is 6.27, weight deacy loss is 6.42, training accuracy is 0.562500, time 229.889 samples/sec
+epoch 18, total_step 277280, total loss is 13.50 , inference loss is 7.08, weight deacy loss is 6.42, training accuracy is 0.500000, time 218.959 samples/sec
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+epoch 18, total_step 277320, total loss is 10.43 , inference loss is 4.01, weight deacy loss is 6.42, training accuracy is 0.562500, time 217.883 samples/sec
+epoch 18, total_step 277340, total loss is 11.37 , inference loss is 4.95, weight deacy loss is 6.42, training accuracy is 0.562500, time 216.286 samples/sec
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+epoch 18, total_step 277480, total loss is 13.33 , inference loss is 6.90, weight deacy loss is 6.42, training accuracy is 0.593750, time 223.337 samples/sec
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+epoch 18, total_step 277680, total loss is 12.56 , inference loss is 6.13, weight deacy loss is 6.42, training accuracy is 0.718750, time 233.606 samples/sec
+epoch 18, total_step 277700, total loss is 9.18 , inference loss is 2.75, weight deacy loss is 6.43, training accuracy is 0.687500, time 237.954 samples/sec
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+epoch 18, total_step 277740, total loss is 10.18 , inference loss is 3.76, weight deacy loss is 6.43, training accuracy is 0.656250, time 216.904 samples/sec
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+epoch 18, total_step 277780, total loss is 8.88 , inference loss is 2.46, weight deacy loss is 6.43, training accuracy is 0.656250, time 225.399 samples/sec
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+epoch 18, total_step 277880, total loss is 9.62 , inference loss is 3.19, weight deacy loss is 6.43, training accuracy is 0.687500, time 224.945 samples/sec
+epoch 18, total_step 277900, total loss is 15.68 , inference loss is 9.25, weight deacy loss is 6.43, training accuracy is 0.625000, time 226.003 samples/sec
+epoch 18, total_step 277920, total loss is 10.65 , inference loss is 4.22, weight deacy loss is 6.43, training accuracy is 0.562500, time 216.219 samples/sec
+epoch 18, total_step 277940, total loss is 8.03 , inference loss is 1.60, weight deacy loss is 6.43, training accuracy is 0.843750, time 183.721 samples/sec
+epoch 18, total_step 277960, total loss is 9.13 , inference loss is 2.70, weight deacy loss is 6.43, training accuracy is 0.812500, time 228.555 samples/sec
+epoch 18, total_step 277980, total loss is 12.04 , inference loss is 5.61, weight deacy loss is 6.43, training accuracy is 0.656250, time 218.123 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.353662000000003
+best_threshold_index 120 0.9864814814814815
+best_threshold_index 120 0.9861111111111112
+best_threshold_index 120 0.9855555555555555
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+best_threshold_index 120 0.9853703703703703
+best_threshold_index 120 0.9842592592592593
+best_threshold_index 120 0.9848148148148148
+[lfw][278000]XNorm: 21.163878
+[lfw][278000]Accuracy-Flip: 0.98550+-0.00597
+test accuracy is:  0.9855
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+epoch 18, total_step 278420, total loss is 11.50 , inference loss is 5.07, weight deacy loss is 6.43, training accuracy is 0.781250, time 227.342 samples/sec
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+epoch 18, total_step 279060, total loss is 9.75 , inference loss is 3.32, weight deacy loss is 6.42, training accuracy is 0.875000, time 226.131 samples/sec
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+epoch 18, total_step 279100, total loss is 12.52 , inference loss is 6.09, weight deacy loss is 6.42, training accuracy is 0.562500, time 226.020 samples/sec
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+epoch 18, total_step 279940, total loss is 15.20 , inference loss is 8.77, weight deacy loss is 6.43, training accuracy is 0.593750, time 214.713 samples/sec
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+epoch 18, total_step 279980, total loss is 12.80 , inference loss is 6.37, weight deacy loss is 6.43, training accuracy is 0.500000, time 219.999 samples/sec
+testing verification..
+(12000, 512)
+infer time 23.99685099999998
+best_threshold_index 126 0.9872222222222222
+best_threshold_index 126 0.9859259259259259
+best_threshold_index 126 0.9864814814814815
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+best_threshold_index 126 0.985
+best_threshold_index 126 0.9855555555555555
+[lfw][280000]XNorm: 20.391007
+[lfw][280000]Accuracy-Flip: 0.98617+-0.00675
+test accuracy is:  0.9861666666666666
+epoch 18, total_step 280000, total loss is 13.46 , inference loss is 7.03, weight deacy loss is 6.43, training accuracy is 0.500000, time 16.822 samples/sec
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+epoch 18, total_step 280100, total loss is 9.14 , inference loss is 2.71, weight deacy loss is 6.43, training accuracy is 0.687500, time 218.006 samples/sec
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+epoch 18, total_step 280160, total loss is 9.05 , inference loss is 2.62, weight deacy loss is 6.43, training accuracy is 0.687500, time 235.487 samples/sec
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+epoch 18, total_step 280220, total loss is 9.27 , inference loss is 2.84, weight deacy loss is 6.43, training accuracy is 0.687500, time 221.694 samples/sec
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+epoch 18, total_step 280260, total loss is 9.21 , inference loss is 2.78, weight deacy loss is 6.43, training accuracy is 0.718750, time 299.297 samples/sec
+epoch 18, total_step 280280, total loss is 12.85 , inference loss is 6.42, weight deacy loss is 6.43, training accuracy is 0.718750, time 215.844 samples/sec
+epoch 18, total_step 280300, total loss is 11.91 , inference loss is 5.48, weight deacy loss is 6.43, training accuracy is 0.531250, time 229.966 samples/sec
+epoch 18, total_step 280320, total loss is 14.94 , inference loss is 8.51, weight deacy loss is 6.43, training accuracy is 0.500000, time 218.540 samples/sec
+epoch 18, total_step 280340, total loss is 14.01 , inference loss is 7.58, weight deacy loss is 6.43, training accuracy is 0.500000, time 221.491 samples/sec
+epoch 18, total_step 280360, total loss is 10.79 , inference loss is 4.36, weight deacy loss is 6.43, training accuracy is 0.656250, time 227.518 samples/sec
+epoch 18, total_step 280380, total loss is 13.87 , inference loss is 7.44, weight deacy loss is 6.43, training accuracy is 0.343750, time 221.985 samples/sec
+epoch 18, total_step 280400, total loss is 14.30 , inference loss is 7.87, weight deacy loss is 6.43, training accuracy is 0.593750, time 215.566 samples/sec
+epoch 18, total_step 280420, total loss is 13.46 , inference loss is 7.03, weight deacy loss is 6.43, training accuracy is 0.500000, time 228.905 samples/sec
+epoch 18, total_step 280440, total loss is 13.42 , inference loss is 6.99, weight deacy loss is 6.43, training accuracy is 0.593750, time 220.548 samples/sec
+epoch 18, total_step 280460, total loss is 10.37 , inference loss is 3.94, weight deacy loss is 6.43, training accuracy is 0.750000, time 217.553 samples/sec
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+epoch 18, total_step 280500, total loss is 11.16 , inference loss is 4.73, weight deacy loss is 6.43, training accuracy is 0.562500, time 12.004 samples/sec
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+epoch 18, total_step 281000, total loss is 12.18 , inference loss is 5.75, weight deacy loss is 6.43, training accuracy is 0.656250, time 229.583 samples/sec
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+epoch 18, total_step 281200, total loss is 12.85 , inference loss is 6.42, weight deacy loss is 6.43, training accuracy is 0.437500, time 226.003 samples/sec
+epoch 18, total_step 281220, total loss is 13.26 , inference loss is 6.83, weight deacy loss is 6.43, training accuracy is 0.593750, time 226.240 samples/sec
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+epoch 18, total_step 281500, total loss is 14.53 , inference loss is 8.10, weight deacy loss is 6.43, training accuracy is 0.437500, time 204.430 samples/sec
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+epoch 18, total_step 281560, total loss is 16.00 , inference loss is 9.56, weight deacy loss is 6.43, training accuracy is 0.531250, time 228.558 samples/sec
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+epoch 18, total_step 281600, total loss is 12.13 , inference loss is 5.70, weight deacy loss is 6.43, training accuracy is 0.468750, time 297.181 samples/sec
+epoch 18, total_step 281620, total loss is 9.28 , inference loss is 2.85, weight deacy loss is 6.43, training accuracy is 0.718750, time 211.643 samples/sec
+epoch 18, total_step 281640, total loss is 12.64 , inference loss is 6.20, weight deacy loss is 6.43, training accuracy is 0.500000, time 210.449 samples/sec
+epoch 18, total_step 281660, total loss is 12.98 , inference loss is 6.55, weight deacy loss is 6.43, training accuracy is 0.593750, time 214.074 samples/sec
+epoch 18, total_step 281680, total loss is 17.98 , inference loss is 11.55, weight deacy loss is 6.43, training accuracy is 0.437500, time 211.280 samples/sec
+epoch 18, total_step 281700, total loss is 9.28 , inference loss is 2.85, weight deacy loss is 6.43, training accuracy is 0.625000, time 13.397 samples/sec
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+epoch 18, total_step 281780, total loss is 13.38 , inference loss is 6.95, weight deacy loss is 6.43, training accuracy is 0.437500, time 222.175 samples/sec
+epoch 18, total_step 281800, total loss is 9.65 , inference loss is 3.21, weight deacy loss is 6.43, training accuracy is 0.593750, time 210.785 samples/sec
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+epoch 18, total_step 281900, total loss is 9.21 , inference loss is 2.78, weight deacy loss is 6.43, training accuracy is 0.625000, time 196.889 samples/sec
+epoch 18, total_step 281920, total loss is 10.61 , inference loss is 4.18, weight deacy loss is 6.43, training accuracy is 0.468750, time 224.988 samples/sec
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+epoch 18, total_step 281980, total loss is 15.42 , inference loss is 8.99, weight deacy loss is 6.43, training accuracy is 0.468750, time 193.606 samples/sec
+testing verification..
+(12000, 512)
+infer time 25.439507999999968
+best_threshold_index 117 0.9885185185185185
+best_threshold_index 117 0.9875925925925926
+best_threshold_index 117 0.9881481481481481
+best_threshold_index 117 0.9883333333333333
+best_threshold_index 117 0.9885185185185185
+best_threshold_index 117 0.9872222222222222
+best_threshold_index 117 0.9887037037037038
+best_threshold_index 117 0.9875925925925926
+best_threshold_index 121 0.9868518518518519
+best_threshold_index 117 0.987037037037037
+[lfw][282000]XNorm: 19.891002
+[lfw][282000]Accuracy-Flip: 0.98750+-0.00554
+test accuracy is:  0.9874999999999998
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+epoch 18, total_step 282020, total loss is 13.80 , inference loss is 7.37, weight deacy loss is 6.43, training accuracy is 0.625000, time 206.684 samples/sec
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+epoch 18, total_step 282100, total loss is 11.33 , inference loss is 4.90, weight deacy loss is 6.43, training accuracy is 0.500000, time 196.762 samples/sec
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+epoch 18, total_step 282140, total loss is 9.31 , inference loss is 2.88, weight deacy loss is 6.43, training accuracy is 0.687500, time 224.078 samples/sec
+epoch 18, total_step 282160, total loss is 12.90 , inference loss is 6.47, weight deacy loss is 6.43, training accuracy is 0.593750, time 232.875 samples/sec
+epoch 18, total_step 282180, total loss is 15.65 , inference loss is 9.22, weight deacy loss is 6.43, training accuracy is 0.500000, time 219.073 samples/sec
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+epoch 18, total_step 282960, total loss is 12.19 , inference loss is 5.76, weight deacy loss is 6.43, training accuracy is 0.593750, time 185.243 samples/sec
+epoch 18, total_step 282980, total loss is 10.60 , inference loss is 4.17, weight deacy loss is 6.43, training accuracy is 0.531250, time 221.466 samples/sec
+epoch 18, total_step 283000, total loss is 9.63 , inference loss is 3.20, weight deacy loss is 6.43, training accuracy is 0.781250, time 213.360 samples/sec
+epoch 18, total_step 283020, total loss is 12.06 , inference loss is 5.63, weight deacy loss is 6.43, training accuracy is 0.437500, time 213.976 samples/sec
+epoch 18, total_step 283040, total loss is 10.98 , inference loss is 4.55, weight deacy loss is 6.43, training accuracy is 0.468750, time 220.960 samples/sec
+epoch 18, total_step 283060, total loss is 12.81 , inference loss is 6.37, weight deacy loss is 6.43, training accuracy is 0.437500, time 205.476 samples/sec
+epoch 18, total_step 283080, total loss is 10.13 , inference loss is 3.70, weight deacy loss is 6.43, training accuracy is 0.687500, time 211.144 samples/sec
+epoch 18, total_step 283100, total loss is 16.72 , inference loss is 10.29, weight deacy loss is 6.43, training accuracy is 0.468750, time 213.851 samples/sec
+epoch 18, total_step 283120, total loss is 12.87 , inference loss is 6.44, weight deacy loss is 6.43, training accuracy is 0.593750, time 209.446 samples/sec
+epoch 18, total_step 283140, total loss is 10.16 , inference loss is 3.73, weight deacy loss is 6.43, training accuracy is 0.656250, time 299.510 samples/sec
+epoch 18, total_step 283160, total loss is 11.83 , inference loss is 5.40, weight deacy loss is 6.43, training accuracy is 0.500000, time 198.054 samples/sec
+epoch 18, total_step 283180, total loss is 13.50 , inference loss is 7.06, weight deacy loss is 6.43, training accuracy is 0.406250, time 220.829 samples/sec
+epoch 18, total_step 283200, total loss is 9.83 , inference loss is 3.40, weight deacy loss is 6.43, training accuracy is 0.625000, time 12.363 samples/sec
+epoch 18, total_step 283220, total loss is 11.43 , inference loss is 5.00, weight deacy loss is 6.43, training accuracy is 0.562500, time 195.172 samples/sec
+epoch 18, total_step 283240, total loss is 17.64 , inference loss is 11.21, weight deacy loss is 6.43, training accuracy is 0.500000, time 208.737 samples/sec
+epoch 18, total_step 283260, total loss is 12.15 , inference loss is 5.72, weight deacy loss is 6.43, training accuracy is 0.468750, time 223.491 samples/sec
+epoch 18, total_step 283280, total loss is 11.45 , inference loss is 5.02, weight deacy loss is 6.43, training accuracy is 0.531250, time 244.555 samples/sec
+epoch 18, total_step 283300, total loss is 11.99 , inference loss is 5.55, weight deacy loss is 6.43, training accuracy is 0.562500, time 297.786 samples/sec
+epoch 18, total_step 283320, total loss is 13.65 , inference loss is 7.21, weight deacy loss is 6.43, training accuracy is 0.531250, time 208.145 samples/sec
+epoch 18, total_step 283340, total loss is 10.94 , inference loss is 4.51, weight deacy loss is 6.43, training accuracy is 0.625000, time 213.373 samples/sec
+epoch 18, total_step 283360, total loss is 15.60 , inference loss is 9.16, weight deacy loss is 6.43, training accuracy is 0.468750, time 227.963 samples/sec
+epoch 18, total_step 283380, total loss is 8.46 , inference loss is 2.03, weight deacy loss is 6.43, training accuracy is 0.750000, time 215.399 samples/sec
+epoch 18, total_step 283400, total loss is 12.54 , inference loss is 6.10, weight deacy loss is 6.43, training accuracy is 0.656250, time 213.432 samples/sec
+epoch 18, total_step 283420, total loss is 10.49 , inference loss is 4.05, weight deacy loss is 6.43, training accuracy is 0.562500, time 209.897 samples/sec
+epoch 18, total_step 283440, total loss is 14.86 , inference loss is 8.43, weight deacy loss is 6.43, training accuracy is 0.593750, time 295.469 samples/sec
+epoch 18, total_step 283460, total loss is 15.39 , inference loss is 8.95, weight deacy loss is 6.43, training accuracy is 0.375000, time 298.470 samples/sec
+epoch 18, total_step 283480, total loss is 9.73 , inference loss is 3.30, weight deacy loss is 6.43, training accuracy is 0.625000, time 196.967 samples/sec
+epoch 18, total_step 283500, total loss is 11.91 , inference loss is 5.48, weight deacy loss is 6.43, training accuracy is 0.562500, time 12.646 samples/sec
+epoch 18, total_step 283520, total loss is 12.59 , inference loss is 6.16, weight deacy loss is 6.43, training accuracy is 0.531250, time 300.370 samples/sec
+epoch 18, total_step 283540, total loss is 13.75 , inference loss is 7.32, weight deacy loss is 6.43, training accuracy is 0.562500, time 216.194 samples/sec
+epoch 18, total_step 283560, total loss is 10.72 , inference loss is 4.29, weight deacy loss is 6.43, training accuracy is 0.687500, time 208.486 samples/sec
+epoch 18, total_step 283580, total loss is 12.44 , inference loss is 6.00, weight deacy loss is 6.43, training accuracy is 0.656250, time 194.671 samples/sec
+epoch 18, total_step 283600, total loss is 11.34 , inference loss is 4.91, weight deacy loss is 6.43, training accuracy is 0.593750, time 220.026 samples/sec
+epoch 18, total_step 283620, total loss is 13.36 , inference loss is 6.92, weight deacy loss is 6.43, training accuracy is 0.625000, time 215.172 samples/sec
+epoch 18, total_step 283640, total loss is 13.29 , inference loss is 6.86, weight deacy loss is 6.43, training accuracy is 0.593750, time 200.931 samples/sec
+epoch 18, total_step 283660, total loss is 9.99 , inference loss is 3.56, weight deacy loss is 6.43, training accuracy is 0.625000, time 207.432 samples/sec
+epoch 18, total_step 283680, total loss is 10.85 , inference loss is 4.42, weight deacy loss is 6.43, training accuracy is 0.500000, time 210.187 samples/sec
+epoch 18, total_step 283700, total loss is 12.13 , inference loss is 5.70, weight deacy loss is 6.43, training accuracy is 0.687500, time 212.240 samples/sec
+epoch 18, total_step 283720, total loss is 12.28 , inference loss is 5.85, weight deacy loss is 6.43, training accuracy is 0.593750, time 208.336 samples/sec
+epoch 18, total_step 283740, total loss is 13.48 , inference loss is 7.05, weight deacy loss is 6.43, training accuracy is 0.468750, time 204.325 samples/sec
+epoch 18, total_step 283760, total loss is 11.10 , inference loss is 4.67, weight deacy loss is 6.43, training accuracy is 0.562500, time 210.657 samples/sec
+epoch 18, total_step 283780, total loss is 11.12 , inference loss is 4.69, weight deacy loss is 6.43, training accuracy is 0.562500, time 207.996 samples/sec
+epoch 18, total_step 283800, total loss is 8.79 , inference loss is 2.36, weight deacy loss is 6.43, training accuracy is 0.718750, time 13.702 samples/sec
+epoch 18, total_step 283820, total loss is 10.93 , inference loss is 4.50, weight deacy loss is 6.43, training accuracy is 0.593750, time 221.950 samples/sec
+epoch 18, total_step 283840, total loss is 14.77 , inference loss is 8.33, weight deacy loss is 6.43, training accuracy is 0.500000, time 223.900 samples/sec
+epoch 18, total_step 283860, total loss is 13.13 , inference loss is 6.70, weight deacy loss is 6.43, training accuracy is 0.437500, time 238.786 samples/sec
+epoch 18, total_step 283880, total loss is 14.62 , inference loss is 8.19, weight deacy loss is 6.43, training accuracy is 0.468750, time 212.653 samples/sec
+epoch 18, total_step 283900, total loss is 14.94 , inference loss is 8.51, weight deacy loss is 6.43, training accuracy is 0.500000, time 214.535 samples/sec
+epoch 18, total_step 283920, total loss is 13.15 , inference loss is 6.72, weight deacy loss is 6.43, training accuracy is 0.531250, time 217.717 samples/sec
+epoch 18, total_step 283940, total loss is 12.00 , inference loss is 5.57, weight deacy loss is 6.43, training accuracy is 0.562500, time 196.666 samples/sec
+epoch 18, total_step 283960, total loss is 10.09 , inference loss is 3.66, weight deacy loss is 6.43, training accuracy is 0.468750, time 222.803 samples/sec
+epoch 18, total_step 283980, total loss is 12.13 , inference loss is 5.70, weight deacy loss is 6.43, training accuracy is 0.656250, time 216.837 samples/sec
+testing verification..
+(12000, 512)
+infer time 24.22726800000003
+best_threshold_index 122 0.9892592592592593
+best_threshold_index 122 0.9888888888888889
+best_threshold_index 116 0.9883333333333333
+best_threshold_index 114 0.9894444444444445
+best_threshold_index 122 0.9892592592592593
+best_threshold_index 122 0.9881481481481481
+best_threshold_index 122 0.9896296296296296
+best_threshold_index 122 0.9885185185185185
+best_threshold_index 122 0.9875925925925926
+best_threshold_index 122 0.9879629629629629
+[lfw][284000]XNorm: 20.702124
+[lfw][284000]Accuracy-Flip: 0.98767+-0.00624
+test accuracy is:  0.9876666666666665
+Traceback (most recent call last):
+  File "/usr/local/python3.7.5/lib/python3.7/site-packages/tensorflow_core/python/client/session.py", line 1365, in _do_call
+    return fn(*args)
+  File "/usr/local/python3.7.5/lib/python3.7/site-packages/tensorflow_core/python/client/session.py", line 1350, in _run_fn
+    target_list, run_metadata)
+  File "/usr/local/python3.7.5/lib/python3.7/site-packages/tensorflow_core/python/client/session.py", line 1443, in _call_tf_sessionrun
+    run_metadata)
+tensorflow.python.framework.errors_impl.InternalError: GeOp11_0GEOP::::DoRunAsync Failed
+Error Message is : 
+EL0004: Failed to allocate memory.
+        Possible Cause: Available memory is insufficient.
+        Solution: Close applications not in use.
+        TraceBack (most recent call last):
+        rtMalloc execute failed, reason=[driver error:out of memory][FUNC:FuncErrorReason][FILE:error_message_manage.cc][LINE:49]
+        Call rtMalloc fail, purpose:feature map,used for op input and output., size:6861537792, device_id:6[FUNC:MallocMemory][FILE:graph_mem_allocator.cc][LINE:56]
+        MallocFeatureMapMem fail, data_size:6861537792, model_id:2159, check invalid[FUNC:InitFeatureMapAndP2PMem][FILE:davinci_model.cc][LINE:373]
+
+	 [[{{node GeOp11_0}}]]
+
+During handling of the above exception, another exception occurred:
+
+Traceback (most recent call last):
+  File "train_nets.py", line 179, in <module>
+    options=config_pb2.RunOptions(report_tensor_allocations_upon_oom=True))
+  File "/usr/local/python3.7.5/lib/python3.7/site-packages/tensorflow_core/python/client/session.py", line 956, in run
+    run_metadata_ptr)
+  File "/usr/local/python3.7.5/lib/python3.7/site-packages/tensorflow_core/python/client/session.py", line 1180, in _run
+    feed_dict_tensor, options, run_metadata)
+  File "/usr/local/python3.7.5/lib/python3.7/site-packages/tensorflow_core/python/client/session.py", line 1359, in _do_run
+    run_metadata)
+  File "/usr/local/python3.7.5/lib/python3.7/site-packages/tensorflow_core/python/client/session.py", line 1384, in _do_call
+    raise type(e)(node_def, op, message)
+tensorflow.python.framework.errors_impl.InternalError: GeOp11_0GEOP::::DoRunAsync Failed
+Error Message is : 
+EL0004: Failed to allocate memory.
+        Possible Cause: Available memory is insufficient.
+        Solution: Close applications not in use.
+        TraceBack (most recent call last):
+        rtMalloc execute failed, reason=[driver error:out of memory][FUNC:FuncErrorReason][FILE:error_message_manage.cc][LINE:49]
+        Call rtMalloc fail, purpose:feature map,used for op input and output., size:6861537792, device_id:6[FUNC:MallocMemory][FILE:graph_mem_allocator.cc][LINE:56]
+        MallocFeatureMapMem fail, data_size:6861537792, model_id:2159, check invalid[FUNC:InitFeatureMapAndP2PMem][FILE:davinci_model.cc][LINE:373]
+
+	 [[{{node GeOp11_0}}]]
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/figures/if_v100_perfermance.log b/TensorFlow/contrib/cv/InsightFace_TF/figures/if_v100_perfermance.log
new file mode 100644
index 0000000000000000000000000000000000000000..83ac97bbbd9d90dab1660696fa095b3c324d62e8
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/figures/if_v100_perfermance.log
@@ -0,0 +1,1552 @@
+nohup: ignoring input
+/root/anaconda3/envs/if/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
+  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
+/root/anaconda3/envs/if/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
+  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
+/root/anaconda3/envs/if/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
+  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
+/root/anaconda3/envs/if/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
+  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
+/root/anaconda3/envs/if/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
+  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
+/root/anaconda3/envs/if/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
+  np_resource = np.dtype([("resource", np.ubyte, 1)])
+/root/anaconda3/envs/if/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
+  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
+/root/anaconda3/envs/if/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
+  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
+/root/anaconda3/envs/if/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
+  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
+/root/anaconda3/envs/if/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
+  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
+/root/anaconda3/envs/if/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
+  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
+/root/anaconda3/envs/if/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
+  np_resource = np.dtype([("resource", np.ubyte, 1)])
+WARNING:tensorflow:From /root/anaconda3/envs/if/lib/python3.7/site-packages/tensorlayer/layers.py:28: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.
+
+WARNING:tensorflow:From /root/anaconda3/envs/if/lib/python3.7/site-packages/tensorlayer/layers.py:4030: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.
+
+WARNING:tensorflow:From /root/dave/InsightFace_TF/InsightFace_TF_GPU_2/train_nets.py:50: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.
+
+WARNING:tensorflow:From /root/dave/InsightFace_TF/InsightFace_TF_GPU_2/train_nets.py:51: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.
+
+WARNING:tensorflow:From /root/dave/InsightFace_TF/InsightFace_TF_GPU_2/data/mx2tfrecords.py:63: The name tf.FixedLenFeature is deprecated. Please use tf.io.FixedLenFeature instead.
+
+WARNING:tensorflow:From /root/dave/InsightFace_TF/InsightFace_TF_GPU_2/data/mx2tfrecords.py:65: The name tf.parse_single_example is deprecated. Please use tf.io.parse_single_example instead.
+
+2022-12-02 19:10:44.549375: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1
+2022-12-02 19:10:45.107187: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-12-02 19:10:45.108611: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: 
+name: Tesla V100-PCIE-32GB major: 7 minor: 0 memoryClockRate(GHz): 1.38
+pciBusID: 0000:00:0d.0
+2022-12-02 19:10:45.108849: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
+2022-12-02 19:10:45.109850: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
+2022-12-02 19:10:45.110803: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0
+2022-12-02 19:10:45.111076: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0
+2022-12-02 19:10:45.112244: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0
+2022-12-02 19:10:45.113217: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0
+2022-12-02 19:10:45.116300: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
+2022-12-02 19:10:45.116417: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-12-02 19:10:45.117858: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-12-02 19:10:45.119188: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
+WARNING:tensorflow:From /root/dave/InsightFace_TF/InsightFace_TF_GPU_2/train_nets.py:64: DatasetV1.make_initializable_iterator (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
+Instructions for updating:
+Use `for ... in dataset:` to iterate over a dataset. If using `tf.estimator`, return the `Dataset` object directly from your input function. As a last resort, you can use `tf.compat.v1.data.make_initializable_iterator(dataset)`.
+begin db lfw convert.
+loading bin 1000
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+(12000, 112, 112, 3)
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+(12000, 112, 112, 3)
+  [TL] InputLayer  resnet_v1_100/input_layer: (?, 112, 112, 3)
+  [TL] Conv2dLayer resnet_v1_100/conv1: shape:[3, 3, 3, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/bn0: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/bn0/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/bn0/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/prelu0: channel_shared:False
+  [TL] PoolLayer   resnet_v1_100/block1/unit_1/bottleneck_v1/shortcut: ksize:[1, 1, 1, 1] strides:[1, 2, 2, 1] padding:SAME pool:max_pool
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn1/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv1: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn2/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block1/unit_1/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] PadLayer   resnet_v1_100/block1/unit_1/bottleneck_v1/padding_conv2: paddings:[[0, 0], [1, 1], [1, 1], [0, 0]] mode:CONSTANT
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv2: shape:[3, 3, 64, 64] strides:[1, 2, 2, 1] pad:VALID act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block1/unit_1/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block1/unit_1/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block1/unit_1/bottleneck_v1/combine_layer: size:(?, 56, 56, 64) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn1/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv1: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn2/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block1/unit_2/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv2: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block1/unit_2/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block1/unit_2/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block1/unit_2/bottleneck_v1/combine_layer: size:(?, 56, 56, 64) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn1/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv1: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn2/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block1/unit_3/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv2: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block1/unit_3/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block1/unit_3/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block1/unit_3/bottleneck_v1/combine_layer: size:(?, 56, 56, 64) fn:add
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_1/bottleneck_v1/shortcut_conv: shape:[1, 1, 64, 128] strides:[1, 2, 2, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_1/bottleneck_v1/shortcut_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_1/bottleneck_v1/shortcut_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_1/bottleneck_v1/shortcut_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn1/Identity:0", shape=(64,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(64,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv1: shape:[3, 3, 64, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_1/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] PadLayer   resnet_v1_100/block2/unit_1/bottleneck_v1/padding_conv2: paddings:[[0, 0], [1, 1], [1, 1], [0, 0]] mode:CONSTANT
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 2, 2, 1] pad:VALID act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_1/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_1/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_1/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_2/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_2/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_2/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_2/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_3/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_3/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_3/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_3/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_4/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_4/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_4/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_4/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_5/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_5/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_5/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_5/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_6/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_6/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_6/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_6/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_7/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_7/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_7/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_7/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_8/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_8/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_8/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_8/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_9/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_9/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_9/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_9/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_10/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_10/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_10/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_10/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_11/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_11/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_11/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_11/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_12/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_12/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_12/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_12/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn2/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block2/unit_13/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block2/unit_13/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block2/unit_13/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_13/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_1/bottleneck_v1/shortcut_conv: shape:[1, 1, 128, 256] strides:[1, 2, 2, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_1/bottleneck_v1/shortcut_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_1/bottleneck_v1/shortcut_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_1/bottleneck_v1/shortcut_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn1/Identity:0", shape=(128,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(128,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv1: shape:[3, 3, 128, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_1/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] PadLayer   resnet_v1_100/block3/unit_1/bottleneck_v1/padding_conv2: paddings:[[0, 0], [1, 1], [1, 1], [0, 0]] mode:CONSTANT
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 2, 2, 1] pad:VALID act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_1/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_1/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_1/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_2/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_2/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_2/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_2/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_3/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_3/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_3/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_3/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_4/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_4/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_4/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_4/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_5/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_5/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_5/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_5/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_6/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_6/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_6/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_6/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_7/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_7/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_7/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_7/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_8/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_8/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_8/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_8/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_9/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_9/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_9/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_9/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_10/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_10/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_10/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_10/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_11/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_11/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_11/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_11/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_12/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_12/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_12/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_12/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_13/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_13/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_13/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_13/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_14/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_14/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_14/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_14/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_15/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_15/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_15/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_15/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_16/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_16/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_16/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_16/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_17/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_17/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_17/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_17/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_18/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_18/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_18/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_18/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_19/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_19/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_19/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_19/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_20/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_20/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_20/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_20/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_21/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_21/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_21/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_21/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_22/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_22/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_22/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_22/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_23/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_23/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_23/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_23/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_24/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_24/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_24/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_24/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_25/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_25/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_25/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_25/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_26/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_26/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_26/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_26/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_27/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_27/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_27/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_27/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_28/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_28/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_28/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_28/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_29/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_29/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_29/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_29/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn2/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block3/unit_30/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block3/unit_30/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block3/unit_30/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_30/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_1/bottleneck_v1/shortcut_conv: shape:[1, 1, 256, 512] strides:[1, 2, 2, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_1/bottleneck_v1/shortcut_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block4/unit_1/bottleneck_v1/shortcut_bn/BatchNorm/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_1/bottleneck_v1/shortcut_bn/BatchNorm/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn1/Identity:0", shape=(256,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(256,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv1: shape:[3, 3, 256, 512] strides:[1, 1, 1, 1] pad:SAME act:identityWARNING:tensorflow:From /root/dave/InsightFace_TF/InsightFace_TF_GPU_2/nets/L_Resnet_E_IR_fix_issue9.py:314: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
+Instructions for updating:
+Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
+WARNING:tensorflow:From /root/dave/InsightFace_TF/InsightFace_TF_GPU_2/losses/face_losses.py:20: calling norm (from tensorflow.python.ops.linalg_ops) with keep_dims is deprecated and will be removed in a future version.
+Instructions for updating:
+keep_dims is deprecated, use keepdims instead
+WARNING:tensorflow:From /root/dave/InsightFace_TF/InsightFace_TF_GPU_2/losses/face_losses.py:21: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
+Instructions for updating:
+Deprecated in favor of operator or tf.math.divide.
+WARNING:tensorflow:From /root/dave/InsightFace_TF/InsightFace_TF_GPU_2/losses/face_losses.py:40: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
+Instructions for updating:
+Use tf.where in 2.0, which has the same broadcast rule as np.where
+
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn2/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block4/unit_1/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] PadLayer   resnet_v1_100/block4/unit_1/bottleneck_v1/padding_conv2: paddings:[[0, 0], [1, 1], [1, 1], [0, 0]] mode:CONSTANT
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv2: shape:[3, 3, 512, 512] strides:[1, 2, 2, 1] pad:VALID act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block4/unit_1/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_1/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block4/unit_1/bottleneck_v1/combine_layer: size:(?, 7, 7, 512) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn1/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv1: shape:[3, 3, 512, 512] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn2/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block4/unit_2/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv2: shape:[3, 3, 512, 512] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block4/unit_2/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_2/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block4/unit_2/bottleneck_v1/combine_layer: size:(?, 7, 7, 512) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn1/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn1/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv1: shape:[3, 3, 512, 512] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn2/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn2/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] PReluLayer resnet_v1_100/block4/unit_3/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv2: shape:[3, 3, 512, 512] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/block4/unit_3/bottleneck_v1/conv2_bn/BatchNorm/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/block4/unit_3/bottleneck_v1/conv2_bn/BatchNorm/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] ElementwiseLayer resnet_v1_100/block4/unit_3/bottleneck_v1/combine_layer: size:(?, 7, 7, 512) fn:add
+  [TL] BatchNormLayer resnet_v1_100/E_BN1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/E_BN1/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/E_BN1/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] ReshapeLayer resnet_v1_100/E_Reshapelayer: (?, 25088)
+  [TL] DenseLayer  resnet_v1_100/E_DenseLayer: 512 identity
+  [TL] BatchNormLayer resnet_v1_100/E_BN2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+Tensor("resnet_v1_100/E_BN2/Identity:0", shape=(512,), dtype=float32)
+Tensor("resnet_v1_100/E_BN2/Identity_1:0", shape=(512,), dtype=float32)
+  [TL] InputLayer  resnet_v1_100/input_layer: (?, 112, 112, 3)
+  [TL] Conv2dLayer resnet_v1_100/conv1: shape:[3, 3, 3, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/bn0: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/prelu0: channel_shared:False
+  [TL] PoolLayer   resnet_v1_100/block1/unit_1/bottleneck_v1/shortcut: ksize:[1, 1, 1, 1] strides:[1, 2, 2, 1] padding:SAME pool:max_pool
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv1: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block1/unit_1/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] PadLayer   resnet_v1_100/block1/unit_1/bottleneck_v1/padding_conv2: paddings:[[0, 0], [1, 1], [1, 1], [0, 0]] mode:CONSTANT
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv2: shape:[3, 3, 64, 64] strides:[1, 2, 2, 1] pad:VALID act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_1/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block1/unit_1/bottleneck_v1/combine_layer: size:(?, 56, 56, 64) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv1: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block1/unit_2/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv2: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_2/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block1/unit_2/bottleneck_v1/combine_layer: size:(?, 56, 56, 64) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv1: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block1/unit_3/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv2: shape:[3, 3, 64, 64] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block1/unit_3/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block1/unit_3/bottleneck_v1/combine_layer: size:(?, 56, 56, 64) fn:add
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_1/bottleneck_v1/shortcut_conv: shape:[1, 1, 64, 128] strides:[1, 2, 2, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_1/bottleneck_v1/shortcut_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv1: shape:[3, 3, 64, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_1/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] PadLayer   resnet_v1_100/block2/unit_1/bottleneck_v1/padding_conv2: paddings:[[0, 0], [1, 1], [1, 1], [0, 0]] mode:CONSTANT
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 2, 2, 1] pad:VALID act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_1/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_1/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_2/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_2/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_2/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_3/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_3/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_3/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_4/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_4/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_4/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_5/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_5/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_5/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_6/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_6/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_6/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_7/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_7/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_7/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_8/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_8/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_8/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_9/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_9/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_9/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_10/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_10/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_10/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_11/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_11/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_11/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_12/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_12/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_12/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv1: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block2/unit_13/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv2: shape:[3, 3, 128, 128] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block2/unit_13/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block2/unit_13/bottleneck_v1/combine_layer: size:(?, 28, 28, 128) fn:add
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_1/bottleneck_v1/shortcut_conv: shape:[1, 1, 128, 256] strides:[1, 2, 2, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_1/bottleneck_v1/shortcut_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv1: shape:[3, 3, 128, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_1/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] PadLayer   resnet_v1_100/block3/unit_1/bottleneck_v1/padding_conv2: paddings:[[0, 0], [1, 1], [1, 1], [0, 0]] mode:CONSTANT
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 2, 2, 1] pad:VALID act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_1/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_1/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_2/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_2/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_2/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_3/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_3/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_3/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_4/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_4/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_4/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_5/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_5/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_5/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_6/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_6/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_6/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_7/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_7/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_7/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_8/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_8/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_8/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_9/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_9/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_9/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_10/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_10/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_10/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_11/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_11/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_11/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_12/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_12/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_12/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_13/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_13/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_13/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_14/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_14/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_14/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_15/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_15/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_15/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_16/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_16/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_16/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_17/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_17/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_17/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_18/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_18/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_18/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_19/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_19/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_19/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_20/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_20/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_20/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_21/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_21/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_21/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_22/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_22/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_22/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_23/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_23/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_23/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_24/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_24/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_24/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_25/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_25/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_25/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_26/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_26/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_26/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_27/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_27/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_27/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_28/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_28/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_28/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_29/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_29/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_29/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv1: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block3/unit_30/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv2: shape:[3, 3, 256, 256] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block3/unit_30/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block3/unit_30/bottleneck_v1/combine_layer: size:(?, 14, 14, 256) fn:add
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_1/bottleneck_v1/shortcut_conv: shape:[1, 1, 256, 512] strides:[1, 2, 2, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_1/bottleneck_v1/shortcut_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv1: shape:[3, 3, 256, 512] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block4/unit_1/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] PadLayer   resnet_v1_100/block4/unit_1/bottleneck_v1/padding_conv2: paddings:[[0, 0], [1, 1], [1, 1], [0, 0]] mode:CONSTANT
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv2: shape:[3, 3, 512, 512] strides:[1, 2, 2, 1] pad:VALID act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_1/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block4/unit_1/bottleneck_v1/combine_layer: size:(?, 7, 7, 512) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv1: shape:[3, 3, 512, 512] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block4/unit_2/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv2: shape:[3, 3, 512, 512] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_2/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block4/unit_2/bottleneck_v1/combine_layer: size:(?, 7, 7, 512) fn:add
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv1: shape:[3, 3, 512, 512] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] PReluLayer resnet_v1_100/block4/unit_3/bottleneck_v1/prelu_layer: channel_shared:False
+  [TL] Conv2dLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv2: shape:[3, 3, 512, 512] strides:[1, 1, 1, 1] pad:SAME act:identity
+  [TL] BatchNormLayer resnet_v1_100/block4/unit_3/bottleneck_v1/conv2_bn/BatchNorm: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ElementwiseLayer resnet_v1_100/block4/unit_3/bottleneck_v1/combine_layer: size:(?, 7, 7, 512) fn:add
+  [TL] BatchNormLayer resnet_v1_100/E_BN1: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [TL] ReshapeLayer resnet_v1_100/E_Reshapelayer: (?, 25088)
+  [TL] DenseLayer  resnet_v1_100/E_DenseLayer: 512 identity
+  [TL] BatchNormLayer resnet_v1_100/E_BN2: decay:0.900000 epsilon:0.000020 act:identity is_train:True
+  [*] geting variables with W_conv2d
+  got   0: resnet_v1_100/conv1/W_conv2d:0   (3, 3, 3, 64)
+  got   1: resnet_v1_100/block1/unit_1/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 64, 64)
+  got   2: resnet_v1_100/block1/unit_1/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 64, 64)
+  got   3: resnet_v1_100/block1/unit_2/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 64, 64)
+  got   4: resnet_v1_100/block1/unit_2/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 64, 64)
+  got   5: resnet_v1_100/block1/unit_3/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 64, 64)
+  got   6: resnet_v1_100/block1/unit_3/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 64, 64)
+  got   7: resnet_v1_100/block2/unit_1/bottleneck_v1/shortcut_conv/W_conv2d:0   (1, 1, 64, 128)
+  got   8: resnet_v1_100/block2/unit_1/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 64, 128)
+  got   9: resnet_v1_100/block2/unit_1/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  10: resnet_v1_100/block2/unit_2/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  11: resnet_v1_100/block2/unit_2/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  12: resnet_v1_100/block2/unit_3/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  13: resnet_v1_100/block2/unit_3/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  14: resnet_v1_100/block2/unit_4/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  15: resnet_v1_100/block2/unit_4/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  16: resnet_v1_100/block2/unit_5/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  17: resnet_v1_100/block2/unit_5/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  18: resnet_v1_100/block2/unit_6/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  19: resnet_v1_100/block2/unit_6/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  20: resnet_v1_100/block2/unit_7/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  21: resnet_v1_100/block2/unit_7/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  22: resnet_v1_100/block2/unit_8/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  23: resnet_v1_100/block2/unit_8/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  24: resnet_v1_100/block2/unit_9/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  25: resnet_v1_100/block2/unit_9/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  26: resnet_v1_100/block2/unit_10/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  27: resnet_v1_100/block2/unit_10/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  28: resnet_v1_100/block2/unit_11/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  29: resnet_v1_100/block2/unit_11/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  30: resnet_v1_100/block2/unit_12/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  31: resnet_v1_100/block2/unit_12/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  32: resnet_v1_100/block2/unit_13/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 128)
+  got  33: resnet_v1_100/block2/unit_13/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 128, 128)
+  got  34: resnet_v1_100/block3/unit_1/bottleneck_v1/shortcut_conv/W_conv2d:0   (1, 1, 128, 256)
+  got  35: resnet_v1_100/block3/unit_1/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 128, 256)
+  got  36: resnet_v1_100/block3/unit_1/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  37: resnet_v1_100/block3/unit_2/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  38: resnet_v1_100/block3/unit_2/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  39: resnet_v1_100/block3/unit_3/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  40: resnet_v1_100/block3/unit_3/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  41: resnet_v1_100/block3/unit_4/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  42: resnet_v1_100/block3/unit_4/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  43: resnet_v1_100/block3/unit_5/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  44: resnet_v1_100/block3/unit_5/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  45: resnet_v1_100/block3/unit_6/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  46: resnet_v1_100/block3/unit_6/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  47: resnet_v1_100/block3/unit_7/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  48: resnet_v1_100/block3/unit_7/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  49: resnet_v1_100/block3/unit_8/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  50: resnet_v1_100/block3/unit_8/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  51: resnet_v1_100/block3/unit_9/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  52: resnet_v1_100/block3/unit_9/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  53: resnet_v1_100/block3/unit_10/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  54: resnet_v1_100/block3/unit_10/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  55: resnet_v1_100/block3/unit_11/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  56: resnet_v1_100/block3/unit_11/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  57: resnet_v1_100/block3/unit_12/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  58: resnet_v1_100/block3/unit_12/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  59: resnet_v1_100/block3/unit_13/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  60: resnet_v1_100/block3/unit_13/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  61: resnet_v1_100/block3/unit_14/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  62: resnet_v1_100/block3/unit_14/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  63: resnet_v1_100/block3/unit_15/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  64: resnet_v1_100/block3/unit_15/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  65: resnet_v1_100/block3/unit_16/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  66: resnet_v1_100/block3/unit_16/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  67: resnet_v1_100/block3/unit_17/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  68: resnet_v1_100/block3/unit_17/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  69: resnet_v1_100/block3/unit_18/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  70: resnet_v1_100/block3/unit_18/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  71: resnet_v1_100/block3/unit_19/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  72: resnet_v1_100/block3/unit_19/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  73: resnet_v1_100/block3/unit_20/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  74: resnet_v1_100/block3/unit_20/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  75: resnet_v1_100/block3/unit_21/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  76: resnet_v1_100/block3/unit_21/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  77: resnet_v1_100/block3/unit_22/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  78: resnet_v1_100/block3/unit_22/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  79: resnet_v1_100/block3/unit_23/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  80: resnet_v1_100/block3/unit_23/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  81: resnet_v1_100/block3/unit_24/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  82: resnet_v1_100/block3/unit_24/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  83: resnet_v1_100/block3/unit_25/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  84: resnet_v1_100/block3/unit_25/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  85: resnet_v1_100/block3/unit_26/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  86: resnet_v1_100/block3/unit_26/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  87: resnet_v1_100/block3/unit_27/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  88: resnet_v1_100/block3/unit_27/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  89: resnet_v1_100/block3/unit_28/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  90: resnet_v1_100/block3/unit_28/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  91: resnet_v1_100/block3/unit_29/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  92: resnet_v1_100/block3/unit_29/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  93: resnet_v1_100/block3/unit_30/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 256)
+  got  94: resnet_v1_100/block3/unit_30/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 256, 256)
+  got  95: resnet_v1_100/block4/unit_1/bottleneck_v1/shortcut_conv/W_conv2d:0   (1, 1, 256, 512)
+  got  96: resnet_v1_100/block4/unit_1/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 256, 512)
+  got  97: resnet_v1_100/block4/unit_1/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 512, 512)
+  got  98: resnet_v1_100/block4/unit_2/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 512, 512)
+  got  99: resnet_v1_100/block4/unit_2/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 512, 512)
+  got 100: resnet_v1_100/block4/unit_3/bottleneck_v1/conv1/W_conv2d:0   (3, 3, 512, 512)
+  got 101: resnet_v1_100/block4/unit_3/bottleneck_v1/conv2/W_conv2d:0   (3, 3, 512, 512)
+  [*] geting variables with resnet_v1_50/E_DenseLayer/W
+  [*] geting variables with embedding_weights
+  got   0: arcface_loss/embedding_weights:0   (512, 85164)
+  [*] geting variables with gamma
+  got   0: resnet_v1_100/bn0/gamma:0   (64,)
+  got   1: resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn1/gamma:0   (64,)
+  got   2: resnet_v1_100/block1/unit_1/bottleneck_v1/conv1_bn2/gamma:0   (64,)
+  got   3: resnet_v1_100/block1/unit_1/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (64,)
+  got   4: resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn1/gamma:0   (64,)
+  got   5: resnet_v1_100/block1/unit_2/bottleneck_v1/conv1_bn2/gamma:0   (64,)
+  got   6: resnet_v1_100/block1/unit_2/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (64,)
+  got   7: resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn1/gamma:0   (64,)
+  got   8: resnet_v1_100/block1/unit_3/bottleneck_v1/conv1_bn2/gamma:0   (64,)
+  got   9: resnet_v1_100/block1/unit_3/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (64,)
+  got  10: resnet_v1_100/block2/unit_1/bottleneck_v1/shortcut_bn/BatchNorm/gamma:0   (128,)
+  got  11: resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn1/gamma:0   (64,)
+  got  12: resnet_v1_100/block2/unit_1/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  13: resnet_v1_100/block2/unit_1/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  14: resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  15: resnet_v1_100/block2/unit_2/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  16: resnet_v1_100/block2/unit_2/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  17: resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  18: resnet_v1_100/block2/unit_3/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  19: resnet_v1_100/block2/unit_3/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  20: resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  21: resnet_v1_100/block2/unit_4/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  22: resnet_v1_100/block2/unit_4/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  23: resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  24: resnet_v1_100/block2/unit_5/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  25: resnet_v1_100/block2/unit_5/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  26: resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  27: resnet_v1_100/block2/unit_6/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  28: resnet_v1_100/block2/unit_6/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  29: resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  30: resnet_v1_100/block2/unit_7/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  31: resnet_v1_100/block2/unit_7/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  32: resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  33: resnet_v1_100/block2/unit_8/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  34: resnet_v1_100/block2/unit_8/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  35: resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  36: resnet_v1_100/block2/unit_9/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  37: resnet_v1_100/block2/unit_9/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  38: resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  39: resnet_v1_100/block2/unit_10/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  40: resnet_v1_100/block2/unit_10/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  41: resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  42: resnet_v1_100/block2/unit_11/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  43: resnet_v1_100/block2/unit_11/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  44: resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  45: resnet_v1_100/block2/unit_12/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  46: resnet_v1_100/block2/unit_12/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  47: resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  48: resnet_v1_100/block2/unit_13/bottleneck_v1/conv1_bn2/gamma:0   (128,)
+  got  49: resnet_v1_100/block2/unit_13/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (128,)
+  got  50: resnet_v1_100/block3/unit_1/bottleneck_v1/shortcut_bn/BatchNorm/gamma:0   (256,)
+  got  51: resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn1/gamma:0   (128,)
+  got  52: resnet_v1_100/block3/unit_1/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  53: resnet_v1_100/block3/unit_1/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  54: resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  55: resnet_v1_100/block3/unit_2/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  56: resnet_v1_100/block3/unit_2/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  57: resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  58: resnet_v1_100/block3/unit_3/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  59: resnet_v1_100/block3/unit_3/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  60: resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  61: resnet_v1_100/block3/unit_4/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  62: resnet_v1_100/block3/unit_4/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  63: resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  64: resnet_v1_100/block3/unit_5/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  65: resnet_v1_100/block3/unit_5/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  66: resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  67: resnet_v1_100/block3/unit_6/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  68: resnet_v1_100/block3/unit_6/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  69: resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  70: resnet_v1_100/block3/unit_7/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  71: resnet_v1_100/block3/unit_7/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  72: resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  73: resnet_v1_100/block3/unit_8/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  74: resnet_v1_100/block3/unit_8/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  75: resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  76: resnet_v1_100/block3/unit_9/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  77: resnet_v1_100/block3/unit_9/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  78: resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  79: resnet_v1_100/block3/unit_10/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  80: resnet_v1_100/block3/unit_10/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  81: resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  82: resnet_v1_100/block3/unit_11/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  83: resnet_v1_100/block3/unit_11/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  84: resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  85: resnet_v1_100/block3/unit_12/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  86: resnet_v1_100/block3/unit_12/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  87: resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  88: resnet_v1_100/block3/unit_13/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  89: resnet_v1_100/block3/unit_13/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  90: resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  91: resnet_v1_100/block3/unit_14/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  92: resnet_v1_100/block3/unit_14/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  93: resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  94: resnet_v1_100/block3/unit_15/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  95: resnet_v1_100/block3/unit_15/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  96: resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got  97: resnet_v1_100/block3/unit_16/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got  98: resnet_v1_100/block3/unit_16/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got  99: resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 100: resnet_v1_100/block3/unit_17/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 101: resnet_v1_100/block3/unit_17/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 102: resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 103: resnet_v1_100/block3/unit_18/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 104: resnet_v1_100/block3/unit_18/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 105: resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 106: resnet_v1_100/block3/unit_19/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 107: resnet_v1_100/block3/unit_19/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 108: resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 109: resnet_v1_100/block3/unit_20/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 110: resnet_v1_100/block3/unit_20/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 111: resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 112: resnet_v1_100/block3/unit_21/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 113: resnet_v1_100/block3/unit_21/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 114: resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 115: resnet_v1_100/block3/unit_22/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 116: resnet_v1_100/block3/unit_22/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 117: resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 118: resnet_v1_100/block3/unit_23/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 119: resnet_v1_100/block3/unit_23/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 120: resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 121: resnet_v1_100/block3/unit_24/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 122: resnet_v1_100/block3/unit_24/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 123: resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 124: resnet_v1_100/block3/unit_25/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 125: resnet_v1_100/block3/unit_25/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 126: resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 127: resnet_v1_100/block3/unit_26/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 128: resnet_v1_100/block3/unit_26/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 129: resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 130: resnet_v1_100/block3/unit_27/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 131: resnet_v1_100/block3/unit_27/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 132: resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 133: resnet_v1_100/block3/unit_28/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 134: resnet_v1_100/block3/unit_28/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 135: resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 136: resnet_v1_100/block3/unit_29/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 137: resnet_v1_100/block3/unit_29/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 138: resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 139: resnet_v1_100/block3/unit_30/bottleneck_v1/conv1_bn2/gamma:0   (256,)
+  got 140: resnet_v1_100/block3/unit_30/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (256,)
+  got 141: resnet_v1_100/block4/unit_1/bottleneck_v1/shortcut_bn/BatchNorm/gamma:0   (512,)
+  got 142: resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn1/gamma:0   (256,)
+  got 143: resnet_v1_100/block4/unit_1/bottleneck_v1/conv1_bn2/gamma:0   (512,)
+  got 144: resnet_v1_100/block4/unit_1/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (512,)
+  got 145: resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn1/gamma:0   (512,)
+  got 146: resnet_v1_100/block4/unit_2/bottleneck_v1/conv1_bn2/gamma:0   (512,)
+  got 147: resnet_v1_100/block4/unit_2/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (512,)
+  got 148: resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn1/gamma:0   (512,)
+  got 149: resnet_v1_100/block4/unit_3/bottleneck_v1/conv1_bn2/gamma:0   (512,)
+  got 150: resnet_v1_100/block4/unit_3/bottleneck_v1/conv2_bn/BatchNorm/gamma:0   (512,)
+  got 151: resnet_v1_100/E_BN1/gamma:0   (512,)
+  [*] geting variables with alphas
+  got   0: resnet_v1_100/prelu0/alphas:0   (64,)
+  got   1: resnet_v1_100/block1/unit_1/bottleneck_v1/prelu_layer/alphas:0   (64,)
+  got   2: resnet_v1_100/block1/unit_2/bottleneck_v1/prelu_layer/alphas:0   (64,)
+  got   3: resnet_v1_100/block1/unit_3/bottleneck_v1/prelu_layer/alphas:0   (64,)
+  got   4: resnet_v1_100/block2/unit_1/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got   5: resnet_v1_100/block2/unit_2/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got   6: resnet_v1_100/block2/unit_3/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got   7: resnet_v1_100/block2/unit_4/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got   8: resnet_v1_100/block2/unit_5/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got   9: resnet_v1_100/block2/unit_6/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got  10: resnet_v1_100/block2/unit_7/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got  11: resnet_v1_100/block2/unit_8/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got  12: resnet_v1_100/block2/unit_9/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got  13: resnet_v1_100/block2/unit_10/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got  14: resnet_v1_100/block2/unit_11/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got  15: resnet_v1_100/block2/unit_12/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got  16: resnet_v1_100/block2/unit_13/bottleneck_v1/prelu_layer/alphas:0   (128,)
+  got  17: resnet_v1_100/block3/unit_1/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  18: resnet_v1_100/block3/unit_2/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  19: resnet_v1_100/block3/unit_3/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  20: resnet_v1_100/block3/unit_4/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  21: resnet_v1_100/block3/unit_5/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  22: resnet_v1_100/block3/unit_6/bottleneck_v1/prelu_layer/alphas:0   (256,)WARNING:tensorflow:From /root/dave/InsightFace_TF/InsightFace_TF_GPU_2/train_nets.py:112: The name tf.train.piecewise_constant is deprecated. Please use tf.compat.v1.train.piecewise_constant instead.
+
+WARNING:tensorflow:From /root/dave/InsightFace_TF/InsightFace_TF_GPU_2/train_nets.py:114: The name tf.train.MomentumOptimizer is deprecated. Please use tf.compat.v1.train.MomentumOptimizer instead.
+
+2022-12-02 19:11:50.971551: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 AVX512F FMA
+2022-12-02 19:11:50.978062: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2600000000 Hz
+2022-12-02 19:11:50.978391: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xf905610 executing computations on platform Host. Devices:
+2022-12-02 19:11:50.978417: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>
+2022-12-02 19:11:51.079465: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-12-02 19:11:51.080958: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xf92d0d0 executing computations on platform CUDA. Devices:
+2022-12-02 19:11:51.080988: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): Tesla V100-PCIE-32GB, Compute Capability 7.0
+2022-12-02 19:11:51.081191: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-12-02 19:11:51.082576: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: 
+name: Tesla V100-PCIE-32GB major: 7 minor: 0 memoryClockRate(GHz): 1.38
+pciBusID: 0000:00:0d.0
+2022-12-02 19:11:51.082664: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
+2022-12-02 19:11:51.082690: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
+2022-12-02 19:11:51.082714: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0
+2022-12-02 19:11:51.082735: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0
+2022-12-02 19:11:51.082765: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0
+2022-12-02 19:11:51.082786: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0
+2022-12-02 19:11:51.082808: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
+2022-12-02 19:11:51.082901: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-12-02 19:11:51.084306: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-12-02 19:11:51.085660: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
+2022-12-02 19:11:51.085739: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
+2022-12-02 19:11:51.088069: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
+2022-12-02 19:11:51.088095: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 
+2022-12-02 19:11:51.088104: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N 
+2022-12-02 19:11:51.088216: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-12-02 19:11:51.089645: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-12-02 19:11:51.091029: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 30555 MB memory) -> physical GPU (device: 0, name: Tesla V100-PCIE-32GB, pci bus id: 0000:00:0d.0, compute capability: 7.0)
+WARNING:tensorflow:From /root/dave/InsightFace_TF/InsightFace_TF_GPU_2/train_nets.py:130: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.
+
+WARNING:tensorflow:From /root/dave/InsightFace_TF/InsightFace_TF_GPU_2/train_nets.py:135: The name tf.summary.histogram is deprecated. Please use tf.compat.v1.summary.histogram instead.
+
+WARNING:tensorflow:From /root/dave/InsightFace_TF/InsightFace_TF_GPU_2/train_nets.py:140: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.
+
+WARNING:tensorflow:From /root/dave/InsightFace_TF/InsightFace_TF_GPU_2/train_nets.py:145: The name tf.summary.merge is deprecated. Please use tf.compat.v1.summary.merge instead.
+
+WARNING:tensorflow:From /root/dave/InsightFace_TF/InsightFace_TF_GPU_2/train_nets.py:147: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.
+
+2022-12-02 19:12:00.632160: W tensorflow/compiler/jit/mark_for_compilation_pass.cc:1412] (One-time warning): Not using XLA:CPU for cluster because envvar TF_XLA_FLAGS=--tf_xla_cpu_global_jit was not set.  If you want XLA:CPU, either set that envvar, or use experimental_jit_scope to enable XLA:CPU.  To confirm that XLA is active, pass --vmodule=xla_compilation_cache=1 (as a proper command-line flag, not via TF_XLA_FLAGS) or set the envvar XLA_FLAGS=--xla_hlo_profile.
+2022-12-02 19:12:37.229855: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
+2022-12-02 19:12:37.500333: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
+
+  got  23: resnet_v1_100/block3/unit_7/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  24: resnet_v1_100/block3/unit_8/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  25: resnet_v1_100/block3/unit_9/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  26: resnet_v1_100/block3/unit_10/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  27: resnet_v1_100/block3/unit_11/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  28: resnet_v1_100/block3/unit_12/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  29: resnet_v1_100/block3/unit_13/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  30: resnet_v1_100/block3/unit_14/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  31: resnet_v1_100/block3/unit_15/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  32: resnet_v1_100/block3/unit_16/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  33: resnet_v1_100/block3/unit_17/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  34: resnet_v1_100/block3/unit_18/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  35: resnet_v1_100/block3/unit_19/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  36: resnet_v1_100/block3/unit_20/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  37: resnet_v1_100/block3/unit_21/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  38: resnet_v1_100/block3/unit_22/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  39: resnet_v1_100/block3/unit_23/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  40: resnet_v1_100/block3/unit_24/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  41: resnet_v1_100/block3/unit_25/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  42: resnet_v1_100/block3/unit_26/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  43: resnet_v1_100/block3/unit_27/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  44: resnet_v1_100/block3/unit_28/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  45: resnet_v1_100/block3/unit_29/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  46: resnet_v1_100/block3/unit_30/bottleneck_v1/prelu_layer/alphas:0   (256,)
+  got  47: resnet_v1_100/block4/unit_1/bottleneck_v1/prelu_layer/alphas:0   (512,)
+  got  48: resnet_v1_100/block4/unit_2/bottleneck_v1/prelu_layer/alphas:0   (512,)
+  got  49: resnet_v1_100/block4/unit_3/bottleneck_v1/prelu_layer/alphas:0   (512,)
+[640000, 960000, 1280000]
+epoch 0, total_step 20, total loss is 58.93 , inference loss is 44.46, weight deacy loss is 14.47, training accuracy is 0.000000, time 80.302 samples/sec
+epoch 0, total_step 40, total loss is 57.10 , inference loss is 42.62, weight deacy loss is 14.47, training accuracy is 0.000000, time 81.657 samples/sec
+epoch 0, total_step 60, total loss is 55.01 , inference loss is 40.53, weight deacy loss is 14.47, training accuracy is 0.000000, time 78.763 samples/sec
+epoch 0, total_step 80, total loss is 51.63 , inference loss is 37.16, weight deacy loss is 14.47, training accuracy is 0.000000, time 80.031 samples/sec
+epoch 0, total_step 100, total loss is 52.69 , inference loss is 38.22, weight deacy loss is 14.47, training accuracy is 0.000000, time 80.972 samples/sec
+epoch 0, total_step 120, total loss is 50.58 , inference loss is 36.10, weight deacy loss is 14.47, training accuracy is 0.000000, time 80.001 samples/sec
+epoch 0, total_step 140, total loss is 52.84 , inference loss is 38.37, weight deacy loss is 14.47, training accuracy is 0.000000, time 81.194 samples/sec
+epoch 0, total_step 160, total loss is 52.77 , inference loss is 38.31, weight deacy loss is 14.47, training accuracy is 0.000000, time 80.557 samples/sec
+epoch 0, total_step 180, total loss is 52.79 , inference loss is 38.32, weight deacy loss is 14.47, training accuracy is 0.000000, time 79.738 samples/sec
+epoch 0, total_step 200, total loss is 52.04 , inference loss is 37.57, weight deacy loss is 14.47, training accuracy is 0.000000, time 81.152 samples/sec
+epoch 0, total_step 220, total loss is 53.48 , inference loss is 39.01, weight deacy loss is 14.46, training accuracy is 0.000000, time 79.425 samples/sec
+epoch 0, total_step 240, total loss is 51.62 , inference loss is 37.16, weight deacy loss is 14.46, training accuracy is 0.000000, time 81.547 samples/sec
+epoch 0, total_step 260, total loss is 46.71 , inference loss is 32.25, weight deacy loss is 14.46, training accuracy is 0.000000, time 78.698 samples/sec
+epoch 0, total_step 280, total loss is 49.36 , inference loss is 34.90, weight deacy loss is 14.46, training accuracy is 0.000000, time 79.106 samples/sec
+epoch 0, total_step 300, total loss is 52.60 , inference loss is 38.14, weight deacy loss is 14.46, training accuracy is 0.000000, time 78.632 samples/sec
+epoch 0, total_step 320, total loss is 49.78 , inference loss is 35.32, weight deacy loss is 14.46, training accuracy is 0.000000, time 80.554 samples/sec
+epoch 0, total_step 340, total loss is 51.65 , inference loss is 37.20, weight deacy loss is 14.46, training accuracy is 0.031250, time 81.581 samples/sec
+epoch 0, total_step 360, total loss is 51.36 , inference loss is 36.91, weight deacy loss is 14.45, training accuracy is 0.000000, time 79.464 samples/sec
+epoch 0, total_step 380, total loss is 49.51 , inference loss is 35.06, weight deacy loss is 14.45, training accuracy is 0.000000, time 81.285 samples/sec
+epoch 0, total_step 400, total loss is 50.16 , inference loss is 35.71, weight deacy loss is 14.45, training accuracy is 0.000000, time 80.461 samples/sec
+epoch 0, total_step 420, total loss is 46.60 , inference loss is 32.15, weight deacy loss is 14.45, training accuracy is 0.000000, time 79.361 samples/sec
+epoch 0, total_step 440, total loss is 47.09 , inference loss is 32.64, weight deacy loss is 14.45, training accuracy is 0.000000, time 81.191 samples/sec
+epoch 0, total_step 460, total loss is 44.51 , inference loss is 30.07, weight deacy loss is 14.45, training accuracy is 0.031250, time 81.208 samples/sec
+epoch 0, total_step 480, total loss is 47.41 , inference loss is 32.97, weight deacy loss is 14.44, training accuracy is 0.000000, time 81.569 samples/sec
+epoch 0, total_step 500, total loss is 46.68 , inference loss is 32.24, weight deacy loss is 14.44, training accuracy is 0.000000, time 80.453 samples/sec
+epoch 0, total_step 520, total loss is 47.01 , inference loss is 32.57, weight deacy loss is 14.44, training accuracy is 0.000000, time 79.177 samples/sec
+epoch 0, total_step 540, total loss is 48.83 , inference loss is 34.40, weight deacy loss is 14.44, training accuracy is 0.000000, time 81.401 samples/sec
+epoch 0, total_step 560, total loss is 47.72 , inference loss is 33.28, weight deacy loss is 14.44, training accuracy is 0.000000, time 79.275 samples/sec
+epoch 0, total_step 580, total loss is 51.74 , inference loss is 37.30, weight deacy loss is 14.44, training accuracy is 0.000000, time 80.160 samples/sec
+epoch 0, total_step 600, total loss is 47.55 , inference loss is 33.11, weight deacy loss is 14.43, training accuracy is 0.000000, time 81.104 samples/sec
+epoch 0, total_step 620, total loss is 48.41 , inference loss is 33.98, weight deacy loss is 14.43, training accuracy is 0.000000, time 78.750 samples/sec
+epoch 0, total_step 640, total loss is 48.72 , inference loss is 34.29, weight deacy loss is 14.43, training accuracy is 0.031250, time 78.792 samples/sec
+epoch 0, total_step 660, total loss is 48.29 , inference loss is 33.86, weight deacy loss is 14.43, training accuracy is 0.031250, time 80.242 samples/sec
+epoch 0, total_step 680, total loss is 43.06 , inference loss is 28.63, weight deacy loss is 14.43, training accuracy is 0.031250, time 80.291 samples/sec
+epoch 0, total_step 700, total loss is 47.08 , inference loss is 32.65, weight deacy loss is 14.43, training accuracy is 0.031250, time 80.610 samples/sec
+epoch 0, total_step 720, total loss is 47.33 , inference loss is 32.90, weight deacy loss is 14.42, training accuracy is 0.000000, time 79.080 samples/sec
+epoch 0, total_step 740, total loss is 48.12 , inference loss is 33.70, weight deacy loss is 14.42, training accuracy is 0.000000, time 80.171 samples/sec
+epoch 0, total_step 760, total loss is 48.31 , inference loss is 33.89, weight deacy loss is 14.42, training accuracy is 0.000000, time 79.259 samples/sec
+epoch 0, total_step 780, total loss is 46.71 , inference loss is 32.30, weight deacy loss is 14.42, training accuracy is 0.000000, time 81.302 samples/sec
\ No newline at end of file
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diff --git a/TensorFlow/contrib/cv/InsightFace_TF/losses/__init__.py b/TensorFlow/contrib/cv/InsightFace_TF/losses/__init__.py
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diff --git a/TensorFlow/contrib/cv/InsightFace_TF/losses/face_losses.py b/TensorFlow/contrib/cv/InsightFace_TF/losses/face_losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..be97b115bcab122fc571c1e6ebb71a50090da98f
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/losses/face_losses.py
@@ -0,0 +1,122 @@
+from npu_bridge.npu_init import *
+import tensorflow as tf
+import math
+
+
+def arcface_loss(embedding, labels, out_num, w_init=None, s=64., m=0.5):
+    '''
+    :param embedding: the input embedding vectors
+    :param labels:  the input labels, the shape should be eg: (batch_size, 1)
+    :param s: scalar value default is 64
+    :param out_num: output class num
+    :param m: the margin value, default is 0.5
+    :return: the final cacualted output, this output is send into the tf.nn.softmax directly
+    '''
+    cos_m = math.cos(m)
+    sin_m = math.sin(m)
+    mm = sin_m * m  # issue 1
+    threshold = math.cos(math.pi - m)
+    with tf.variable_scope('arcface_loss'):
+        # inputs and weights norm
+        embedding_norm = tf.norm(embedding, axis=1, keep_dims=True)
+        embedding = tf.div(embedding, embedding_norm, name='norm_embedding')
+        weights = tf.get_variable(name='embedding_weights', shape=(embedding.get_shape().as_list()[-1], out_num),
+                                  initializer=w_init, dtype=tf.float32)
+        weights_norm = tf.norm(weights, axis=0, keep_dims=True)
+        weights = tf.div(weights, weights_norm, name='norm_weights')
+        # cos(theta+m)
+        cos_t = tf.matmul(embedding, weights, name='cos_t')
+        cos_t2 = tf.square(cos_t, name='cos_2')
+        sin_t2 = tf.subtract(1., cos_t2, name='sin_2')
+        sin_t = tf.sqrt(sin_t2, name='sin_t')
+        cos_mt = s * tf.subtract(tf.multiply(cos_t, cos_m), tf.multiply(sin_t, sin_m), name='cos_mt')
+
+        # this condition controls the theta+m should in range [0, pi]
+        #      0<=theta+m<=pi
+        #     -m<=theta<=pi-m
+        cond_v = cos_t - threshold
+        cond = tf.cast(tf.nn.relu(cond_v, name='if_else'), dtype=tf.bool)
+
+        keep_val = s*(cos_t - mm)
+        cos_mt_temp = tf.where(cond, cos_mt, keep_val)
+
+        mask = tf.one_hot(labels, depth=out_num, name='one_hot_mask')
+        # mask = tf.squeeze(mask, 1)
+        inv_mask = tf.subtract(1., mask, name='inverse_mask')
+
+        s_cos_t = tf.multiply(s, cos_t, name='scalar_cos_t')
+
+        output = tf.add(tf.multiply(s_cos_t, inv_mask), tf.multiply(cos_mt_temp, mask), name='arcface_loss_output')
+    return output
+
+
+def cosineface_losses(embedding, labels, out_num, w_init=None, s=30., m=0.4):
+    '''
+    :param embedding: the input embedding vectors
+    :param labels:  the input labels, the shape should be eg: (batch_size, 1)
+    :param s: scalar value, default is 30
+    :param out_num: output class num
+    :param m: the margin value, default is 0.4
+    :return: the final cacualted output, this output is send into the tf.nn.softmax directly
+    '''
+    with tf.variable_scope('cosineface_loss'):
+        # inputs and weights norm
+        embedding_norm = tf.norm(embedding, axis=1, keep_dims=True)
+        embedding = tf.div(embedding, embedding_norm, name='norm_embedding')
+        weights = tf.get_variable(name='embedding_weights', shape=(embedding.get_shape().as_list()[-1], out_num),
+                                  initializer=w_init, dtype=tf.float32)
+        weights_norm = tf.norm(weights, axis=0, keep_dims=True)
+        weights = tf.div(weights, weights_norm, name='norm_weights')
+        # cos_theta - m
+        cos_t = tf.matmul(embedding, weights, name='cos_t')
+        cos_t_m = tf.subtract(cos_t, m, name='cos_t_m')
+
+        mask = tf.one_hot(labels, depth=out_num, name='one_hot_mask')
+        inv_mask = tf.subtract(1., mask, name='inverse_mask')
+
+        output = tf.add(s * tf.multiply(cos_t, inv_mask), s * tf.multiply(cos_t_m, mask), name='cosineface_loss_output')
+    return output
+
+
+def combine_loss_val(embedding, labels, w_init, out_num, margin_a, margin_m, margin_b, s):
+    '''
+    This code is contributed by RogerLo. Thanks for you contribution.
+
+    :param embedding: the input embedding vectors
+    :param labels:  the input labels, the shape should be eg: (batch_size, 1)
+    :param s: scalar value default is 64
+    :param out_num: output class num
+    :param m: the margin value, default is 0.5
+    :return: the final cacualted output, this output is send into the tf.nn.softmax directly
+    '''
+    weights = tf.get_variable(name='embedding_weights', shape=(embedding.get_shape().as_list()[-1], out_num),
+                              initializer=w_init, dtype=tf.float32)
+    weights_unit = tf.nn.l2_normalize(weights, axis=0)
+    embedding_unit = tf.nn.l2_normalize(embedding, axis=1)
+    cos_t = tf.matmul(embedding_unit, weights_unit)
+    ordinal = tf.constant(list(range(0, embedding.get_shape().as_list()[0])), tf.int64)
+    ordinal_y = tf.stack([ordinal, labels], axis=1)
+    zy = cos_t * s
+    sel_cos_t = tf.gather_nd(zy, ordinal_y)
+    if margin_a != 1.0 or margin_m != 0.0 or margin_b != 0.0:
+        if margin_a == 1.0 and margin_m == 0.0:
+            s_m = s * margin_b
+            new_zy = sel_cos_t - s_m
+        else:
+            cos_value = sel_cos_t / s
+            t = tf.acos(cos_value)
+            if margin_a != 1.0:
+                t = t * margin_a
+            if margin_m > 0.0:
+                t = t + margin_m
+            body = tf.cos(t)
+            if margin_b > 0.0:
+                body = body - margin_b
+            new_zy = body * s
+    updated_logits = tf.add(zy, tf.scatter_nd(ordinal_y, tf.subtract(new_zy, sel_cos_t), zy.get_shape()))
+    loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, logits=updated_logits))
+    predict_cls = tf.argmax(updated_logits, 1)
+    accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.cast(predict_cls, tf.int64), tf.cast(labels, tf.int64)), 'float'))
+    predict_cls_s = tf.argmax(zy, 1)
+    accuracy_s = tf.reduce_mean(tf.cast(tf.equal(tf.cast(predict_cls_s, tf.int64), tf.cast(labels, tf.int64)), 'float'))
+    return zy, loss, accuracy, accuracy_s, predict_cls_s
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/nets/L_Resnet_E_IR.py b/TensorFlow/contrib/cv/InsightFace_TF/nets/L_Resnet_E_IR.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8e8f26d98add1887f1505996cd8b53ee950dace
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/nets/L_Resnet_E_IR.py
@@ -0,0 +1,418 @@
+from npu_bridge.npu_init import *
+import tensorflow as tf
+import tensorlayer as tl
+from tensorflow.contrib.layers.python.layers import utils
+import collections
+from tensorlayer.layers import Layer, list_remove_repeat
+
+
+class ElementwiseLayer(Layer):
+    """
+    The :class:`ElementwiseLayer` class combines multiple :class:`Layer` which have the same output shapes by a given elemwise-wise operation.
+
+    Parameters
+    ----------
+    layer : a list of :class:`Layer` instances
+        The `Layer` class feeding into this layer.
+    combine_fn : a TensorFlow elemwise-merge function
+        e.g. AND is ``tf.minimum`` ;  OR is ``tf.maximum`` ; ADD is ``tf.add`` ; MUL is ``tf.multiply`` and so on.
+        See `TensorFlow Math API <https://www.tensorflow.org/versions/master/api_docs/python/math_ops.html#math>`_ .
+    name : a string or None
+        An optional name to attach to this layer.
+    """
+    def __init__(
+        self,
+        layer = [],
+        combine_fn = tf.minimum,
+        name ='elementwise_layer',
+        act = None,
+    ):
+        Layer.__init__(self, name=name)
+
+        if act:
+            print("  [TL] ElementwiseLayer %s: size:%s fn:%s, act:%s" % (
+            self.name, layer[0].outputs.get_shape(), combine_fn.__name__, act.__name__))
+        else:
+            print("  [TL] ElementwiseLayer %s: size:%s fn:%s" % (
+            self.name, layer[0].outputs.get_shape(), combine_fn.__name__))
+
+        self.outputs = layer[0].outputs
+        # print(self.outputs._shape, type(self.outputs._shape))
+        for l in layer[1:]:
+            # assert str(self.outputs.get_shape()) == str(l.outputs.get_shape()), "Hint: the input shapes should be the same. %s != %s" %  (self.outputs.get_shape() , str(l.outputs.get_shape()))
+            self.outputs = combine_fn(self.outputs, l.outputs, name=name)
+        if act:
+            self.outputs = act(self.outputs)
+        self.all_layers = list(layer[0].all_layers)
+        self.all_params = list(layer[0].all_params)
+        self.all_drop = dict(layer[0].all_drop)
+
+        for i in range(1, len(layer)):
+            self.all_layers.extend(list(layer[i].all_layers))
+            self.all_params.extend(list(layer[i].all_params))
+            self.all_drop.update(dict(layer[i].all_drop))
+
+        self.all_layers = list_remove_repeat(self.all_layers)
+        self.all_params = list_remove_repeat(self.all_params)
+
+
+class BatchNormLayer(Layer):
+    """
+    The :class:`BatchNormLayer` class is a normalization layer, see ``tf.nn.batch_normalization`` and ``tf.nn.moments``.
+
+    Batch normalization on fully-connected or convolutional maps.
+
+    ```
+        https://www.tensorflow.org/api_docs/python/tf/cond
+        If x < y, the tf.add operation will be executed and tf.square operation will not be executed.
+        Since z is needed for at least one branch of the cond, the tf.multiply operation is always executed, unconditionally.
+    ```
+
+    Parameters
+    -----------
+    layer : a :class:`Layer` instance
+        The `Layer` class feeding into this layer.
+    decay : float, default is 0.9.
+        A decay factor for ExponentialMovingAverage, use larger value for large dataset.
+    epsilon : float
+        A small float number to avoid dividing by 0.
+    act : activation function.
+    is_train : boolean
+        Whether train or inference.
+    beta_init : beta initializer
+        The initializer for initializing beta
+    gamma_init : gamma initializer
+        The initializer for initializing gamma
+    dtype : tf.float32 (default) or tf.float16
+    name : a string or None
+        An optional name to attach to this layer.
+
+    References
+    ----------
+    - `Source <https://github.com/ry/tensorflow-resnet/blob/master/resnet.py>`_
+    - `stackoverflow <http://stackoverflow.com/questions/38312668/how-does-one-do-inference-with-batch-normalization-with-tensor-flow>`_
+
+    """
+
+    def __init__(
+            self,
+            layer=None,
+            decay=0.9,
+            epsilon=2e-5,
+            act=tf.identity,
+            is_train=False,
+            fix_gamma=True,
+            beta_init=tf.zeros_initializer,
+            gamma_init=tf.random_normal_initializer(mean=1.0, stddev=0.002),  # tf.ones_initializer,
+            # dtype = tf.float32,
+            trainable=None,
+            name='batchnorm_layer',
+    ):
+        Layer.__init__(self, name=name)
+        self.inputs = layer.outputs
+        print("  [TL] BatchNormLayer %s: decay:%f epsilon:%f act:%s is_train:%s" % (self.name, decay, epsilon, act.__name__, is_train))
+        x_shape = self.inputs.get_shape()
+        params_shape = x_shape[-1:]
+
+        from tensorflow.python.training import moving_averages
+        from tensorflow.python.ops import control_flow_ops
+
+        with tf.variable_scope(name) as vs:
+            axis = list(range(len(x_shape) - 1))
+
+            ## 1. beta, gamma
+            if tf.__version__ > '0.12.1' and beta_init == tf.zeros_initializer:
+                beta_init = beta_init()
+            beta = tf.get_variable('beta', shape=params_shape, initializer=beta_init, dtype=tf.float32, trainable=is_train)  #, restore=restore)
+
+            gamma = tf.get_variable(
+                'gamma',
+                shape=params_shape,
+                initializer=gamma_init,
+                dtype=tf.float32,
+                trainable=fix_gamma,
+            )  #restore=restore)
+
+            ## 2.
+            if tf.__version__ > '0.12.1':
+                moving_mean_init = tf.zeros_initializer()
+            else:
+                moving_mean_init = tf.zeros_initializer
+            moving_mean = tf.get_variable('moving_mean', params_shape, initializer=moving_mean_init, dtype=tf.float32, trainable=False)  #   restore=restore)
+            moving_variance = tf.get_variable(
+                'moving_variance',
+                params_shape,
+                initializer=tf.constant_initializer(1.),
+                dtype=tf.float32,
+                trainable=False,
+            )  #   restore=restore)
+
+            ## 3.
+            # These ops will only be preformed when training.
+            mean, variance = tf.nn.moments(self.inputs, axis)
+            try:  # TF12
+                update_moving_mean = moving_averages.assign_moving_average(moving_mean, mean, decay, zero_debias=False)  # if zero_debias=True, has bias
+                update_moving_variance = moving_averages.assign_moving_average(
+                    moving_variance, variance, decay, zero_debias=False)  # if zero_debias=True, has bias
+                # print("TF12 moving")
+            except Exception as e:  # TF11
+                update_moving_mean = moving_averages.assign_moving_average(moving_mean, mean, decay)
+                update_moving_variance = moving_averages.assign_moving_average(moving_variance, variance, decay)
+                # print("TF11 moving")
+
+            def mean_var_with_update():
+                with tf.control_dependencies([update_moving_mean, update_moving_variance]):
+                    return tf.identity(mean), tf.identity(variance)
+            if trainable:
+                mean, var = mean_var_with_update()
+                print(mean)
+                print(var)
+                self.outputs = act(tf.nn.batch_normalization(self.inputs, mean, var, beta, gamma, epsilon))
+            else:
+                self.outputs = act(tf.nn.batch_normalization(self.inputs, moving_mean, moving_variance, beta, gamma, epsilon))
+            variables = [beta, gamma, moving_mean, moving_variance]
+        self.all_layers = list(layer.all_layers)
+        self.all_params = list(layer.all_params)
+        self.all_drop = dict(layer.all_drop)
+        self.all_layers.extend([self.outputs])
+        self.all_params.extend(variables)
+
+
+def subsample(inputs, factor, scope=None):
+    if factor == 1:
+        return inputs
+    else:
+        return tl.layers.MaxPool2d(inputs, [1, 1], strides=(factor, factor), name=scope)
+
+
+def conv2d_same(inputs, num_outputs, kernel_size, strides, rate=1, w_init=None, scope=None, trainable=None):
+    '''
+    Reference slim resnet
+    :param inputs:
+    :param num_outputs:
+    :param kernel_size:
+    :param strides:
+    :param rate:
+    :param scope:
+    :return:
+    '''
+    if strides == 1:
+        if rate == 1:
+            nets = tl.layers.Conv2d(inputs, n_filter=num_outputs, filter_size=(kernel_size, kernel_size), b_init=None,
+                                   strides=(strides, strides), W_init=w_init, act=None, padding='SAME', name=scope,
+                                    use_cudnn_on_gpu=True)
+            nets = BatchNormLayer(nets, act=tf.identity, is_train=True, trainable=trainable, name=scope+'_bn/BatchNorm')
+        else:
+            nets = tl.layers.AtrousConv2dLayer(inputs, n_filter=num_outputs, filter_size=(kernel_size, kernel_size),
+                                               rate=rate, act=None, W_init=w_init, padding='SAME', name=scope)
+            nets = BatchNormLayer(nets, act=tf.identity, is_train=True, trainable=trainable, name=scope+'_bn/BatchNorm')
+        return nets
+    else:
+        kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)
+        pad_total = kernel_size_effective - 1
+        pad_beg = pad_total // 2
+        pad_end = pad_total - pad_beg
+        inputs = tl.layers.PadLayer(inputs, [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]], name='padding_%s' % scope)
+        if rate == 1:
+            nets = tl.layers.Conv2d(inputs, n_filter=num_outputs, filter_size=(kernel_size, kernel_size), b_init=None,
+                                    strides=(strides, strides), W_init=w_init, act=None, padding='VALID', name=scope,
+                                    use_cudnn_on_gpu=True)
+            nets = BatchNormLayer(nets, act=tf.identity, is_train=True, trainable=trainable, name=scope+'_bn/BatchNorm')
+        else:
+            nets = tl.layers.AtrousConv2dLayer(inputs, n_filter=num_outputs, filter_size=(kernel_size, kernel_size), b_init=None,
+                                              rate=rate, act=None, W_init=w_init, padding='SAME', name=scope)
+            nets = BatchNormLayer(nets, act=tf.identity, is_train=True, trainable=trainable, name=scope+'_bn/BatchNorm')
+        return nets
+
+
+def bottleneck_IR(inputs, depth, depth_bottleneck, stride, rate=1, w_init=None, scope=None, trainable=None):
+    with tf.variable_scope(scope, 'bottleneck_v1') as sc:
+        depth_in = utils.last_dimension(inputs.outputs.get_shape(), min_rank=4)
+        if depth == depth_in:
+            shortcut = subsample(inputs, stride, 'shortcut')
+        else:
+            shortcut = tl.layers.Conv2d(inputs, depth, filter_size=(1, 1), strides=(stride, stride), act=None,
+                                        W_init=w_init, b_init=None, name='shortcut_conv', use_cudnn_on_gpu=True)
+            shortcut = BatchNormLayer(shortcut, act=tf.identity, is_train=True, trainable=trainable, name='shortcut_bn/BatchNorm')
+        # bottleneck layer 1
+        residual = BatchNormLayer(inputs, act=tf.identity, is_train=True, trainable=trainable, name='conv1_bn1')
+        residual = tl.layers.Conv2d(residual, depth_bottleneck, filter_size=(3, 3), strides=(1, 1), act=None, b_init=None,
+                                    W_init=w_init, name='conv1', use_cudnn_on_gpu=True)
+        residual = BatchNormLayer(residual, act=tf.identity, is_train=True, trainable=trainable, name='conv1_bn2')
+        # bottleneck prelu
+        residual = tl.layers.PReluLayer(residual)
+        # bottleneck layer 2
+        residual = conv2d_same(residual, depth, kernel_size=3, strides=stride, rate=rate, w_init=w_init, scope='conv2', trainable=trainable)
+        output = ElementwiseLayer(layer=[shortcut, residual],
+                                  combine_fn=tf.add,
+                                  name='combine_layer',
+                                  act=None)
+        return output
+
+
+def bottleneck_IR_SE(inputs, depth, depth_bottleneck, stride, rate=1, w_init=None, scope=None, trainable=None):
+    with tf.variable_scope(scope, 'bottleneck_v1') as sc:
+        depth_in = utils.last_dimension(inputs.outputs.get_shape(), min_rank=4)
+        if depth == depth_in:
+            shortcut = subsample(inputs, stride, 'shortcut')
+        else:
+            shortcut = tl.layers.Conv2d(inputs, depth, filter_size=(1, 1), strides=(stride, stride), act=None,
+                                        W_init=w_init, b_init=None, name='shortcut_conv', use_cudnn_on_gpu=True)
+            shortcut = BatchNormLayer(shortcut, act=tf.identity, is_train=True, trainable=trainable, name='shortcut_bn/BatchNorm')
+        # bottleneck layer 1
+        residual = BatchNormLayer(inputs, act=tf.identity, is_train=True, trainable=trainable, name='conv1_bn1')
+        residual = tl.layers.Conv2d(residual, depth_bottleneck, filter_size=(3, 3), strides=(1, 1), act=None, b_init=None,
+                                    W_init=w_init, name='conv1', use_cudnn_on_gpu=True)
+        residual = BatchNormLayer(residual, act=tf.identity, is_train=True, trainable=trainable, name='conv1_bn2')
+        # bottleneck prelu
+        residual = tl.layers.PReluLayer(residual)
+        # bottleneck layer 2
+        residual = conv2d_same(residual, depth, kernel_size=3, strides=stride, rate=rate, w_init=w_init, scope='conv2', trainable=trainable)
+        # squeeze
+        squeeze = tl.layers.InputLayer(tf.reduce_mean(residual.outputs, axis=[1, 2]), name='squeeze_layer')
+        # excitation
+        excitation1 = tl.layers.DenseLayer(squeeze, n_units=int(depth/16.0), act=tf.nn.relu,
+                                           W_init=w_init, name='excitation_1')
+        # excitation1 = tl.layers.PReluLayer(excitation1, name='excitation_prelu')
+        excitation2 = tl.layers.DenseLayer(excitation1, n_units=depth, act=tf.nn.sigmoid,
+                                           W_init=w_init, name='excitation_2')
+        # scale
+        scale = tl.layers.ReshapeLayer(excitation2, shape=[tf.shape(excitation2.outputs)[0], 1, 1, depth], name='excitation_reshape')
+
+        residual_se = ElementwiseLayer(layer=[residual, scale],
+                                       combine_fn=tf.multiply,
+                                       name='scale_layer',
+                                       act=None)
+
+        output = ElementwiseLayer(layer=[shortcut, residual_se],
+                                  combine_fn=tf.add,
+                                  name='combine_layer',
+                                  act=tf.nn.relu)
+        return output
+
+
+def resnet(inputs, bottle_neck, blocks, w_init=None, trainable=None, reuse=False, keep_rate=None, scope=None):
+    with tf.variable_scope(scope, reuse=reuse):
+        # inputs = tf.subtract(inputs, 127.5)
+        # inputs = tf.multiply(inputs, 0.0078125)
+        net_inputs = tl.layers.InputLayer(inputs, name='input_layer')
+        if bottle_neck:
+            net = tl.layers.Conv2d(net_inputs, n_filter=64, filter_size=(3, 3), strides=(1, 1),
+                                   act=None, W_init=w_init, b_init=None, name='conv1', use_cudnn_on_gpu=True)
+            net = BatchNormLayer(net, act=tf.identity, name='bn0', is_train=True, trainable=trainable)
+            net = tl.layers.PReluLayer(net, name='prelu0')
+        else:
+            raise ValueError('The standard resnet must support the bottleneck layer')
+        for block in blocks:
+            with tf.variable_scope(block.scope):
+                for i, var in enumerate(block.args):
+                    with tf.variable_scope('unit_%d' % (i+1)):
+                        net = block.unit_fn(net, depth=var['depth'], depth_bottleneck=var['depth_bottleneck'],
+                                            w_init=w_init, stride=var['stride'], rate=var['rate'], scope=None,
+                                            trainable=trainable)
+        net = BatchNormLayer(net, act=tf.identity, is_train=True, name='E_BN1', trainable=trainable)
+        # net = tl.layers.DropoutLayer(net, keep=0.4, name='E_Dropout')
+        net.outputs = npu_ops.dropout(net.outputs, keep_prob=keep_rate, name='E_Dropout')
+        net_shape = net.outputs.get_shape()
+        net = tl.layers.ReshapeLayer(net, shape=[-1, net_shape[1]*net_shape[2]*net_shape[3]], name='E_Reshapelayer')
+        net = tl.layers.DenseLayer(net, n_units=512, W_init=w_init, name='E_DenseLayer')
+        net = BatchNormLayer(net, act=tf.identity, is_train=True, fix_gamma=False, trainable=trainable, name='E_BN2')
+        return net
+
+
+class Block(collections.namedtuple('Block', ['scope', 'unit_fn', 'args'])):
+    """A named tuple describing a ResNet block.
+
+    Its parts are:
+      scope: The scope of the `Block`.
+      unit_fn: The ResNet unit function which takes as input a `Tensor` and
+        returns another `Tensor` with the output of the ResNet unit.
+      args: A list of length equal to the number of units in the `Block`. The list
+        contains one (depth, depth_bottleneck, stride) tuple for each unit in the
+        block to serve as argument to unit_fn.
+    """
+
+
+def resnetse_v1_block(scope, base_depth, num_units, stride, rate=1, unit_fn=None):
+  """Helper function for creating a resnet_v1 bottleneck block.
+
+  Args:
+    scope: The scope of the block.
+    base_depth: The depth of the bottleneck layer for each unit.
+    num_units: The number of units in the block.
+    stride: The stride of the block, implemented as a stride in the last unit.
+      All other units have stride=1.
+
+  Returns:
+    A resnet_v1 bottleneck block.
+  """
+  return Block(scope, unit_fn, [{
+      'depth': base_depth * 4,
+      'depth_bottleneck': base_depth,
+      'stride': stride,
+      'rate': rate
+  }] + [{
+      'depth': base_depth * 4,
+      'depth_bottleneck': base_depth,
+      'stride': 1,
+      'rate': rate
+  }] * (num_units - 1))
+
+
+def get_resnet(inputs, num_layers, type=None, w_init=None, trainable=None, sess=None, reuse=False, keep_rate=None):
+    if type == 'ir':
+        unit_fn = bottleneck_IR
+    elif type == 'se_ir':
+        unit_fn = bottleneck_IR_SE
+    else:
+        raise ValueError('the input fn is unknown')
+
+    if num_layers == 50:
+        blocks = [
+            resnetse_v1_block('block1', base_depth=64, num_units=3, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block2', base_depth=128, num_units=4, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block3', base_depth=256, num_units=14, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block4', base_depth=512, num_units=3, stride=2, rate=1, unit_fn=unit_fn)
+        ]
+    elif num_layers == 101:
+        blocks = [
+            resnetse_v1_block('block1', base_depth=64, num_units=3, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block2', base_depth=128, num_units=13, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block3', base_depth=256, num_units=30, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block4', base_depth=512, num_units=3, stride=2, rate=1, unit_fn=unit_fn)
+        ]
+    elif num_layers == 152:
+        blocks = [
+            resnetse_v1_block('block1', base_depth=64, num_units=3, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block2', base_depth=128, num_units=8, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block3', base_depth=256, num_units=36, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block4', base_depth=512, num_units=3, stride=2, rate=1, unit_fn=unit_fn)
+        ]
+    else:
+        raise ValueError('Resnet layer %d is not supported now.' % num_layers)
+    net = resnet(inputs=inputs,
+                 bottle_neck=True,
+                 blocks=blocks,
+                 w_init=w_init,
+                 trainable=trainable,
+                 reuse=reuse,
+                 keep_rate = keep_rate,
+                 scope='resnet_v1_%d' % num_layers)
+    return net
+
+
+if __name__ == '__main__':
+        x = tf.placeholder(dtype=tf.float32, shape=[None, 112, 112, 3], name='input_place')
+        sess = tf.Session(config=npu_config_proto())
+        # w_init = tf.truncated_normal_initializer(mean=10, stddev=5e-2)
+        w_init = tf.contrib.layers.xavier_initializer(uniform=False)
+        # test resnetse
+        nets = get_resnet(x, 50, type='ir', w_init=w_init, sess=sess)
+        tl.layers.initialize_global_variables(sess)
+
+        for p in tl.layers.get_variables_with_name('W_conv2d', True, True):
+            print(p.op.name)
+        print('##############'*30)
+        with sess:
+            nets.print_params()
+
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/nets/L_Resnet_E_IR_GBN.py b/TensorFlow/contrib/cv/InsightFace_TF/nets/L_Resnet_E_IR_GBN.py
new file mode 100644
index 0000000000000000000000000000000000000000..49a76e607ec61570d409dbd1c2a2c906ef1a89b0
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/nets/L_Resnet_E_IR_GBN.py
@@ -0,0 +1,354 @@
+from npu_bridge.npu_init import *
+import tensorflow as tf
+import tensorlayer as tl
+from tensorflow.contrib.layers.python.layers import utils
+import collections
+from tensorlayer.layers import Layer, list_remove_repeat
+from tl_layers_modify import GroupNormLayer
+
+
+class ElementwiseLayer(Layer):
+    """
+    The :class:`ElementwiseLayer` class combines multiple :class:`Layer` which have the same output shapes by a given elemwise-wise operation.
+
+    Parameters
+    ----------
+    layer : a list of :class:`Layer` instances
+        The `Layer` class feeding into this layer.
+    combine_fn : a TensorFlow elemwise-merge function
+        e.g. AND is ``tf.minimum`` ;  OR is ``tf.maximum`` ; ADD is ``tf.add`` ; MUL is ``tf.multiply`` and so on.
+        See `TensorFlow Math API <https://www.tensorflow.org/versions/master/api_docs/python/math_ops.html#math>`_ .
+    name : a string or None
+        An optional name to attach to this layer.
+    """
+    def __init__(
+        self,
+        layer = [],
+        combine_fn = tf.minimum,
+        name ='elementwise_layer',
+        act = None,
+    ):
+        Layer.__init__(self, name=name)
+
+        if act:
+            print("  [TL] ElementwiseLayer %s: size:%s fn:%s, act:%s" % (
+            self.name, layer[0].outputs.get_shape(), combine_fn.__name__, act.__name__))
+        else:
+            print("  [TL] ElementwiseLayer %s: size:%s fn:%s" % (
+            self.name, layer[0].outputs.get_shape(), combine_fn.__name__))
+
+        self.outputs = layer[0].outputs
+        # print(self.outputs._shape, type(self.outputs._shape))
+        for l in layer[1:]:
+            # assert str(self.outputs.get_shape()) == str(l.outputs.get_shape()), "Hint: the input shapes should be the same. %s != %s" %  (self.outputs.get_shape() , str(l.outputs.get_shape()))
+            self.outputs = combine_fn(self.outputs, l.outputs, name=name)
+        if act:
+            self.outputs = act(self.outputs)
+        self.all_layers = list(layer[0].all_layers)
+        self.all_params = list(layer[0].all_params)
+        self.all_drop = dict(layer[0].all_drop)
+
+        for i in range(1, len(layer)):
+            self.all_layers.extend(list(layer[i].all_layers))
+            self.all_params.extend(list(layer[i].all_params))
+            self.all_drop.update(dict(layer[i].all_drop))
+
+        self.all_layers = list_remove_repeat(self.all_layers)
+        self.all_params = list_remove_repeat(self.all_params)
+
+
+def subsample(inputs, factor, scope=None):
+    if factor == 1:
+        return inputs
+    else:
+        return tl.layers.MaxPool2d(inputs, [1, 1], strides=(factor, factor), name=scope)
+
+
+def conv2d_same(inputs, num_outputs, kernel_size, strides, rate=1, w_init=None, scope=None, trainable=None):
+    '''
+    Reference slim resnet
+    :param inputs:
+    :param num_outputs:
+    :param kernel_size:
+    :param strides:
+    :param rate:
+    :param scope:
+    :return:
+    '''
+    if strides == 1:
+        if rate == 1:
+            nets = tl.layers.Conv2d(inputs, n_filter=num_outputs, filter_size=(kernel_size, kernel_size), b_init=None,
+                                   strides=(strides, strides), W_init=w_init, act=None, padding='SAME', name=scope,
+                                    use_cudnn_on_gpu=True)
+            nets = GroupNormLayer(layer=nets, act=tf.identity, name=scope+'_bn/GroupNorm')
+        else:
+            nets = tl.layers.AtrousConv2dLayer(inputs, n_filter=num_outputs, filter_size=(kernel_size, kernel_size),
+                                               rate=rate, act=None, W_init=w_init, padding='SAME', name=scope)
+            nets = GroupNormLayer(layer=nets, act=tf.identity, name=scope+'_bn/GroupNorm')
+        return nets
+    else:
+        kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)
+        pad_total = kernel_size_effective - 1
+        pad_beg = pad_total // 2
+        pad_end = pad_total - pad_beg
+        inputs = tl.layers.PadLayer(inputs, [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]], name='padding_%s' % scope)
+        if rate == 1:
+            nets = tl.layers.Conv2d(inputs, n_filter=num_outputs, filter_size=(kernel_size, kernel_size), b_init=None,
+                                    strides=(strides, strides), W_init=w_init, act=None, padding='VALID', name=scope,
+                                    use_cudnn_on_gpu=True)
+            nets = GroupNormLayer(layer=nets, act=tf.identity, name=scope+'_bn/GroupNorm')
+        else:
+            nets = tl.layers.AtrousConv2dLayer(inputs, n_filter=num_outputs, filter_size=(kernel_size, kernel_size), b_init=None,
+                                              rate=rate, act=None, W_init=w_init, padding='SAME', name=scope)
+            nets = GroupNormLayer(layer=nets, act=tf.identity, name=scope+'_bn/GroupNorm')
+        return nets
+
+
+def bottleneck(inputs, depth, depth_bottleneck, stride, rate=1, scope=None):
+    with tf.variable_scope(scope, 'bottleneck_v1') as sc:
+        depth_in = utils.last_dimension(inputs.outputs.get_shape(), min_rank=4)
+        if depth == depth_in:
+            shortcut = subsample(inputs, stride, 'shortcut')
+        else:
+            shortcut = tl.layers.Conv2d(inputs, depth, filter_size=(1, 1), strides=(stride, stride), act=None,
+                                        b_init=None, name='shortcut_conv')
+            shortcut = GroupNormLayer(layer=shortcut, act=tf.identity, name='shortcut_bn/BatchNorm')
+        # bottleneck layer 1
+        residual = tl.layers.Conv2d(inputs, depth_bottleneck, filter_size=(1, 1), strides=(1, 1), act=None, b_init=None,
+                                    name='conv1')
+        residual = GroupNormLayer(layer=residual, act=tf.nn.relu, name='conv1_bn/BatchNorm')
+
+        # bottleneck layer 2
+        residual = conv2d_same(residual, depth_bottleneck, kernel_size=3, strides= stride, rate=rate, scope='conv2')
+
+        # bottleneck layer 3
+        residual = tl.layers.Conv2d(residual, depth, filter_size=(1, 1), strides=(1, 1), act=None, b_init=None,
+                                    name='conv3')
+        residual = GroupNormLayer(layer=residual, act=tf.identity, name='conv3_bn/BatchNorm',
+                                  scale_init=tf.constant_initializer(0.0))
+        output = ElementwiseLayer(layer=[shortcut, residual],
+                                  combine_fn=tf.add,
+                                  name='combine_layer',
+                                  act=tf.nn.relu)
+        return output
+
+
+def bottleneck_IR(inputs, depth, depth_bottleneck, stride, rate=1, w_init=None, scope=None, trainable=None):
+    with tf.variable_scope(scope, 'bottleneck_v1') as sc:
+        depth_in = utils.last_dimension(inputs.outputs.get_shape(), min_rank=4)
+        if depth == depth_in:
+            shortcut = subsample(inputs, stride, 'shortcut')
+        else:
+            shortcut = tl.layers.Conv2d(inputs, depth, filter_size=(1, 1), strides=(stride, stride), act=None,
+                                        W_init=w_init, b_init=None, name='shortcut_conv', use_cudnn_on_gpu=True)
+            shortcut = GroupNormLayer(layer=shortcut, act=tf.identity, name='shortcut_bn/BatchNorm')
+        # bottleneck layer 1
+        residual = GroupNormLayer(layer=inputs, act=tf.identity, name='conv1_bn1')
+        residual = tl.layers.Conv2d(residual, depth_bottleneck, filter_size=(3, 3), strides=(1, 1), act=None, b_init=None,
+                                    W_init=w_init, name='conv1', use_cudnn_on_gpu=True)
+        residual = GroupNormLayer(layer=residual, act=tf.identity, name='conv1_bn2')
+        # bottleneck prelu
+        residual = tl.layers.PReluLayer(residual)
+        # bottleneck layer 2
+        residual = conv2d_same(residual, depth, kernel_size=3, strides=stride, rate=rate, w_init=w_init, scope='conv2', trainable=trainable)
+        output = ElementwiseLayer(layer=[shortcut, residual],
+                                  combine_fn=tf.add,
+                                  name='combine_layer',
+                                  act=None)
+        return output
+
+
+def bottleneck_IR_SE(inputs, depth, depth_bottleneck, stride, rate=1, w_init=None, scope=None, trainable=None):
+    with tf.variable_scope(scope, 'bottleneck_v1') as sc:
+        depth_in = utils.last_dimension(inputs.outputs.get_shape(), min_rank=4)
+        if depth == depth_in:
+            shortcut = subsample(inputs, stride, 'shortcut')
+        else:
+            shortcut = tl.layers.Conv2d(inputs, depth, filter_size=(1, 1), strides=(stride, stride), act=None,
+                                        W_init=w_init, b_init=None, name='shortcut_conv', use_cudnn_on_gpu=True)
+            shortcut = GroupNormLayer(layer=shortcut, act=tf.identity, name='shortcut_bn/BatchNorm')
+        residual = GroupNormLayer(layer=inputs, act=tf.identity, name='conv1_bn1')
+        residual = tl.layers.Conv2d(residual, depth_bottleneck, filter_size=(3, 3), strides=(1, 1), act=None, b_init=None,
+                                    W_init=w_init, name='conv1', use_cudnn_on_gpu=True)
+        residual = GroupNormLayer(layer=residual, act=tf.identity, name='conv1_bn2')
+        # bottleneck prelu
+        residual = tl.layers.PReluLayer(residual)
+        # bottleneck layer 2
+        residual = conv2d_same(residual, depth, kernel_size=3, strides=stride, rate=rate, w_init=w_init, scope='conv2', trainable=trainable)
+        # squeeze
+        squeeze = tl.layers.InputLayer(tf.reduce_mean(residual.outputs, axis=[1, 2]), name='squeeze_layer')
+        # excitation
+        excitation1 = tl.layers.DenseLayer(squeeze, n_units=int(depth/16.0), act=tf.nn.relu,
+                                           W_init=w_init, name='excitation_1')
+        # excitation1 = tl.layers.PReluLayer(excitation1, name='excitation_prelu')
+        excitation2 = tl.layers.DenseLayer(excitation1, n_units=depth, act=tf.nn.sigmoid,
+                                           W_init=w_init, name='excitation_2')
+        # scale
+        scale = tl.layers.ReshapeLayer(excitation2, shape=[tf.shape(excitation2.outputs)[0], 1, 1, depth], name='excitation_reshape')
+
+        residual_se = ElementwiseLayer(layer=[residual, scale],
+                                       combine_fn=tf.multiply,
+                                       name='scale_layer',
+                                       act=None)
+
+        output = ElementwiseLayer(layer=[shortcut, residual_se],
+                                  combine_fn=tf.add,
+                                  name='combine_layer',
+                                  act=tf.nn.relu)
+        return output
+
+
+def resnet(inputs, bottle_neck, blocks, w_init=None, trainable=None, scope=None):
+    with tf.variable_scope(scope):
+        net_inputs = tl.layers.InputLayer(inputs, name='input_layer')
+        if bottle_neck:
+            net = tl.layers.Conv2d(net_inputs, n_filter=64, filter_size=(3, 3), strides=(1, 1),
+                                   act=None, W_init=w_init, b_init=None, name='conv1', use_cudnn_on_gpu=True)
+            net = GroupNormLayer(layer=net, act=tf.identity, name='group_norm_0')
+            net = tl.layers.PReluLayer(net, name='prelu0')
+        else:
+            raise ValueError('The standard resnet must support the bottleneck layer')
+        for block in blocks:
+            with tf.variable_scope(block.scope):
+                for i, var in enumerate(block.args):
+                    with tf.variable_scope('unit_%d' % (i+1)):
+                        net = block.unit_fn(net, depth=var['depth'], depth_bottleneck=var['depth_bottleneck'],
+                                            w_init=w_init, stride=var['stride'], rate=var['rate'], scope=None,
+                                            trainable=trainable)
+        net = GroupNormLayer(layer=net, act=tf.identity, name='E_GN_0')
+        net = tl.layers.DropoutLayer(net, keep=0.4, name='E_Dropout')
+        net_shape = net.outputs.get_shape()
+        net = tl.layers.ReshapeLayer(net, shape=[-1, net_shape[1]*net_shape[2]*net_shape[3]], name='E_Reshapelayer')
+        net = tl.layers.DenseLayer(net, n_units=512, W_init=w_init, name='E_DenseLayer')
+        # net = GroupNormLayer(layer=net, act=tf.identity, name='E_GN_1')
+        return net
+
+
+class Block(collections.namedtuple('Block', ['scope', 'unit_fn', 'args'])):
+    """A named tuple describing a ResNet block.
+
+    Its parts are:
+      scope: The scope of the `Block`.
+      unit_fn: The ResNet unit function which takes as input a `Tensor` and
+        returns another `Tensor` with the output of the ResNet unit.
+      args: A list of length equal to the number of units in the `Block`. The list
+        contains one (depth, depth_bottleneck, stride) tuple for each unit in the
+        block to serve as argument to unit_fn.
+    """
+
+
+def resnetse_v1_block(scope, base_depth, num_units, stride, rate=1, unit_fn=None):
+  """Helper function for creating a resnet_v1 bottleneck block.
+
+  Args:
+    scope: The scope of the block.
+    base_depth: The depth of the bottleneck layer for each unit.
+    num_units: The number of units in the block.
+    stride: The stride of the block, implemented as a stride in the last unit.
+      All other units have stride=1.
+
+  Returns:
+    A resnet_v1 bottleneck block.
+  """
+  return Block(scope, unit_fn, [{
+      'depth': base_depth * 4,
+      'depth_bottleneck': base_depth,
+      'stride': stride,
+      'rate': rate
+  }] + [{
+      'depth': base_depth * 4,
+      'depth_bottleneck': base_depth,
+      'stride': 1,
+      'rate': rate
+  }] * (num_units - 1))
+
+
+def resnetse_v1_block_2(scope, base_depth, num_units, stride, rate=1, unit_fn=None):
+  """Helper function for creating a resnet_v1 bottleneck block.
+
+  Args:
+    scope: The scope of the block.
+    base_depth: The depth of the bottleneck layer for each unit.
+    num_units: The number of units in the block.
+    stride: The stride of the block, implemented as a stride in the last unit.
+      All other units have stride=1.
+
+  Returns:
+    A resnet_v1 bottleneck block.
+  """
+  return Block(scope, unit_fn, [{
+      'depth': base_depth * 4,
+      'depth_bottleneck': base_depth,
+      'stride': 1,
+      'rate': rate
+  }] * (num_units - 1) + [{
+      'depth': base_depth * 4,
+      'depth_bottleneck': base_depth,
+      'stride': stride,
+      'rate': rate
+  }])
+
+
+def get_resnet(inputs, num_layers, type=None, w_init=None, trainable=None, sess=None):
+    if type == 'ir':
+        unit_fn = bottleneck_IR
+    elif type == 'se_ir':
+        unit_fn = bottleneck_IR_SE
+    # elif type == 'resnet':
+    #     unit_fn = bottleneck
+    #     blocks = [
+    #         resnetse_v1_block_2('block1', base_depth=64, num_units=3, stride=2, rate=1, unit_fn=unit_fn),
+    #         resnetse_v1_block_2('block2', base_depth=128, num_units=4, stride=2, rate=1, unit_fn=unit_fn),
+    #         resnetse_v1_block_2('block3', base_depth=256, num_units=6, stride=2, rate=1, unit_fn=unit_fn),
+    #         resnetse_v1_block_2('block4', base_depth=512, num_units=3, stride=1, rate=1, unit_fn=unit_fn)
+    #     ]
+    else:
+        raise ValueError('the input fn is unknown')
+
+    if num_layers == 50:
+        blocks = [
+            resnetse_v1_block('block1', base_depth=64, num_units=3, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block2', base_depth=128, num_units=4, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block3', base_depth=256, num_units=14, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block4', base_depth=512, num_units=3, stride=2, rate=1, unit_fn=unit_fn)
+        ]
+    elif num_layers == 101:
+        blocks = [
+            resnetse_v1_block('block1', base_depth=64, num_units=3, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block2', base_depth=128, num_units=13, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block3', base_depth=256, num_units=30, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block4', base_depth=512, num_units=3, stride=2, rate=1, unit_fn=unit_fn)
+        ]
+    elif num_layers == 152:
+        blocks = [
+            resnetse_v1_block('block1', base_depth=64, num_units=3, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block2', base_depth=128, num_units=8, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block3', base_depth=256, num_units=36, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block4', base_depth=512, num_units=3, stride=2, rate=1, unit_fn=unit_fn)
+        ]
+    else:
+        raise ValueError('Resnet layer %d is not supported now.' % num_layers)
+    net = resnet(inputs=inputs,
+                 bottle_neck=True,
+                 blocks=blocks,
+                 w_init=w_init,
+                 trainable=trainable,
+                 scope='resnet_v1_%d' % num_layers)
+    return net
+
+
+if __name__ == '__main__':
+        x = tf.placeholder(dtype=tf.float32, shape=[None, 112, 112, 3], name='input_place')
+        sess = tf.Session(config=npu_config_proto())
+        # w_init = tf.truncated_normal_initializer(mean=10, stddev=5e-2)
+        w_init = tf.contrib.layers.xavier_initializer(uniform=False)
+        # test resnetse
+        nets = get_resnet(x, 50, type='ir', w_init=w_init, sess=sess)
+        tl.layers.initialize_global_variables(sess)
+
+        for p in tl.layers.get_variables_with_name('W_conv2d', True, True):
+            print(p.op.name)
+        print('##############'*30)
+        with sess:
+            nets.print_params()
+
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/nets/L_Resnet_E_IR_MGPU.py b/TensorFlow/contrib/cv/InsightFace_TF/nets/L_Resnet_E_IR_MGPU.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f96317c6c36425f4c1007881a014110b70273b1
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/nets/L_Resnet_E_IR_MGPU.py
@@ -0,0 +1,242 @@
+from npu_bridge.npu_init import *
+import tensorflow as tf
+import tensorlayer as tl
+from tensorflow.contrib.layers.python.layers import utils
+import collections
+from tl_layers_modify import ElementwiseLayer, BatchNormLayer, Conv2d, PReluLayer, DenseLayer
+
+
+def subsample(inputs, factor, scope=None):
+    if factor == 1:
+        return inputs
+    else:
+        return tl.layers.MaxPool2d(inputs, [1, 1], strides=(factor, factor), name=scope)
+
+
+def conv2d_same(inputs, num_outputs, kernel_size, strides, rate=1, w_init=None, scope=None, trainable=None):
+    '''
+    Reference slim resnet
+    :param inputs:
+    :param num_outputs:
+    :param kernel_size:
+    :param strides:
+    :param rate:
+    :param scope:
+    :return:
+    '''
+    if strides == 1:
+        if rate == 1:
+            nets = Conv2d(inputs, n_filter=num_outputs, filter_size=(kernel_size, kernel_size), b_init=None,
+                                   strides=(strides, strides), W_init=w_init, act=None, padding='SAME', name=scope,
+                                    use_cudnn_on_gpu=True)
+            nets = BatchNormLayer(nets, act=tf.identity, is_train=True, trainable=trainable, name=scope+'_bn/BatchNorm')
+        else:
+            nets = tl.layers.AtrousConv2dLayer(inputs, n_filter=num_outputs, filter_size=(kernel_size, kernel_size),
+                                               rate=rate, act=None, W_init=w_init, padding='SAME', name=scope)
+            nets = BatchNormLayer(nets, act=tf.identity, is_train=True, trainable=trainable, name=scope+'_bn/BatchNorm')
+        return nets
+    else:
+        kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)
+        pad_total = kernel_size_effective - 1
+        pad_beg = pad_total // 2
+        pad_end = pad_total - pad_beg
+        inputs = tl.layers.PadLayer(inputs, [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]], name='padding_%s' % scope)
+        if rate == 1:
+            nets = Conv2d(inputs, n_filter=num_outputs, filter_size=(kernel_size, kernel_size), b_init=None,
+                                    strides=(strides, strides), W_init=w_init, act=None, padding='VALID', name=scope,
+                                    use_cudnn_on_gpu=True)
+            nets = BatchNormLayer(nets, act=tf.identity, is_train=True, trainable=trainable, name=scope+'_bn/BatchNorm')
+        else:
+            nets = tl.layers.AtrousConv2dLayer(inputs, n_filter=num_outputs, filter_size=(kernel_size, kernel_size), b_init=None,
+                                              rate=rate, act=None, W_init=w_init, padding='SAME', name=scope)
+            nets = BatchNormLayer(nets, act=tf.identity, is_train=True, trainable=trainable, name=scope+'_bn/BatchNorm')
+        return nets
+
+
+def bottleneck_IR(inputs, depth, depth_bottleneck, stride, rate=1, w_init=None, scope=None, trainable=None):
+    with tf.variable_scope(scope, 'bottleneck_v1') as sc:
+        depth_in = utils.last_dimension(inputs.outputs.get_shape(), min_rank=4)
+        if depth == depth_in:
+            shortcut = subsample(inputs, stride, 'shortcut')
+        else:
+            shortcut = Conv2d(inputs, depth, filter_size=(1, 1), strides=(stride, stride), act=None,
+                                        W_init=w_init, b_init=None, name='shortcut_conv', use_cudnn_on_gpu=True)
+            shortcut = BatchNormLayer(shortcut, act=tf.identity, is_train=True, trainable=trainable, name='shortcut_bn/BatchNorm')
+        # bottleneck layer 1
+        residual = BatchNormLayer(inputs, act=tf.identity, is_train=True, trainable=trainable, name='conv1_bn1')
+        residual = Conv2d(residual, depth_bottleneck, filter_size=(3, 3), strides=(1, 1), act=None, b_init=None,
+                                    W_init=w_init, name='conv1', use_cudnn_on_gpu=True)
+        residual = BatchNormLayer(residual, act=tf.identity, is_train=True, trainable=trainable, name='conv1_bn2')
+        # bottleneck prelu
+        residual = PReluLayer(residual)
+        # bottleneck layer 2
+        residual = conv2d_same(residual, depth, kernel_size=3, strides=stride, rate=rate, w_init=w_init, scope='conv2', trainable=trainable)
+        output = ElementwiseLayer(layer=[shortcut, residual],
+                                  combine_fn=tf.add,
+                                  name='combine_layer',
+                                  act=None)
+        return output
+
+
+def bottleneck_IR_SE(inputs, depth, depth_bottleneck, stride, rate=1, w_init=None, scope=None, trainable=None):
+    with tf.variable_scope(scope, 'bottleneck_v1') as sc:
+        depth_in = utils.last_dimension(inputs.outputs.get_shape(), min_rank=4)
+        if depth == depth_in:
+            shortcut = subsample(inputs, stride, 'shortcut')
+        else:
+            shortcut = Conv2d(inputs, depth, filter_size=(1, 1), strides=(stride, stride), act=None,
+                                        W_init=w_init, b_init=None, name='shortcut_conv', use_cudnn_on_gpu=True)
+            shortcut = BatchNormLayer(shortcut, act=tf.identity, is_train=True, trainable=trainable, name='shortcut_bn/BatchNorm')
+        # bottleneck layer 1
+        residual = BatchNormLayer(inputs, act=tf.identity, is_train=True, trainable=trainable, name='conv1_bn1')
+        residual = Conv2d(residual, depth_bottleneck, filter_size=(3, 3), strides=(1, 1), act=None, b_init=None,
+                                    W_init=w_init, name='conv1', use_cudnn_on_gpu=True)
+        residual = BatchNormLayer(residual, act=tf.identity, is_train=True, trainable=trainable, name='conv1_bn2')
+        # bottleneck prelu
+        residual = PReluLayer(residual)
+        # bottleneck layer 2
+        residual = conv2d_same(residual, depth, kernel_size=3, strides=stride, rate=rate, w_init=w_init, scope='conv2', trainable=trainable)
+        # squeeze
+        squeeze = tl.layers.InputLayer(tf.reduce_mean(residual.outputs, axis=[1, 2]), name='squeeze_layer')
+        # excitation
+        excitation1 = DenseLayer(squeeze, n_units=int(depth/16.0), act=tf.nn.relu,
+                                           W_init=w_init, name='excitation_1')
+        # excitation1 = tl.layers.PReluLayer(excitation1, name='excitation_prelu')
+        excitation2 = DenseLayer(excitation1, n_units=depth, act=tf.nn.sigmoid,
+                                           W_init=w_init, name='excitation_2')
+        # scale
+        scale = tl.layers.ReshapeLayer(excitation2, shape=[tf.shape(excitation2.outputs)[0], 1, 1, depth], name='excitation_reshape')
+
+        residual_se = ElementwiseLayer(layer=[residual, scale],
+                                       combine_fn=tf.multiply,
+                                       name='scale_layer',
+                                       act=None)
+
+        output = ElementwiseLayer(layer=[shortcut, residual_se],
+                                  combine_fn=tf.add,
+                                  name='combine_layer',
+                                  act=tf.nn.relu)
+        return output
+
+
+def resnet(inputs, bottle_neck, blocks, w_init=None, trainable=None, keep_rate=None, scope=None):
+    with tf.variable_scope(scope):
+        net_inputs = tl.layers.InputLayer(inputs, name='input_layer')
+        if bottle_neck:
+            net = Conv2d(net_inputs, n_filter=64, filter_size=(3, 3), strides=(1, 1),
+                                   act=None, W_init=w_init, b_init=None, name='conv1', use_cudnn_on_gpu=True)
+            net = BatchNormLayer(net, act=tf.identity, name='bn0', is_train=True, trainable=trainable)
+            net = PReluLayer(net, name='prelu0')
+        else:
+            raise ValueError('The standard resnet must support the bottleneck layer')
+        for block in blocks:
+            with tf.variable_scope(block.scope):
+                for i, var in enumerate(block.args):
+                    with tf.variable_scope('unit_%d' % (i+1)):
+                        net = block.unit_fn(net, depth=var['depth'], depth_bottleneck=var['depth_bottleneck'],
+                                            w_init=w_init, stride=var['stride'], rate=var['rate'], scope=None,
+                                            trainable=trainable)
+        net = BatchNormLayer(net, act=tf.identity, is_train=True, name='E_BN1', trainable=trainable)
+        net = tl.layers.DropoutLayer(net, keep=keep_rate, name='E_Dropout')
+        net_shape = net.outputs.get_shape()
+        net = tl.layers.ReshapeLayer(net, shape=[-1, net_shape[1]*net_shape[2]*net_shape[3]], name='E_Reshapelayer')
+        net = DenseLayer(net, n_units=512, W_init=w_init, name='E_DenseLayer')
+        net = BatchNormLayer(net, act=tf.identity, is_train=True, fix_gamma=False, trainable=trainable, name='E_BN2')
+        return net
+
+
+class Block(collections.namedtuple('Block', ['scope', 'unit_fn', 'args'])):
+    """A named tuple describing a ResNet block.
+
+    Its parts are:
+      scope: The scope of the `Block`.
+      unit_fn: The ResNet unit function which takes as input a `Tensor` and
+        returns another `Tensor` with the output of the ResNet unit.
+      args: A list of length equal to the number of units in the `Block`. The list
+        contains one (depth, depth_bottleneck, stride) tuple for each unit in the
+        block to serve as argument to unit_fn.
+    """
+
+
+def resnetse_v1_block(scope, base_depth, num_units, stride, rate=1, unit_fn=None):
+  """Helper function for creating a resnet_v1 bottleneck block.
+
+  Args:
+    scope: The scope of the block.
+    base_depth: The depth of the bottleneck layer for each unit.
+    num_units: The number of units in the block.
+    stride: The stride of the block, implemented as a stride in the last unit.
+      All other units have stride=1.
+
+  Returns:
+    A resnet_v1 bottleneck block.
+  """
+  return Block(scope, unit_fn, [{
+      'depth': base_depth,
+      'depth_bottleneck': base_depth,
+      'stride': stride,
+      'rate': rate
+  }] + [{
+      'depth': base_depth,
+      'depth_bottleneck': base_depth,
+      'stride': 1,
+      'rate': rate
+  }] * (num_units - 1))
+
+
+def get_resnet(inputs, num_layers, type=None, w_init=None, trainable=None, keep_rate=None, sess=None):
+    if type == 'ir':
+        unit_fn = bottleneck_IR
+    elif type == 'se_ir':
+        unit_fn = bottleneck_IR_SE
+    else:
+        raise ValueError('the input fn is unknown')
+
+    if num_layers == 50:
+        blocks = [
+            resnetse_v1_block('block1', base_depth=64, num_units=3, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block2', base_depth=128, num_units=4, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block3', base_depth=256, num_units=14, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block4', base_depth=512, num_units=3, stride=2, rate=1, unit_fn=unit_fn)
+        ]
+    elif num_layers == 100:
+        blocks = [
+            resnetse_v1_block('block1', base_depth=64, num_units=3, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block2', base_depth=128, num_units=13, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block3', base_depth=256, num_units=30, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block4', base_depth=512, num_units=3, stride=2, rate=1, unit_fn=unit_fn)
+        ]
+    elif num_layers == 152:
+        blocks = [
+            resnetse_v1_block('block1', base_depth=64, num_units=3, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block2', base_depth=128, num_units=8, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block3', base_depth=256, num_units=36, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block4', base_depth=512, num_units=3, stride=2, rate=1, unit_fn=unit_fn)
+        ]
+    else:
+        raise ValueError('Resnet layer %d is not supported now.' % num_layers)
+    net = resnet(inputs=inputs,
+                 bottle_neck=True,
+                 blocks=blocks,
+                 w_init=w_init,
+                 trainable=trainable,
+                 keep_rate=keep_rate,
+                 scope='resnet_v1_%d' % num_layers)
+    return net
+
+
+if __name__ == '__main__':
+        x = tf.placeholder(dtype=tf.float32, shape=[None, 112, 112, 3], name='input_place')
+        sess = tf.Session(config=npu_config_proto())
+        # w_init = tf.truncated_normal_initializer(mean=10, stddev=5e-2)
+        w_init = tf.contrib.layers.xavier_initializer(uniform=False)
+        # test resnetse
+        nets = get_resnet(x, 50, type='ir', w_init=w_init, sess=sess)
+        tl.layers.initialize_global_variables(sess)
+
+        for p in tl.layers.get_variables_with_name('W_conv2d', True, True):
+            print(p.op.name)
+        print('##############'*30)
+        with sess:
+            nets.print_params()
+
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/nets/L_Resnet_E_IR_RBN.py b/TensorFlow/contrib/cv/InsightFace_TF/nets/L_Resnet_E_IR_RBN.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6a54a4600e30fc286ef2219314ba4651e375807
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/nets/L_Resnet_E_IR_RBN.py
@@ -0,0 +1,322 @@
+from npu_bridge.npu_init import *
+import tensorflow as tf
+import tensorlayer as tl
+from tensorflow.contrib.layers.python.layers import utils
+import collections
+from tensorlayer.layers import Layer, list_remove_repeat
+
+
+class ElementwiseLayer(Layer):
+    """
+    The :class:`ElementwiseLayer` class combines multiple :class:`Layer` which have the same output shapes by a given elemwise-wise operation.
+
+    Parameters
+    ----------
+    layer : a list of :class:`Layer` instances
+        The `Layer` class feeding into this layer.
+    combine_fn : a TensorFlow elemwise-merge function
+        e.g. AND is ``tf.minimum`` ;  OR is ``tf.maximum`` ; ADD is ``tf.add`` ; MUL is ``tf.multiply`` and so on.
+        See `TensorFlow Math API <https://www.tensorflow.org/versions/master/api_docs/python/math_ops.html#math>`_ .
+    name : a string or None
+        An optional name to attach to this layer.
+    """
+    def __init__(
+        self,
+        layer = [],
+        combine_fn = tf.minimum,
+        name ='elementwise_layer',
+        act = None,
+    ):
+        Layer.__init__(self, name=name)
+
+        if act:
+            print("  [TL] ElementwiseLayer %s: size:%s fn:%s, act:%s" % (
+            self.name, layer[0].outputs.get_shape(), combine_fn.__name__, act.__name__))
+        else:
+            print("  [TL] ElementwiseLayer %s: size:%s fn:%s" % (
+            self.name, layer[0].outputs.get_shape(), combine_fn.__name__))
+
+        self.outputs = layer[0].outputs
+        # print(self.outputs._shape, type(self.outputs._shape))
+        for l in layer[1:]:
+            # assert str(self.outputs.get_shape()) == str(l.outputs.get_shape()), "Hint: the input shapes should be the same. %s != %s" %  (self.outputs.get_shape() , str(l.outputs.get_shape()))
+            self.outputs = combine_fn(self.outputs, l.outputs, name=name)
+        if act:
+            self.outputs = act(self.outputs)
+        self.all_layers = list(layer[0].all_layers)
+        self.all_params = list(layer[0].all_params)
+        self.all_drop = dict(layer[0].all_drop)
+
+        for i in range(1, len(layer)):
+            self.all_layers.extend(list(layer[i].all_layers))
+            self.all_params.extend(list(layer[i].all_params))
+            self.all_drop.update(dict(layer[i].all_drop))
+
+        self.all_layers = list_remove_repeat(self.all_layers)
+        self.all_params = list_remove_repeat(self.all_params)
+
+
+def subsample(inputs, factor, scope=None):
+    if factor == 1:
+        return inputs
+    else:
+        return tl.layers.MaxPool2d(inputs, [1, 1], strides=(factor, factor), name=scope)
+
+
+def conv2d_same(inputs, num_outputs, kernel_size, strides, rate=1, w_init=None, scope=None, trainable=None):
+    '''
+    Reference slim resnet
+    :param inputs:
+    :param num_outputs:
+    :param kernel_size:
+    :param strides:
+    :param rate:
+    :param scope:
+    :return:
+    '''
+    if strides == 1:
+        if rate == 1:
+            nets = tl.layers.Conv2d(inputs, n_filter=num_outputs, filter_size=(kernel_size, kernel_size), b_init=None,
+                                   strides=(strides, strides), W_init=w_init, act=None, padding='SAME', name=scope,
+                                    use_cudnn_on_gpu=True)
+            nets.outputs = tf.layers.batch_normalization(inputs=nets.outputs,
+                                                         momentum=0.9,
+                                                         training=trainable,
+                                                         renorm=True,
+                                                         renorm_clipping={'rmax':3, 'rmin':0.3333,
+                                                                          'dmax':5},
+                                                         renorm_momentum=0.9,
+                                                         name=scope+'_bn/BatchNorm')
+        else:
+            nets = tl.layers.AtrousConv2dLayer(inputs, n_filter=num_outputs, filter_size=(kernel_size, kernel_size),
+                                               rate=rate, act=None, W_init=w_init, padding='SAME', name=scope)
+            nets.outputs = tf.layers.batch_normalization(inputs=nets.outputs,
+                                                         momentum=0.9,
+                                                         training=trainable,
+                                                         renorm=True,
+                                                         renorm_clipping={'rmax':3, 'rmin':0.3333,
+                                                                          'dmax':5},
+                                                         renorm_momentum=0.9,
+                                                         name=scope+'_bn/BatchNorm')
+        return nets
+    else:
+        kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)
+        pad_total = kernel_size_effective - 1
+        pad_beg = pad_total // 2
+        pad_end = pad_total - pad_beg
+        inputs = tl.layers.PadLayer(inputs, [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]], name='padding_%s' % scope)
+        if rate == 1:
+            nets = tl.layers.Conv2d(inputs, n_filter=num_outputs, filter_size=(kernel_size, kernel_size), b_init=None,
+                                    strides=(strides, strides), W_init=w_init, act=None, padding='VALID', name=scope,
+                                    use_cudnn_on_gpu=True)
+            nets.outputs = tf.layers.batch_normalization(inputs=nets.outputs,
+                                                         momentum=0.9,
+                                                         training=trainable,
+                                                         renorm=True,
+                                                         renorm_clipping={'rmax':3, 'rmin':0.3333,
+                                                                          'dmax':5},
+                                                         renorm_momentum=0.9,
+                                                         name=scope+'_bn/BatchNorm')
+        else:
+            nets = tl.layers.AtrousConv2dLayer(inputs, n_filter=num_outputs, filter_size=(kernel_size, kernel_size), b_init=None,
+                                              rate=rate, act=None, W_init=w_init, padding='SAME', name=scope)
+            nets.outputs = tf.layers.batch_normalization(inputs=nets.outputs,
+                                                         momentum=0.9,
+                                                         training=trainable,
+                                                         renorm=True,
+                                                         renorm_clipping={'rmax':3, 'rmin':0.3333,
+                                                                          'dmax':5},
+                                                         renorm_momentum=0.9,
+                                                         name=scope+'_bn/BatchNorm')
+        return nets
+
+
+def bottleneck_IR(inputs, depth, depth_bottleneck, stride, rate=1, w_init=None, scope=None, trainable=None):
+    with tf.variable_scope(scope, 'bottleneck_v1') as sc:
+        depth_in = utils.last_dimension(inputs.outputs.get_shape(), min_rank=4)
+        if depth == depth_in:
+            shortcut = subsample(inputs, stride, 'shortcut')
+        else:
+            shortcut = tl.layers.Conv2d(inputs, depth, filter_size=(1, 1), strides=(stride, stride), act=None,
+                                        W_init=w_init, b_init=None, name='shortcut_conv', use_cudnn_on_gpu=True)
+            shortcut.outputs = tf.layers.batch_normalization(inputs=shortcut.outputs,
+                                                             momentum=0.9,
+                                                             training=trainable,
+                                                             renorm=True,
+                                                             renorm_clipping={'rmax': 3, 'rmin': 0.3333,
+                                                                              'dmax': 5},
+                                                             renorm_momentum=0.9,
+                                                             name='shortcut_bn/BatchNorm')
+        # bottleneck layer 1
+        inputs.outputs = tf.layers.batch_normalization(inputs=inputs.outputs,
+                                                         momentum=0.9,
+                                                         training=trainable,
+                                                         renorm=True,
+                                                         renorm_clipping={'rmax': 3, 'rmin': 0.3333,
+                                                                          'dmax': 5},
+                                                         renorm_momentum=0.9,
+                                                         name='conv1_bn1')
+        residual = tl.layers.Conv2d(inputs, depth_bottleneck, filter_size=(3, 3), strides=(1, 1), act=None, b_init=None,
+                                    W_init=w_init, name='conv1', use_cudnn_on_gpu=True)
+        residual.outputs = tf.layers.batch_normalization(inputs=residual.outputs,
+                                                         momentum=0.9,
+                                                         training=trainable,
+                                                         renorm=True,
+                                                         renorm_clipping={'rmax': 3, 'rmin': 0.3333,
+                                                                          'dmax': 5},
+                                                         renorm_momentum=0.9,
+                                                         name='conv1_bn2')
+        # bottleneck prelu
+        residual = tl.layers.PReluLayer(residual)
+        # bottleneck layer 2
+        residual = conv2d_same(residual, depth, kernel_size=3, strides=stride, rate=rate, w_init=w_init, scope='conv2', trainable=trainable)
+        output = ElementwiseLayer(layer=[shortcut, residual],
+                                  combine_fn=tf.add,
+                                  name='combine_layer',
+                                  act=None)
+        return output
+
+
+def resnet(inputs, bottle_neck, blocks, w_init=None, trainable=None, scope=None):
+    with tf.variable_scope(scope):
+        # inputs = tf.subtract(inputs, 127.5)
+        # inputs = tf.multiply(inputs, 0.0078125)
+        net_inputs = tl.layers.InputLayer(inputs, name='input_layer')
+        if bottle_neck:
+            net = tl.layers.Conv2d(net_inputs, n_filter=64, filter_size=(3, 3), strides=(1, 1),
+                                   act=None, W_init=w_init, b_init=None, name='conv1', use_cudnn_on_gpu=True)
+            net.outputs = tf.layers.batch_normalization(inputs=net.outputs,
+                                                             momentum=0.9,
+                                                             training=trainable,
+                                                             renorm=True,
+                                                             renorm_clipping={'rmax': 3, 'rmin': 0.3333,
+                                                                              'dmax': 5},
+                                                             renorm_momentum=0.9,
+                                                             name='bn0')
+            net = tl.layers.PReluLayer(net, name='prelu0')
+        else:
+            raise ValueError('The standard resnet must support the bottleneck layer')
+        for block in blocks:
+            with tf.variable_scope(block.scope):
+                for i, var in enumerate(block.args):
+                    with tf.variable_scope('unit_%d' % (i+1)):
+                        net = block.unit_fn(net, depth=var['depth'], depth_bottleneck=var['depth_bottleneck'],
+                                            w_init=w_init, stride=var['stride'], rate=var['rate'], scope=None,
+                                            trainable=trainable)
+        net.outputs = tf.layers.batch_normalization(inputs=net.outputs,
+                                                    momentum=0.9,
+                                                    training=trainable,
+                                                    renorm=True,
+                                                    renorm_clipping={'rmax': 3, 'rmin': 0.3333,
+                                                                     'dmax': 5},
+                                                    renorm_momentum=0.9,
+                                                    name='E_BN1')
+        net = tl.layers.DropoutLayer(net, keep=0.4, name='E_Dropout')
+        net_shape = net.outputs.get_shape()
+        net = tl.layers.ReshapeLayer(net, shape=[-1, net_shape[1]*net_shape[2]*net_shape[3]], name='E_Reshapelayer')
+        net = tl.layers.DenseLayer(net, n_units=512, W_init=w_init, name='E_DenseLayer')
+        net.outputs = tf.layers.batch_normalization(inputs=net.outputs,
+                                                    momentum=0.9,
+                                                    training=trainable,
+                                                    renorm=True,
+                                                    renorm_clipping={'rmax': 3, 'rmin': 0.3333,
+                                                                     'dmax': 5},
+                                                    renorm_momentum=0.9,
+                                                    name='E_BN2')
+        return net
+
+
+class Block(collections.namedtuple('Block', ['scope', 'unit_fn', 'args'])):
+    """A named tuple describing a ResNet block.
+
+    Its parts are:
+      scope: The scope of the `Block`.
+      unit_fn: The ResNet unit function which takes as input a `Tensor` and
+        returns another `Tensor` with the output of the ResNet unit.
+      args: A list of length equal to the number of units in the `Block`. The list
+        contains one (depth, depth_bottleneck, stride) tuple for each unit in the
+        block to serve as argument to unit_fn.
+    """
+
+
+def resnetse_v1_block(scope, base_depth, num_units, stride, rate=1, unit_fn=None):
+  """Helper function for creating a resnet_v1 bottleneck block.
+
+  Args:
+    scope: The scope of the block.
+    base_depth: The depth of the bottleneck layer for each unit.
+    num_units: The number of units in the block.
+    stride: The stride of the block, implemented as a stride in the last unit.
+      All other units have stride=1.
+
+  Returns:
+    A resnet_v1 bottleneck block.
+  """
+  return Block(scope, unit_fn, [{
+      'depth': base_depth * 4,
+      'depth_bottleneck': base_depth,
+      'stride': stride,
+      'rate': rate
+  }] + [{
+      'depth': base_depth * 4,
+      'depth_bottleneck': base_depth,
+      'stride': 1,
+      'rate': rate
+  }] * (num_units - 1))
+
+
+def get_resnet(inputs, num_layers, type=None, w_init=None, trainable=None, sess=None):
+    if type == 'ir':
+        unit_fn = bottleneck_IR
+    # elif type == 'se_ir':
+    #     unit_fn = bottleneck_IR_SE
+    else:
+        raise ValueError('the input fn is unknown')
+
+    if num_layers == 50:
+        blocks = [
+            resnetse_v1_block('block1', base_depth=64, num_units=3, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block2', base_depth=128, num_units=4, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block3', base_depth=256, num_units=14, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block4', base_depth=512, num_units=3, stride=2, rate=1, unit_fn=unit_fn)
+        ]
+    elif num_layers == 101:
+        blocks = [
+            resnetse_v1_block('block1', base_depth=64, num_units=3, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block2', base_depth=128, num_units=13, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block3', base_depth=256, num_units=30, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block4', base_depth=512, num_units=3, stride=2, rate=1, unit_fn=unit_fn)
+        ]
+    elif num_layers == 152:
+        blocks = [
+            resnetse_v1_block('block1', base_depth=64, num_units=3, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block2', base_depth=128, num_units=8, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block3', base_depth=256, num_units=36, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block4', base_depth=512, num_units=3, stride=2, rate=1, unit_fn=unit_fn)
+        ]
+    else:
+        raise ValueError('Resnet layer %d is not supported now.' % num_layers)
+    net = resnet(inputs=inputs,
+                 bottle_neck=True,
+                 blocks=blocks,
+                 w_init=w_init,
+                 trainable=trainable,
+                 scope='resnet_v1_%d' % num_layers)
+    return net
+
+
+if __name__ == '__main__':
+        x = tf.placeholder(dtype=tf.float32, shape=[None, 112, 112, 3], name='input_place')
+        sess = tf.Session(config=npu_config_proto())
+        # w_init = tf.truncated_normal_initializer(mean=10, stddev=5e-2)
+        w_init = tf.contrib.layers.xavier_initializer(uniform=False)
+        # test resnetse
+        nets = get_resnet(x, 50, type='ir', w_init=w_init, sess=sess)
+        tl.layers.initialize_global_variables(sess)
+
+        for p in tl.layers.get_variables_with_name('W_conv2d', True, True):
+            print(p.op.name)
+        print('##############'*30)
+        with sess:
+            nets.print_params()
+
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/nets/L_Resnet_E_IR_fix_issue9.py b/TensorFlow/contrib/cv/InsightFace_TF/nets/L_Resnet_E_IR_fix_issue9.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7b67ca444157cf6e041651f2ebe796b45cf7052
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/nets/L_Resnet_E_IR_fix_issue9.py
@@ -0,0 +1,418 @@
+from npu_bridge.npu_init import *
+import tensorflow as tf
+import tensorlayer as tl
+from tensorflow.contrib.layers.python.layers import utils
+import collections
+from tensorlayer.layers import Layer, list_remove_repeat
+
+
+class ElementwiseLayer(Layer):
+    """
+    The :class:`ElementwiseLayer` class combines multiple :class:`Layer` which have the same output shapes by a given elemwise-wise operation.
+
+    Parameters
+    ----------
+    layer : a list of :class:`Layer` instances
+        The `Layer` class feeding into this layer.
+    combine_fn : a TensorFlow elemwise-merge function
+        e.g. AND is ``tf.minimum`` ;  OR is ``tf.maximum`` ; ADD is ``tf.add`` ; MUL is ``tf.multiply`` and so on.
+        See `TensorFlow Math API <https://www.tensorflow.org/versions/master/api_docs/python/math_ops.html#math>`_ .
+    name : a string or None
+        An optional name to attach to this layer.
+    """
+    def __init__(
+        self,
+        layer = [],
+        combine_fn = tf.minimum,
+        name ='elementwise_layer',
+        act = None,
+    ):
+        Layer.__init__(self, name=name)
+
+        if act:
+            print("  [TL] ElementwiseLayer %s: size:%s fn:%s, act:%s" % (
+            self.name, layer[0].outputs.get_shape(), combine_fn.__name__, act.__name__))
+        else:
+            print("  [TL] ElementwiseLayer %s: size:%s fn:%s" % (
+            self.name, layer[0].outputs.get_shape(), combine_fn.__name__))
+
+        self.outputs = layer[0].outputs
+        # print(self.outputs._shape, type(self.outputs._shape))
+        for l in layer[1:]:
+            # assert str(self.outputs.get_shape()) == str(l.outputs.get_shape()), "Hint: the input shapes should be the same. %s != %s" %  (self.outputs.get_shape() , str(l.outputs.get_shape()))
+            self.outputs = combine_fn(self.outputs, l.outputs, name=name)
+        if act:
+            self.outputs = act(self.outputs)
+        self.all_layers = list(layer[0].all_layers)
+        self.all_params = list(layer[0].all_params)
+        self.all_drop = dict(layer[0].all_drop)
+
+        for i in range(1, len(layer)):
+            self.all_layers.extend(list(layer[i].all_layers))
+            self.all_params.extend(list(layer[i].all_params))
+            self.all_drop.update(dict(layer[i].all_drop))
+
+        self.all_layers = list_remove_repeat(self.all_layers)
+        self.all_params = list_remove_repeat(self.all_params)
+
+
+class BatchNormLayer(Layer):
+    """
+    The :class:`BatchNormLayer` class is a normalization layer, see ``tf.nn.batch_normalization`` and ``tf.nn.moments``.
+
+    Batch normalization on fully-connected or convolutional maps.
+
+    ```
+        https://www.tensorflow.org/api_docs/python/tf/cond
+        If x < y, the tf.add operation will be executed and tf.square operation will not be executed.
+        Since z is needed for at least one branch of the cond, the tf.multiply operation is always executed, unconditionally.
+    ```
+
+    Parameters
+    -----------
+    layer : a :class:`Layer` instance
+        The `Layer` class feeding into this layer.
+    decay : float, default is 0.9.
+        A decay factor for ExponentialMovingAverage, use larger value for large dataset.
+    epsilon : float
+        A small float number to avoid dividing by 0.
+    act : activation function.
+    is_train : boolean
+        Whether train or inference.
+    beta_init : beta initializer
+        The initializer for initializing beta
+    gamma_init : gamma initializer
+        The initializer for initializing gamma
+    dtype : tf.float32 (default) or tf.float16
+    name : a string or None
+        An optional name to attach to this layer.
+
+    References
+    ----------
+    - `Source <https://github.com/ry/tensorflow-resnet/blob/master/resnet.py>`_
+    - `stackoverflow <http://stackoverflow.com/questions/38312668/how-does-one-do-inference-with-batch-normalization-with-tensor-flow>`_
+
+    """
+
+    def __init__(
+            self,
+            layer=None,
+            decay=0.9,
+            epsilon=2e-5,
+            act=tf.identity,
+            is_train=False,
+            fix_gamma=True,
+            beta_init=tf.zeros_initializer,
+            gamma_init=tf.random_normal_initializer(mean=1.0, stddev=0.002),  # tf.ones_initializer,
+            # dtype = tf.float32,
+            trainable=None,
+            name='batchnorm_layer',
+    ):
+        Layer.__init__(self, name=name)
+        self.inputs = layer.outputs
+        print("  [TL] BatchNormLayer %s: decay:%f epsilon:%f act:%s is_train:%s" % (self.name, decay, epsilon, act.__name__, is_train))
+        x_shape = self.inputs.get_shape()
+        params_shape = x_shape[-1:]
+
+        from tensorflow.python.training import moving_averages
+        from tensorflow.python.ops import control_flow_ops
+
+        with tf.variable_scope(name) as vs:
+            axis = list(range(len(x_shape) - 1))
+
+            ## 1. beta, gamma
+            if tf.__version__ > '0.12.1' and beta_init == tf.zeros_initializer:
+                beta_init = beta_init()
+            beta = tf.get_variable('beta', shape=params_shape, initializer=beta_init, dtype=tf.float32, trainable=is_train)  #, restore=restore)
+
+            gamma = tf.get_variable(
+                'gamma',
+                shape=params_shape,
+                initializer=gamma_init,
+                dtype=tf.float32,
+                trainable=fix_gamma,
+            )  #restore=restore)
+
+            ## 2.
+            if tf.__version__ > '0.12.1':
+                moving_mean_init = tf.zeros_initializer()
+            else:
+                moving_mean_init = tf.zeros_initializer
+            moving_mean = tf.get_variable('moving_mean', params_shape, initializer=moving_mean_init, dtype=tf.float32, trainable=False)  #   restore=restore)
+            moving_variance = tf.get_variable(
+                'moving_variance',
+                params_shape,
+                initializer=tf.constant_initializer(1.),
+                dtype=tf.float32,
+                trainable=False,
+            )  #   restore=restore)
+
+            ## 3.
+            # These ops will only be preformed when training.
+            mean, variance = tf.nn.moments(self.inputs, axis)
+            try:  # TF12
+                update_moving_mean = moving_averages.assign_moving_average(moving_mean, mean, decay, zero_debias=False)  # if zero_debias=True, has bias
+                update_moving_variance = moving_averages.assign_moving_average(
+                    moving_variance, variance, decay, zero_debias=False)  # if zero_debias=True, has bias
+                # print("TF12 moving")
+            except Exception as e:  # TF11
+                update_moving_mean = moving_averages.assign_moving_average(moving_mean, mean, decay)
+                update_moving_variance = moving_averages.assign_moving_average(moving_variance, variance, decay)
+                # print("TF11 moving")
+
+            def mean_var_with_update():
+                with tf.control_dependencies([update_moving_mean, update_moving_variance]):
+                    return tf.identity(mean), tf.identity(variance)
+            if trainable:
+                mean, var = mean_var_with_update()
+                print(mean)
+                print(var)
+                self.outputs = act(tf.nn.batch_normalization(self.inputs, mean, var, beta, gamma, epsilon))
+            else:
+                self.outputs = act(tf.nn.batch_normalization(self.inputs, moving_mean, moving_variance, beta, gamma, epsilon))
+            variables = [beta, gamma, moving_mean, moving_variance]
+        self.all_layers = list(layer.all_layers)
+        self.all_params = list(layer.all_params)
+        self.all_drop = dict(layer.all_drop)
+        self.all_layers.extend([self.outputs])
+        self.all_params.extend(variables)
+
+
+def subsample(inputs, factor, scope=None):
+    if factor == 1:
+        return inputs
+    else:
+        return tl.layers.MaxPool2d(inputs, [1, 1], strides=(factor, factor), name=scope)
+
+
+def conv2d_same(inputs, num_outputs, kernel_size, strides, rate=1, w_init=None, scope=None, trainable=None):
+    '''
+    Reference slim resnet
+    :param inputs:
+    :param num_outputs:
+    :param kernel_size:
+    :param strides:
+    :param rate:
+    :param scope:
+    :return:
+    '''
+    if strides == 1:
+        if rate == 1:
+            nets = tl.layers.Conv2d(inputs, n_filter=num_outputs, filter_size=(kernel_size, kernel_size), b_init=None,
+                                   strides=(strides, strides), W_init=w_init, act=None, padding='SAME', name=scope,
+                                    use_cudnn_on_gpu=True)
+            nets = BatchNormLayer(nets, act=tf.identity, is_train=True, trainable=trainable, name=scope+'_bn/BatchNorm')
+        else:
+            nets = tl.layers.AtrousConv2dLayer(inputs, n_filter=num_outputs, filter_size=(kernel_size, kernel_size),
+                                               rate=rate, act=None, W_init=w_init, padding='SAME', name=scope)
+            nets = BatchNormLayer(nets, act=tf.identity, is_train=True, trainable=trainable, name=scope+'_bn/BatchNorm')
+        return nets
+    else:
+        kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)
+        pad_total = kernel_size_effective - 1
+        pad_beg = pad_total // 2
+        pad_end = pad_total - pad_beg
+        inputs = tl.layers.PadLayer(inputs, [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]], name='padding_%s' % scope)
+        if rate == 1:
+            nets = tl.layers.Conv2d(inputs, n_filter=num_outputs, filter_size=(kernel_size, kernel_size), b_init=None,
+                                    strides=(strides, strides), W_init=w_init, act=None, padding='VALID', name=scope,
+                                    use_cudnn_on_gpu=True)
+            nets = BatchNormLayer(nets, act=tf.identity, is_train=True, trainable=trainable, name=scope+'_bn/BatchNorm')
+        else:
+            nets = tl.layers.AtrousConv2dLayer(inputs, n_filter=num_outputs, filter_size=(kernel_size, kernel_size), b_init=None,
+                                              rate=rate, act=None, W_init=w_init, padding='SAME', name=scope)
+            nets = BatchNormLayer(nets, act=tf.identity, is_train=True, trainable=trainable, name=scope+'_bn/BatchNorm')
+        return nets
+
+
+def bottleneck_IR(inputs, depth, depth_bottleneck, stride, rate=1, w_init=None, scope=None, trainable=None):
+    with tf.variable_scope(scope, 'bottleneck_v1') as sc:
+        depth_in = utils.last_dimension(inputs.outputs.get_shape(), min_rank=4)
+        if depth == depth_in:
+            shortcut = subsample(inputs, stride, 'shortcut')
+        else:
+            shortcut = tl.layers.Conv2d(inputs, depth, filter_size=(1, 1), strides=(stride, stride), act=None,
+                                        W_init=w_init, b_init=None, name='shortcut_conv', use_cudnn_on_gpu=True)
+            shortcut = BatchNormLayer(shortcut, act=tf.identity, is_train=True, trainable=trainable, name='shortcut_bn/BatchNorm')
+        # bottleneck layer 1
+        residual = BatchNormLayer(inputs, act=tf.identity, is_train=True, trainable=trainable, name='conv1_bn1')
+        residual = tl.layers.Conv2d(residual, depth_bottleneck, filter_size=(3, 3), strides=(1, 1), act=None, b_init=None,
+                                    W_init=w_init, name='conv1', use_cudnn_on_gpu=True)
+        residual = BatchNormLayer(residual, act=tf.identity, is_train=True, trainable=trainable, name='conv1_bn2')
+        # bottleneck prelu
+        residual = tl.layers.PReluLayer(residual)
+        # bottleneck layer 2
+        residual = conv2d_same(residual, depth, kernel_size=3, strides=stride, rate=rate, w_init=w_init, scope='conv2', trainable=trainable)
+        output = ElementwiseLayer(layer=[shortcut, residual],
+                                  combine_fn=tf.add,
+                                  name='combine_layer',
+                                  act=None)
+        return output
+
+
+def bottleneck_IR_SE(inputs, depth, depth_bottleneck, stride, rate=1, w_init=None, scope=None, trainable=None):
+    with tf.variable_scope(scope, 'bottleneck_v1') as sc:
+        depth_in = utils.last_dimension(inputs.outputs.get_shape(), min_rank=4)
+        if depth == depth_in:
+            shortcut = subsample(inputs, stride, 'shortcut')
+        else:
+            shortcut = tl.layers.Conv2d(inputs, depth, filter_size=(1, 1), strides=(stride, stride), act=None,
+                                        W_init=w_init, b_init=None, name='shortcut_conv', use_cudnn_on_gpu=True)
+            shortcut = BatchNormLayer(shortcut, act=tf.identity, is_train=True, trainable=trainable, name='shortcut_bn/BatchNorm')
+        # bottleneck layer 1
+        residual = BatchNormLayer(inputs, act=tf.identity, is_train=True, trainable=trainable, name='conv1_bn1')
+        residual = tl.layers.Conv2d(residual, depth_bottleneck, filter_size=(3, 3), strides=(1, 1), act=None, b_init=None,
+                                    W_init=w_init, name='conv1', use_cudnn_on_gpu=True)
+        residual = BatchNormLayer(residual, act=tf.identity, is_train=True, trainable=trainable, name='conv1_bn2')
+        # bottleneck prelu
+        residual = tl.layers.PReluLayer(residual)
+        # bottleneck layer 2
+        residual = conv2d_same(residual, depth, kernel_size=3, strides=stride, rate=rate, w_init=w_init, scope='conv2', trainable=trainable)
+        # squeeze
+        squeeze = tl.layers.InputLayer(tf.reduce_mean(residual.outputs, axis=[1, 2]), name='squeeze_layer')
+        # excitation
+        excitation1 = tl.layers.DenseLayer(squeeze, n_units=int(depth/16.0), act=tf.nn.relu,
+                                           W_init=w_init, name='excitation_1')
+        # excitation1 = tl.layers.PReluLayer(excitation1, name='excitation_prelu')
+        excitation2 = tl.layers.DenseLayer(excitation1, n_units=depth, act=tf.nn.sigmoid,
+                                           W_init=w_init, name='excitation_2')
+        # scale
+        scale = tl.layers.ReshapeLayer(excitation2, shape=[tf.shape(excitation2.outputs)[0], 1, 1, depth], name='excitation_reshape')
+
+        residual_se = ElementwiseLayer(layer=[residual, scale],
+                                       combine_fn=tf.multiply,
+                                       name='scale_layer',
+                                       act=None)
+
+        output = ElementwiseLayer(layer=[shortcut, residual_se],
+                                  combine_fn=tf.add,
+                                  name='combine_layer',
+                                  act=tf.nn.relu)
+        return output
+
+
+def resnet(inputs, bottle_neck, blocks, w_init=None, trainable=None, reuse=False, keep_rate=None, scope=None):
+    with tf.variable_scope(scope, reuse=reuse):
+        # inputs = tf.subtract(inputs, 127.5)
+        # inputs = tf.multiply(inputs, 0.0078125)
+        net_inputs = tl.layers.InputLayer(inputs, name='input_layer')
+        if bottle_neck:
+            net = tl.layers.Conv2d(net_inputs, n_filter=64, filter_size=(3, 3), strides=(1, 1),
+                                   act=None, W_init=w_init, b_init=None, name='conv1', use_cudnn_on_gpu=True)
+            net = BatchNormLayer(net, act=tf.identity, name='bn0', is_train=True, trainable=trainable)
+            net = tl.layers.PReluLayer(net, name='prelu0')
+        else:
+            raise ValueError('The standard resnet must support the bottleneck layer')
+        for block in blocks:
+            with tf.variable_scope(block.scope):
+                for i, var in enumerate(block.args):
+                    with tf.variable_scope('unit_%d' % (i+1)):
+                        net = block.unit_fn(net, depth=var['depth'], depth_bottleneck=var['depth_bottleneck'],
+                                            w_init=w_init, stride=var['stride'], rate=var['rate'], scope=None,
+                                            trainable=trainable)
+        net = BatchNormLayer(net, act=tf.identity, is_train=True, name='E_BN1', trainable=trainable)
+        # net = tl.layers.DropoutLayer(net, keep=0.4, name='E_Dropout')
+        net.outputs = npu_ops.dropout(net.outputs, keep_prob=keep_rate, name='E_Dropout')
+        net_shape = net.outputs.get_shape()
+        net = tl.layers.ReshapeLayer(net, shape=[-1, net_shape[1]*net_shape[2]*net_shape[3]], name='E_Reshapelayer')
+        net = tl.layers.DenseLayer(net, n_units=512, W_init=w_init, name='E_DenseLayer')
+        net = BatchNormLayer(net, act=tf.identity, is_train=True, fix_gamma=False, trainable=trainable, name='E_BN2')
+        return net
+
+
+class Block(collections.namedtuple('Block', ['scope', 'unit_fn', 'args'])):
+    """A named tuple describing a ResNet block.
+
+    Its parts are:
+      scope: The scope of the `Block`.
+      unit_fn: The ResNet unit function which takes as input a `Tensor` and
+        returns another `Tensor` with the output of the ResNet unit.
+      args: A list of length equal to the number of units in the `Block`. The list
+        contains one (depth, depth_bottleneck, stride) tuple for each unit in the
+        block to serve as argument to unit_fn.
+    """
+
+
+def resnetse_v1_block(scope, base_depth, num_units, stride, rate=1, unit_fn=None):
+  """Helper function for creating a resnet_v1 bottleneck block.
+
+  Args:
+    scope: The scope of the block.
+    base_depth: The depth of the bottleneck layer for each unit.
+    num_units: The number of units in the block.
+    stride: The stride of the block, implemented as a stride in the last unit.
+      All other units have stride=1.
+
+  Returns:
+    A resnet_v1 bottleneck block.
+  """
+  return Block(scope, unit_fn, [{
+      'depth': base_depth,
+      'depth_bottleneck': base_depth,
+      'stride': stride,
+      'rate': rate
+  }] + [{
+      'depth': base_depth,
+      'depth_bottleneck': base_depth,
+      'stride': 1,
+      'rate': rate
+  }] * (num_units - 1))
+
+
+def get_resnet(inputs, num_layers, type=None, w_init=None, trainable=None, sess=None, reuse=False, keep_rate=None):
+    if type == 'ir':
+        unit_fn = bottleneck_IR
+    elif type == 'se_ir':
+        unit_fn = bottleneck_IR_SE
+    else:
+        raise ValueError('the input fn is unknown')
+
+    if num_layers == 50:
+        blocks = [
+            resnetse_v1_block('block1', base_depth=64, num_units=3, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block2', base_depth=128, num_units=4, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block3', base_depth=256, num_units=14, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block4', base_depth=512, num_units=3, stride=2, rate=1, unit_fn=unit_fn)
+        ]
+    elif num_layers == 100:
+        blocks = [
+            resnetse_v1_block('block1', base_depth=64, num_units=3, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block2', base_depth=128, num_units=13, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block3', base_depth=256, num_units=30, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block4', base_depth=512, num_units=3, stride=2, rate=1, unit_fn=unit_fn)
+        ]
+    elif num_layers == 152:
+        blocks = [
+            resnetse_v1_block('block1', base_depth=64, num_units=3, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block2', base_depth=128, num_units=8, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block3', base_depth=256, num_units=36, stride=2, rate=1, unit_fn=unit_fn),
+            resnetse_v1_block('block4', base_depth=512, num_units=3, stride=2, rate=1, unit_fn=unit_fn)
+        ]
+    else:
+        raise ValueError('Resnet layer %d is not supported now.' % num_layers)
+    net = resnet(inputs=inputs,
+                 bottle_neck=True,
+                 blocks=blocks,
+                 w_init=w_init,
+                 trainable=trainable,
+                 reuse=reuse,
+                 keep_rate=keep_rate,
+                 scope='resnet_v1_%d' % num_layers)
+    return net
+
+
+if __name__ == '__main__':
+        x = tf.placeholder(dtype=tf.float32, shape=[None, 112, 112, 3], name='input_place')
+        sess = tf.Session(config=npu_config_proto())
+        # w_init = tf.truncated_normal_initializer(mean=10, stddev=5e-2)
+        w_init = tf.contrib.layers.xavier_initializer(uniform=False)
+        # test resnetse
+        nets = get_resnet(x, 50, type='ir', w_init=w_init, sess=sess)
+        tl.layers.initialize_global_variables(sess)
+
+        for p in tl.layers.get_variables_with_name('W_conv2d', True, True):
+            print(p.op.name)
+        print('##############'*30)
+        with sess:
+            nets.print_params()
+
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/nets/__init__.py b/TensorFlow/contrib/cv/InsightFace_TF/nets/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/nets/imagenet_classes.py b/TensorFlow/contrib/cv/InsightFace_TF/nets/imagenet_classes.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7d7ab7ec1b2f691191f20477a12386cf9602212
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/nets/imagenet_classes.py
@@ -0,0 +1,1000 @@
+class_names = '''tench, Tinca tinca
+goldfish, Carassius auratus
+great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias
+tiger shark, Galeocerdo cuvieri
+hammerhead, hammerhead shark
+electric ray, crampfish, numbfish, torpedo
+stingray
+cock
+hen
+ostrich, Struthio camelus
+brambling, Fringilla montifringilla
+goldfinch, Carduelis carduelis
+house finch, linnet, Carpodacus mexicanus
+junco, snowbird
+indigo bunting, indigo finch, indigo bird, Passerina cyanea
+robin, American robin, Turdus migratorius
+bulbul
+jay
+magpie
+chickadee
+water ouzel, dipper
+kite
+bald eagle, American eagle, Haliaeetus leucocephalus
+vulture
+great grey owl, great gray owl, Strix nebulosa
+European fire salamander, Salamandra salamandra
+common newt, Triturus vulgaris
+eft
+spotted salamander, Ambystoma maculatum
+axolotl, mud puppy, Ambystoma mexicanum
+bullfrog, Rana catesbeiana
+tree frog, tree-frog
+tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui
+loggerhead, loggerhead turtle, Caretta caretta
+leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea
+mud turtle
+terrapin
+box turtle, box tortoise
+banded gecko
+common iguana, iguana, Iguana iguana
+American chameleon, anole, Anolis carolinensis
+whiptail, whiptail lizard
+agama
+frilled lizard, Chlamydosaurus kingi
+alligator lizard
+Gila monster, Heloderma suspectum
+green lizard, Lacerta viridis
+African chameleon, Chamaeleo chamaeleon
+Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis
+African crocodile, Nile crocodile, Crocodylus niloticus
+American alligator, Alligator mississipiensis
+triceratops
+thunder snake, worm snake, Carphophis amoenus
+ringneck snake, ring-necked snake, ring snake
+hognose snake, puff adder, sand viper
+green snake, grass snake
+king snake, kingsnake
+garter snake, grass snake
+water snake
+vine snake
+night snake, Hypsiglena torquata
+boa constrictor, Constrictor constrictor
+rock python, rock snake, Python sebae
+Indian cobra, Naja naja
+green mamba
+sea snake
+horned viper, cerastes, sand viper, horned asp, Cerastes cornutus
+diamondback, diamondback rattlesnake, Crotalus adamanteus
+sidewinder, horned rattlesnake, Crotalus cerastes
+trilobite
+harvestman, daddy longlegs, Phalangium opilio
+scorpion
+black and gold garden spider, Argiope aurantia
+barn spider, Araneus cavaticus
+garden spider, Aranea diademata
+black widow, Latrodectus mactans
+tarantula
+wolf spider, hunting spider
+tick
+centipede
+black grouse
+ptarmigan
+ruffed grouse, partridge, Bonasa umbellus
+prairie chicken, prairie grouse, prairie fowl
+peacock
+quail
+partridge
+African grey, African gray, Psittacus erithacus
+macaw
+sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita
+lorikeet
+coucal
+bee eater
+hornbill
+hummingbird
+jacamar
+toucan
+drake
+red-breasted merganser, Mergus serrator
+goose
+black swan, Cygnus atratus
+tusker
+echidna, spiny anteater, anteater
+platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus
+wallaby, brush kangaroo
+koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus
+wombat
+jellyfish
+sea anemone, anemone
+brain coral
+flatworm, platyhelminth
+nematode, nematode worm, roundworm
+conch
+snail
+slug
+sea slug, nudibranch
+chiton, coat-of-mail shell, sea cradle, polyplacophore
+chambered nautilus, pearly nautilus, nautilus
+Dungeness crab, Cancer magister
+rock crab, Cancer irroratus
+fiddler crab
+king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica
+American lobster, Northern lobster, Maine lobster, Homarus americanus
+spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish
+crayfish, crawfish, crawdad, crawdaddy
+hermit crab
+isopod
+white stork, Ciconia ciconia
+black stork, Ciconia nigra
+spoonbill
+flamingo
+little blue heron, Egretta caerulea
+American egret, great white heron, Egretta albus
+bittern
+crane
+limpkin, Aramus pictus
+European gallinule, Porphyrio porphyrio
+American coot, marsh hen, mud hen, water hen, Fulica americana
+bustard
+ruddy turnstone, Arenaria interpres
+red-backed sandpiper, dunlin, Erolia alpina
+redshank, Tringa totanus
+dowitcher
+oystercatcher, oyster catcher
+pelican
+king penguin, Aptenodytes patagonica
+albatross, mollymawk
+grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus
+killer whale, killer, orca, grampus, sea wolf, Orcinus orca
+dugong, Dugong dugon
+sea lion
+Chihuahua
+Japanese spaniel
+Maltese dog, Maltese terrier, Maltese
+Pekinese, Pekingese, Peke
+Shih-Tzu
+Blenheim spaniel
+papillon
+toy terrier
+Rhodesian ridgeback
+Afghan hound, Afghan
+basset, basset hound
+beagle
+bloodhound, sleuthhound
+bluetick
+black-and-tan coonhound
+Walker hound, Walker foxhound
+English foxhound
+redbone
+borzoi, Russian wolfhound
+Irish wolfhound
+Italian greyhound
+whippet
+Ibizan hound, Ibizan Podenco
+Norwegian elkhound, elkhound
+otterhound, otter hound
+Saluki, gazelle hound
+Scottish deerhound, deerhound
+Weimaraner
+Staffordshire bullterrier, Staffordshire bull terrier
+American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier
+Bedlington terrier
+Border terrier
+Kerry blue terrier
+Irish terrier
+Norfolk terrier
+Norwich terrier
+Yorkshire terrier
+wire-haired fox terrier
+Lakeland terrier
+Sealyham terrier, Sealyham
+Airedale, Airedale terrier
+cairn, cairn terrier
+Australian terrier
+Dandie Dinmont, Dandie Dinmont terrier
+Boston bull, Boston terrier
+miniature schnauzer
+giant schnauzer
+standard schnauzer
+Scotch terrier, Scottish terrier, Scottie
+Tibetan terrier, chrysanthemum dog
+silky terrier, Sydney silky
+soft-coated wheaten terrier
+West Highland white terrier
+Lhasa, Lhasa apso
+flat-coated retriever
+curly-coated retriever
+golden retriever
+Labrador retriever
+Chesapeake Bay retriever
+German short-haired pointer
+vizsla, Hungarian pointer
+English setter
+Irish setter, red setter
+Gordon setter
+Brittany spaniel
+clumber, clumber spaniel
+English springer, English springer spaniel
+Welsh springer spaniel
+cocker spaniel, English cocker spaniel, cocker
+Sussex spaniel
+Irish water spaniel
+kuvasz
+schipperke
+groenendael
+malinois
+briard
+kelpie
+komondor
+Old English sheepdog, bobtail
+Shetland sheepdog, Shetland sheep dog, Shetland
+collie
+Border collie
+Bouvier des Flandres, Bouviers des Flandres
+Rottweiler
+German shepherd, German shepherd dog, German police dog, alsatian
+Doberman, Doberman pinscher
+miniature pinscher
+Greater Swiss Mountain dog
+Bernese mountain dog
+Appenzeller
+EntleBucher
+boxer
+bull mastiff
+Tibetan mastiff
+French bulldog
+Great Dane
+Saint Bernard, St Bernard
+Eskimo dog, husky
+malamute, malemute, Alaskan malamute
+Siberian husky
+dalmatian, coach dog, carriage dog
+affenpinscher, monkey pinscher, monkey dog
+basenji
+pug, pug-dog
+Leonberg
+Newfoundland, Newfoundland dog
+Great Pyrenees
+Samoyed, Samoyede
+Pomeranian
+chow, chow chow
+keeshond
+Brabancon griffon
+Pembroke, Pembroke Welsh corgi
+Cardigan, Cardigan Welsh corgi
+toy poodle
+miniature poodle
+standard poodle
+Mexican hairless
+timber wolf, grey wolf, gray wolf, Canis lupus
+white wolf, Arctic wolf, Canis lupus tundrarum
+red wolf, maned wolf, Canis rufus, Canis niger
+coyote, prairie wolf, brush wolf, Canis latrans
+dingo, warrigal, warragal, Canis dingo
+dhole, Cuon alpinus
+African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus
+hyena, hyaena
+red fox, Vulpes vulpes
+kit fox, Vulpes macrotis
+Arctic fox, white fox, Alopex lagopus
+grey fox, gray fox, Urocyon cinereoargenteus
+tabby, tabby cat
+tiger cat
+Persian cat
+Siamese cat, Siamese
+Egyptian cat
+cougar, puma, catamount, mountain lion, painter, panther, Felis concolor
+lynx, catamount
+leopard, Panthera pardus
+snow leopard, ounce, Panthera uncia
+jaguar, panther, Panthera onca, Felis onca
+lion, king of beasts, Panthera leo
+tiger, Panthera tigris
+cheetah, chetah, Acinonyx jubatus
+brown bear, bruin, Ursus arctos
+American black bear, black bear, Ursus americanus, Euarctos americanus
+ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus
+sloth bear, Melursus ursinus, Ursus ursinus
+mongoose
+meerkat, mierkat
+tiger beetle
+ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle
+ground beetle, carabid beetle
+long-horned beetle, longicorn, longicorn beetle
+leaf beetle, chrysomelid
+dung beetle
+rhinoceros beetle
+weevil
+fly
+bee
+ant, emmet, pismire
+grasshopper, hopper
+cricket
+walking stick, walkingstick, stick insect
+cockroach, roach
+mantis, mantid
+cicada, cicala
+leafhopper
+lacewing, lacewing fly
+dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk
+damselfly
+admiral
+ringlet, ringlet butterfly
+monarch, monarch butterfly, milkweed butterfly, Danaus plexippus
+cabbage butterfly
+sulphur butterfly, sulfur butterfly
+lycaenid, lycaenid butterfly
+starfish, sea star
+sea urchin
+sea cucumber, holothurian
+wood rabbit, cottontail, cottontail rabbit
+hare
+Angora, Angora rabbit
+hamster
+porcupine, hedgehog
+fox squirrel, eastern fox squirrel, Sciurus niger
+marmot
+beaver
+guinea pig, Cavia cobaya
+sorrel
+zebra
+hog, pig, grunter, squealer, Sus scrofa
+wild boar, boar, Sus scrofa
+warthog
+hippopotamus, hippo, river horse, Hippopotamus amphibius
+ox
+water buffalo, water ox, Asiatic buffalo, Bubalus bubalis
+bison
+ram, tup
+bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis
+ibex, Capra ibex
+hartebeest
+impala, Aepyceros melampus
+gazelle
+Arabian camel, dromedary, Camelus dromedarius
+llama
+weasel
+mink
+polecat, fitch, foulmart, foumart, Mustela putorius
+black-footed ferret, ferret, Mustela nigripes
+otter
+skunk, polecat, wood pussy
+badger
+armadillo
+three-toed sloth, ai, Bradypus tridactylus
+orangutan, orang, orangutang, Pongo pygmaeus
+gorilla, Gorilla gorilla
+chimpanzee, chimp, Pan troglodytes
+gibbon, Hylobates lar
+siamang, Hylobates syndactylus, Symphalangus syndactylus
+guenon, guenon monkey
+patas, hussar monkey, Erythrocebus patas
+baboon
+macaque
+langur
+colobus, colobus monkey
+proboscis monkey, Nasalis larvatus
+marmoset
+capuchin, ringtail, Cebus capucinus
+howler monkey, howler
+titi, titi monkey
+spider monkey, Ateles geoffroyi
+squirrel monkey, Saimiri sciureus
+Madagascar cat, ring-tailed lemur, Lemur catta
+indri, indris, Indri indri, Indri brevicaudatus
+Indian elephant, Elephas maximus
+African elephant, Loxodonta africana
+lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens
+giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca
+barracouta, snoek
+eel
+coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch
+rock beauty, Holocanthus tricolor
+anemone fish
+sturgeon
+gar, garfish, garpike, billfish, Lepisosteus osseus
+lionfish
+puffer, pufferfish, blowfish, globefish
+abacus
+abaya
+academic gown, academic robe, judge's robe
+accordion, piano accordion, squeeze box
+acoustic guitar
+aircraft carrier, carrier, flattop, attack aircraft carrier
+airliner
+airship, dirigible
+altar
+ambulance
+amphibian, amphibious vehicle
+analog clock
+apiary, bee house
+apron
+ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin
+assault rifle, assault gun
+backpack, back pack, knapsack, packsack, rucksack, haversack
+bakery, bakeshop, bakehouse
+balance beam, beam
+balloon
+ballpoint, ballpoint pen, ballpen, Biro
+Band Aid
+banjo
+bannister, banister, balustrade, balusters, handrail
+barbell
+barber chair
+barbershop
+barn
+barometer
+barrel, cask
+barrow, garden cart, lawn cart, wheelbarrow
+baseball
+basketball
+bassinet
+bassoon
+bathing cap, swimming cap
+bath towel
+bathtub, bathing tub, bath, tub
+beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
+beacon, lighthouse, beacon light, pharos
+beaker
+bearskin, busby, shako
+beer bottle
+beer glass
+bell cote, bell cot
+bib
+bicycle-built-for-two, tandem bicycle, tandem
+bikini, two-piece
+binder, ring-binder
+binoculars, field glasses, opera glasses
+birdhouse
+boathouse
+bobsled, bobsleigh, bob
+bolo tie, bolo, bola tie, bola
+bonnet, poke bonnet
+bookcase
+bookshop, bookstore, bookstall
+bottlecap
+bow
+bow tie, bow-tie, bowtie
+brass, memorial tablet, plaque
+brassiere, bra, bandeau
+breakwater, groin, groyne, mole, bulwark, seawall, jetty
+breastplate, aegis, egis
+broom
+bucket, pail
+buckle
+bulletproof vest
+bullet train, bullet
+butcher shop, meat market
+cab, hack, taxi, taxicab
+caldron, cauldron
+candle, taper, wax light
+cannon
+canoe
+can opener, tin opener
+cardigan
+car mirror
+carousel, carrousel, merry-go-round, roundabout, whirligig
+carpenter's kit, tool kit
+carton
+car wheel
+cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM
+cassette
+cassette player
+castle
+catamaran
+CD player
+cello, violoncello
+cellular telephone, cellular phone, cellphone, cell, mobile phone
+chain
+chainlink fence
+chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour
+chain saw, chainsaw
+chest
+chiffonier, commode
+chime, bell, gong
+china cabinet, china closet
+Christmas stocking
+church, church building
+cinema, movie theater, movie theatre, movie house, picture palace
+cleaver, meat cleaver, chopper
+cliff dwelling
+cloak
+clog, geta, patten, sabot
+cocktail shaker
+coffee mug
+coffeepot
+coil, spiral, volute, whorl, helix
+combination lock
+computer keyboard, keypad
+confectionery, confectionary, candy store
+container ship, containership, container vessel
+convertible
+corkscrew, bottle screw
+cornet, horn, trumpet, trump
+cowboy boot
+cowboy hat, ten-gallon hat
+cradle
+crane
+crash helmet
+crate
+crib, cot
+Crock Pot
+croquet ball
+crutch
+cuirass
+dam, dike, dyke
+desk
+desktop computer
+dial telephone, dial phone
+diaper, nappy, napkin
+digital clock
+digital watch
+dining table, board
+dishrag, dishcloth
+dishwasher, dish washer, dishwashing machine
+disk brake, disc brake
+dock, dockage, docking facility
+dogsled, dog sled, dog sleigh
+dome
+doormat, welcome mat
+drilling platform, offshore rig
+drum, membranophone, tympan
+drumstick
+dumbbell
+Dutch oven
+electric fan, blower
+electric guitar
+electric locomotive
+entertainment center
+envelope
+espresso maker
+face powder
+feather boa, boa
+file, file cabinet, filing cabinet
+fireboat
+fire engine, fire truck
+fire screen, fireguard
+flagpole, flagstaff
+flute, transverse flute
+folding chair
+football helmet
+forklift
+fountain
+fountain pen
+four-poster
+freight car
+French horn, horn
+frying pan, frypan, skillet
+fur coat
+garbage truck, dustcart
+gasmask, respirator, gas helmet
+gas pump, gasoline pump, petrol pump, island dispenser
+goblet
+go-kart
+golf ball
+golfcart, golf cart
+gondola
+gong, tam-tam
+gown
+grand piano, grand
+greenhouse, nursery, glasshouse
+grille, radiator grille
+grocery store, grocery, food market, market
+guillotine
+hair slide
+hair spray
+half track
+hammer
+hamper
+hand blower, blow dryer, blow drier, hair dryer, hair drier
+hand-held computer, hand-held microcomputer
+handkerchief, hankie, hanky, hankey
+hard disc, hard disk, fixed disk
+harmonica, mouth organ, harp, mouth harp
+harp
+harvester, reaper
+hatchet
+holster
+home theater, home theatre
+honeycomb
+hook, claw
+hoopskirt, crinoline
+horizontal bar, high bar
+horse cart, horse-cart
+hourglass
+iPod
+iron, smoothing iron
+jack-o'-lantern
+jean, blue jean, denim
+jeep, landrover
+jersey, T-shirt, tee shirt
+jigsaw puzzle
+jinrikisha, ricksha, rickshaw
+joystick
+kimono
+knee pad
+knot
+lab coat, laboratory coat
+ladle
+lampshade, lamp shade
+laptop, laptop computer
+lawn mower, mower
+lens cap, lens cover
+letter opener, paper knife, paperknife
+library
+lifeboat
+lighter, light, igniter, ignitor
+limousine, limo
+liner, ocean liner
+lipstick, lip rouge
+Loafer
+lotion
+loudspeaker, speaker, speaker unit, loudspeaker system, speaker system
+loupe, jeweler's loupe
+lumbermill, sawmill
+magnetic compass
+mailbag, postbag
+mailbox, letter box
+maillot
+maillot, tank suit
+manhole cover
+maraca
+marimba, xylophone
+mask
+matchstick
+maypole
+maze, labyrinth
+measuring cup
+medicine chest, medicine cabinet
+megalith, megalithic structure
+microphone, mike
+microwave, microwave oven
+military uniform
+milk can
+minibus
+miniskirt, mini
+minivan
+missile
+mitten
+mixing bowl
+mobile home, manufactured home
+Model T
+modem
+monastery
+monitor
+moped
+mortar
+mortarboard
+mosque
+mosquito net
+motor scooter, scooter
+mountain bike, all-terrain bike, off-roader
+mountain tent
+mouse, computer mouse
+mousetrap
+moving van
+muzzle
+nail
+neck brace
+necklace
+nipple
+notebook, notebook computer
+obelisk
+oboe, hautboy, hautbois
+ocarina, sweet potato
+odometer, hodometer, mileometer, milometer
+oil filter
+organ, pipe organ
+oscilloscope, scope, cathode-ray oscilloscope, CRO
+overskirt
+oxcart
+oxygen mask
+packet
+paddle, boat paddle
+paddlewheel, paddle wheel
+padlock
+paintbrush
+pajama, pyjama, pj's, jammies
+palace
+panpipe, pandean pipe, syrinx
+paper towel
+parachute, chute
+parallel bars, bars
+park bench
+parking meter
+passenger car, coach, carriage
+patio, terrace
+pay-phone, pay-station
+pedestal, plinth, footstall
+pencil box, pencil case
+pencil sharpener
+perfume, essence
+Petri dish
+photocopier
+pick, plectrum, plectron
+pickelhaube
+picket fence, paling
+pickup, pickup truck
+pier
+piggy bank, penny bank
+pill bottle
+pillow
+ping-pong ball
+pinwheel
+pirate, pirate ship
+pitcher, ewer
+plane, carpenter's plane, woodworking plane
+planetarium
+plastic bag
+plate rack
+plow, plough
+plunger, plumber's helper
+Polaroid camera, Polaroid Land camera
+pole
+police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria
+poncho
+pool table, billiard table, snooker table
+pop bottle, soda bottle
+pot, flowerpot
+potter's wheel
+power drill
+prayer rug, prayer mat
+printer
+prison, prison house
+projectile, missile
+projector
+puck, hockey puck
+punching bag, punch bag, punching ball, punchball
+purse
+quill, quill pen
+quilt, comforter, comfort, puff
+racer, race car, racing car
+racket, racquet
+radiator
+radio, wireless
+radio telescope, radio reflector
+rain barrel
+recreational vehicle, RV, R.V.
+reel
+reflex camera
+refrigerator, icebox
+remote control, remote
+restaurant, eating house, eating place, eatery
+revolver, six-gun, six-shooter
+rifle
+rocking chair, rocker
+rotisserie
+rubber eraser, rubber, pencil eraser
+rugby ball
+rule, ruler
+running shoe
+safe
+safety pin
+saltshaker, salt shaker
+sandal
+sarong
+sax, saxophone
+scabbard
+scale, weighing machine
+school bus
+schooner
+scoreboard
+screen, CRT screen
+screw
+screwdriver
+seat belt, seatbelt
+sewing machine
+shield, buckler
+shoe shop, shoe-shop, shoe store
+shoji
+shopping basket
+shopping cart
+shovel
+shower cap
+shower curtain
+ski
+ski mask
+sleeping bag
+slide rule, slipstick
+sliding door
+slot, one-armed bandit
+snorkel
+snowmobile
+snowplow, snowplough
+soap dispenser
+soccer ball
+sock
+solar dish, solar collector, solar furnace
+sombrero
+soup bowl
+space bar
+space heater
+space shuttle
+spatula
+speedboat
+spider web, spider's web
+spindle
+sports car, sport car
+spotlight, spot
+stage
+steam locomotive
+steel arch bridge
+steel drum
+stethoscope
+stole
+stone wall
+stopwatch, stop watch
+stove
+strainer
+streetcar, tram, tramcar, trolley, trolley car
+stretcher
+studio couch, day bed
+stupa, tope
+submarine, pigboat, sub, U-boat
+suit, suit of clothes
+sundial
+sunglass
+sunglasses, dark glasses, shades
+sunscreen, sunblock, sun blocker
+suspension bridge
+swab, swob, mop
+sweatshirt
+swimming trunks, bathing trunks
+swing
+switch, electric switch, electrical switch
+syringe
+table lamp
+tank, army tank, armored combat vehicle, armoured combat vehicle
+tape player
+teapot
+teddy, teddy bear
+television, television system
+tennis ball
+thatch, thatched roof
+theater curtain, theatre curtain
+thimble
+thresher, thrasher, threshing machine
+throne
+tile roof
+toaster
+tobacco shop, tobacconist shop, tobacconist
+toilet seat
+torch
+totem pole
+tow truck, tow car, wrecker
+toyshop
+tractor
+trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi
+tray
+trench coat
+tricycle, trike, velocipede
+trimaran
+tripod
+triumphal arch
+trolleybus, trolley coach, trackless trolley
+trombone
+tub, vat
+turnstile
+typewriter keyboard
+umbrella
+unicycle, monocycle
+upright, upright piano
+vacuum, vacuum cleaner
+vase
+vault
+velvet
+vending machine
+vestment
+viaduct
+violin, fiddle
+volleyball
+waffle iron
+wall clock
+wallet, billfold, notecase, pocketbook
+wardrobe, closet, press
+warplane, military plane
+washbasin, handbasin, washbowl, lavabo, wash-hand basin
+washer, automatic washer, washing machine
+water bottle
+water jug
+water tower
+whiskey jug
+whistle
+wig
+window screen
+window shade
+Windsor tie
+wine bottle
+wing
+wok
+wooden spoon
+wool, woolen, woollen
+worm fence, snake fence, snake-rail fence, Virginia fence
+wreck
+yawl
+yurt
+web site, website, internet site, site
+comic book
+crossword puzzle, crossword
+street sign
+traffic light, traffic signal, stoplight
+book jacket, dust cover, dust jacket, dust wrapper
+menu
+plate
+guacamole
+consomme
+hot pot, hotpot
+trifle
+ice cream, icecream
+ice lolly, lolly, lollipop, popsicle
+French loaf
+bagel, beigel
+pretzel
+cheeseburger
+hotdog, hot dog, red hot
+mashed potato
+head cabbage
+broccoli
+cauliflower
+zucchini, courgette
+spaghetti squash
+acorn squash
+butternut squash
+cucumber, cuke
+artichoke, globe artichoke
+bell pepper
+cardoon
+mushroom
+Granny Smith
+strawberry
+orange
+lemon
+fig
+pineapple, ananas
+banana
+jackfruit, jak, jack
+custard apple
+pomegranate
+hay
+carbonara
+chocolate sauce, chocolate syrup
+dough
+meat loaf, meatloaf
+pizza, pizza pie
+potpie
+burrito
+red wine
+espresso
+cup
+eggnog
+alp
+bubble
+cliff, drop, drop-off
+coral reef
+geyser
+lakeside, lakeshore
+promontory, headland, head, foreland
+sandbar, sand bar
+seashore, coast, seacoast, sea-coast
+valley, vale
+volcano
+ballplayer, baseball player
+groom, bridegroom
+scuba diver
+rapeseed
+daisy
+yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum
+corn
+acorn
+hip, rose hip, rosehip
+buckeye, horse chestnut, conker
+coral fungus
+agaric
+gyromitra
+stinkhorn, carrion fungus
+earthstar
+hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa
+bolete
+ear, spike, capitulum
+toilet tissue, toilet paper, bathroom tissue'''.split("\n")
\ No newline at end of file
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/nets/nets_utils.py b/TensorFlow/contrib/cv/InsightFace_TF/nets/nets_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..16b0bf54c8edc6769a9baed666b89b375da5c11f
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/nets/nets_utils.py
@@ -0,0 +1,31 @@
+from npu_bridge.npu_init import *
+from tensorflow.python import pywrap_tensorflow
+import collections
+import numpy as np
+
+
+var_stat = collections.namedtuple('stats', ['mean', 'median', 'std'])
+
+
+def get_variables_in_checkpoint_file(file_name):
+    try:
+        reader = pywrap_tensorflow.NewCheckpointReader(file_name)
+        var_to_shape_map = reader.get_variable_to_shape_map()
+        return var_to_shape_map
+    except Exception as e:  # pylint: disable=broad-except
+        print(str(e))
+        if "corrupted compressed block contents" in str(e):
+            print("It's likely that your checkpoint file has been compressed "
+                  "with SNAPPY.")
+
+
+def get_tensor_static_val(file_name, all_tensors, all_tensor_names):
+    reader = pywrap_tensorflow.NewCheckpointReader(file_name)
+    vars_dict = {}
+    if all_tensors or all_tensor_names:
+      var_to_shape_map = reader.get_variable_to_shape_map()
+      for key in sorted(var_to_shape_map):
+        if all_tensors:
+          vars_dict[key] = var_stat(np.mean(reader.get_tensor(key)), np.median(reader.get_tensor(key)),
+                                    np.std(reader.get_tensor(key)))
+    return vars_dict
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/nets/networks.py b/TensorFlow/contrib/cv/InsightFace_TF/nets/networks.py
new file mode 100644
index 0000000000000000000000000000000000000000..e345b80c190d8f7fb3a0759c2561142dd6cedf31
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/nets/networks.py
@@ -0,0 +1,20 @@
+from npu_bridge.npu_init import *
+import tensorflow as tf
+from vgg16 import get_vgg16
+from vgg19 import get_vgg19
+
+
+def get_model(inputs, sess, type, pretrained=True):
+    if type == 'vgg16':
+        return get_vgg16(inputs, sess, pretrained)
+    elif type == 'vgg19':
+        return get_vgg19(inputs, sess, pretrained)
+
+
+if __name__ == '__main__':
+    tfconfig = tf.ConfigProto(allow_soft_placement=True)
+    x = tf.placeholder(dtype=tf.float32, shape=[None, 224, 224, 3], name='inpust')
+    with tf.Session(config=npu_config_proto(config_proto=tfconfig)) as sess:
+        network = get_model(x, sess, type='vgg19', pretrained=True)
+        network.print_params()
+        network.print_layers()
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/nets/readme.md b/TensorFlow/contrib/cv/InsightFace_TF/nets/readme.md
new file mode 100644
index 0000000000000000000000000000000000000000..614cc24f3bd22216287d40d1633330a4633671c4
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/nets/readme.md
@@ -0,0 +1,5 @@
+1. `vgg16.py`
+   the vgg16 model, input is an image that with shape 224*224 and mean substract. The input should be first resized to 224*224, and then substract the mean. The channels of the input image is `RGB`.
+2. 'vgg19.py'
+   the vgg19 model. The input should be first normalized to [0, 1], then resized to 224*224 and then do substract to mean, the channel of the input should be `RGB`.
+3. 'resnet'
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/nets/resnet.py b/TensorFlow/contrib/cv/InsightFace_TF/nets/resnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..71d23518587c11dd8c6c49dc9998a957e4ec40a4
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/nets/resnet.py
@@ -0,0 +1,502 @@
+from npu_bridge.npu_init import *
+import tensorflow as tf
+import tensorlayer as tl
+from tensorflow.contrib.layers.python.layers import utils
+import collections
+from tensorlayer.layers import Layer, list_remove_repeat
+from nets_utils import get_variables_in_checkpoint_file
+
+
+class ElementwiseLayer(Layer):
+    """
+    The :class:`ElementwiseLayer` class combines multiple :class:`Layer` which have the same output shapes by a given elemwise-wise operation.
+
+    Parameters
+    ----------
+    layer : a list of :class:`Layer` instances
+        The `Layer` class feeding into this layer.
+    combine_fn : a TensorFlow elemwise-merge function
+        e.g. AND is ``tf.minimum`` ;  OR is ``tf.maximum`` ; ADD is ``tf.add`` ; MUL is ``tf.multiply`` and so on.
+        See `TensorFlow Math API <https://www.tensorflow.org/versions/master/api_docs/python/math_ops.html#math>`_ .
+    name : a string or None
+        An optional name to attach to this layer.
+    """
+    def __init__(
+        self,
+        layer = [],
+        combine_fn = tf.minimum,
+        name ='elementwise_layer',
+        act = None,
+    ):
+        Layer.__init__(self, name=name)
+
+        if act:
+            print("  [TL] ElementwiseLayer %s: size:%s fn:%s, act:%s" % (
+            self.name, layer[0].outputs.get_shape(), combine_fn.__name__, act.__name__))
+        else:
+            print("  [TL] ElementwiseLayer %s: size:%s fn:%s" % (
+            self.name, layer[0].outputs.get_shape(), combine_fn.__name__))
+
+        self.outputs = layer[0].outputs
+        # print(self.outputs._shape, type(self.outputs._shape))
+        for l in layer[1:]:
+            # assert str(self.outputs.get_shape()) == str(l.outputs.get_shape()), "Hint: the input shapes should be the same. %s != %s" %  (self.outputs.get_shape() , str(l.outputs.get_shape()))
+            self.outputs = combine_fn(self.outputs, l.outputs, name=name)
+        if act:
+            self.outputs = act(self.outputs)
+        self.all_layers = list(layer[0].all_layers)
+        self.all_params = list(layer[0].all_params)
+        self.all_drop = dict(layer[0].all_drop)
+
+        for i in range(1, len(layer)):
+            self.all_layers.extend(list(layer[i].all_layers))
+            self.all_params.extend(list(layer[i].all_params))
+            self.all_drop.update(dict(layer[i].all_drop))
+
+        self.all_layers = list_remove_repeat(self.all_layers)
+        self.all_params = list_remove_repeat(self.all_params)
+
+
+def subsample(inputs, factor, scope=None):
+    if factor == 1:
+        return inputs
+    else:
+        return tl.layers.MaxPool2d(inputs, [1, 1], strides=(factor, factor), name=scope)
+
+
+def conv2d_same(inputs, num_outputs, kernel_size, strides, rate=1, scope=None):
+    '''
+    Reference slim resnet
+    :param inputs:
+    :param num_outputs:
+    :param kernel_size:
+    :param strides:
+    :param rate:
+    :param scope:
+    :return:
+    '''
+    if strides == 1:
+        if rate == 1:
+            nets = tl.layers.Conv2d(inputs, n_filter=num_outputs, filter_size=(kernel_size, kernel_size), b_init=None,
+                                   strides=(strides, strides), act=None, padding='SAME', name=scope)
+            nets = tl.layers.BatchNormLayer(nets, act=tf.nn.relu, is_train=True, name=scope+'_bn/BatchNorm')
+        else:
+            nets = tl.layers.AtrousConv2dLayer(inputs, n_filter=num_outputs, filter_size=(kernel_size, kernel_size),
+                                               rate=rate, act=None, padding='SAME', name=scope)
+            nets = tl.layers.BatchNormLayer(nets, act=tf.nn.relu, is_train=True, name=scope+'_bn/BatchNorm')
+        return nets
+    else:
+        kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)
+        pad_total = kernel_size_effective - 1
+        pad_beg = pad_total // 2
+        pad_end = pad_total - pad_beg
+        inputs = tl.layers.PadLayer(inputs, [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]], name='padding_%s' % scope)
+        if rate == 1:
+            nets = tl.layers.Conv2d(inputs, n_filter=num_outputs, filter_size=(kernel_size, kernel_size), b_init=None,
+                                    strides=(strides, strides), act=None, padding='VALID', name=scope)
+            nets = tl.layers.BatchNormLayer(nets, act=tf.nn.relu, is_train=True, name=scope+'_bn/BatchNorm')
+        else:
+            nets = tl.layers.AtrousConv2dLayer(inputs, n_filter=num_outputs, filter_size=(kernel_size, kernel_size), b_init=None,
+                                              rate=rate, act=None, padding='SAME', name=scope)
+            nets = tl.layers.BatchNormLayer(nets, act=tf.nn.relu, is_train=True, name=scope+'_bn/BatchNorm')
+        return nets
+
+
+def bottleneck(inputs, depth, depth_bottleneck, stride, rate=1, scope=None):
+    with tf.variable_scope(scope, 'bottleneck_v1') as sc:
+        depth_in = utils.last_dimension(inputs.outputs.get_shape(), min_rank=4)
+        if depth == depth_in:
+            shortcut = subsample(inputs, stride, 'shortcut')
+        else:
+            shortcut = tl.layers.Conv2d(inputs, depth, filter_size=(1, 1), strides=(stride, stride), act=None,
+                                        b_init=None, name='shortcut_conv')
+            shortcut = tl.layers.BatchNormLayer(shortcut, act=tf.identity, is_train=True, name='shortcut_bn/BatchNorm')
+        # bottleneck layer 1
+        residual = tl.layers.Conv2d(inputs, depth_bottleneck, filter_size=(1, 1), strides=(1, 1), act=None, b_init=None,
+                                    name='conv1')
+        residual = tl.layers.BatchNormLayer(residual, act=tf.nn.relu, is_train=True, name='conv1_bn/BatchNorm')
+
+        # bottleneck layer 2
+        residual = conv2d_same(residual, depth_bottleneck, kernel_size=3, strides= stride, rate=rate, scope='conv2')
+
+        # bottleneck layer 3
+        residual = tl.layers.Conv2d(residual, depth, filter_size=(1, 1), strides=(1, 1), act=None, b_init=None,
+                                    name='conv3')
+        residual = tl.layers.BatchNormLayer(residual, act=tf.identity, is_train=True, name='conv3_bn/BatchNorm')
+
+        output = ElementwiseLayer(layer=[shortcut, residual],
+                                  combine_fn=tf.add,
+                                  name='combine_layer',
+                                  act=tf.nn.relu)
+        return output
+
+
+def bottleneck_SE(inputs, depth, depth_bottleneck, stride, rate=1, scope=None):
+    with tf.variable_scope(scope, 'bottleneck_v1') as sc:
+        depth_in = utils.last_dimension(inputs.outputs.get_shape(), min_rank=4)
+        if depth == depth_in:
+            shortcut = subsample(inputs, stride, 'shortcut')
+        else:
+            shortcut = tl.layers.Conv2d(inputs, depth, filter_size=(1, 1), strides=(stride, stride), act=None,
+                                        b_init=None, name='shortcut_conv')
+            shortcut = tl.layers.BatchNormLayer(shortcut, act=tf.identity, is_train=True, name='shortcut_bn/BatchNorm')
+        # bottleneck layer 1
+        residual = tl.layers.Conv2d(inputs, depth_bottleneck, filter_size=(1, 1), strides=(1, 1), act=None, b_init=None,
+                                    name='conv1')
+        residual = tl.layers.BatchNormLayer(residual, act=tf.nn.relu, is_train=True, name='conv1_bn/BatchNorm')
+
+        # bottleneck layer 2
+        residual = conv2d_same(residual, depth_bottleneck, kernel_size=3, strides= stride, rate=rate, scope='conv2')
+
+        # bottleneck layer 3
+        residual = tl.layers.Conv2d(residual, depth, filter_size=(1, 1), strides=(1, 1), act=None, b_init=None,
+                                    name='conv3')
+        residual = tl.layers.BatchNormLayer(residual, act=tf.identity, is_train=True, name='conv3_bn/BatchNorm')
+
+        # squeeze
+        squeeze = tl.layers.InputLayer(tf.reduce_mean(residual.outputs, axis=[1, 2]), name='squeeze_layer')
+        # excitation
+        excitation1 = tl.layers.DenseLayer(squeeze, n_units=int(depth/16.0), act=tf.nn.relu, name='excitation_1')
+        excitation2 = tl.layers.DenseLayer(excitation1, n_units=depth, act=tf.nn.sigmoid, name='excitation_2')
+        # scale
+        scale = tl.layers.ReshapeLayer(excitation2, shape=[tf.shape(excitation2.outputs)[0], 1, 1, depth], name='excitation_reshape')
+
+        residual_se = ElementwiseLayer(layer=[residual, scale],
+                                    combine_fn=tf.multiply,
+                                    name='scale_layer',
+                                    act=None)
+
+        output = ElementwiseLayer(layer=[shortcut, residual_se],
+                                  combine_fn=tf.add,
+                                  name='combine_layer',
+                                  act=tf.nn.relu)
+        return output
+
+
+def bottleneck_Xt(inputs, depth, stride, cardinality, cardinality_dim, rate=1, scope=None):
+    with tf.variable_scope(scope, 'bottleneck_v1') as sc:
+        depth_in = utils.last_dimension(inputs.outputs.get_shape(), min_rank=4)
+        if depth == depth_in:
+            shortcut = subsample(inputs, stride, 'shortcut')
+        else:
+            shortcut = tl.layers.Conv2d(inputs, depth, filter_size=(1, 1), strides=(stride, stride), act=None,
+                                        b_init=None, name='shortcut_conv')
+            shortcut = tl.layers.BatchNormLayer(shortcut, act=tf.identity, is_train=True, name='shortcut_bn/BatchNorm')
+
+        cardinality_layers = []
+        for i in range(cardinality):
+            # bottleneck layer 1
+            residual = tl.layers.Conv2d(inputs, cardinality_dim, filter_size=(1, 1), strides=(1, 1), act=None, b_init=None,
+                                        name='cardinality_%d/conv1' % i)
+            residual = tl.layers.BatchNormLayer(residual, act=tf.nn.relu, is_train=True, name='cardinality_%d/conv1_bn/BatchNorm' % i)
+
+            # bottleneck layer 2
+            residual = conv2d_same(residual, cardinality_dim, kernel_size=3, strides= stride, rate=rate, scope='cardinality_%d/conv2' % i)
+
+            # bottleneck layer 3
+            residual = tl.layers.Conv2d(residual, depth, filter_size=(1, 1), strides=(1, 1), act=None, b_init=None,
+                                        name='cardinality_%d/conv3' % i)
+            residual = tl.layers.BatchNormLayer(residual, act=tf.identity, is_train=True, name='cardinality_%d/conv3_bn/BatchNorm' % i)
+            cardinality_layers.append(residual)
+
+        residual_total = ElementwiseLayer(layer=cardinality_layers,
+                                          combine_fn=tf.add,
+                                          name='cardinality_cmobine',
+                                          act=None)
+        with tf.control_dependencies([residual_total.outputs]):
+            output = ElementwiseLayer(layer=[shortcut, residual_total],
+                                          combine_fn=tf.add,
+                                          name='combine_layer',
+                                          act=tf.nn.relu)
+        return output
+
+
+def resnet(inputs, bottle_neck, blocks, num_classes=1000, scope=None, type=None):
+    # mean_rgb_var = tf.Variable()
+    with tf.variable_scope(scope):
+        mean_rgb_var = tf.Variable(dtype=tf.float32, name='mean_rgb', trainable=False, initial_value=[128.0, 128.0, 128.0])
+        rgb_mean_dims = tf.reshape(mean_rgb_var, shape=[1, 1, 1, 3])
+        inputs = tf.subtract(inputs, rgb_mean_dims)
+        net_inputs = tl.layers.InputLayer(inputs, name='input_layer')
+        if bottle_neck:
+            net = conv2d_same(net_inputs, 64, 7, strides=2, rate=1, scope='conv1')
+            net = tl.layers.MaxPool2d(net, (2, 2), padding='SAME', name='pool1')
+        else:
+            raise ValueError('The standard resnet must support the bottleneck layer')
+        for block in blocks:
+            with tf.variable_scope(block.scope):
+                for i, var in enumerate(block.args):
+                    with tf.variable_scope('unit_%d' % (i+1)):
+                        if type=='resnext':
+                            net = block.unit_fn(net, depth=var['depth'], stride=var['stride'],
+                                                cardinality=var['cardinality'],
+                                                cardinality_dim=var['cardinality_dim'], rate=1, scope=None)
+                        else:
+                            net = block.unit_fn(net, depth=var['depth'], depth_bottleneck=var['depth_bottleneck'],
+                                                stride=var['stride'], rate=var['rate'], scope=None)
+        net.outputs = tf.reduce_mean(net.outputs, [1, 2], keep_dims=True)
+        net = tl.layers.Conv2d(net, num_classes, filter_size=(1, 1), strides=(1, 1), act=None, name='logits')
+        net.outputs = tf.squeeze(net.outputs, [1, 2], name='SpatialSqueeze')
+        return net
+
+
+class Block(collections.namedtuple('Block', ['scope', 'unit_fn', 'args'])):
+    """A named tuple describing a ResNet block.
+
+    Its parts are:
+      scope: The scope of the `Block`.
+      unit_fn: The ResNet unit function which takes as input a `Tensor` and
+        returns another `Tensor` with the output of the ResNet unit.
+      args: A list of length equal to the number of units in the `Block`. The list
+        contains one (depth, depth_bottleneck, stride) tuple for each unit in the
+        block to serve as argument to unit_fn.
+    """
+
+
+def resnet_v1_block(scope, base_depth, num_units, stride, rate=1):
+  """Helper function for creating a resnet_v1 bottleneck block.
+
+  Args:
+    scope: The scope of the block.
+    base_depth: The depth of the bottleneck layer for each unit.
+    num_units: The number of units in the block.
+    stride: The stride of the block, implemented as a stride in the last unit.
+      All other units have stride=1.
+
+  Returns:
+    A resnet_v1 bottleneck block.
+  """
+  return Block(scope, bottleneck, [{
+      'depth': base_depth * 4,
+      'depth_bottleneck': base_depth,
+      'stride': 1,
+      'rate': rate
+  }] * (num_units - 1) + [{
+      'depth': base_depth * 4,
+      'depth_bottleneck': base_depth,
+      'stride': stride,
+      'rate': rate
+  }])
+
+
+def resnetse_v1_block(scope, base_depth, num_units, stride, rate=1):
+  """Helper function for creating a resnet_v1 bottleneck block.
+
+  Args:
+    scope: The scope of the block.
+    base_depth: The depth of the bottleneck layer for each unit.
+    num_units: The number of units in the block.
+    stride: The stride of the block, implemented as a stride in the last unit.
+      All other units have stride=1.
+
+  Returns:
+    A resnet_v1 bottleneck block.
+  """
+  return Block(scope, bottleneck_SE, [{
+      'depth': base_depth * 4,
+      'depth_bottleneck': base_depth,
+      'stride': 1,
+      'rate': rate
+  }] * (num_units - 1) + [{
+      'depth': base_depth * 4,
+      'depth_bottleneck': base_depth,
+      'stride': stride,
+      'rate': rate
+  }])
+
+
+def resnext_v1_block(scope, base_depth, num_units, stride, cardinality, cardinality_dim, rate=1):
+  """Helper function for creating a resnet_v1 bottleneck block.
+
+  Args:
+    scope: The scope of the block.
+    base_depth: The depth of the bottleneck layer for each unit.
+    num_units: The number of units in the block.
+    stride: The stride of the block, implemented as a stride in the last unit.
+      All other units have stride=1.
+
+  Returns:
+    A resnet_v1 bottleneck block.
+  """
+  return Block(scope, bottleneck_Xt, [{
+      'depth': base_depth * 4,
+      'stride': 1,
+      'rate': rate,
+      'cardinality': cardinality,
+      'cardinality_dim': cardinality_dim
+  }] * (num_units - 1) + [{
+      'depth': base_depth * 4,
+      'stride': stride,
+      'rate': 1,
+      'cardinality': cardinality,
+      'cardinality_dim': cardinality_dim
+  }])
+
+
+def get_resnet(inputs, num_classes, num_layers, type='resnet', sess=None, pretrained=True):
+    '''
+    :param inputs: inputs is an tensorflow placeholder
+    :param num_classes:
+    :param num_layers:
+    :param type: choose weather using se xt or just oridinary resnet  ['resnet', 'resnetse', 'resnext']
+    :return:
+    '''
+    if type == 'resnet':
+        if num_layers == 50:
+            blocks = [
+                resnet_v1_block('block1', base_depth=64, num_units=3, stride=2, rate=1),
+                resnet_v1_block('block2', base_depth=128, num_units=4, stride=2, rate=1),
+                resnet_v1_block('block3', base_depth=256, num_units=6, stride=2, rate=1),
+                resnet_v1_block('block4', base_depth=512, num_units=3, stride=1, rate=1)
+            ]
+            ckpt_file_path = '../model_weights/resnet_v1_50.ckpt'
+        elif num_layers == 101:
+            blocks = [
+                resnet_v1_block('block1', base_depth=64, num_units=3, stride=2, rate=1),
+                resnet_v1_block('block2', base_depth=128, num_units=4, stride=2, rate=1),
+                resnet_v1_block('block3', base_depth=256, num_units=23, stride=2, rate=1),
+                resnet_v1_block('block4', base_depth=512, num_units=3, stride=1, rate=1)
+            ]
+            ckpt_file_path = '../model_weights/resnet_v1_101.ckpt'
+        elif num_layers == 152:
+            blocks = [
+                resnet_v1_block('block1', base_depth=64, num_units=3, stride=2, rate=1),
+                resnet_v1_block('block2', base_depth=128, num_units=8, stride=2, rate=1),
+                resnet_v1_block('block3', base_depth=256, num_units=36, stride=2, rate=1),
+                resnet_v1_block('block4', base_depth=512, num_units=3, stride=1, rate=1)
+            ]
+            ckpt_file_path = '../model_weights/resnet_v1_152.ckpt'
+        else:
+            raise ValueError('Resnet layer %d is not supported now.' % num_layers)
+        net = resnet(inputs=inputs,
+                     bottle_neck=True,
+                     blocks=blocks,
+                     num_classes=num_classes,
+                     scope='resnet_v1_%d' % num_layers,
+                     type=type)
+        if pretrained:
+            var_ckpt = get_variables_in_checkpoint_file(ckpt_file_path)
+            vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
+            vars_dict = {}
+            for var in vars:
+                var_name = var.op.name
+                var_name_new = var_name
+                if '_bn' in var_name:
+                    var_name_new = var_name_new.replace('_bn', '')
+                if 'W_conv2d' in var_name:
+                    var_name_new = var_name_new.replace('W_conv2d', 'weights')
+                if 'b_conv2d' in var_name:
+                    var_name_new = var_name_new.replace('b_conv2d', 'biases')
+                if 'shortcut_conv' in var_name:
+                    var_name_new = var_name_new.replace('shortcut_conv', 'shortcut')
+                print(var_name_new)
+                if var_name_new in var_ckpt:
+                    vars_dict[var_name_new] = var
+            tl.layers.initialize_global_variables(sess)
+            if len(vars_dict.keys()) > 0:
+                saver = tf.train.Saver(vars_dict)
+                saver.restore(sess, ckpt_file_path)
+            return net
+        else:
+            return net
+    elif type == 'resnetse':
+        if pretrained:
+            raise ValueError('resnetse have no pretrained model!')
+        if num_layers == 50:
+            blocks = [
+                resnetse_v1_block('block1', base_depth=64, num_units=3, stride=2, rate=1),
+                resnetse_v1_block('block2', base_depth=128, num_units=4, stride=2, rate=1),
+                resnetse_v1_block('block3', base_depth=256, num_units=6, stride=2, rate=1),
+                resnetse_v1_block('block4', base_depth=512, num_units=3, stride=1, rate=1)
+            ]
+        elif num_layers == 101:
+            blocks = [
+                resnetse_v1_block('block1', base_depth=64, num_units=3, stride=2, rate=1),
+                resnetse_v1_block('block2', base_depth=128, num_units=4, stride=2, rate=1),
+                resnetse_v1_block('block3', base_depth=256, num_units=23, stride=2, rate=1),
+                resnetse_v1_block('block4', base_depth=512, num_units=3, stride=1, rate=1)
+            ]
+        elif num_layers == 152:
+            blocks = [
+                resnetse_v1_block('block1', base_depth=64, num_units=3, stride=2, rate=1),
+                resnetse_v1_block('block2', base_depth=128, num_units=8, stride=2, rate=1),
+                resnetse_v1_block('block3', base_depth=256, num_units=36, stride=2, rate=1),
+                resnetse_v1_block('block4', base_depth=512, num_units=3, stride=1, rate=1)
+            ]
+        else:
+            raise ValueError('Resnet layer %d is not supported now.' % num_layers)
+        net = resnet(inputs=inputs,
+                     bottle_neck=True,
+                     blocks=blocks,
+                     num_classes=num_classes,
+                     scope='resnet_v1_%d' % num_layers,
+                     type=type)
+        return net
+    elif type == 'resnext':
+        if pretrained:
+            raise ValueError('resnetse have no pretrained model!')
+        if num_layers == 50:
+            blocks = [
+                resnext_v1_block('block1', base_depth=64, num_units=3, stride=2, rate=1, cardinality=32,
+                                 cardinality_dim=4),
+                resnext_v1_block('block2', base_depth=128, num_units=4, stride=2, rate=1, cardinality=32,
+                                 cardinality_dim=4),
+                resnext_v1_block('block3', base_depth=256, num_units=6, stride=2, rate=1, cardinality=32,
+                                 cardinality_dim=4),
+                resnext_v1_block('block4', base_depth=512, num_units=3, stride=1, rate=1, cardinality=32,
+                                 cardinality_dim=4)
+            ]
+        elif num_layers == 101:
+            blocks = [
+                resnext_v1_block('block1', base_depth=64, num_units=3, stride=2, rate=1, cardinality=32,
+                                 cardinality_dim=4),
+                resnext_v1_block('block2', base_depth=128, num_units=4, stride=2, rate=1, cardinality=32,
+                                 cardinality_dim=4),
+                resnext_v1_block('block3', base_depth=256, num_units=23, stride=2, rate=1, cardinality=32,
+                                 cardinality_dim=4),
+                resnext_v1_block('block4', base_depth=512, num_units=3, stride=1, rate=1, cardinality=32,
+                                 cardinality_dim=4)
+            ]
+        elif num_layers == 152:
+            blocks = [
+                resnext_v1_block('block1', base_depth=64, num_units=3, stride=2, rate=1, cardinality=32,
+                                 cardinality_dim=4),
+                resnext_v1_block('block2', base_depth=128, num_units=8, stride=2, rate=1, cardinality=32,
+                                 cardinality_dim=4),
+                resnext_v1_block('block3', base_depth=256, num_units=36, stride=2, rate=1, cardinality=32,
+                                 cardinality_dim=4),
+                resnext_v1_block('block4', base_depth=512, num_units=3, stride=1, rate=1, cardinality=32,
+                                 cardinality_dim=4)
+            ]
+        else:
+            raise ValueError('Resnet layer %d is not supported now.' % num_layers)
+        net = resnet(inputs=inputs,
+                     bottle_neck=True,
+                     blocks=blocks,
+                     num_classes=num_classes,
+                     scope='resnet_v1_%d' % num_layers,
+                     type=type)
+        return net
+    else:
+        raise ValueError('resnet type %s does not support now!' % type)
+
+
+if __name__ == '__main__':
+        x = tf.placeholder(dtype=tf.float32, shape=[1, 224, 224, 3], name='input_place')
+        sess = tf.Session(config=npu_config_proto())
+        # # test resnet
+        # nets = get_resnet(x, 1000, 50, type='resnet', sess=sess, pretrained=True)
+        # with sess:
+        #     nets.print_params()
+
+        # # test resnetse
+        # nets = get_resnet(x, 1000, 50, type='resnetse', sess=sess, pretrained=False)
+        # tl.layers.initialize_global_variables(sess)
+        # with sess:
+        #     nets.print_params()
+
+        # test resnext
+        nets = get_resnet(x, 1000, 50, type='resnext', sess=sess, pretrained=False)
+        tl.layers.initialize_global_variables(sess)
+        with sess:
+            nets.print_params()
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/nets/tl_layers_modify.py b/TensorFlow/contrib/cv/InsightFace_TF/nets/tl_layers_modify.py
new file mode 100644
index 0000000000000000000000000000000000000000..f76e6e926b054e1354f85fdb6a56be1a65998eec
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/nets/tl_layers_modify.py
@@ -0,0 +1,567 @@
+from npu_bridge.npu_init import *
+import tensorflow as tf
+import tensorlayer as tl
+from tensorlayer.layers import Layer, list_remove_repeat
+
+
+D_TYPE = tf.float32
+TF_GRAPHKEYS_VARIABLES = tf.GraphKeys.GLOBAL_VARIABLES
+
+
+class ElementwiseLayer(Layer):
+    """
+    The :class:`ElementwiseLayer` class combines multiple :class:`Layer` which have the same output shapes by a given elemwise-wise operation.
+
+    Parameters
+    ----------
+    layer : a list of :class:`Layer` instances
+        The `Layer` class feeding into this layer.
+    combine_fn : a TensorFlow elemwise-merge function
+        e.g. AND is ``tf.minimum`` ;  OR is ``tf.maximum`` ; ADD is ``tf.add`` ; MUL is ``tf.multiply`` and so on.
+        See `TensorFlow Math API <https://www.tensorflow.org/versions/master/api_docs/python/math_ops.html#math>`_ .
+    name : a string or None
+        An optional name to attach to this layer.
+    """
+    def __init__(
+        self,
+        layer = [],
+        combine_fn = tf.minimum,
+        name ='elementwise_layer',
+        act = None,
+    ):
+        Layer.__init__(self, name=name)
+
+        if act:
+            print("  [TL] ElementwiseLayer %s: size:%s fn:%s, act:%s" % (
+            self.name, layer[0].outputs.get_shape(), combine_fn.__name__, act.__name__))
+        else:
+            print("  [TL] ElementwiseLayer %s: size:%s fn:%s" % (
+            self.name, layer[0].outputs.get_shape(), combine_fn.__name__))
+
+        self.outputs = layer[0].outputs
+        # print(self.outputs._shape, type(self.outputs._shape))
+        for l in layer[1:]:
+            # assert str(self.outputs.get_shape()) == str(l.outputs.get_shape()), "Hint: the input shapes should be the same. %s != %s" %  (self.outputs.get_shape() , str(l.outputs.get_shape()))
+            self.outputs = combine_fn(self.outputs, l.outputs, name=name)
+        if act:
+            self.outputs = act(self.outputs)
+        self.all_layers = list(layer[0].all_layers)
+        self.all_params = list(layer[0].all_params)
+        self.all_drop = dict(layer[0].all_drop)
+
+        for i in range(1, len(layer)):
+            self.all_layers.extend(list(layer[i].all_layers))
+            self.all_params.extend(list(layer[i].all_params))
+            self.all_drop.update(dict(layer[i].all_drop))
+
+        self.all_layers = list_remove_repeat(self.all_layers)
+        self.all_params = list_remove_repeat(self.all_params)
+
+
+class BatchNormLayer(Layer):
+    """
+    The :class:`BatchNormLayer` class is a normalization layer, see ``tf.nn.batch_normalization`` and ``tf.nn.moments``.
+
+    Batch normalization on fully-connected or convolutional maps.
+
+    ```
+        https://www.tensorflow.org/api_docs/python/tf/cond
+        If x < y, the tf.add operation will be executed and tf.square operation will not be executed.
+        Since z is needed for at least one branch of the cond, the tf.multiply operation is always executed, unconditionally.
+    ```
+
+    Parameters
+    -----------
+    layer : a :class:`Layer` instance
+        The `Layer` class feeding into this layer.
+    decay : float, default is 0.9.
+        A decay factor for ExponentialMovingAverage, use larger value for large dataset.
+    epsilon : float
+        A small float number to avoid dividing by 0.
+    act : activation function.
+    is_train : boolean
+        Whether train or inference.
+    beta_init : beta initializer
+        The initializer for initializing beta
+    gamma_init : gamma initializer
+        The initializer for initializing gamma
+    dtype : tf.float32 (default) or tf.float16
+    name : a string or None
+        An optional name to attach to this layer.
+
+    References
+    ----------
+    - `Source <https://github.com/ry/tensorflow-resnet/blob/master/resnet.py>`_
+    - `stackoverflow <http://stackoverflow.com/questions/38312668/how-does-one-do-inference-with-batch-normalization-with-tensor-flow>`_
+
+    """
+
+    def __init__(
+            self,
+            layer=None,
+            decay=0.9,
+            epsilon=2e-5,
+            act=tf.identity,
+            is_train=False,
+            fix_gamma=True,
+            beta_init=tf.zeros_initializer,
+            gamma_init=tf.random_normal_initializer(mean=1.0, stddev=0.002),  # tf.ones_initializer,
+            # dtype = tf.float32,
+            trainable=None,
+            name='batchnorm_layer',
+    ):
+        Layer.__init__(self, name=name)
+        self.inputs = layer.outputs
+        print("  [TL] BatchNormLayer %s: decay:%f epsilon:%f act:%s is_train:%s" % (self.name, decay, epsilon, act.__name__, is_train))
+        x_shape = self.inputs.get_shape()
+        params_shape = x_shape[-1:]
+
+        from tensorflow.python.training import moving_averages
+        from tensorflow.python.ops import control_flow_ops
+
+        with tf.variable_scope(name) as vs:
+            axis = list(range(len(x_shape) - 1))
+
+            ## 1. beta, gamma
+            if tf.__version__ > '0.12.1' and beta_init == tf.zeros_initializer:
+                beta_init = beta_init()
+            with tf.device('/cpu:0'):
+                beta = tf.get_variable('beta', shape=params_shape, initializer=beta_init, dtype=tf.float32, trainable=is_train)  #, restore=restore)
+
+                gamma = tf.get_variable(
+                    'gamma',
+                    shape=params_shape,
+                    initializer=gamma_init,
+                    dtype=tf.float32,
+                    trainable=fix_gamma,
+                )  #restore=restore)
+
+            ## 2.
+            if tf.__version__ > '0.12.1':
+                moving_mean_init = tf.zeros_initializer()
+            else:
+                moving_mean_init = tf.zeros_initializer
+            with tf.device('/cpu:0'):
+                moving_mean = tf.get_variable('moving_mean', params_shape, initializer=moving_mean_init, dtype=tf.float32, trainable=False)  #   restore=restore)
+                moving_variance = tf.get_variable(
+                    'moving_variance',
+                    params_shape,
+                    initializer=tf.constant_initializer(1.),
+                    dtype=tf.float32,
+                    trainable=False,
+                )  #   restore=restore)
+
+            ## 3.
+            # These ops will only be preformed when training.
+            mean, variance = tf.nn.moments(self.inputs, axis)
+            try:  # TF12
+                update_moving_mean = moving_averages.assign_moving_average(moving_mean, mean, decay, zero_debias=False)  # if zero_debias=True, has bias
+                update_moving_variance = moving_averages.assign_moving_average(
+                    moving_variance, variance, decay, zero_debias=False)  # if zero_debias=True, has bias
+                # print("TF12 moving")
+            except Exception as e:  # TF11
+                update_moving_mean = moving_averages.assign_moving_average(moving_mean, mean, decay)
+                update_moving_variance = moving_averages.assign_moving_average(moving_variance, variance, decay)
+                # print("TF11 moving")
+
+            def mean_var_with_update():
+                with tf.control_dependencies([update_moving_mean, update_moving_variance]):
+                    return tf.identity(mean), tf.identity(variance)
+            if trainable:
+                mean, var = mean_var_with_update()
+                print(mean)
+                print(var)
+                self.outputs = act(tf.nn.batch_normalization(self.inputs, mean, var, beta, gamma, epsilon))
+            else:
+                self.outputs = act(tf.nn.batch_normalization(self.inputs, moving_mean, moving_variance, beta, gamma, epsilon))
+            variables = [beta, gamma, moving_mean, moving_variance]
+        self.all_layers = list(layer.all_layers)
+        self.all_params = list(layer.all_params)
+        self.all_drop = dict(layer.all_drop)
+        self.all_layers.extend([self.outputs])
+        self.all_params.extend(variables)
+
+
+def Conv2d(
+        net,
+        n_filter=32,
+        filter_size=(3, 3),
+        strides=(1, 1),
+        act=None,
+        padding='SAME',
+        W_init=tf.truncated_normal_initializer(stddev=0.02),
+        b_init=tf.constant_initializer(value=0.0),
+        W_init_args={},
+        b_init_args={},
+        use_cudnn_on_gpu=None,
+        data_format=None,
+        name='conv2d',
+):
+    """Wrapper for :class:`Conv2dLayer`, if you don't understand how to use :class:`Conv2dLayer`, this function may be easier.
+
+    Parameters
+    ----------
+    net : TensorLayer layer.
+    n_filter : number of filter.
+    filter_size : tuple (height, width) for filter size.
+    strides : tuple (height, width) for strides.
+    act : None or activation function.
+    others : see :class:`Conv2dLayer`.
+
+    Examples
+    --------
+    >>> w_init = tf.truncated_normal_initializer(stddev=0.01)
+    >>> b_init = tf.constant_initializer(value=0.0)
+    >>> inputs = InputLayer(x, name='inputs')
+    >>> conv1 = Conv2d(inputs, 64, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv1_1')
+    >>> conv1 = Conv2d(conv1, 64, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv1_2')
+    >>> pool1 = MaxPool2d(conv1, (2, 2), padding='SAME', name='pool1')
+    >>> conv2 = Conv2d(pool1, 128, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv2_1')
+    >>> conv2 = Conv2d(conv2, 128, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv2_2')
+    >>> pool2 = MaxPool2d(conv2, (2, 2), padding='SAME', name='pool2')
+    """
+    assert len(strides) == 2, "len(strides) should be 2, Conv2d and Conv2dLayer are different."
+    if act is None:
+        act = tf.identity
+
+    try:
+        pre_channel = int(net.outputs.get_shape()[-1])
+    except:  # if pre_channel is ?, it happens when using Spatial Transformer Net
+        pre_channel = 1
+        print("[warnings] unknow input channels, set to 1")
+    net = Conv2dLayer(
+        net,
+        act=act,
+        shape=[filter_size[0], filter_size[1], pre_channel, n_filter],  # 32 features for each 5x5 patch
+        strides=[1, strides[0], strides[1], 1],
+        padding=padding,
+        W_init=W_init,
+        W_init_args=W_init_args,
+        b_init=b_init,
+        b_init_args=b_init_args,
+        use_cudnn_on_gpu=use_cudnn_on_gpu,
+        data_format=data_format,
+        name=name)
+    return net
+
+
+class Conv2dLayer(Layer):
+    """
+    The :class:`Conv2dLayer` class is a 2D CNN layer, see `tf.nn.conv2d <https://www.tensorflow.org/versions/master/api_docs/python/nn.html#conv2d>`_.
+
+    Parameters
+    ----------
+    layer : a :class:`Layer` instance
+        The `Layer` class feeding into this layer.
+    act : activation function
+        The function that is applied to the layer activations.
+    shape : list of shape
+        shape of the filters, [filter_height, filter_width, in_channels, out_channels].
+    strides : a list of ints.
+        The stride of the sliding window for each dimension of input.\n
+        It Must be in the same order as the dimension specified with format.
+    padding : a string from: "SAME", "VALID".
+        The type of padding algorithm to use.
+    W_init : weights initializer
+        The initializer for initializing the weight matrix.
+    b_init : biases initializer or None
+        The initializer for initializing the bias vector. If None, skip biases.
+    W_init_args : dictionary
+        The arguments for the weights tf.get_variable().
+    b_init_args : dictionary
+        The arguments for the biases tf.get_variable().
+    use_cudnn_on_gpu : bool, default is None.
+    data_format : string "NHWC" or "NCHW", default is "NHWC"
+    name : a string or None
+        An optional name to attach to this layer.
+
+    Notes
+    ------
+    - shape = [h, w, the number of output channel of previous layer, the number of output channels]
+    - the number of output channel of a layer is its last dimension.
+
+    Examples
+    --------
+    >>> x = tf.placeholder(tf.float32, shape=[None, 28, 28, 1])
+    >>> network = tl.layers.InputLayer(x, name='input_layer')
+    >>> network = tl.layers.Conv2dLayer(network,
+    ...                   act = tf.nn.relu,
+    ...                   shape = [5, 5, 1, 32],  # 32 features for each 5x5 patch
+    ...                   strides=[1, 1, 1, 1],
+    ...                   padding='SAME',
+    ...                   W_init=tf.truncated_normal_initializer(stddev=5e-2),
+    ...                   W_init_args={},
+    ...                   b_init = tf.constant_initializer(value=0.0),
+    ...                   b_init_args = {},
+    ...                   name ='cnn_layer1')     # output: (?, 28, 28, 32)
+    >>> network = tl.layers.PoolLayer(network,
+    ...                   ksize=[1, 2, 2, 1],
+    ...                   strides=[1, 2, 2, 1],
+    ...                   padding='SAME',
+    ...                   pool = tf.nn.max_pool,
+    ...                   name ='pool_layer1',)   # output: (?, 14, 14, 32)
+
+    >>> Without TensorLayer, you can implement 2d convolution as follow.
+    >>> W = tf.Variable(W_init(shape=[5, 5, 1, 32], ), name='W_conv')
+    >>> b = tf.Variable(b_init(shape=[32], ), name='b_conv')
+    >>> outputs = tf.nn.relu( tf.nn.conv2d(inputs, W,
+    ...                       strides=[1, 1, 1, 1],
+    ...                       padding='SAME') + b )
+    """
+
+    def __init__(
+            self,
+            layer=None,
+            act=tf.identity,
+            shape=[5, 5, 1, 100],
+            strides=[1, 1, 1, 1],
+            padding='SAME',
+            W_init=tf.truncated_normal_initializer(stddev=0.02),
+            b_init=tf.constant_initializer(value=0.0),
+            W_init_args={},
+            b_init_args={},
+            use_cudnn_on_gpu=None,
+            data_format=None,
+            name='cnn_layer',
+    ):
+        Layer.__init__(self, name=name)
+        self.inputs = layer.outputs
+        print("  [TL] Conv2dLayer %s: shape:%s strides:%s pad:%s act:%s" % (self.name, str(shape), str(strides), padding, act.__name__))
+
+        with tf.variable_scope(name) as vs:
+            with tf.device('/cpu:0'):
+                W = tf.get_variable(name='W_conv2d', shape=shape, initializer=W_init, dtype=D_TYPE, **W_init_args)
+            if b_init:
+                with tf.device('/cpu:0'):
+                    b = tf.get_variable(name='b_conv2d', shape=(shape[-1]), initializer=b_init, dtype=D_TYPE, **b_init_args)
+                self.outputs = act(
+                    tf.nn.conv2d(self.inputs, W, strides=strides, padding=padding, use_cudnn_on_gpu=use_cudnn_on_gpu, data_format=data_format) + b)
+            else:
+                self.outputs = act(tf.nn.conv2d(self.inputs, W, strides=strides, padding=padding, use_cudnn_on_gpu=use_cudnn_on_gpu, data_format=data_format))
+
+        self.all_layers = list(layer.all_layers)
+        self.all_params = list(layer.all_params)
+        self.all_drop = dict(layer.all_drop)
+        self.all_layers.extend([self.outputs])
+        if b_init:
+            self.all_params.extend([W, b])
+        else:
+            self.all_params.extend([W])
+
+
+## Special activation
+class PReluLayer(Layer):
+    """
+    The :class:`PReluLayer` class is Parametric Rectified Linear layer.
+
+    Parameters
+    ----------
+    x : A `Tensor` with type `float`, `double`, `int32`, `int64`, `uint8`,
+        `int16`, or `int8`.
+    channel_shared : `bool`. Single weight is shared by all channels
+    a_init : alpha initializer, default zero constant.
+        The initializer for initializing the alphas.
+    a_init_args : dictionary
+        The arguments for the weights initializer.
+    name : A name for this activation op (optional).
+
+    References
+    -----------
+    - `Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification <http://arxiv.org/pdf/1502.01852v1.pdf>`_
+    """
+
+    def __init__(
+            self,
+            layer=None,
+            channel_shared=False,
+            a_init=tf.constant_initializer(value=0.0),
+            a_init_args={},
+            # restore = True,
+            name="prelu_layer"):
+        Layer.__init__(self, name=name)
+        self.inputs = layer.outputs
+        print("  [TL] PReluLayer %s: channel_shared:%s" % (self.name, channel_shared))
+        if channel_shared:
+            w_shape = (1, )
+        else:
+            w_shape = int(self.inputs.get_shape()[-1])
+
+        # with tf.name_scope(name) as scope:
+        with tf.variable_scope(name) as vs:
+            with tf.device('/cpu:0'):
+                alphas = tf.get_variable(name='alphas', shape=w_shape, initializer=a_init, dtype=D_TYPE, **a_init_args)
+            try:  ## TF 1.0
+                self.outputs = tf.nn.relu(self.inputs) + tf.multiply(alphas, (self.inputs - tf.abs(self.inputs))) * 0.5
+            except:  ## TF 0.12
+                self.outputs = tf.nn.relu(self.inputs) + tf.mul(alphas, (self.inputs - tf.abs(self.inputs))) * 0.5
+
+        self.all_layers = list(layer.all_layers)
+        self.all_params = list(layer.all_params)
+        self.all_drop = dict(layer.all_drop)
+
+        self.all_layers.extend([self.outputs])
+        self.all_params.extend([alphas])
+
+
+## Dense layer
+class DenseLayer(Layer):
+    """
+    The :class:`DenseLayer` class is a fully connected layer.
+
+    Parameters
+    ----------
+    layer : a :class:`Layer` instance
+        The `Layer` class feeding into this layer.
+    n_units : int
+        The number of units of the layer.
+    act : activation function
+        The function that is applied to the layer activations.
+    W_init : weights initializer
+        The initializer for initializing the weight matrix.
+    b_init : biases initializer or None
+        The initializer for initializing the bias vector. If None, skip biases.
+    W_init_args : dictionary
+        The arguments for the weights tf.get_variable.
+    b_init_args : dictionary
+        The arguments for the biases tf.get_variable.
+    name : a string or None
+        An optional name to attach to this layer.
+
+    Examples
+    --------
+    >>> network = tl.layers.InputLayer(x, name='input_layer')
+    >>> network = tl.layers.DenseLayer(
+    ...                 network,
+    ...                 n_units=800,
+    ...                 act = tf.nn.relu,
+    ...                 W_init=tf.truncated_normal_initializer(stddev=0.1),
+    ...                 name ='relu_layer'
+    ...                 )
+
+    >>> Without TensorLayer, you can do as follow.
+    >>> W = tf.Variable(
+    ...     tf.random_uniform([n_in, n_units], -1.0, 1.0), name='W')
+    >>> b = tf.Variable(tf.zeros(shape=[n_units]), name='b')
+    >>> y = tf.nn.relu(tf.matmul(inputs, W) + b)
+
+    Notes
+    -----
+    If the input to this layer has more than two axes, it need to flatten the
+    input by using :class:`FlattenLayer` in this case.
+    """
+
+    def __init__(
+            self,
+            layer=None,
+            n_units=100,
+            act=tf.identity,
+            W_init=tf.truncated_normal_initializer(stddev=0.1),
+            b_init=tf.constant_initializer(value=0.0),
+            W_init_args={},
+            b_init_args={},
+            name='dense_layer',
+    ):
+        Layer.__init__(self, name=name)
+        self.inputs = layer.outputs
+        if self.inputs.get_shape().ndims != 2:
+            raise Exception("The input dimension must be rank 2, please reshape or flatten it")
+
+        n_in = int(self.inputs.get_shape()[-1])
+        self.n_units = n_units
+        print("  [TL] DenseLayer  %s: %d %s" % (self.name, self.n_units, act.__name__))
+        with tf.variable_scope(name) as vs:
+            with tf.device('/cpu:0'):
+                W = tf.get_variable(name='W', shape=(n_in, n_units), initializer=W_init, dtype=D_TYPE, **W_init_args)
+            if b_init is not None:
+                try:
+                    with tf.device('/cpu:0'):
+                        b = tf.get_variable(name='b', shape=(n_units), initializer=b_init, dtype=D_TYPE, **b_init_args)
+                except:  # If initializer is a constant, do not specify shape.
+                    with tf.device('/cpu:0'):
+                        b = tf.get_variable(name='b', initializer=b_init, dtype=D_TYPE, **b_init_args)
+                self.outputs = act(tf.matmul(self.inputs, W) + b)
+            else:
+                self.outputs = act(tf.matmul(self.inputs, W))
+
+        # Hint : list(), dict() is pass by value (shallow), without them, it is
+        # pass by reference.
+        self.all_layers = list(layer.all_layers)
+        self.all_params = list(layer.all_params)
+        self.all_drop = dict(layer.all_drop)
+        self.all_layers.extend([self.outputs])
+        if b_init is not None:
+            self.all_params.extend([W, b])
+        else:
+            self.all_params.extend([W])
+
+
+def get_shape(input_tensor):
+    static_shape = input_tensor.get_shape().as_list()
+    dynamic_shape = tf.unstack(tf.shape(input_tensor))
+    final_shapes = [shapes[1] if shapes[0] is None else shapes[0] for shapes in zip(static_shape, dynamic_shape)]
+    return final_shapes
+
+
+## Group Batch Normalization layer
+class GroupNormLayer(Layer):
+    """The :class:`GroupNormLayer` class is a for instance normalization.
+       The implementation is based on paper [Group Normalization](https://arxiv.org/pdf/1803.08494.pdf)
+    Parameters
+    -----------
+    layer : a :class:`Layer` instance
+        The `Layer` class feeding into this layer.
+    act : activation function.
+    epsilon : float
+        A small float number.
+    scale_init : beta initializer
+        The initializer for initializing beta
+    offset_init : gamma initializer
+        The initializer for initializing gamma
+    G: the group number
+    name : a string or None
+        An optional name to attach to this layer.
+    """
+    def __init__(
+            self,
+            layer=None,
+            act=tf.identity,
+            epsilon=1e-5,
+            scale_init=tf.constant_initializer(1.0),
+            offset_init=tf.constant_initializer(0.0),
+            G=32,
+            name='group_norm',
+    ):
+        Layer.__init__(self, name=name)
+        self.inputs = layer.outputs
+        print("  [TL] GroupNormLayer %s: epsilon:%f act:%s" % (self.name, epsilon, act.__name__))
+        inputs_shape = get_shape(layer.outputs)
+        G = tf.minimum(G, inputs_shape[-1])
+        # [N, H, W, C] to [N, C, H, W]
+        temp_input = tf.transpose(self.inputs, [0, 3, 1, 2])
+        temp_input = tf.reshape(temp_input, [inputs_shape[0], G, inputs_shape[-1]//G, inputs_shape[1], inputs_shape[2]],
+                                name='group_reshape1')
+        with tf.variable_scope(name) as vs:
+            mean, var = tf.nn.moments(temp_input, [2, 3, 4], keep_dims=True)
+            scale = tf.get_variable('scale', shape=[1, inputs_shape[-1], 1, 1], initializer=scale_init, dtype=D_TYPE)
+            offset = tf.get_variable('offset', shape=[1, inputs_shape[-1], 1, 1], initializer=offset_init, dtype=D_TYPE)
+            temp_input = (temp_input - mean) / tf.sqrt(var + epsilon)
+            temp_input = tf.reshape(temp_input, shape=[inputs_shape[0], inputs_shape[-1], inputs_shape[1], inputs_shape[2]],
+                                    name='group_reshape2')
+            self.outputs = scale * temp_input + offset
+            self.outputs = tf.transpose(self.outputs, [0, 2, 3, 1])
+            self.outputs = act(self.outputs)
+            variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)
+        self.all_layers = list(layer.all_layers)
+        self.all_params = list(layer.all_params)
+        self.all_drop = dict(layer.all_drop)
+        self.all_layers.extend([self.outputs])
+        self.all_params.extend(variables)
+
+
+if __name__ == '__main__':
+    import numpy as np
+    a = np.random.randn(5, 32, 32, 64)
+    b = tf.placeholder(dtype=tf.float32, name='placeholder_b', shape=(None, 32, 32, 64))
+    inputs_tl = tl.layers.InputLayer(b, name='inputs_layer')
+    gp = GroupNormLayer(layer=inputs_tl)
+
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/nets/vgg16.py b/TensorFlow/contrib/cv/InsightFace_TF/nets/vgg16.py
new file mode 100644
index 0000000000000000000000000000000000000000..550527910cd3e10ecec1dd3a064ba30eda09e5d7
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/nets/vgg16.py
@@ -0,0 +1,209 @@
+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+"""
+VGG-16 for ImageNet.
+Introduction
+----------------
+VGG is a convolutional neural network model proposed by K. Simonyan and A. Zisserman
+from the University of Oxford in the paper “Very Deep Convolutional Networks for
+Large-Scale Image Recognition”  . The model achieves 92.7% top-5 test accuracy in ImageNet,
+which is a dataset of over 14 million images belonging to 1000 classes.
+Download Pre-trained Model
+----------------------------
+- Model weights in this example - vgg16_weights.npz : http://www.cs.toronto.edu/~frossard/post/vgg16/
+- Caffe VGG 16 model : https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-readme-md
+- Tool to convert the Caffe models to TensorFlow's : https://github.com/ethereon/caffe-tensorflow
+Note
+------
+- For simplified CNN layer see "Convolutional layer (Simplified)"
+in read the docs website.
+- When feeding other images to the model be sure to properly resize or crop them
+beforehand. Distorted images might end up being misclassified. One way of safely
+feeding images of multiple sizes is by doing center cropping, as shown in the
+
+The input image type is
+    from scipy.misc import imread, imresize
+    img1 = imread('data/laska.png', mode='RGB')  # test data in github
+    img1 = imresize(img1, (224, 224))
+So the input image is three channels, and is RGB.
+
+following snippet:
+#   >>> image_h, image_w, _ = np.shape(img)
+#   >>> shorter_side = min(image_h, image_w)
+#   >>> scale = 224. / shorter_side
+#   >>> image_h, image_w = np.ceil([scale * image_h, scale * image_w]).astype('int32')
+#   >>> img = imresize(img, (image_h, image_w))
+#   >>> crop_x = (image_w - 224) / 2
+#   >>> crop_y = (image_h - 224) / 2
+#   >>> img = img[crop_y:crop_y+224,crop_x:crop_x+224,:]
+"""
+from npu_bridge.npu_init import *
+
+import tensorlayer as tl
+from tensorlayer.layers import *
+from scipy.misc import imread, imresize
+from nets.imagenet_classes import *
+import os
+
+
+def _conv_layers(net_in):
+    with tf.name_scope('preprocess'):
+        # Notice that we include a preprocessing layer that takes the RGB image
+        # with pixels values in the range of 0-255 and subtracts the mean image
+        # values (calculated over the entire ImageNet training set).
+        mean = tf.constant([123.68, 116.779, 103.939], dtype=tf.float32, shape=[1, 1, 1, 3], name='img_mean')
+        net_in.outputs = net_in.outputs - mean
+
+    # conv1
+    network = Conv2dLayer(
+        net_in,
+        act=tf.nn.relu,
+        shape=[3, 3, 3, 64],  # 64 features for each 3x3 patch
+        strides=[1, 1, 1, 1],
+        padding='SAME',
+        name='conv1_1')
+    network = Conv2dLayer(
+        network,
+        act=tf.nn.relu,
+        shape=[3, 3, 64, 64],  # 64 features for each 3x3 patch
+        strides=[1, 1, 1, 1],
+        padding='SAME',
+        name='conv1_2')
+    network = PoolLayer(network, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', pool=tf.nn.max_pool, name='pool1')
+
+    # conv2
+    network = Conv2dLayer(
+        network,
+        act=tf.nn.relu,
+        shape=[3, 3, 64, 128],  # 128 features for each 3x3 patch
+        strides=[1, 1, 1, 1],
+        padding='SAME',
+        name='conv2_1')
+    network = Conv2dLayer(
+        network,
+        act=tf.nn.relu,
+        shape=[3, 3, 128, 128],  # 128 features for each 3x3 patch
+        strides=[1, 1, 1, 1],
+        padding='SAME',
+        name='conv2_2')
+    network = PoolLayer(network, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', pool=tf.nn.max_pool, name='pool2')
+
+    # conv3
+    network = Conv2dLayer(
+        network,
+        act=tf.nn.relu,
+        shape=[3, 3, 128, 256],  # 256 features for each 3x3 patch
+        strides=[1, 1, 1, 1],
+        padding='SAME',
+        name='conv3_1')
+    network = Conv2dLayer(
+        network,
+        act=tf.nn.relu,
+        shape=[3, 3, 256, 256],  # 256 features for each 3x3 patch
+        strides=[1, 1, 1, 1],
+        padding='SAME',
+        name='conv3_2')
+    network = Conv2dLayer(
+        network,
+        act=tf.nn.relu,
+        shape=[3, 3, 256, 256],  # 256 features for each 3x3 patch
+        strides=[1, 1, 1, 1],
+        padding='SAME',
+        name='conv3_3')
+    network = PoolLayer(network, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', pool=tf.nn.max_pool, name='pool3')
+
+    # conv4
+    network = Conv2dLayer(
+        network,
+        act=tf.nn.relu,
+        shape=[3, 3, 256, 512],  # 512 features for each 3x3 patch
+        strides=[1, 1, 1, 1],
+        padding='SAME',
+        name='conv4_1')
+    network = Conv2dLayer(
+        network,
+        act=tf.nn.relu,
+        shape=[3, 3, 512, 512],  # 512 features for each 3x3 patch
+        strides=[1, 1, 1, 1],
+        padding='SAME',
+        name='conv4_2')
+    network = Conv2dLayer(
+        network,
+        act=tf.nn.relu,
+        shape=[3, 3, 512, 512],  # 512 features for each 3x3 patch
+        strides=[1, 1, 1, 1],
+        padding='SAME',
+        name='conv4_3')
+    network = PoolLayer(network, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', pool=tf.nn.max_pool, name='pool4')
+
+    # conv5
+    network = Conv2dLayer(
+        network,
+        act=tf.nn.relu,
+        shape=[3, 3, 512, 512],  # 512 features for each 3x3 patch
+        strides=[1, 1, 1, 1],
+        padding='SAME',
+        name='conv5_1')
+    network = Conv2dLayer(
+        network,
+        act=tf.nn.relu,
+        shape=[3, 3, 512, 512],  # 512 features for each 3x3 patch
+        strides=[1, 1, 1, 1],
+        padding='SAME',
+        name='conv5_2')
+    network = Conv2dLayer(
+        network,
+        act=tf.nn.relu,
+        shape=[3, 3, 512, 512],  # 512 features for each 3x3 patch
+        strides=[1, 1, 1, 1],
+        padding='SAME',
+        name='conv5_3')
+    network = PoolLayer(network, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', pool=tf.nn.max_pool, name='pool5')
+    return network
+
+
+def _fc_layers(net):
+    network = FlattenLayer(net, name='flatten')
+    network = DenseLayer(network, n_units=4096, act=tf.nn.relu, name='fc1_relu')
+    network = DenseLayer(network, n_units=4096, act=tf.nn.relu, name='fc2_relu')
+    network = DenseLayer(network, n_units=1000, act=tf.identity, name='fc3_relu')
+    return network
+
+
+def get_vgg16(x, sess=None, pretrained=True):
+    net_in = InputLayer(x, name='input')
+    net_cnn = _conv_layers(net_in)  # simplified CNN APIs
+    network = _fc_layers(net_cnn)
+
+    if pretrained:
+        npz = np.load('../model_weights/vgg16_weights.npz')
+        params = []
+        for val in sorted(npz.items()):
+            print("  Loading %s" % str(val[1].shape))
+            params.append(val[1])
+        tl.files.assign_params(sess, params, network)
+        return network
+    else:
+        tl.layers.initialize_global_variables(sess)
+        return network
+
+
+if __name__ == '__main__':
+    DATA_PATH = '/home/aurora/workspaces2/PycharmProjects/tensorflow/tensorlayer/example/data'
+
+    x = tf.placeholder(tf.float32, [None, 224, 224, 3])
+    tfconfig = tf.ConfigProto(allow_soft_placement=True)
+    with tf.Session(config=npu_config_proto(config_proto=tfconfig)) as sess:
+        network = get_vgg16(x, sess, pretrained=True)
+        y = network.outputs
+        network.print_params()
+        network.print_layers()
+        img1 = imread(os.path.join(DATA_PATH, 'laska.png'), mode='RGB')  # test data in github
+        img1 = imresize(img1, (224, 224))
+        probs = tf.nn.softmax(y)
+        start_time = time.time()
+        prob = sess.run(probs, feed_dict={x: [img1]})[0]
+        print("  End time : %.5ss" % (time.time() - start_time))
+        preds = (np.argsort(prob)[::-1])[0:5]
+        for p in preds:
+            print(class_names[p], prob[p])
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/nets/vgg19.py b/TensorFlow/contrib/cv/InsightFace_TF/nets/vgg19.py
new file mode 100644
index 0000000000000000000000000000000000000000..a5646cd648b229d3b7f1b1ab7211acdba055855c
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/nets/vgg19.py
@@ -0,0 +1,240 @@
+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+"""
+VGG-19 for ImageNet.
+Pre-trained model in this example - VGG19 NPZ and
+trainable examples of VGG16/19 in TensorFlow can be found here:
+https://github.com/machrisaa/tensorflow-vgg
+For simplified CNN layer see "Convolutional layer (Simplified)"
+in read the docs website.
+"""
+from npu_bridge.npu_init import *
+
+import os
+import time
+
+import numpy as np
+import skimage
+import skimage.io
+import skimage.transform
+import tensorflow as tf
+from scipy.misc import imread, imresize
+import tensorlayer as tl
+from tensorlayer.layers import *
+from imagenet_classes import *
+
+
+DATA_PATH = '/home/aurora/workspaces2/PycharmProjects/tensorflow/tensorlayer/example/data'
+VGG_MEAN = [103.939, 116.779, 123.68]
+
+
+def load_image(path):
+    # load image
+    img = skimage.io.imread(path)
+    img = img / 255.0
+    if ((0 <= img).all() and (img <= 1.0).all()) is False:
+        raise Exception("image value should be [0, 1]")
+    # print "Original Image Shape: ", img.shape
+    # we crop image from center
+    short_edge = min(img.shape[:2])
+    yy = int((img.shape[0] - short_edge) / 2)
+    xx = int((img.shape[1] - short_edge) / 2)
+    crop_img = img[yy:yy + short_edge, xx:xx + short_edge]
+    # resize to 224, 224
+    resized_img = skimage.transform.resize(crop_img, (224, 224))
+    return resized_img
+
+
+def print_prob(prob):
+    synset = class_names
+    # print prob
+    pred = np.argsort(prob)[::-1]
+    # Get top1 label
+    top1 = synset[pred[0]]
+    print("Top1: ", top1, prob[pred[0]])
+    # Get top5 label
+    top5 = [(synset[pred[i]], prob[pred[i]]) for i in range(5)]
+    print("Top5: ", top5)
+    return top1
+
+
+def _Vgg19(rgb):
+    """
+    Build the VGG 19 Model
+    Parameters
+    -----------
+    rgb : rgb image placeholder [batch, height, width, 3] values scaled [0, 1]
+    """
+    start_time = time.time()
+    print("build model started")
+    rgb_scaled = rgb * 255.0
+    # Convert RGB to BGR
+    if tf.__version__ <= '0.11':
+        red, green, blue = tf.split(3, 3, rgb_scaled)
+    else:  # TF 1.0
+        print(rgb_scaled)
+        red, green, blue = tf.split(rgb_scaled, 3, 3)
+    if red.get_shape().as_list()[1:] != [224, 224, 1]:
+        raise Exception("image size unmatch")
+    if green.get_shape().as_list()[1:] != [224, 224, 1]:
+        raise Exception("image size unmatch")
+    if blue.get_shape().as_list()[1:] != [224, 224, 1]:
+        raise Exception("image size unmatch")
+    if tf.__version__ <= '0.11':
+        bgr = tf.concat(3, [
+            blue - VGG_MEAN[0],
+            green - VGG_MEAN[1],
+            red - VGG_MEAN[2],
+        ])
+    else:
+        bgr = tf.concat(
+            [
+                blue - VGG_MEAN[0],
+                green - VGG_MEAN[1],
+                red - VGG_MEAN[2],
+            ], axis=3)
+    if bgr.get_shape().as_list()[1:] != [224, 224, 3]:
+        raise Exception("image size unmatch")
+    # input layer
+    net_in = InputLayer(bgr, name='input')
+    # conv1
+    network = Conv2dLayer(net_in, act=tf.nn.relu, shape=[3, 3, 3, 64], strides=[1, 1, 1, 1], padding='SAME', name='conv1_1')
+    network = Conv2dLayer(network, act=tf.nn.relu, shape=[3, 3, 64, 64], strides=[1, 1, 1, 1], padding='SAME', name='conv1_2')
+    network = PoolLayer(network, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', pool=tf.nn.max_pool, name='pool1')
+    # conv2
+    network = Conv2dLayer(network, act=tf.nn.relu, shape=[3, 3, 64, 128], strides=[1, 1, 1, 1], padding='SAME', name='conv2_1')
+    network = Conv2dLayer(network, act=tf.nn.relu, shape=[3, 3, 128, 128], strides=[1, 1, 1, 1], padding='SAME', name='conv2_2')
+    network = PoolLayer(network, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', pool=tf.nn.max_pool, name='pool2')
+    # conv3
+    network = Conv2dLayer(network, act=tf.nn.relu, shape=[3, 3, 128, 256], strides=[1, 1, 1, 1], padding='SAME', name='conv3_1')
+    network = Conv2dLayer(network, act=tf.nn.relu, shape=[3, 3, 256, 256], strides=[1, 1, 1, 1], padding='SAME', name='conv3_2')
+    network = Conv2dLayer(network, act=tf.nn.relu, shape=[3, 3, 256, 256], strides=[1, 1, 1, 1], padding='SAME', name='conv3_3')
+    network = Conv2dLayer(network, act=tf.nn.relu, shape=[3, 3, 256, 256], strides=[1, 1, 1, 1], padding='SAME', name='conv3_4')
+    network = PoolLayer(network, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', pool=tf.nn.max_pool, name='pool3')
+    # conv4
+    network = Conv2dLayer(network, act=tf.nn.relu, shape=[3, 3, 256, 512], strides=[1, 1, 1, 1], padding='SAME', name='conv4_1')
+    network = Conv2dLayer(network, act=tf.nn.relu, shape=[3, 3, 512, 512], strides=[1, 1, 1, 1], padding='SAME', name='conv4_2')
+    network = Conv2dLayer(network, act=tf.nn.relu, shape=[3, 3, 512, 512], strides=[1, 1, 1, 1], padding='SAME', name='conv4_3')
+    network = Conv2dLayer(network, act=tf.nn.relu, shape=[3, 3, 512, 512], strides=[1, 1, 1, 1], padding='SAME', name='conv4_4')
+    network = PoolLayer(network, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', pool=tf.nn.max_pool, name='pool4')
+    # conv5
+    network = Conv2dLayer(network, act=tf.nn.relu, shape=[3, 3, 512, 512], strides=[1, 1, 1, 1], padding='SAME', name='conv5_1')
+    network = Conv2dLayer(network, act=tf.nn.relu, shape=[3, 3, 512, 512], strides=[1, 1, 1, 1], padding='SAME', name='conv5_2')
+    network = Conv2dLayer(network, act=tf.nn.relu, shape=[3, 3, 512, 512], strides=[1, 1, 1, 1], padding='SAME', name='conv5_3')
+    network = Conv2dLayer(network, act=tf.nn.relu, shape=[3, 3, 512, 512], strides=[1, 1, 1, 1], padding='SAME', name='conv5_4')
+    network = PoolLayer(network, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', pool=tf.nn.max_pool, name='pool5')
+    # fc 6~8
+    network = FlattenLayer(network, name='flatten')
+    network = DenseLayer(network, n_units=4096, act=tf.nn.relu, name='fc6')
+    network = DenseLayer(network, n_units=4096, act=tf.nn.relu, name='fc7')
+    network = DenseLayer(network, n_units=1000, act=tf.identity, name='fc8')
+    print("build model finished: %fs" % (time.time() - start_time))
+    return network
+
+
+def _Vgg19_simple_api(rgb):
+    """
+    Build the VGG 19 Model
+    Parameters
+    -----------
+    rgb : rgb image placeholder [batch, height, width, 3] values scaled [0, 1]
+    """
+    start_time = time.time()
+    print("build model started")
+    rgb_scaled = rgb * 255.0
+    # Convert RGB to BGR
+    if tf.__version__ <= '0.11':
+        red, green, blue = tf.split(3, 3, rgb_scaled)
+    else:  # TF 1.0
+        print(rgb_scaled)
+        red, green, blue = tf.split(rgb_scaled, 3, 3)
+    if red.get_shape().as_list()[1:] != [224, 224, 1]:
+        raise Exception("image size unmatch")
+    if green.get_shape().as_list()[1:] != [224, 224, 1]:
+        raise Exception("image size unmatch")
+    if blue.get_shape().as_list()[1:] != [224, 224, 1]:
+        raise Exception("image size unmatch")
+    if tf.__version__ <= '0.11':
+        bgr = tf.concat(3, [
+            blue - VGG_MEAN[0],
+            green - VGG_MEAN[1],
+            red - VGG_MEAN[2],
+        ])
+    else:
+        bgr = tf.concat(
+            [
+                blue - VGG_MEAN[0],
+                green - VGG_MEAN[1],
+                red - VGG_MEAN[2],
+            ], axis=3)
+    if bgr.get_shape().as_list()[1:] != [224, 224, 3]:
+        raise Exception("image size unmatch")
+    # input layer
+    net_in = InputLayer(bgr, name='input')
+    # conv1
+    network = Conv2d(net_in, n_filter=64, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv1_1')
+    network = Conv2d(network, n_filter=64, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv1_2')
+    network = MaxPool2d(network, filter_size=(2, 2), strides=(2, 2), padding='SAME', name='pool1')
+    # conv2
+    network = Conv2d(network, n_filter=128, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv2_1')
+    network = Conv2d(network, n_filter=128, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv2_2')
+    network = MaxPool2d(network, filter_size=(2, 2), strides=(2, 2), padding='SAME', name='pool2')
+    # conv3
+    network = Conv2d(network, n_filter=256, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv3_1')
+    network = Conv2d(network, n_filter=256, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv3_2')
+    network = Conv2d(network, n_filter=256, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv3_3')
+    network = Conv2d(network, n_filter=256, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv3_4')
+    network = MaxPool2d(network, filter_size=(2, 2), strides=(2, 2), padding='SAME', name='pool3')
+    # conv4
+    network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv4_1')
+    network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv4_2')
+    network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv4_3')
+    network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv4_4')
+    network = MaxPool2d(network, filter_size=(2, 2), strides=(2, 2), padding='SAME', name='pool4')
+    # conv5
+    network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv5_1')
+    network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv5_2')
+    network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv5_3')
+    network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv5_4')
+    network = MaxPool2d(network, filter_size=(2, 2), strides=(2, 2), padding='SAME', name='pool5')
+    # fc 6~8
+    network = FlattenLayer(network, name='flatten')
+    network = DenseLayer(network, n_units=4096, act=tf.nn.relu, name='fc6')
+    network = DenseLayer(network, n_units=4096, act=tf.nn.relu, name='fc7')
+    network = DenseLayer(network, n_units=1000, act=tf.identity, name='fc8')
+    print("build model finished: %fs" % (time.time() - start_time))
+    return network
+
+
+def get_vgg19(inputs, sess=None, pretrained=True):
+    network = _Vgg19(inputs)
+    if pretrained:
+        vgg19_npy_path = "../model_weights/vgg19.npy"
+        npz = np.load(vgg19_npy_path, encoding='latin1').item()
+        params = []
+        for val in sorted(npz.items()):
+            W = np.asarray(val[1][0])
+            b = np.asarray(val[1][1])
+            print("  Loading %s: %s, %s" % (val[0], W.shape, b.shape))
+            params.extend([W, b])
+        print("Restoring model from npz file")
+        tl.files.assign_params(sess, params, network)
+        return network
+    else:
+        tl.layers.initialize_global_variables(sess)
+        return network
+
+
+if __name__ == '__main__':
+    sess = tf.InteractiveSession(config=npu_config_proto())
+    x = tf.placeholder("float", [None, 224, 224, 3])
+    network = get_vgg19(x, sess)
+    y = network.outputs
+    probs = tf.nn.softmax(y, name="prob")
+    img1 = load_image(os.path.join(DATA_PATH, "tiger.jpeg"))  # test data in github
+    img1 = img1.reshape((1, 224, 224, 3))
+    start_time = time.time()
+    prob = sess.run(probs, feed_dict={x: img1})
+    print("End time : %.5ss" % (time.time() - start_time))
+
+    print_prob(prob[0])
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/requirements.txt b/TensorFlow/contrib/cv/InsightFace_TF/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..74202e087c0bac8702f38da9163d52ad98bdfae6
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/requirements.txt
@@ -0,0 +1,92 @@
+absl-py==0.12.0
+astor==0.8.1
+astunparse==1.6.3
+attrs==20.3.0
+Automat==20.2.0
+Babel==2.9.1
+cached-property==1.5.2
+cachetools==4.2.2
+certifi==2020.12.5
+cffi==1.14.5
+chardet==4.0.0
+constantly==15.1.0
+cryptography==3.4.6
+cssselect==1.1.0
+cycler==0.10.0
+decorator==5.1.0
+easygui===0.98.0-unreleased
+Faker==8.2.1
+flatbuffers==1.12
+gast==0.3.3
+google-auth==1.30.0
+google-auth-oauthlib==0.4.4
+google-pasta==0.2.0
+grpcio==1.32.0
+h5py==2.10.0
+hyperlink==21.0.0
+idna==2.10
+importlib-metadata==4.0.1
+incremental==17.5.0
+itemadapter==0.2.0
+itemloaders==1.0.4
+jmespath==0.10.0
+joblib==1.0.1
+Keras==2.2.5
+Keras-Applications==1.0.8
+Keras-Preprocessing==1.1.2
+keras-resnet==0.2.0
+kiwisolver==1.3.1
+lxml==4.6.2
+Markdown==3.3.4
+mxnet==1.9.0
+numpy==1.19.3
+oauthlib==3.1.0
+opencv-python==4.6.0.66
+opt-einsum==3.3.0
+paddlepaddle==2.2.0
+
+parsel==1.6.0
+Pillow==8.1.1
+Protego==0.1.16
+protobuf==3.15.8
+pyasn1==0.4.8
+pyasn1-modules==0.2.8
+pycparser==2.20
+PyDispatcher==2.0.5
+PyHamcrest==2.0.2
+pyOpenSSL==20.0.1
+pyparsing==2.4.7
+python-dateutil==2.8.1
+pytz==2021.1
+PyYAML==5.4.1
+queuelib==1.5.0
+requests==2.25.1
+requests-oauthlib==1.3.0
+rsa==4.7.2
+scikit-learn==0.24.2
+scipy==1.6.3
+Scrapy==2.4.1
+service-identity==18.1.0
+six==1.15.0
+sklearn==0.0
+tensorboard==1.14.0
+tensorboard-data-server==0.6.0
+tensorboard-plugin-wit==1.8.0
+tensorflow==1.14.0
+tensorflow-estimator==1.14.0
+tensorflow-gpu==1.14.0
+tensorlayer==1.7.0
+termcolor==1.1.0
+text-unidecode==1.3
+threadpoolctl==2.1.0
+torch==1.8.1
+torchtext==0.9.1
+tqdm==4.61.0
+Twisted==20.3.0
+typing-extensions==3.7.4.3
+urllib3==1.26.4
+w3lib==1.22.0
+Werkzeug==1.0.1
+wrapt==1.12.1
+zipp==3.4.1
+zope.interface==5.2.0
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/test/benchmark/README.md b/TensorFlow/contrib/cv/InsightFace_TF/test/benchmark/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..3b4a360a7b0e1ea8593259b1fd0a6d58b5cf1da9
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/test/benchmark/README.md
@@ -0,0 +1 @@
+##### Test max batch size
\ No newline at end of file
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/test/benchmark/__init__.py b/TensorFlow/contrib/cv/InsightFace_TF/test/benchmark/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/test/benchmark/gluon_batchsize_test.py b/TensorFlow/contrib/cv/InsightFace_TF/test/benchmark/gluon_batchsize_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..761fc8a7e73de2817de7734a3025bc0bc24d0feb
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/test/benchmark/gluon_batchsize_test.py
@@ -0,0 +1,62 @@
+from npu_bridge.npu_init import *
+from mxnet import gluon
+import mxnet as mx
+from mxnet import ndarray as nd
+import utils_final as utils
+import mxnet.gluon.nn as nn
+from mxnet import init
+import os
+from mxnet import initializer
+from mxnet.gluon.block import HybridBlock
+
+
+def prelu():
+    pass
+
+
+def inference():
+    net = gluon.nn.Sequential()
+    with net.name_scope():
+        net.add(nn.Conv2D(channels=64, kernel_size=3, padding=1))
+        net.add(nn.BatchNorm(axis=1, center=True, scale=True))
+        # net.add(mx.sym.LeakyReLU(data=net, act_type='prelu', name='prelu1'))
+        net.add(nn.Conv2D(channels=64, kernel_size=3, padding=1))
+        net.add(nn.BatchNorm(axis=1, center=True, scale=True))
+        net.add(nn.Conv2D(channels=64, kernel_size=3, padding=1, strides=2))
+        net.add(nn.BatchNorm(axis=1, center=True, scale=True))
+
+        net.add(nn.Conv2D(channels=128, kernel_size=3, padding=1))
+        net.add(nn.BatchNorm(axis=1, center=True, scale=True))
+        net.add(nn.Conv2D(channels=128, kernel_size=3, padding=1))
+        net.add(nn.BatchNorm(axis=1, center=True, scale=True))
+        net.add(nn.Conv2D(channels=128, kernel_size=3, padding=1, strides=2))
+        net.add(nn.BatchNorm(axis=1, center=True, scale=True))
+
+        net.add(nn.Conv2D(channels=256, kernel_size=3, padding=1))
+        net.add(nn.BatchNorm(axis=1, center=True, scale=True))
+        net.add(nn.Conv2D(channels=256, kernel_size=3, padding=1))
+        net.add(nn.BatchNorm(axis=1, center=True, scale=True))
+        net.add(nn.Conv2D(channels=256, kernel_size=3, padding=1, strides=2))
+        net.add(nn.BatchNorm(axis=1, center=True, scale=True))
+
+        net.add(nn.Flatten())
+        net.add(nn.Dense(10))
+    return net
+
+
+if __name__ == '__main__':
+    # without prelu and bn    7000< max batch size <8000
+    # with bn only            3000< max batch size <4000
+    os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+    batch_size = 3000
+    train_data, test_data = utils.load_data_mnist(batch_size=batch_size)
+    ctx = utils.try_gpu()
+    net = inference()
+    print(net)
+    net.initialize(ctx=ctx, init=init.Xavier())
+    softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
+    trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.01})
+    utils.train(train_data, test_data, net, softmax_cross_entropy, trainer, ctx, num_epochs=10)
+
+
+
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/test/benchmark/mxnet_batchsize_test.py b/TensorFlow/contrib/cv/InsightFace_TF/test/benchmark/mxnet_batchsize_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..592c3e41a763ccbaed755dc48cd717ed2d876564
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/test/benchmark/mxnet_batchsize_test.py
@@ -0,0 +1,68 @@
+from npu_bridge.npu_init import *
+import mxnet as mx
+import mxnet.ndarray as nd
+import os
+
+
+if __name__ == '__main__':
+    # without bn and prelu  max batchsize (40000, 50000)
+    # with bn max batchsize (20000, 30000)
+    # with prelu batchsize (20000, 30000)
+    # with bn and prelu max batchsize (10000, 20000)
+    os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+    batch_size = 10000
+    mnist = mx.test_utils.get_mnist()
+    print(mnist['train_data'].shape)
+    train_iter = mx.io.NDArrayIter(mnist['train_data'], mnist['train_label'], batch_size, shuffle=True)
+
+    # inference
+    data = mx.sym.var('data')
+    # first conv layer
+    net = mx.sym.Convolution(data=data, kernel=(3, 3), num_filter=64)
+    net = mx.sym.BatchNorm(data=net, fix_gamma=False, eps=2e-5, name='_bn1')
+    net = mx.sym.LeakyReLU(data=net, act_type='prelu', name='_preul1')
+    net = mx.sym.Convolution(data=data, kernel=(3, 3), num_filter=64)
+    net = mx.sym.BatchNorm(data=net, fix_gamma=False, eps=2e-5, name='_bn2')
+    net = mx.sym.LeakyReLU(data=net, act_type='prelu', name='_preul2')
+    net = mx.sym.Convolution(data=data, kernel=(3, 3), stride=(2, 2), num_filter=64)
+    net = mx.sym.BatchNorm(data=net, fix_gamma=False, eps=2e-5, name='_bn3')
+    net = mx.sym.LeakyReLU(data=net, act_type='prelu', name='_preul3')
+
+    net = mx.sym.Convolution(data=data, kernel=(3, 3), num_filter=128)
+    net = mx.sym.BatchNorm(data=net, fix_gamma=False, eps=2e-5, name='_bn4')
+    net = mx.sym.LeakyReLU(data=net, act_type='prelu', name='_preul4')
+    net = mx.sym.Convolution(data=data, kernel=(3, 3), num_filter=128)
+    net = mx.sym.BatchNorm(data=net, fix_gamma=False, eps=2e-5, name='_bn5')
+    net = mx.sym.LeakyReLU(data=net, act_type='prelu', name='_preul5')
+    net = mx.sym.Convolution(data=data, kernel=(3, 3), stride=(2, 2), num_filter=128)
+    net = mx.sym.BatchNorm(data=net, fix_gamma=False, eps=2e-5, name='_bn6')
+    net = mx.sym.LeakyReLU(data=net, act_type='prelu', name='_preul6')
+
+    net = mx.sym.Convolution(data=data, kernel=(3, 3), num_filter=256)
+    net = mx.sym.BatchNorm(data=net, fix_gamma=False, eps=2e-5, name='_bn7')
+    net = mx.sym.LeakyReLU(data=net, act_type='prelu', name='_preul7')
+    net = mx.sym.Convolution(data=data, kernel=(3, 3), num_filter=256)
+    net = mx.sym.BatchNorm(data=net, fix_gamma=False, eps=2e-5, name='_bn8')
+    net = mx.sym.LeakyReLU(data=net, act_type='prelu', name='_preul8')
+    net = mx.sym.Convolution(data=data, kernel=(3, 3), stride=(2, 2), num_filter=256)
+    net = mx.sym.BatchNorm(data=net, fix_gamma=False, eps=2e-5, name='_bn9')
+    net = mx.sym.LeakyReLU(data=net, act_type='prelu', name='_preul9')
+
+    flatten = mx.sym.flatten(data=net)
+    # MNIST has 10 classes
+    fc3 = mx.sym.FullyConnected(data=flatten, num_hidden=10)
+    # Softmax with cross entropy loss
+    mlp = mx.sym.SoftmaxOutput(data=fc3, name='softmax')
+
+    import logging
+
+    logging.getLogger().setLevel(logging.DEBUG)  # logging to stdout
+    # create a trainable module on GPU
+    mlp_model = mx.mod.Module(symbol=mlp, context=mx.gpu())
+    mlp_model.fit(train_iter,  # train data
+                  optimizer='sgd',  # use SGD to train
+                  optimizer_params={'learning_rate': 0.1},  # use fixed learning rate
+                  eval_metric='acc',  # report accuracy during training
+                  batch_end_callback=mx.callback.Speedometer(batch_size, 100),
+                  # output progress for each 100 data batches
+                  num_epoch=10)  # train for at most 10 dataset passes
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/test/benchmark/resnet_slim_benchmark.py b/TensorFlow/contrib/cv/InsightFace_TF/test/benchmark/resnet_slim_benchmark.py
new file mode 100644
index 0000000000000000000000000000000000000000..017aae37a60499fdfc8e4223d1356737a21dca60
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/test/benchmark/resnet_slim_benchmark.py
@@ -0,0 +1,28 @@
+from npu_bridge.npu_init import *
+import tensorflow as tf
+import tensorflow.contrib.slim.nets as nets
+import numpy as np
+
+
+slim = tf.contrib.slim
+resnet = nets.resnet_v1
+
+if __name__ == '__main__':
+    output_shape = 85164
+    batch_size = 64
+    image = tf.placeholder(name='input_x', shape=[None, 224, 224, 3], dtype=tf.float32)
+    labels = tf.placeholder(name='input_label', shape=[None, output_shape], dtype=tf.float32)
+    with slim.arg_scope(nets.resnet_utils.resnet_arg_scope()):
+        resnet_50, end_points = resnet.resnet_v1_50(inputs=image, num_classes=output_shape, scope='resnet_v1_50')
+        prob = tf.squeeze(resnet_50, axis=[1, 2])
+    probabilities = tf.reduce_mean(tf.nn.softmax(prob, dim=-1))
+    losses = tf.norm(tf.subtract(probabilities, labels))
+    train_op = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(losses)
+    sess = tf.Session(config=npu_config_proto())
+    saver = tf.train.Saver()
+    sess.run(tf.global_variables_initializer())
+    while True:
+        datasets = np.random.randn(batch_size, 224, 224, 3).astype(np.float32)
+        datasets_labels = np.random.randn(batch_size, output_shape).astype(np.float32)
+        losses_val, _ = sess.run([losses, train_op], feed_dict={image: datasets, labels: datasets_labels})
+        print(losses_val)
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/test/benchmark/resnet_tl_benchmark.py b/TensorFlow/contrib/cv/InsightFace_TF/test/benchmark/resnet_tl_benchmark.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c9afe3fa52347cec5b0959771b26cfe842e98d1
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/test/benchmark/resnet_tl_benchmark.py
@@ -0,0 +1,30 @@
+from npu_bridge.npu_init import *
+import tensorflow as tf
+import tensorflow.contrib.slim.nets as nets
+import numpy as np
+from nets.resnet import get_resnet
+
+
+slim = tf.contrib.slim
+resnet = nets.resnet_v1
+
+if __name__ == '__main__':
+    output_shape = 85164
+    batch_size = 128
+    image = tf.placeholder(name='input_x', shape=[None, 224, 224, 3], dtype=tf.float32)
+    labels = tf.placeholder(name='input_label', shape=[None, output_shape], dtype=tf.float32)
+    with slim.arg_scope(nets.resnet_utils.resnet_arg_scope()):
+        nets = get_resnet(image, output_shape, 50, type='resnet', sess=None, pretrained=False)
+    print(nets.outputs)
+    probabilities = tf.reduce_mean(tf.nn.softmax(nets.outputs, dim=-1))
+    print(probabilities)
+    losses = tf.norm(tf.subtract(probabilities, labels))
+    train_op = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(losses)
+    sess = tf.Session(config=npu_config_proto())
+    saver = tf.train.Saver()
+    sess.run(tf.global_variables_initializer())
+    while True:
+        datasets = np.random.randn(batch_size, 224, 224, 3).astype(np.float32)
+        datasets_labels = np.random.randn(batch_size, output_shape).astype(np.float32)
+        losses_val, _ = sess.run([losses, train_op], feed_dict={image: datasets, labels: datasets_labels})
+        print(losses_val)
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/test/benchmark/tensorlayer_batchsize_test.py b/TensorFlow/contrib/cv/InsightFace_TF/test/benchmark/tensorlayer_batchsize_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..7b9a3883653fc75266a3a8e6d519f5fb4eb6d428
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/test/benchmark/tensorlayer_batchsize_test.py
@@ -0,0 +1,89 @@
+from npu_bridge.npu_init import *
+import tensorflow as tf
+import tensorlayer as tl
+import os
+
+
+def inference(x):
+    w_init_method = tf.contrib.layers.xavier_initializer(uniform=True)
+    # define the network
+    network = tl.layers.InputLayer(x, name='input')
+    network = tl.layers.Conv2d(network, n_filter=64, filter_size=(3, 3), strides=(1, 1), padding='SAME', act=None,
+                               W_init=w_init_method, name='conv1_1')
+    network = tl.layers.BatchNormLayer(network, act=tf.identity, is_train=True, name='bn1')
+    network = tl.layers.PReluLayer(network, name='prelu1')
+    network = tl.layers.Conv2d(network, n_filter=64, filter_size=(3, 3), strides=(1, 1), padding='SAME', act=None,
+                               W_init=w_init_method, name='conv1_2')
+    network = tl.layers.BatchNormLayer(network, act=tf.identity, is_train=True, name='bn2')
+    network = tl.layers.PReluLayer(network, name='prelu2')
+    network = tl.layers.Conv2d(network, n_filter=64, filter_size=(3, 3), strides=(2, 2), padding='SAME', act=None,
+                               W_init=w_init_method, name='conv1_3')
+    network = tl.layers.BatchNormLayer(network, act=tf.identity, is_train=True, name='bn3')
+    network = tl.layers.PReluLayer(network, name='prelu3')
+
+    network = tl.layers.Conv2d(network, n_filter=128, filter_size=(3, 3), strides=(1, 1), padding='SAME', act=None,
+                               W_init=w_init_method, name='conv2_1')
+    network = tl.layers.BatchNormLayer(network, act=tf.identity, is_train=True, name='bn4')
+    network = tl.layers.PReluLayer(network, name='prelu4')
+
+    network = tl.layers.Conv2d(network, n_filter=128, filter_size=(3, 3), strides=(1, 1), padding='SAME', act=None,
+                               W_init=w_init_method, name='conv2_2')
+    network = tl.layers.BatchNormLayer(network, act=tf.identity, is_train=True, name='bn5')
+    network = tl.layers.PReluLayer(network, name='prelu5')
+    network = tl.layers.Conv2d(network, n_filter=128, filter_size=(3, 3), strides=(2, 2), padding='SAME', act=None,
+                               W_init=w_init_method, name='conv2_3')
+    network = tl.layers.BatchNormLayer(network, act=tf.identity, is_train=True, name='bn6')
+    network = tl.layers.PReluLayer(network, name='prelu6')
+
+    network = tl.layers.Conv2d(network, n_filter=256, filter_size=(3, 3), strides=(1, 1), padding='SAME', act=None,
+                               W_init=w_init_method, name='conv3_1')
+    network = tl.layers.BatchNormLayer(network, act=tf.identity, is_train=True, name='bn7')
+    network = tl.layers.PReluLayer(network, name='prelu7')
+    network = tl.layers.Conv2d(network, n_filter=256, filter_size=(3, 3), strides=(1, 1), padding='SAME', act=None,
+                               W_init=w_init_method, name='conv3_2')
+    network = tl.layers.BatchNormLayer(network, act=tf.identity, is_train=True, name='bn8')
+    network = tl.layers.PReluLayer(network, name='prelu8')
+    network = tl.layers.Conv2d(network, n_filter=256, filter_size=(3, 3), strides=(2, 2), padding='SAME', act=None,
+                               W_init=w_init_method, name='conv3_3')
+    network = tl.layers.BatchNormLayer(network, act=tf.identity, is_train=True, name='bn9')
+    network = tl.layers.PReluLayer(network, name='prelu9')
+
+    network = tl.layers.FlattenLayer(network, name='flatten')
+    network = tl.layers.DenseLayer(network, 10)
+
+    return network.outputs
+
+
+if __name__ == '__main__':
+    # without bn prelu     8000< max batch size <9000
+    # with bn only         5000< max batch size <6000
+    # with prelu only      3000< max batch size <4000
+    # with bn and prelu    2000< max batch size <3000
+    os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+    batch_size = 2000
+    n_epoch = 10
+    # prepare data
+    X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1, 28, 28, 1))
+    # define placeholder
+    x = tf.placeholder(tf.float32, shape=[None, 28, 28, 1], name='x')
+    y_ = tf.placeholder(tf.int64, shape=[None], name='y_')
+
+    output = inference(x)
+    cost = tl.cost.cross_entropy(output, y_, 'cost')
+    train_op = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)
+
+    sess = tf.Session(config=npu_config_proto())
+    tl.layers.initialize_global_variables(sess)
+
+    correct_prediction = tf.equal(tf.argmax(output, 1), y_)
+    acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
+
+    for epoch in range(n_epoch):
+        train_loss, train_acc, n_batch = 0, 0, 0
+        for X_train_a, y_train_a in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=True):
+            feed_dict = {x: X_train_a, y_: y_train_a}
+            _, err, ac = sess.run([train_op, cost, acc], feed_dict=feed_dict)
+            train_loss += err
+            train_acc += ac
+            n_batch += 1
+        print("epoch %d, train acc: %f" % (epoch, (train_acc / n_batch)))
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/test/benchmark/utils_final.py b/TensorFlow/contrib/cv/InsightFace_TF/test/benchmark/utils_final.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb65184e4bb7d837f8dd108a30eac834392b7b03
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/test/benchmark/utils_final.py
@@ -0,0 +1,386 @@
+from npu_bridge.npu_init import *
+from math import exp
+from mxnet import gluon
+from mxnet import autograd
+from mxnet import nd
+from mxnet import image
+from mxnet.gluon import nn
+import mxnet as mx
+import numpy as np
+from time import time
+import matplotlib.pyplot as plt
+import random
+
+
+class DataLoader(object):
+    """similiar to gluon.data.DataLoader, but might be faster.
+
+    The main difference this data loader tries to read more exmaples each
+    time. But the limits are 1) all examples in dataset have the same shape, 2)
+    data transfomer needs to process multiple examples at each time
+    """
+
+    def __init__(self, dataset, batch_size, shuffle, transform=None):
+        self.dataset = dataset
+        self.batch_size = batch_size
+        self.shuffle = shuffle
+        self.transform = transform
+
+    def __iter__(self):
+        data = self.dataset[:]
+        X = data[0]
+        y = nd.array(data[1])
+        n = X.shape[0]
+        if self.shuffle:
+            idx = np.arange(n)
+            np.random.shuffle(idx)
+            X = nd.array(X.asnumpy()[idx])
+            y = nd.array(y.asnumpy()[idx])
+
+        for i in range(n // self.batch_size):
+            if self.transform is not None:
+                yield self.transform(X[i * self.batch_size:(i + 1) * self.batch_size],
+                                     y[i * self.batch_size:(i + 1) * self.batch_size])
+            else:
+                yield (X[i * self.batch_size:(i + 1) * self.batch_size],
+                       y[i * self.batch_size:(i + 1) * self.batch_size])
+
+    def __len__(self):
+        return len(self.dataset) // self.batch_size
+
+
+def load_data_fashion_mnist(batch_size, resize=None, root="~/.mxnet/datasets/fashion-mnist"):
+    """download the fashion mnist dataest and then load into memory"""
+
+    def transform_mnist(data, label):
+        # Transform a batch of examples.
+        if resize:
+            n = data.shape[0]
+            new_data = nd.zeros((n, resize, resize, data.shape[3]))
+            for i in range(n):
+                new_data[i] = image.imresize(data[i], resize, resize)
+            data = new_data
+        # change data from batch x height x width x channel to batch x channel x height x width
+        return nd.transpose(data.astype('float32'), (0, 3, 1, 2)) / 255, label.astype('float32')
+
+    mnist_train = gluon.data.vision.FashionMNIST(root=root, train=True, transform=None)
+    mnist_test = gluon.data.vision.FashionMNIST(root=root, train=False, transform=None)
+    # Transform later to avoid memory explosion.
+    train_data = DataLoader(mnist_train, batch_size, shuffle=True, transform=transform_mnist)
+    test_data = DataLoader(mnist_test, batch_size, shuffle=False, transform=transform_mnist)
+    return (train_data, test_data)
+
+
+def load_data_mnist(batch_size, resize=None, root="~/.mxnet/datasets/mnist"):
+    """download the fashion mnist dataest and then load into memory"""
+
+    def transform_mnist(data, label):
+        # Transform a batch of examples.
+        if resize:
+            n = data.shape[0]
+            new_data = nd.zeros((n, resize, resize, data.shape[3]))
+            for i in range(n):
+                new_data[i] = image.imresize(data[i], resize, resize)
+            data = new_data
+        # change data from batch x height x width x channel to batch x channel x height x width
+        return nd.transpose(data.astype('float32'), (0, 3, 1, 2)) / 255, label.astype('float32')
+
+    mnist_train = gluon.data.vision.MNIST(root=root, train=True, transform=None)
+    mnist_test = gluon.data.vision.MNIST(root=root, train=False, transform=None)
+    # Transform later to avoid memory explosion.
+    train_data = DataLoader(mnist_train, batch_size, shuffle=True, transform=transform_mnist)
+    test_data = DataLoader(mnist_test, batch_size, shuffle=False, transform=transform_mnist)
+    return (train_data, test_data)
+
+
+def try_gpu():
+    """If GPU is available, return mx.gpu(0); else return mx.cpu()"""
+    try:
+        ctx = mx.gpu()
+        _ = nd.array([0], ctx=ctx)
+    except:
+        ctx = mx.cpu()
+    return ctx
+
+
+def try_all_gpus():
+    """Return all available GPUs, or [mx.gpu()] if there is no GPU"""
+    ctx_list = []
+    try:
+        for i in range(16):
+            ctx = mx.gpu(i)
+            _ = nd.array([0], ctx=ctx)
+            ctx_list.append(ctx)
+    except:
+        pass
+    if not ctx_list:
+        ctx_list = [mx.cpu()]
+    return ctx_list
+
+
+def SGD(params, lr):
+    for param in params:
+        param[:] = param - lr * param.grad
+
+
+def accuracy(output, label):
+    return nd.mean(output.argmax(axis=1) == label).asscalar()
+
+
+def _get_batch(batch, ctx):
+    """return data and label on ctx"""
+    if isinstance(batch, mx.io.DataBatch):
+        data = batch.data[0]
+        label = batch.label[0]
+    else:
+        data, label = batch
+    return (gluon.utils.split_and_load(data, ctx),
+            gluon.utils.split_and_load(label, ctx),
+            data.shape[0])
+
+
+def evaluate_accuracy(data_iterator, net, ctx=[mx.cpu()]):
+    if isinstance(ctx, mx.Context):
+        ctx = [ctx]
+    acc = nd.array([0])
+    n = 0.
+    if isinstance(data_iterator, mx.io.MXDataIter):
+        data_iterator.reset()
+    for batch in data_iterator:
+        data, label, batch_size = _get_batch(batch, ctx)
+        for X, y in zip(data, label):
+            acc += nd.sum(net(X).argmax(axis=1) == y).copyto(mx.cpu())
+            n += y.size
+        acc.wait_to_read()  # don't push too many operators into backend
+    return acc.asscalar() / n
+
+
+def train(train_data, test_data, net, loss, trainer, ctx, num_epochs, print_batches=None):
+    """Train a network"""
+    print("Start training on ", ctx)
+    if isinstance(ctx, mx.Context):
+        ctx = [ctx]
+    for epoch in range(num_epochs):
+        train_loss, train_acc, n, m = 0.0, 0.0, 0.0, 0.0
+        if isinstance(train_data, mx.io.MXDataIter):
+            train_data.reset()
+        start = time()
+        for i, batch in enumerate(train_data):
+            data, label, batch_size = _get_batch(batch, ctx)
+            losses = []
+            with autograd.record():
+                outputs = [net(X) for X in data]
+                losses = [loss(yhat, y) for yhat, y in zip(outputs, label)]
+            for l in losses:
+                l.backward()
+            train_acc += sum([(yhat.argmax(axis=1) == y).sum().asscalar()
+                              for yhat, y in zip(outputs, label)])
+            train_loss += sum([l.sum().asscalar() for l in losses])
+            trainer.step(batch_size)
+            n += batch_size
+            m += sum([y.size for y in label])
+            if print_batches and (i + 1) % print_batches == 0:
+                print("Batch %d. Loss: %f, Train acc %f" % (
+                    n, train_loss / n, train_acc / m
+                ))
+
+        test_acc = evaluate_accuracy(test_data, net, ctx)
+        print("Epoch %d. Loss: %.3f, Train acc %.2f, Test acc %.2f, Time %.1f sec" % (
+            epoch, train_loss / n, train_acc / m, test_acc, time() - start
+        ))
+
+
+class Residual(nn.HybridBlock):
+    def __init__(self, channels, same_shape=True, **kwargs):
+        super(Residual, self).__init__(**kwargs)
+        self.same_shape = same_shape
+        with self.name_scope():
+            strides = 1 if same_shape else 2
+            self.conv1 = nn.Conv2D(channels, kernel_size=3, padding=1,
+                                   strides=strides)
+            self.bn1 = nn.BatchNorm()
+            self.conv2 = nn.Conv2D(channels, kernel_size=3, padding=1)
+            self.bn2 = nn.BatchNorm()
+            if not same_shape:
+                self.conv3 = nn.Conv2D(channels, kernel_size=1,
+                                       strides=strides)
+
+    def hybrid_forward(self, F, x):
+        out = F.relu(self.bn1(self.conv1(x)))
+        out = self.bn2(self.conv2(out))
+        if not self.same_shape:
+            x = self.conv3(x)
+        return F.relu(out + x)
+
+
+def resnet18(num_classes):
+    net = nn.HybridSequential()
+    with net.name_scope():
+        net.add(
+            nn.BatchNorm(),
+            nn.Conv2D(64, kernel_size=3, strides=1),
+            nn.MaxPool2D(pool_size=3, strides=2),
+            Residual(64),
+            Residual(64),
+            Residual(128, same_shape=False),
+            Residual(128),
+            Residual(256, same_shape=False),
+            Residual(256),
+            nn.GlobalAvgPool2D(),
+            nn.Dense(num_classes)
+        )
+    return net
+
+
+def show_images(imgs, nrows, ncols, figsize=None):
+    """plot a list of images"""
+    if not figsize:
+        figsize = (ncols, nrows)
+    _, figs = plt.subplots(nrows, ncols, figsize=figsize)
+    for i in range(nrows):
+        for j in range(ncols):
+            figs[i][j].imshow(imgs[i * ncols + j].asnumpy())
+            figs[i][j].axes.get_xaxis().set_visible(False)
+            figs[i][j].axes.get_yaxis().set_visible(False)
+    plt.show()
+
+
+def data_iter_random(corpus_indices, batch_size, num_steps, ctx=None):
+    """Sample mini-batches in a random order from sequential data."""
+    # Subtract 1 because label indices are corresponding input indices + 1.
+    num_examples = (len(corpus_indices) - 1) // num_steps
+    epoch_size = num_examples // batch_size
+    # Randomize samples.
+    example_indices = list(range(num_examples))
+    random.shuffle(example_indices)
+
+    def _data(pos):
+        return corpus_indices[pos: pos + num_steps]
+
+    for i in range(epoch_size):
+        # Read batch_size random samples each time.
+        i = i * batch_size
+        batch_indices = example_indices[i: i + batch_size]
+        data = nd.array(
+            [_data(j * num_steps) for j in batch_indices], ctx=ctx)
+        label = nd.array(
+            [_data(j * num_steps + 1) for j in batch_indices], ctx=ctx)
+        yield data, label
+
+
+def data_iter_consecutive(corpus_indices, batch_size, num_steps, ctx=None):
+    """Sample mini-batches in a consecutive order from sequential data."""
+    corpus_indices = nd.array(corpus_indices, ctx=ctx)
+    data_len = len(corpus_indices)
+    batch_len = data_len // batch_size
+
+    indices = corpus_indices[0: batch_size * batch_len].reshape((
+        batch_size, batch_len))
+    # Subtract 1 because label indices are corresponding input indices + 1.
+    epoch_size = (batch_len - 1) // num_steps
+
+    for i in range(epoch_size):
+        i = i * num_steps
+        data = indices[:, i: i + num_steps]
+        label = indices[:, i + 1: i + num_steps + 1]
+        yield data, label
+
+
+def grad_clipping(params, clipping_norm, ctx):
+    """Gradient clipping."""
+    if clipping_norm is not None:
+        norm = nd.array([0.0], ctx)
+        for p in params:
+            norm += nd.sum(p.grad ** 2)
+        norm = nd.sqrt(norm).asscalar()
+        if norm > clipping_norm:
+            for p in params:
+                p.grad[:] *= clipping_norm / norm
+
+
+def predict_rnn(rnn, prefix, num_chars, params, hidden_dim, ctx, idx_to_char,
+                char_to_idx, get_inputs, is_lstm=False):
+    """Predict the next chars given the prefix."""
+    prefix = prefix.lower()
+    state_h = nd.zeros(shape=(1, hidden_dim), ctx=ctx)
+    if is_lstm:
+        state_c = nd.zeros(shape=(1, hidden_dim), ctx=ctx)
+    output = [char_to_idx[prefix[0]]]
+    for i in range(num_chars + len(prefix)):
+        X = nd.array([output[-1]], ctx=ctx)
+        if is_lstm:
+            Y, state_h, state_c = rnn(get_inputs(X), state_h, state_c, *params)
+        else:
+            Y, state_h = rnn(get_inputs(X), state_h, *params)
+        if i < len(prefix) - 1:
+            next_input = char_to_idx[prefix[i + 1]]
+        else:
+            next_input = int(Y[0].argmax(axis=1).asscalar())
+        output.append(next_input)
+    return ''.join([idx_to_char[i] for i in output])
+
+
+def train_and_predict_rnn(rnn, is_random_iter, epochs, num_steps, hidden_dim,
+                          learning_rate, clipping_norm, batch_size,
+                          pred_period, pred_len, seqs, get_params, get_inputs,
+                          ctx, corpus_indices, idx_to_char, char_to_idx,
+                          is_lstm=False):
+    """Train an RNN model and predict the next item in the sequence."""
+    if is_random_iter:
+        data_iter = data_iter_random
+    else:
+        data_iter = data_iter_consecutive
+    params = get_params()
+
+    softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
+
+    for e in range(1, epochs + 1):
+        # If consecutive sampling is used, in the same epoch, the hidden state
+        # is initialized only at the beginning of the epoch.
+        if not is_random_iter:
+            state_h = nd.zeros(shape=(batch_size, hidden_dim), ctx=ctx)
+            if is_lstm:
+                state_c = nd.zeros(shape=(batch_size, hidden_dim), ctx=ctx)
+        train_loss, num_examples = 0, 0
+        for data, label in data_iter(corpus_indices, batch_size, num_steps,
+                                     ctx):
+            # If random sampling is used, the hidden state has to be
+            # initialized for each mini-batch.
+            if is_random_iter:
+                state_h = nd.zeros(shape=(batch_size, hidden_dim), ctx=ctx)
+                if is_lstm:
+                    state_c = nd.zeros(shape=(batch_size, hidden_dim), ctx=ctx)
+            with autograd.record():
+                # outputs shape: (batch_size, vocab_size)
+                if is_lstm:
+                    outputs, state_h, state_c = rnn(get_inputs(data), state_h,
+                                                    state_c, *params)
+                else:
+                    outputs, state_h = rnn(get_inputs(data), state_h, *params)
+                # Let t_ib_j be the j-th element of the mini-batch at time i.
+                # label shape: (batch_size * num_steps)
+                # label = [t_0b_0, t_0b_1, ..., t_1b_0, t_1b_1, ..., ].
+                label = label.T.reshape((-1,))
+                # Concatenate outputs:
+                # shape: (batch_size * num_steps, vocab_size).
+                outputs = nd.concat(*outputs, dim=0)
+                # Now outputs and label are aligned.
+                loss = softmax_cross_entropy(outputs, label)
+            loss.backward()
+
+            grad_clipping(params, clipping_norm, ctx)
+            SGD(params, learning_rate)
+
+            train_loss += nd.sum(loss).asscalar()
+            num_examples += loss.size
+
+        if e % pred_period == 0:
+            print("Epoch %d. Training perplexity %f" % (e,
+                                                        exp(train_loss / num_examples)))
+            for seq in seqs:
+                print(' - ', predict_rnn(rnn, seq, pred_len, params,
+                                         hidden_dim, ctx, idx_to_char, char_to_idx, get_inputs,
+                                         is_lstm))
+            print()
+
+
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/test/benchmark/vgg19_slim_benchmark.py b/TensorFlow/contrib/cv/InsightFace_TF/test/benchmark/vgg19_slim_benchmark.py
new file mode 100644
index 0000000000000000000000000000000000000000..e3cdc64537f2d6fdd347bb05e797c284df6999ff
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/test/benchmark/vgg19_slim_benchmark.py
@@ -0,0 +1,25 @@
+from npu_bridge.npu_init import *
+import tensorflow as tf
+import tensorflow.contrib.slim.nets as nets
+import numpy as np
+
+slim = tf.contrib.slim
+
+if __name__ == '__main__':
+    output_shape = 1000
+    batch_size = 128
+    image = tf.placeholder(name='input_x', shape=[None, 224, 224, 3], dtype=tf.float32)
+    labels = tf.placeholder(name='input_label', shape=[None, output_shape], dtype=tf.float32)
+    with slim.arg_scope(nets.vgg.vgg_arg_scope()):
+        vgg_19, end_points = nets.vgg.vgg_19(inputs=image, num_classes=output_shape, scope='vgg_19')
+    probabilities = tf.reduce_mean(tf.nn.softmax(vgg_19, dim=-1))
+    losses = tf.norm(tf.subtract(probabilities, labels))
+    train_op = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(losses)
+    sess = tf.Session(config=npu_config_proto())
+    saver = tf.train.Saver()
+    sess.run(tf.global_variables_initializer())
+    while True:
+        datasets = np.random.randn(batch_size, 224, 224, 3).astype(np.float32)
+        datasets_labels = np.random.randn(batch_size, output_shape).astype(np.float32)
+        losses_val, _ = sess.run([losses, train_op], feed_dict={image: datasets, labels: datasets_labels})
+        print(losses_val)
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/test/benchmark/vgg19_tl_benchmark.py b/TensorFlow/contrib/cv/InsightFace_TF/test/benchmark/vgg19_tl_benchmark.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ca1ac5512d546df17fb52a9462026bd15bc40fb
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/test/benchmark/vgg19_tl_benchmark.py
@@ -0,0 +1,23 @@
+from npu_bridge.npu_init import *
+import tensorflow as tf
+from nets.vgg19 import get_vgg19
+import numpy as np
+
+
+if __name__ == '__main__':
+    sess = tf.Session(config=npu_config_proto())
+    x = tf.placeholder(name="inputs_x", shape=[None, 224, 224, 3], dtype=tf.float32)
+    y = tf.placeholder(name='inputs_y', shape=[None, 1000], dtype=tf.float32)
+    network = get_vgg19(x, sess, pretrained=False)
+    outputs_y = network.outputs
+    probs = tf.nn.softmax(outputs_y, name="prob")
+    loss = tf.reduce_mean(tf.subtract(probs, y))
+
+    while True:
+        batch_size = 128
+        datasets_x = np.random.randn(batch_size, 224, 224, 3).astype(np.float32)
+        datasets_y = np.random.randn(batch_size, 1000).astype(np.float32)
+        feed_dict = {x: datasets_x, y: datasets_y}
+        loss_val = sess.run(loss, feed_dict=feed_dict)
+        print('batch size %d, loss value is %.2f' % (batch_size, loss_val))
+
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/test/memory_usage_test.py b/TensorFlow/contrib/cv/InsightFace_TF/test/memory_usage_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..2fb4aaf7bdbe6847f5701efe013c5dc683bc40cb
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/test/memory_usage_test.py
@@ -0,0 +1,124 @@
+from npu_bridge.npu_init import *
+import mxnet as mx
+import argparse
+import PIL.Image
+import io
+import numpy as np
+import cv2
+import tensorflow as tf
+import os
+import sys
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+        description='data path information'
+    )
+    parser.add_argument('--bin_path', default='../datasets/faces_ms1m_112x112/train.rec', type=str,
+                        help='path to the binary image file')
+    parser.add_argument('--idx_path', default='../datasets/faces_ms1m_112x112/train.idx', type=str,
+                        help='path to the image index path')
+    parser.add_argument('--tfrecords_file_path', default='../datasets/tfrecords', type=str,
+                        help='path to the output of tfrecords file path')
+    args = parser.parse_args()
+    return args
+
+
+def mx2tfrecords_mem_test(imgidx, imgrec, args):
+    output_path = os.path.join(args.tfrecords_file_path, 'tran.tfrecords')
+    writer = tf.python_io.TFRecordWriter(output_path)
+    for i in imgidx:
+        img_info = imgrec.read_idx(i)
+        header, img = mx.recordio.unpack(img_info)
+        print(type(img))
+        print(img)
+        print(sys.getsizeof(img))
+        print('#####################')
+        img_mx = mx.image.imdecode(img)
+        print(type(img_mx))
+        print(sys.getsizeof(img_mx))
+        print(img_mx.size)
+        print(img_mx.dtype)
+        print(img_mx.context)
+        print(img_mx.stype)
+        print(img_mx)
+        print('#####################')
+        img_mx_np = img_mx.asnumpy()
+        print(type(img_mx_np))
+        print(sys.getsizeof(img_mx_np))
+        print('#####################')
+        back_mx_ndarray = mx.nd.array(img_mx_np)
+        print(type(back_mx_ndarray))
+        print(sys.getsizeof(back_mx_ndarray))
+        encoded_jpg_io = io.BytesIO(img)
+        print(sys.getsizeof(encoded_jpg_io))
+        image = PIL.Image.open(encoded_jpg_io)
+        np_img = np.array(image)
+        img = cv2.cvtColor(np_img, cv2.COLOR_RGB2BGR)
+        print(sys.getsizeof(img))
+        print('#####################')
+        img_raw = img.tobytes()
+        print(sys.getsizeof(img))
+        print('#####################')
+    writer.close()
+
+
+def mx2tfrecords(imgidx, imgrec, args):
+    output_path = os.path.join(args.tfrecords_file_path, 'tran.tfrecords')
+    writer = tf.python_io.TFRecordWriter(output_path)
+    for i in imgidx:
+        img_info = imgrec.read_idx(i)
+        header, img = mx.recordio.unpack(img_info)
+        # encoded_jpg_io = io.BytesIO(img)
+        # image = PIL.Image.open(encoded_jpg_io)
+        # np_img = np.array(image)
+        # img = cv2.cvtColor(np_img, cv2.COLOR_RGB2BGR)
+        # img_raw = img.tobytes()
+        # images = tf.image.decode_jpeg(img)
+        # images = tf.reshape(images, shape=(112, 112, 3))
+        # r, g, b = tf.split(images, num_or_size_splits=3, axis=-1)
+        # images = tf.concat([b, g, r], axis=-1)
+        # sess = tf.Session()
+        # np_images = sess.run(images)
+        # print(images.shape)
+        # print(type(np_images))
+        # print(sys.getsizeof(np_images))
+        # cv2.imshow('test', np_images)
+        # cv2.waitKey(0)
+        label = int(header.label)
+        example = tf.train.Example(features=tf.train.Features(feature={
+            'image_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img])),
+            "label": tf.train.Feature(int64_list=tf.train.Int64List(value=[label]))
+        }))
+        writer.write(example.SerializeToString())  # Serialize To String
+        if i % 10000 == 0:
+            print('%d num image processed' % i)
+    writer.close()
+
+
+if __name__ == '__main__':
+    # define parameters
+    id2range = {}
+    data_shape = (3, 112, 112)
+    args = parse_args()
+    imgrec = mx.recordio.MXIndexedRecordIO(args.idx_path, args.bin_path, 'r')
+    s = imgrec.read_idx(0)
+    header, _ = mx.recordio.unpack(s)
+    print(header.label)
+    imgidx = list(range(1, int(header.label[0])))
+    seq_identity = range(int(header.label[0]), int(header.label[1]))
+    for identity in seq_identity:
+        s = imgrec.read_idx(identity)
+        header, _ = mx.recordio.unpack(s)
+        a, b = int(header.label[0]), int(header.label[1])
+        id2range[identity] = (a, b)
+    print('id2range', len(id2range))
+
+    # generate tfrecords
+    mx2tfrecords_mem_test(imgidx, imgrec, args)
+
+
+
+
+
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/test/multiple_gpu_test/__init__.py b/TensorFlow/contrib/cv/InsightFace_TF/test/multiple_gpu_test/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/test/multiple_gpu_test/test_mgpu_mnist.py b/TensorFlow/contrib/cv/InsightFace_TF/test/multiple_gpu_test/test_mgpu_mnist.py
new file mode 100644
index 0000000000000000000000000000000000000000..cabed8c75c8a34a23fd7aec9ffd5de48678927c9
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/test/multiple_gpu_test/test_mgpu_mnist.py
@@ -0,0 +1,225 @@
+from npu_bridge.npu_init import *
+import tensorflow as tf
+import tensorlayer as tl
+import os
+
+Layer = tl.layers.Layer
+D_TYPE = tf.float32
+
+class DenseLayer(Layer):
+    """
+    The :class:`DenseLayer` class is a fully connected layer.
+
+    Parameters
+    ----------
+    layer : a :class:`Layer` instance
+        The `Layer` class feeding into this layer.
+    n_units : int
+        The number of units of the layer.
+    act : activation function
+        The function that is applied to the layer activations.
+    W_init : weights initializer
+        The initializer for initializing the weight matrix.
+    b_init : biases initializer or None
+        The initializer for initializing the bias vector. If None, skip biases.
+    W_init_args : dictionary
+        The arguments for the weights tf.get_variable.
+    b_init_args : dictionary
+        The arguments for the biases tf.get_variable.
+    name : a string or None
+        An optional name to attach to this layer.
+
+    Examples
+    --------
+    >>> network = tl.layers.InputLayer(x, name='input_layer')
+    >>> network = tl.layers.DenseLayer(
+    ...                 network,
+    ...                 n_units=800,
+    ...                 act = tf.nn.relu,
+    ...                 W_init=tf.truncated_normal_initializer(stddev=0.1),
+    ...                 name ='relu_layer'
+    ...                 )
+
+    >>> Without TensorLayer, you can do as follow.
+    >>> W = tf.Variable(
+    ...     tf.random_uniform([n_in, n_units], -1.0, 1.0), name='W')
+    >>> b = tf.Variable(tf.zeros(shape=[n_units]), name='b')
+    >>> y = tf.nn.relu(tf.matmul(inputs, W) + b)
+
+    Notes
+    -----
+    If the input to this layer has more than two axes, it need to flatten the
+    input by using :class:`FlattenLayer` in this case.
+    """
+
+    def __init__(
+            self,
+            layer=None,
+            n_units=100,
+            act=tf.identity,
+            W_init=tf.truncated_normal_initializer(stddev=0.1),
+            b_init=tf.constant_initializer(value=0.0),
+            W_init_args={},
+            b_init_args={},
+            name='dense_layer',
+    ):
+        Layer.__init__(self, name=name)
+        self.inputs = layer.outputs
+        if self.inputs.get_shape().ndims != 2:
+            raise Exception("The input dimension must be rank 2, please reshape or flatten it")
+
+        n_in = int(self.inputs.get_shape()[-1])
+        self.n_units = n_units
+        print("  [TL] DenseLayer  %s: %d %s" % (self.name, self.n_units, act.__name__))
+        with tf.variable_scope(name) as vs:
+            with tf.device('/cpu:0'):
+                W = tf.get_variable(name='W', shape=(n_in, n_units), initializer=W_init, dtype=D_TYPE, **W_init_args)
+            if b_init is not None:
+                try:
+                    with tf.device('/cpu:0'):
+                        b = tf.get_variable(name='b', shape=(n_units), initializer=b_init, dtype=D_TYPE, **b_init_args)
+                except:  # If initializer is a constant, do not specify shape.
+                    with tf.device('/cpu:0'):
+                        b = tf.get_variable(name='b', initializer=b_init, dtype=D_TYPE, **b_init_args)
+                self.outputs = act(tf.matmul(self.inputs, W) + b)
+            else:
+                self.outputs = act(tf.matmul(self.inputs, W))
+
+        # Hint : list(), dict() is pass by value (shallow), without them, it is
+        # pass by reference.
+        self.all_layers = list(layer.all_layers)
+        self.all_params = list(layer.all_params)
+        self.all_drop = dict(layer.all_drop)
+        self.all_layers.extend([self.outputs])
+        if b_init is not None:
+            self.all_params.extend([W, b])
+        else:
+            self.all_params.extend([W])
+
+
+def inference(x):
+    network = tl.layers.InputLayer(x, name='input')
+    network = tl.layers.DropoutLayer(network, keep=0.8, name='drop1')
+    network = DenseLayer(network, n_units=800, act=tf.nn.relu, name='relu1')
+    network = tl.layers.DropoutLayer(network, keep=0.5, name='drop2')
+    network = DenseLayer(network, n_units=800, act=tf.nn.relu, name='relu2')
+    network = tl.layers.DropoutLayer(network, keep=0.5, name='drop3')
+    network = DenseLayer(network, n_units=10, act=tf.identity, name='output')
+    y = network.outputs
+    return y
+
+
+def load_data():
+    X_train, y_train, X_val, y_val, X_test, y_test = \
+        tl.files.load_mnist_dataset(shape=(-1, 784), path='/home/aurora/workspaces/data')
+    print('X_train.shape', X_train.shape)
+    print('y_train.shape', y_train.shape)
+    print('X_val.shape', X_val.shape)
+    print('y_val.shape', y_val.shape)
+    print('X_test.shape', X_test.shape)
+    print('y_test.shape', y_test.shape)
+    print('X %s   y %s' % (X_test.dtype, y_test.dtype))
+    return X_train, y_train
+
+
+def tower_losses(inputs, labels):
+    logit = inference(inputs)
+    loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logit, labels=labels, name='cross_entropy')
+    return loss
+
+
+def average_gradients(tower_grads):
+  """Calculate the average gradient for each shared variable across all towers.
+
+  Note that this function provides a synchronization point across all towers.
+
+  Args:
+    tower_grads: List of lists of (gradient, variable) tuples. The outer list
+      is over individual gradients. The inner list is over the gradient
+      calculation for each tower.
+  Returns:
+     List of pairs of (gradient, variable) where the gradient has been averaged
+     across all towers.
+  """
+  average_grads = []
+
+  for grad_and_vars in zip(*tower_grads):
+    # Note that each grad_and_vars looks like the following:
+    #   ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))
+    grads = []
+    for g, g1 in grad_and_vars:
+      # Add 0 dimension to the gradients to represent the tower.
+      expanded_g = tf.expand_dims(g, 0)
+
+      # Append on a 'tower' dimension which we will average over below.
+      grads.append(expanded_g)
+
+    # Average over the 'tower' dimension.
+    grad = tf.concat(axis=0, values=grads)
+    grad = tf.reduce_mean(grad, 0)
+
+    # Keep in mind that the Variables are redundant because they are shared
+    # across towers. So .. we will just return the first tower's pointer to
+    # the Variable.
+    v = grad_and_vars[0][1]
+    grad_and_var = (grad, v)
+    average_grads.append(grad_and_var)
+  return average_grads
+
+
+def train():
+    with tf.Graph().as_default(), tf.device('/cpu:0'):
+        global_step = tf.get_variable(
+            'global_step', [],
+            initializer=tf.constant_initializer(0), trainable=False)
+        # Decay the learning rate exponentially based on the number of steps.
+        lr = tf.train.exponential_decay(0.01,
+                                        global_step,
+                                        10000,
+                                        0.99,
+                                        staircase=True)
+        # Create an optimizer that performs gradient descent.
+        opt = tf.train.GradientDescentOptimizer(lr)
+        tower_grads = []
+        x = tf.placeholder(tf.float32, shape=[None, 784], name='x')
+        y_ = tf.placeholder(tf.int64, shape=[None, ], name='y_')
+        with tf.variable_scope(tf.get_variable_scope()):
+            for i in range(1):
+                with tf.device('/cpu:0'):
+                    with tf.name_scope('%s_%d' % ('tower', i)) as scope:
+                        tl.layers.set_name_reuse(True)
+                        # Dequeues one batch for the GPU
+                        # Calculate the loss for one tower of the CIFAR model. This function
+                        # constructs the entire CIFAR model but shares the variables across
+                        # all towers.
+                        summaries = tf.get_collection(tf.GraphKeys.SUMMARIES, scope)
+                        loss = tower_losses(x, y_)
+                        # Reuse variables for the next tower.
+                        tf.get_variable_scope().reuse_variables()
+                        # Calculate the gradients for the batch of data on this CIFAR tower.
+                        grads = opt.compute_gradients(loss)
+                        # Keep track of the gradients across all towers.
+                        tower_grads.append(grads)
+        # We must calculate the mean of each gradient. Note that this is the
+        # synchronization point across all towers.
+        grads = average_gradients(tower_grads)
+        apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)
+
+        # Track the moving averages of all trainable variables.
+        variable_averages = tf.train.ExponentialMovingAverage(0.9999, global_step)
+        variables_averages_op = variable_averages.apply(tf.trainable_variables())
+
+        train_op = tf.group(apply_gradient_op, variables_averages_op)
+        # Build an initialization operation to run below.
+        init = tf.global_variables_initializer()
+        sess = tf.Session(config=npu_config_proto(config_proto=tf.ConfigProto(
+            allow_soft_placement=True,
+            log_device_placement=True)))
+        sess.run(init)
+
+
+if __name__ == '__main__':
+    os.environ["CUDA_VISIBLE_DEVICES"] = "1"
+    train()
+
+
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/test/multiple_gpu_test/test_tensorlayer.py b/TensorFlow/contrib/cv/InsightFace_TF/test/multiple_gpu_test/test_tensorlayer.py
new file mode 100644
index 0000000000000000000000000000000000000000..73483480df9c16afa9612068cac9f59f7d5a0105
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/test/multiple_gpu_test/test_tensorlayer.py
@@ -0,0 +1,119 @@
+from npu_bridge.npu_init import *
+import tensorflow as tf
+import tensorlayer as tl
+
+Layer = tl.layers.Layer
+D_TYPE = tf.float32
+
+
+class DenseLayer(Layer):
+    """
+    The :class:`DenseLayer` class is a fully connected layer.
+
+    Parameters
+    ----------
+    layer : a :class:`Layer` instance
+        The `Layer` class feeding into this layer.
+    n_units : int
+        The number of units of the layer.
+    act : activation function
+        The function that is applied to the layer activations.
+    W_init : weights initializer
+        The initializer for initializing the weight matrix.
+    b_init : biases initializer or None
+        The initializer for initializing the bias vector. If None, skip biases.
+    W_init_args : dictionary
+        The arguments for the weights tf.get_variable.
+    b_init_args : dictionary
+        The arguments for the biases tf.get_variable.
+    name : a string or None
+        An optional name to attach to this layer.
+
+    Examples
+    --------
+    >>> network = tl.layers.InputLayer(x, name='input_layer')
+    >>> network = tl.layers.DenseLayer(
+    ...                 network,
+    ...                 n_units=800,
+    ...                 act = tf.nn.relu,
+    ...                 W_init=tf.truncated_normal_initializer(stddev=0.1),
+    ...                 name ='relu_layer'
+    ...                 )
+
+    >>> Without TensorLayer, you can do as follow.
+    >>> W = tf.Variable(
+    ...     tf.random_uniform([n_in, n_units], -1.0, 1.0), name='W')
+    >>> b = tf.Variable(tf.zeros(shape=[n_units]), name='b')
+    >>> y = tf.nn.relu(tf.matmul(inputs, W) + b)
+
+    Notes
+    -----
+    If the input to this layer has more than two axes, it need to flatten the
+    input by using :class:`FlattenLayer` in this case.
+    """
+
+    def __init__(
+            self,
+            layer=None,
+            n_units=100,
+            act=tf.identity,
+            W_init=tf.truncated_normal_initializer(stddev=0.1),
+            b_init=tf.constant_initializer(value=0.0),
+            W_init_args={},
+            b_init_args={},
+            name='dense_layer',
+    ):
+        Layer.__init__(self, name=name)
+        self.inputs = layer.outputs
+        if self.inputs.get_shape().ndims != 2:
+            raise Exception("The input dimension must be rank 2, please reshape or flatten it")
+
+        n_in = int(self.inputs.get_shape()[-1])
+        self.n_units = n_units
+        print("  [TL] DenseLayer  %s: %d %s" % (self.name, self.n_units, act.__name__))
+        with tf.variable_scope(name) as vs:
+            with tf.device('/cpu:0'):
+                W = tf.get_variable(name='W', shape=(n_in, n_units), initializer=W_init, dtype=D_TYPE, **W_init_args)
+            if b_init is not None:
+                try:
+                    with tf.device('/cpu:0'):
+                        b = tf.get_variable(name='b', shape=(n_units), initializer=b_init, dtype=D_TYPE, **b_init_args)
+                except:  # If initializer is a constant, do not specify shape.
+                    with tf.device('/cpu:0'):
+                        b = tf.get_variable(name='b', initializer=b_init, dtype=D_TYPE, **b_init_args)
+                self.outputs = act(tf.matmul(self.inputs, W) + b)
+            else:
+                self.outputs = act(tf.matmul(self.inputs, W))
+
+        # Hint : list(), dict() is pass by value (shallow), without them, it is
+        # pass by reference.
+        self.all_layers = list(layer.all_layers)
+        self.all_params = list(layer.all_params)
+        self.all_drop = dict(layer.all_drop)
+        self.all_layers.extend([self.outputs])
+        if b_init is not None:
+            self.all_params.extend([W, b])
+        else:
+            self.all_params.extend([W])
+
+
+def inference():
+        x = tf.placeholder(tf.float32, shape=[None, 784], name='x')
+        network = tl.layers.InputLayer(x, name='input')
+        network = tl.layers.DropoutLayer(network, keep=0.8, name='drop1')
+        network = DenseLayer(network, n_units=800, act=tf.nn.relu, name='relu1')
+        network = tl.layers.DropoutLayer(network, keep=0.5, name='drop2')
+        network = DenseLayer(network, n_units=800, act=tf.nn.relu, name='relu2')
+        network = tl.layers.DropoutLayer(network, keep=0.5, name='drop3')
+        network = DenseLayer(network, n_units=10, act=tf.identity, name='output')
+        return network
+
+
+if __name__ == '__main__':
+    with tf.device('/cpu:0'):
+        network = inference()
+        network.print_layers()
+        sess = tf.Session(config=npu_config_proto(config_proto=tf.ConfigProto(
+                allow_soft_placement=True,
+                log_device_placement=True)))
+        tl.layers.initialize_global_variables(sess)
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/test/resnet_test_static.py b/TensorFlow/contrib/cv/InsightFace_TF/test/resnet_test_static.py
new file mode 100644
index 0000000000000000000000000000000000000000..fd577b914fc834cd87bbeedfd4213743e6d69681
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/test/resnet_test_static.py
@@ -0,0 +1,56 @@
+from npu_bridge.npu_init import *
+from resnet import get_resnet
+import tensorflow as tf
+from nets_utils import get_tensor_static_val
+import numpy as np
+
+
+def resnet_diff_test(layers_num):
+    ckpt_file_path = '../model_weights/resnet_v1_'+str(layers_num)+'.ckpt'
+    x = tf.placeholder(dtype=tf.float32, shape=[1, 224, 224, 3], name='input_place')
+    tfconfig = tf.ConfigProto(allow_soft_placement=True)
+    sess = tf.Session(config=npu_config_proto(config_proto=tfconfig))
+    nets = get_resnet(x, 1000, layers_num, sess)
+    ckpt_static = get_tensor_static_val(ckpt_file_path, all_tensors=True, all_tensor_names=True)
+
+    print('###########'*30)
+    vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
+
+    total_count = 0
+    mean_avg = 0.0
+    median_avg = 0.0
+    std_avg = 0.0
+
+    for var in vars:
+        var_name = var.op.name
+        var_name_new = var_name
+        if '_bn' in var_name:
+            var_name_new = var_name_new.replace('_bn', '')
+        if 'W_conv2d' in var_name:
+            var_name_new = var_name_new.replace('W_conv2d', 'weights')
+        if 'b_conv2d' in var_name:
+            var_name_new = var_name_new.replace('b_conv2d', 'biases')
+        if 'shortcut_conv' in var_name:
+            var_name_new = var_name_new.replace('shortcut_conv', 'shortcut')
+
+        if var_name_new in ckpt_static:
+            print(var_name_new, end=',    ')
+            total_count += 1
+            ckpt_s = ckpt_static[var_name_new]
+            var_val = sess.run(var)
+            mean_diff = np.mean(var_val) - ckpt_s.mean
+            mean_avg += mean_diff
+            median_diff = np.median(var_val) - ckpt_s.median
+            median_avg += median_diff
+            std_diff = np.std(var_val) - ckpt_s.std
+            std_avg += std_diff
+            print('mean_diff: ', mean_diff, 'median_diff: ', median_diff, 'std_diff: ', std_diff)
+
+    print('total_mean_diff', mean_avg/total_count, 'total_mean_diff', median_avg/total_count,
+          'total_std_diff', std_avg/total_count)
+
+
+if __name__ == '__main__':
+    with tf.device('/cpu:0'):
+        resnet_diff_test(50)
+
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/test/test_losses.py b/TensorFlow/contrib/cv/InsightFace_TF/test/test_losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d8dc4c10d83379c66a05ae6a9f2da7700b209b2
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/test/test_losses.py
@@ -0,0 +1,138 @@
+from npu_bridge.npu_init import *
+import tensorflow as tf
+import numpy as np
+from losses.face_losses import cosineface_losses
+import mxnet as mx
+import math
+
+
+def arcface_loss_val(embedding, labels, weights, out_num, s=64., m=0.5):
+    '''
+    :param embedding: the input embedding vectors
+    :param labels:  the input labels, the shape should be eg: (batch_size, 1)
+    :param s: scalar value default is 64
+    :param out_num: output class num
+    :param m: the margin value, default is 0.5
+    :return: the final cacualted output, this output is send into the tf.nn.softmax directly
+    '''
+    cos_m = math.cos(m)
+    sin_m = math.sin(m)
+    mm = sin_m * m * s
+    threshold = math.cos(math.pi - m)
+    with tf.variable_scope('arcface_loss'):
+        # inputs and weights norm
+        embedding_norm = tf.norm(embedding, axis=1, keep_dims=True)
+        embedding = tf.div(embedding, embedding_norm, name='norm_embedding')
+        weights_norm = tf.norm(weights, axis=0, keep_dims=True)
+        weights = tf.div(weights, weights_norm, name='norm_weights')
+        # cos(theta+m)
+        cos_t = tf.matmul(embedding, weights, name='cos_t')
+        cos_t2 = tf.square(cos_t, name='cos_2')
+        sin_t2 = tf.subtract(1., cos_t2, name='sin_2')
+        sin_t = tf.sqrt(sin_t2, name='sin_t')
+        cos_mt = s * tf.subtract(tf.multiply(cos_t, cos_m), tf.multiply(sin_t, sin_m), name='cos_mt')
+
+        # this condition controls the theta+m should in range [0, pi]
+        #      0<=theta+m<=pi
+        #     -m<=theta<=pi-m
+        cond_v = cos_t - threshold
+        cond = tf.cast(tf.nn.relu(cond_v, name='if_else'), dtype=tf.bool)
+
+        keep_val = s * (cos_t - mm)
+        cos_mt_temp = tf.where(cond, cos_mt, keep_val)
+
+        mask = tf.one_hot(labels, depth=out_num, name='one_hot_mask')
+        inv_mask = tf.subtract(1., mask, name='inverse_mask')
+
+        s_cos_t = tf.multiply(s, cos_t, name='scalar_cos_t')
+
+        output = tf.add(tf.multiply(s_cos_t, inv_mask), tf.multiply(cos_mt_temp, mask), name='arcface_loss_output')
+    return output
+
+
+def test_arcface_losses(np_embedding, np_weights):
+    tf_embedding = tf.constant(np_embedding, name='embedding', dtype=tf.float32)
+    labels = tf.constant([1, 3, 2, 1, 1], name='input_labels', dtype=tf.int64)
+    print(labels)
+    tf_weights = tf.constant(np_weights, name='weights')
+    output = arcface_loss_val(embedding=tf_embedding, labels=labels, out_num=10, weights=tf_weights)
+    print(output)
+    sess = tf.Session(config=npu_config_proto())
+    sess.run(tf.global_variables_initializer())
+    results1 = sess.run(output)
+    print(results1)
+    return results1
+
+
+def test_cosineface_losses():
+    np_embedding = np.random.randn(5, 512).astype(dtype=np.float32)
+    tf_embedding = tf.constant(np_embedding, name='embedding', dtype=tf.float32)
+    labels = tf.constant([1, 3, 2, 1, 1], name='input_labels', dtype=tf.int64)
+    output = cosineface_losses(embedding=tf_embedding, labels=labels, out_num=10)
+    sess = tf.Session(config=npu_config_proto())
+    sess.run(tf.global_variables_initializer())
+    print(sess.run(output))
+
+
+def test_mxnet_losses(np_embedding, np_weights):
+    labels = np.array([1, 3, 2, 1, 1]).astype(dtype=np.float32)
+    return mxnet_arcface_val(np_embedding, labels, np_weights)
+
+
+def mxnet_arcface_val(embedding, gt_label, weights):
+    s = 64
+    m = 0.5
+    _weight = mx.symbol.Variable("fc7_weight", shape=(10, 512), lr_mult=1.0)
+    _weight = mx.symbol.L2Normalization(_weight, mode='instance')
+    _embedding = mx.symbol.Variable('mx_embedding', shape=(5, 512), lr_mult=1.0)
+    nembedding = mx.symbol.L2Normalization(_embedding, mode='instance', name='fc1n')*s
+    fc7 = mx.sym.FullyConnected(data=nembedding, weight=_weight, no_bias=True, num_hidden=10, name='fc7')
+
+    _labels = mx.symbol.Variable('labels', shape=(5, ), lr_mult=1.0)
+    zy = mx.sym.pick(fc7, _labels, axis=1)
+    cos_t = zy/s
+
+    cos_m = math.cos(m)
+    sin_m = math.sin(m)
+    mm = math.sin(math.pi - m) * m
+    # threshold = 0.0
+    threshold = math.cos(math.pi - m)
+
+    cond_v = cos_t - threshold
+    cond = mx.symbol.Activation(data=cond_v, act_type='relu')
+
+    body = cos_t * cos_t
+    body = 1.0 - body
+    sin_t = mx.sym.sqrt(body)
+    new_zy = cos_t * cos_m
+    b = sin_t * sin_m
+    new_zy = new_zy - b
+    new_zy = new_zy * s
+
+    zy_keep = zy - s * mm
+    new_zy = mx.sym.where(cond, new_zy, zy_keep)
+
+    diff = new_zy - zy
+    diff = mx.sym.expand_dims(diff, 1)
+    gt_one_hot = mx.sym.one_hot(_labels, depth = 10, on_value = 1.0, off_value = 0.0)
+    body = mx.sym.broadcast_mul(gt_one_hot, diff)
+    fc7 = fc7+body
+    executor = fc7.bind(mx.cpu(), {'fc7_weight': mx.nd.array(weights.T), 'mx_embedding': mx.nd.array(embedding),
+                                   'labels': mx.nd.array(gt_label)})
+    output = executor.forward()
+    print(output)
+    return output
+
+
+if __name__ == '__main__':
+    np_embedding = np.random.randn(5, 512).astype(dtype=np.float32)
+    np_weights = np.random.randn(512, 10).astype(dtype=np.float32)
+    # test arcface_losses output
+    result1 = test_arcface_losses(np_embedding, np_weights)
+    # print('########'*30)
+    print('################')
+    result2 = test_mxnet_losses(np_embedding, np_weights)
+    print(len(result2[0]))
+    print(type(result1))
+    print(type(result2[0].asnumpy()))
+    print(np.mean(result1 - result2[0].asnumpy()))   # 1.26362e-07
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/train_nets.py b/TensorFlow/contrib/cv/InsightFace_TF/train_nets.py
new file mode 100644
index 0000000000000000000000000000000000000000..665318472771791501302c9652cc95bf46c430e3
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/train_nets.py
@@ -0,0 +1,229 @@
+from npu_bridge.npu_init import *
+import tensorflow as tf
+import tensorlayer as tl
+import argparse
+from data.mx2tfrecords import parse_function
+import os
+# from nets.L_Resnet_E_IR import get_resnet
+# from nets.L_Resnet_E_IR_GBN import get_resnet
+from nets.L_Resnet_E_IR_fix_issue9 import get_resnet
+from losses.face_losses import arcface_loss
+from tensorflow.core.protobuf import config_pb2
+import time
+from data.eval_data_reader import load_bin
+from verification import ver_test
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(description='parameters to train net')
+    parser.add_argument('--net_depth', default=100, help='resnet depth, default is 50')
+    parser.add_argument('--epoch', default=200, help='epoch to train the network')
+    parser.add_argument('--batch_size', default=32, help='batch size to train network')
+    parser.add_argument('--lr_steps', default=[40000, 60000, 80000], help='learning rate to train network')
+    parser.add_argument('--momentum', default=0.9, help='learning alg momentum')
+    parser.add_argument('--weight_deacy', default=5e-4, help='learning alg momentum')
+    # parser.add_argument('--eval_datasets', default=['lfw', 'cfp_ff', 'cfp_fp', 'agedb_30'], help='evluation datasets')
+    parser.add_argument('--eval_datasets', default=['lfw'], help='evluation datasets')
+    parser.add_argument('--eval_db_path', default='./datasets/faces_ms1m_112x112', help='evluate datasets base path')
+    parser.add_argument('--image_size', default=[112, 112], help='the image size')
+    parser.add_argument('--num_output', default=85164, help='the image size')
+    parser.add_argument('--tfrecords_file_path', default='./datasets/tfrecords_webface', type=str,
+                        help='path to the output of tfrecords file path')
+    parser.add_argument('--summary_path', default='./output/summary', help='the summary file save path')
+    parser.add_argument('--ckpt_path', default='./output/ckpt', help='the ckpt file save path')
+    parser.add_argument('--log_file_path', default='./output/logs', help='the ckpt file save path')
+    parser.add_argument('--saver_maxkeep', default=100, help='tf.train.Saver max keep ckpt files')
+    parser.add_argument('--buffer_size', default=10000, help='tf dataset api buffer size')
+    parser.add_argument('--log_device_mapping', default=False, help='show device placement log')
+    parser.add_argument('--summary_interval', default=300, help='interval to save summary')
+    parser.add_argument('--ckpt_interval', default=10000, help='intervals to save ckpt file')
+    parser.add_argument('--validate_interval', default=2000, help='intervals to save ckpt file')
+    parser.add_argument('--show_info_interval', default=20, help='intervals to save ckpt file')
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+    os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+    # 1. define global parameters
+    args = get_parser()
+    global_step = tf.Variable(name='global_step', initial_value=0, trainable=False)
+    inc_op = tf.assign_add(global_step, 1, name='increment_global_step')
+    images = tf.placeholder(name='img_inputs', shape=[None, *args.image_size, 3], dtype=tf.float32)
+    labels = tf.placeholder(name='img_labels', shape=[None, ], dtype=tf.int64)
+    # trainable = tf.placeholder(name='trainable_bn', dtype=tf.bool)
+    dropout_rate = tf.placeholder(name='dropout_rate', dtype=tf.float32)
+    # 2 prepare train datasets and test datasets by using tensorflow dataset api
+    # 2.1 train datasets
+    # the image is substracted 127.5 and multiplied 1/128.
+    # random flip left right
+    tfrecords_f = os.path.join(args.tfrecords_file_path, 'tran.tfrecords')
+    print("tfrecords_f path : " + tfrecords_f)
+    dataset = tf.data.TFRecordDataset(tfrecords_f)
+    dataset = dataset.map(parse_function)
+    print("parse_function :" , parse_function)
+    dataset = dataset.shuffle(buffer_size=args.buffer_size)
+    dataset = dataset.batch(args.batch_size, drop_remainder=True)
+    iterator = dataset.make_initializable_iterator()
+    next_element = iterator.get_next()
+    print("let look at the next_element:")
+    print(iterator.__sizeof__())
+    # 2.2 prepare validate datasets
+    ver_list = []
+    ver_name_list = []
+    for db in args.eval_datasets:
+        print('begin db %s convert.' % db)
+        data_set = load_bin(db, args.image_size, args)
+        ver_list.append(data_set)
+        ver_name_list.append(db)
+    # 3. define network, loss, optimize method, learning rate schedule, summary writer, saver
+    # 3.1 inference phase
+    w_init_method = tf.contrib.layers.xavier_initializer(uniform=False)
+    net = get_resnet(images, args.net_depth, type='ir', w_init=w_init_method, trainable=True, keep_rate=dropout_rate)
+    # 3.2 get arcface loss
+    logit = arcface_loss(embedding=net.outputs, labels=labels, w_init=w_init_method, out_num=args.num_output)
+    # test net  because of batch normal layer
+    tl.layers.set_name_reuse(True)
+    test_net = get_resnet(images, args.net_depth, type='ir', w_init=w_init_method, trainable=False, reuse=True, keep_rate=dropout_rate)
+    embedding_tensor = test_net.outputs
+    # 3.3 define the cross entropy
+    inference_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logit, labels=labels))
+    # inference_loss_avg = tf.reduce_mean(inference_loss)
+    # 3.4 define weight deacy losses
+    # for var in tf.trainable_variables():
+    #     print(var.name)
+    # print('##########'*30)
+    wd_loss = 0
+    for weights in tl.layers.get_variables_with_name('W_conv2d', True, True):
+        wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(weights)
+    for W in tl.layers.get_variables_with_name('resnet_v1_50/E_DenseLayer/W', True, True):
+        wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(W)
+    for weights in tl.layers.get_variables_with_name('embedding_weights', True, True):
+        wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(weights)
+    for gamma in tl.layers.get_variables_with_name('gamma', True, True):
+        wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(gamma)
+    # for beta in tl.layers.get_variables_with_name('beta', True, True):
+    #     wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(beta)
+    for alphas in tl.layers.get_variables_with_name('alphas', True, True):
+        wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(alphas)
+    # for bias in tl.layers.get_variables_with_name('resnet_v1_50/E_DenseLayer/b', True, True):
+    #     wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(bias)
+
+    # 3.5 total losses
+    total_loss = inference_loss + wd_loss
+    # 3.6 define the learning rate schedule
+    p = int(512.0/args.batch_size)
+    lr_steps = [p*val for val in args.lr_steps]
+    print(lr_steps)
+    lr = tf.train.piecewise_constant(global_step, boundaries=lr_steps, values=[0.001, 0.0005, 0.0003, 0.0001], name='lr_schedule')
+    # 3.7 define the optimize method
+    opt = tf.train.MomentumOptimizer(learning_rate=lr, momentum=args.momentum)
+    # 3.8 get train op
+    grads = opt.compute_gradients(total_loss)
+    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
+    with tf.control_dependencies(update_ops):
+        train_op = opt.apply_gradients(grads, global_step=global_step)
+    # train_op = opt.minimize(total_loss, global_step=global_step)
+    # 3.9 define the inference accuracy used during validate or test
+    pred = tf.nn.softmax(logit)
+    acc = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(pred, axis=1), labels), dtype=tf.float32))
+    # 3.10 define sess
+    config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=args.log_device_mapping)
+    config.gpu_options.allow_growth = True
+
+    sess = tf.Session(config=npu_config_proto(config_proto=config))
+    # 3.11 summary writer
+    summary = tf.summary.FileWriter(args.summary_path, sess.graph)
+    summaries = []
+    # # 3.11.1 add grad histogram op
+    for grad, var in grads:
+        if grad is not None:
+            summaries.append(tf.summary.histogram(var.op.name + '/gradients', grad))
+    # 3.11.2 add trainabel variable gradients
+    for var in tf.trainable_variables():
+        summaries.append(tf.summary.histogram(var.op.name, var))
+    # 3.11.3 add loss summary
+    summaries.append(tf.summary.scalar('inference_loss', inference_loss))
+    summaries.append(tf.summary.scalar('wd_loss', wd_loss))
+    summaries.append(tf.summary.scalar('total_loss', total_loss))
+    # 3.11.4 add learning rate
+    summaries.append(tf.summary.scalar('leraning_rate', lr))
+    summary_op = tf.summary.merge(summaries)
+    # 3.12 saver
+    saver = tf.train.Saver(max_to_keep=args.saver_maxkeep)
+    # 3.13 init all variables
+    sess.run(tf.global_variables_initializer())
+
+    # restore_saver = tf.train.Saver()
+    # restore_saver.restore(sess, '/home/aurora/workspaces2018/InsightFace_TF/output/ckpt/InsightFace_iter_1110000.ckpt')
+    # 4 begin iteration
+    if not os.path.exists(args.log_file_path):
+        os.makedirs(args.log_file_path)
+    log_file_path = args.log_file_path + '/train' + time.strftime('_%Y-%m-%d-%H-%M', time.localtime(time.time())) + '.log'
+    log_file = open(log_file_path, 'w')
+    # 4 begin iteration
+    count = 0
+    total_accuracy = {}
+
+    for i in range(args.epoch):
+        sess.run(iterator.initializer)
+        while True:
+            try:
+                images_train, labels_train = sess.run(next_element)
+                feed_dict = {images: images_train, labels: labels_train, dropout_rate: 0.4}
+                feed_dict.update(net.all_drop)
+                start = time.time()
+                _, total_loss_val, inference_loss_val, wd_loss_val, _, acc_val = \
+                    sess.run([train_op, total_loss, inference_loss, wd_loss, inc_op, acc],
+                              feed_dict=feed_dict,
+                              options=config_pb2.RunOptions(report_tensor_allocations_upon_oom=True))
+                end = time.time()
+                pre_sec = args.batch_size/(end - start)
+                # print training information
+                if count > 0 and count % args.show_info_interval == 0:
+                    print('epoch %d, total_step %d, total loss is %.2f , inference loss is %.2f, weight deacy '
+                          'loss is %.2f, training accuracy is %.6f, time %.3f samples/sec' %
+                          (i, count, total_loss_val, inference_loss_val, wd_loss_val, acc_val, pre_sec))
+                count += 1
+
+                # save summary
+                if count > 0 and count % args.summary_interval == 0:
+                    feed_dict = {images: images_train, labels: labels_train, dropout_rate: 0.4}
+                    feed_dict.update(net.all_drop)
+                    summary_op_val = sess.run(summary_op, feed_dict=feed_dict)
+                    summary.add_summary(summary_op_val, count)
+
+                # save ckpt files
+                if count > 0 and count % args.ckpt_interval == 0:
+                    filename = 'InsightFace_iter_{:d}'.format(count) + '.ckpt'
+                    filename = os.path.join(args.ckpt_path, filename)
+                    saver.save(sess, filename)
+
+                # validate
+                if count > 0 and count % args.validate_interval == 0:
+                    feed_dict_test ={dropout_rate: 1.0}
+                    feed_dict_test.update(tl.utils.dict_to_one(net.all_drop))
+                    results = ver_test(ver_list=ver_list, ver_name_list=ver_name_list, nbatch=count, sess=sess,
+                             embedding_tensor=embedding_tensor, batch_size=args.batch_size, feed_dict=feed_dict_test,
+                             input_placeholder=images)
+                    print('test accuracy is: ', str(results[0]))
+                    total_accuracy[str(count)] = results[0]
+                    log_file.write('########'*10+'\n')
+                    log_file.write(','.join(list(total_accuracy.keys())) + '\n')
+                    log_file.write(','.join([str(val) for val in list(total_accuracy.values())])+'\n')
+                    log_file.flush()
+                    if max(results) > 0.996:
+                        print('best accuracy is %.5f' % max(results))
+                        filename = 'InsightFace_iter_best_{:d}'.format(count) + '.ckpt'
+                        filename = os.path.join(args.ckpt_path, filename)
+                        saver.save(sess, filename)
+                        log_file.write('######Best Accuracy######'+'\n')
+                        log_file.write(str(max(results))+'\n')
+                        log_file.write(filename+'\n')
+
+                        log_file.flush()
+            except tf.errors.OutOfRangeError:
+                print("End of epoch %d" % i)
+                break
+    log_file.write('\n')
+    log_file.close()
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/train_nets_mgpu.py b/TensorFlow/contrib/cv/InsightFace_TF/train_nets_mgpu.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f500a7aeccb386e1b81812d6d0e537f13e80d5e
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/train_nets_mgpu.py
@@ -0,0 +1,261 @@
+from npu_bridge.npu_init import *
+import tensorflow as tf
+import tensorlayer as tl
+import argparse
+from data.mx2tfrecords import parse_function
+import os
+from nets.L_Resnet_E_IR_MGPU import get_resnet
+from losses.face_losses import arcface_loss
+import time
+from data.eval_data_reader import load_bin
+from verification import ver_test
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(description='parameters to train net')
+    parser.add_argument('--net_depth', default=50, help='resnet depth, default is 50')
+    parser.add_argument('--epoch', default=100000, help='epoch to train the network')
+    parser.add_argument('--batch_size', default=32, help='batch size to train network')
+    parser.add_argument('--lr_steps', default=[40000, 60000, 80000], help='learning rate to train network')
+    parser.add_argument('--momentum', default=0.9, help='learning alg momentum')
+    parser.add_argument('--weight_deacy', default=5e-4, help='learning alg momentum')
+    # parser.add_argument('--eval_datasets', default=['lfw', 'cfp_ff', 'cfp_fp', 'agedb_30'], help='evluation datasets')
+    parser.add_argument('--eval_datasets', default=['lfw', 'cfp_fp'], help='evluation datasets')
+    parser.add_argument('--eval_db_path', default='./datasets/faces_ms1m_112x112', help='evluate datasets base path')
+    parser.add_argument('--image_size', default=[112, 112], help='the image size')
+    parser.add_argument('--num_output', default=85164, help='the image size')
+    parser.add_argument('--tfrecords_file_path', default='./datasets/tfrecords', type=str,
+                        help='path to the output of tfrecords file path')
+    parser.add_argument('--summary_path', default='./output/summary', help='the summary file save path')
+    parser.add_argument('--ckpt_path', default='./output/ckpt', help='the ckpt file save path')
+    parser.add_argument('--saver_maxkeep', default=100, help='tf.train.Saver max keep ckpt files')
+    parser.add_argument('--buffer_size', default=50000, help='tf dataset api buffer size')
+    parser.add_argument('--log_device_mapping', default=False, help='show device placement log')
+    parser.add_argument('--summary_interval', default=300, help='interval to save summary')
+    parser.add_argument('--ckpt_interval', default=5000, help='intervals to save ckpt file')
+    parser.add_argument('--validate_interval', default=2000, help='intervals to save ckpt file')
+    parser.add_argument('--show_info_interval', default=20, help='intervals to show information')
+    parser.add_argument('--num_gpus', default=2, help='the num of gpus')
+    parser.add_argument('--tower_name', default='tower', help='tower name')
+    args = parser.parse_args()
+    return args
+
+
+def average_gradients(tower_grads):
+  """Calculate the average gradient for each shared variable across all towers.
+
+  Note that this function provides a synchronization point across all towers.
+
+  Args:
+    tower_grads: List of lists of (gradient, variable) tuples. The outer list
+      is over individual gradients. The inner list is over the gradient
+      calculation for each tower.
+  Returns:
+     List of pairs of (gradient, variable) where the gradient has been averaged
+     across all towers.
+  """
+  average_grads = []
+  for grad_and_vars in zip(*tower_grads):
+    # Note that each grad_and_vars looks like the following:
+    #   ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))
+    grads = []
+    for g, _ in grad_and_vars:
+      # Add 0 dimension to the gradients to represent the tower.
+      expanded_g = tf.expand_dims(g, 0)
+
+      # Append on a 'tower' dimension which we will average over below.
+      grads.append(expanded_g)
+
+    # Average over the 'tower' dimension.
+    grad = tf.concat(axis=0, values=grads)
+    grad = tf.reduce_mean(grad, 0)
+
+    # Keep in mind that the Variables are redundant because they are shared
+    # across towers. So .. we will just return the first tower's pointer to
+    # the Variable.
+    v = grad_and_vars[0][1]
+    grad_and_var = (grad, v)
+    average_grads.append(grad_and_var)
+  return average_grads
+
+
+if __name__ == '__main__':
+    # os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+    # 1. define global parameters
+    args = get_parser()
+    global_step = tf.Variable(name='global_step', initial_value=0, trainable=False)
+    inc_op = tf.assign_add(global_step, 1, name='increment_global_step')
+    trainable = tf.placeholder(name='trainable_bn', dtype=tf.bool)
+    images = tf.placeholder(name='img_inputs', shape=[None, *args.image_size, 3], dtype=tf.float32)
+    labels = tf.placeholder(name='img_labels', shape=[None, ], dtype=tf.int64)
+    # splits input to different gpu
+    images_s = tf.split(images, num_or_size_splits=args.num_gpus, axis=0)
+    labels_s = tf.split(labels, num_or_size_splits=args.num_gpus, axis=0)
+    # 2 prepare train datasets and test datasets by using tensorflow dataset api
+    # 2.1 train datasets
+    # the image is substracted 127.5 and multiplied 1/128.
+    # random flip left right
+    tfrecords_f = os.path.join(args.tfrecords_file_path, 'tran.tfrecords')
+    dataset = tf.data.TFRecordDataset(tfrecords_f)
+    dataset = dataset.map(parse_function)
+    dataset = dataset.shuffle(buffer_size=args.buffer_size)
+    dataset = dataset.batch(args.batch_size, drop_remainder=True)
+    iterator = dataset.make_initializable_iterator()
+    next_element = iterator.get_next()
+    # 2.2 prepare validate datasets
+    ver_list = []
+    ver_name_list = []
+    for db in args.eval_datasets:
+        print('begin db %s convert.' % db)
+        data_set = load_bin(db, args.image_size, args)
+        ver_list.append(data_set)
+        ver_name_list.append(db)
+
+    # 3. define network, loss, optimize method, learning rate schedule, summary writer, saver
+    # 3.1 inference phase
+    w_init_method = tf.contrib.layers.xavier_initializer(uniform=False)
+    # 3.2 define the learning rate schedule
+    p = int(512.0/args.batch_size)
+    lr_steps = [p*val for val in args.lr_steps]
+    print('learning rate steps: ', lr_steps)
+    lr = tf.train.piecewise_constant(global_step, boundaries=lr_steps, values=[0.001, 0.0001, 0.00005, 0.00001], name='lr_schedule')
+    # 3.3 define the optimize method
+    opt = tf.train.MomentumOptimizer(learning_rate=lr, momentum=args.momentum)
+
+    # Calculate the gradients for each model tower.
+    tower_grads = []
+    tl.layers.set_name_reuse(True)
+    loss_dict = {}
+    drop_dict = {}
+    loss_keys = []
+    with tf.variable_scope(tf.get_variable_scope()):
+      for i in range(args.num_gpus):
+        with tf.device('/cpu:0'):
+          with tf.name_scope('%s_%d' % (args.tower_name, i)) as scope:
+            net = get_resnet(images_s[i], args.net_depth, type='ir', w_init=w_init_method, trainable=trainable)
+            logit = arcface_loss(embedding=net.outputs, labels=labels_s[i], w_init=w_init_method, out_num=args.num_output)
+            # Reuse variables for the next tower.
+            tf.get_variable_scope().reuse_variables()
+            # define the cross entropy
+            inference_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logit, labels=labels_s[i]))
+            # define weight deacy losses
+            wd_loss = 0
+            for weights in tl.layers.get_variables_with_name('W_conv2d', True, True):
+                wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(weights)
+            for W in tl.layers.get_variables_with_name('resnet_v1_50/E_DenseLayer/W', True, True):
+                wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(W)
+            for weights in tl.layers.get_variables_with_name('embedding_weights', True, True):
+                wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(weights)
+            for gamma in tl.layers.get_variables_with_name('gamma', True, True):
+                wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(gamma)
+            for beta in tl.layers.get_variables_with_name('beta', True, True):
+                wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(beta)
+            for alphas in tl.layers.get_variables_with_name('alphas', True, True):
+                wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(alphas)
+            for bias in tl.layers.get_variables_with_name('resnet_v1_50/E_DenseLayer/b', True, True):
+                wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(bias)
+            total_loss = inference_loss + wd_loss
+
+            loss_dict[('inference_loss_%s_%d' % ('gpu', i))] = inference_loss
+            loss_keys.append(('inference_loss_%s_%d' % ('gpu', i)))
+            loss_dict[('wd_loss_%s_%d' % ('gpu', i))] = wd_loss
+            loss_keys.append(('wd_loss_%s_%d' % ('gpu', i)))
+            loss_dict[('total_loss_%s_%d' % ('gpu', i))] = total_loss
+            loss_keys.append(('total_loss_%s_%d' % ('gpu', i)))
+            grads = opt.compute_gradients(total_loss)
+            tower_grads.append(grads)
+            drop_dict.update(net.all_drop)
+            if i == 0:
+                update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
+                pred = tf.nn.softmax(logit)
+                acc = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(pred, axis=1), labels_s[i]), dtype=tf.int64))
+                embedding_tensor_gpu0 = net.outputs
+
+    grads = average_gradients(tower_grads)
+    with tf.control_dependencies(update_ops):
+        # Apply the gradients to adjust the shared variables.
+        train_op = opt.apply_gradients(grads, global_step=global_step)
+
+    config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=args.log_device_mapping)
+    config.gpu_options.allow_growth = True
+    sess = tf.Session(config=npu_config_proto(config_proto=config))
+    # summary writer
+    summary = tf.summary.FileWriter(args.summary_path, sess.graph)
+    summaries = []
+    # add grad histogram op
+    for grad, var in grads:
+        if grad is not None:
+            summaries.append(tf.summary.histogram(var.op.name + '/gradients', grad))
+    # add trainabel variable gradients
+    for var in tf.trainable_variables():
+        summaries.append(tf.summary.histogram(var.op.name, var))
+    # add loss summary
+    for keys, val in loss_dict.items():
+        summaries.append(tf.summary.scalar(keys, val))
+    # add learning rate
+    summaries.append(tf.summary.scalar('leraning_rate', lr))
+    summary_op = tf.summary.merge(summaries)
+
+    # Create a saver.
+    saver = tf.train.Saver(tf.global_variables())
+    # init all variables
+    sess.run(tf.global_variables_initializer())
+
+    drop_dict_test = {keys: 1 for keys in drop_dict.keys()}
+    # begin iteration
+    count = 0
+    for i in range(args.epoch):
+        sess.run(iterator.initializer)
+        while True:
+            try:
+                images_train, labels_train = sess.run(next_element)
+                feed_dict = {images: images_train, labels: labels_train, trainable: True}
+                feed_dict.update(drop_dict)
+                start = time.time()
+                _, _, inference_loss_val_gpu_1, wd_loss_val_gpu_1, total_loss_gpu_1, inference_loss_val_gpu_2, \
+                wd_loss_val_gpu_2, total_loss_gpu_2, acc_val = sess.run([train_op, inc_op, loss_dict[loss_keys[0]],
+                                                                         loss_dict[loss_keys[1]],
+                                                                         loss_dict[loss_keys[2]],
+                                                                         loss_dict[loss_keys[3]],
+                                                                         loss_dict[loss_keys[4]],
+                                                                         loss_dict[loss_keys[5]], acc],
+                                                                         feed_dict=feed_dict)
+                end = time.time()
+                pre_sec = args.batch_size/(end - start)
+                # print training information
+                if count > 0 and count % args.show_info_interval == 0:
+                    print('epoch %d, total_step %d, total loss gpu 1 is %.2f , inference loss gpu 1 is %.2f, weight deacy '
+                          'loss gpu 1 is %.2f, total loss gpu 2 is %.2f , inference loss gpu 2 is %.2f, weight deacy '
+                          'loss gpu 2 is %.2f, training accuracy is %.6f, time %.3f samples/sec' %
+                          (i, count, total_loss_gpu_1, inference_loss_val_gpu_1, wd_loss_val_gpu_1, total_loss_gpu_2,
+                           inference_loss_val_gpu_2, wd_loss_val_gpu_2, acc_val, pre_sec))
+                count += 1
+
+                # save summary
+                if count > 0 and count % args.summary_interval == 0:
+                    feed_dict = {images: images_train, labels: labels_train, trainable: True}
+                    feed_dict.update(drop_dict)
+                    summary_op_val = sess.run(summary_op, feed_dict=feed_dict)
+                    summary.add_summary(summary_op_val, count)
+
+                # save ckpt files
+                if count > 0 and count % args.ckpt_interval == 0:
+                    filename = 'InsightFace_iter_{:d}'.format(count) + '.ckpt'
+                    filename = os.path.join(args.ckpt_path, filename)
+                    saver.save(sess, filename)
+                # # validate
+                if count > 0 and count % args.validate_interval == 0:
+                    feed_dict_test ={trainable: False}
+                    feed_dict_test.update(drop_dict_test)
+                    results = ver_test(ver_list=ver_list, ver_name_list=ver_name_list, nbatch=count, sess=sess,
+                             embedding_tensor=embedding_tensor_gpu0, batch_size=args.batch_size//args.num_gpus, feed_dict=feed_dict_test,
+                             input_placeholder=images_s[0])
+                    if max(results) > 0.99:
+                        print('best accuracy is %.5f' % max(results))
+                        filename = 'InsightFace_iter_best_{:d}'.format(count) + '.ckpt'
+                        filename = os.path.join(args.ckpt_path, filename)
+                        saver.save(sess, filename)
+            except tf.errors.OutOfRangeError:
+                print("End of epoch %d" % i)
+                break
+
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/train_nets_mgpu_new.py b/TensorFlow/contrib/cv/InsightFace_TF/train_nets_mgpu_new.py
new file mode 100644
index 0000000000000000000000000000000000000000..0670cb9a7274a8bb1c7cf6de31fd01a76ff926c2
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/train_nets_mgpu_new.py
@@ -0,0 +1,258 @@
+from npu_bridge.npu_init import *
+import tensorflow as tf
+import tensorlayer as tl
+import argparse
+from data.mx2tfrecords import parse_function
+import os
+from nets.L_Resnet_E_IR_MGPU import get_resnet
+from losses.face_losses import arcface_loss
+import time
+from data.eval_data_reader import load_bin
+from verification import ver_test
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(description='parameters to train net')
+    parser.add_argument('--net_depth', default=100, help='resnet depth, default is 50')
+    parser.add_argument('--epoch', default=100000, help='epoch to train the network')
+    parser.add_argument('--batch_size', default=64, help='batch size to train network')
+    parser.add_argument('--lr_steps', default=[40000, 60000, 80000], help='learning rate to train network')
+    parser.add_argument('--momentum', default=0.9, help='learning alg momentum')
+    parser.add_argument('--weight_deacy', default=5e-4, help='learning alg momentum')
+    # parser.add_argument('--eval_datasets', default=['lfw', 'cfp_ff', 'cfp_fp', 'agedb_30'], help='evluation datasets')
+    parser.add_argument('--eval_datasets', default=['lfw', 'cfp_fp'], help='evluation datasets')
+    parser.add_argument('--eval_db_path', default='./datasets/faces_ms1m_112x112', help='evluate datasets base path')
+    parser.add_argument('--image_size', default=[112, 112], help='the image size')
+    parser.add_argument('--num_output', default=85164, help='the image size')
+    parser.add_argument('--tfrecords_file_path', default='./datasets/tfrecords', type=str,
+                        help='path to the output of tfrecords file path')
+    parser.add_argument('--summary_path', default='./output/summary', help='the summary file save path')
+    parser.add_argument('--ckpt_path', default='./output/ckpt', help='the ckpt file save path')
+    parser.add_argument('--saver_maxkeep', default=100, help='tf.train.Saver max keep ckpt files')
+    parser.add_argument('--buffer_size', default=100000, help='tf dataset api buffer size')
+    parser.add_argument('--log_device_mapping', default=False, help='show device placement log')
+    parser.add_argument('--summary_interval', default=300, help='interval to save summary')
+    parser.add_argument('--ckpt_interval', default=5000, help='intervals to save ckpt file')
+    parser.add_argument('--validate_interval', default=2000, help='intervals to save ckpt file')
+    parser.add_argument('--show_info_interval', default=20, help='intervals to show information')
+    parser.add_argument('--num_gpus', default=2, help='the num of gpus')
+    parser.add_argument('--tower_name', default='tower', help='tower name')
+    args = parser.parse_args()
+    return args
+
+
+def average_gradients(tower_grads):
+  """Calculate the average gradient for each shared variable across all towers.
+
+  Note that this function provides a synchronization point across all towers.
+
+  Args:
+    tower_grads: List of lists of (gradient, variable) tuples. The outer list
+      is over individual gradients. The inner list is over the gradient
+      calculation for each tower.
+  Returns:
+     List of pairs of (gradient, variable) where the gradient has been averaged
+     across all towers.
+  """
+  average_grads = []
+  for grad_and_vars in zip(*tower_grads):
+    # Note that each grad_and_vars looks like the following:
+    #   ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))
+    grads = []
+    for g, _ in grad_and_vars:
+      # Add 0 dimension to the gradients to represent the tower.
+      expanded_g = tf.expand_dims(g, 0)
+
+      # Append on a 'tower' dimension which we will average over below.
+      grads.append(expanded_g)
+
+    # Average over the 'tower' dimension.
+    grad = tf.concat(axis=0, values=grads)
+    grad = tf.reduce_mean(grad, 0)
+
+    # Keep in mind that the Variables are redundant because they are shared
+    # across towers. So .. we will just return the first tower's pointer to
+    # the Variable.
+    v = grad_and_vars[0][1]
+    grad_and_var = (grad, v)
+    average_grads.append(grad_and_var)
+  return average_grads
+
+
+if __name__ == '__main__':
+    # os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+    # 1. define global parameters
+    args = get_parser()
+    global_step = tf.Variable(name='global_step', initial_value=0, trainable=False)
+    inc_op = tf.assign_add(global_step, 1, name='increment_global_step')
+    images = tf.placeholder(name='img_inputs', shape=[None, *args.image_size, 3], dtype=tf.float32)
+    images_test = tf.placeholder(name='img_inputs', shape=[None, *args.image_size, 3], dtype=tf.float32)
+    labels = tf.placeholder(name='img_labels', shape=[None, ], dtype=tf.int64)
+    dropout_rate = tf.placeholder(name='dropout_rate', dtype=tf.float32)
+    # splits input to different gpu
+    images_s = tf.split(images, num_or_size_splits=args.num_gpus, axis=0)
+    labels_s = tf.split(labels, num_or_size_splits=args.num_gpus, axis=0)
+    # 2 prepare train datasets and test datasets by using tensorflow dataset api
+    # 2.1 train datasets
+    # the image is substracted 127.5 and multiplied 1/128.
+    # random flip left right
+    tfrecords_f = os.path.join(args.tfrecords_file_path, 'tran.tfrecords')
+    dataset = tf.data.TFRecordDataset(tfrecords_f)
+    dataset = dataset.map(parse_function)
+    dataset = dataset.shuffle(buffer_size=args.buffer_size)
+    dataset = dataset.batch(args.batch_size, drop_remainder=True)
+    iterator = dataset.make_initializable_iterator()
+    next_element = iterator.get_next()
+    # 2.2 prepare validate datasets
+    ver_list = []
+    ver_name_list = []
+    for db in args.eval_datasets:
+        print('begin db %s convert.' % db)
+        data_set = load_bin(db, args.image_size, args)
+        ver_list.append(data_set)
+        ver_name_list.append(db)
+    # 3. define network, loss, optimize method, learning rate schedule, summary writer, saver
+    # 3.1 inference phase
+    w_init_method = tf.contrib.layers.xavier_initializer(uniform=False)
+    # 3.2 define the learning rate schedule
+    p = int(512.0/args.batch_size)
+    lr_steps = [p*val for val in args.lr_steps]
+    print('learning rate steps: ', lr_steps)
+    lr = tf.train.piecewise_constant(global_step, boundaries=lr_steps, values=[0.001, 0.0005, 0.0003, 0.0001],
+                                     name='lr_schedule')
+    # 3.3 define the optimize method
+    opt = tf.train.MomentumOptimizer(learning_rate=lr, momentum=args.momentum)
+
+    # Calculate the gradients for each model tower.
+    tower_grads = []
+    tl.layers.set_name_reuse(True)
+    loss_dict = {}
+    drop_dict = {}
+    loss_keys = []
+    with tf.variable_scope(tf.get_variable_scope()):
+      for i in range(args.num_gpus):
+        with tf.device('/cpu:0'):
+          with tf.name_scope('%s_%d' % (args.tower_name, i)) as scope:
+            net = get_resnet(images_s[i], args.net_depth, type='ir', w_init=w_init_method, trainable=True, keep_rate=dropout_rate)
+            logit = arcface_loss(embedding=net.outputs, labels=labels_s[i], w_init=w_init_method, out_num=args.num_output)
+            # Reuse variables for the next tower.
+            tf.get_variable_scope().reuse_variables()
+            # define the cross entropy
+            inference_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logit, labels=labels_s[i]))
+            # define weight deacy losses
+            wd_loss = 0
+            for weights in tl.layers.get_variables_with_name('W_conv2d', True, True):
+                wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(weights)
+            for W in tl.layers.get_variables_with_name('resnet_v1_50/E_DenseLayer/W', True, True):
+                wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(W)
+            for weights in tl.layers.get_variables_with_name('embedding_weights', True, True):
+                wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(weights)
+            for gamma in tl.layers.get_variables_with_name('gamma', True, True):
+                wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(gamma)
+            for alphas in tl.layers.get_variables_with_name('alphas', True, True):
+                wd_loss += tf.contrib.layers.l2_regularizer(args.weight_deacy)(alphas)
+            total_loss = inference_loss + wd_loss
+
+            loss_dict[('inference_loss_%s_%d' % ('gpu', i))] = inference_loss
+            loss_keys.append(('inference_loss_%s_%d' % ('gpu', i)))
+            loss_dict[('wd_loss_%s_%d' % ('gpu', i))] = wd_loss
+            loss_keys.append(('wd_loss_%s_%d' % ('gpu', i)))
+            loss_dict[('total_loss_%s_%d' % ('gpu', i))] = total_loss
+            loss_keys.append(('total_loss_%s_%d' % ('gpu', i)))
+            grads = opt.compute_gradients(total_loss)
+            tower_grads.append(grads)
+            if i == 0:
+                test_net = get_resnet(images_test, args.net_depth, type='ir', w_init=w_init_method, trainable=False, keep_rate=dropout_rate)
+                embedding_tensor = test_net.outputs
+                update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
+                pred = tf.nn.softmax(logit)
+                acc = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(pred, axis=1), labels_s[i]), dtype=tf.float32))
+
+    grads = average_gradients(tower_grads)
+    with tf.control_dependencies(update_ops):
+        # Apply the gradients to adjust the shared variables.
+        train_op = opt.apply_gradients(grads, global_step=global_step)
+
+    config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=args.log_device_mapping)
+    config.gpu_options.allow_growth = True
+    sess = tf.Session(config=npu_config_proto(config_proto=config))
+    # summary writer
+    summary = tf.summary.FileWriter(args.summary_path, sess.graph)
+    summaries = []
+    # add grad histogram op
+    for grad, var in grads:
+        if grad is not None:
+            summaries.append(tf.summary.histogram(var.op.name + '/gradients', grad))
+    # add trainabel variable gradients
+    for var in tf.trainable_variables():
+        summaries.append(tf.summary.histogram(var.op.name, var))
+    # add loss summary
+    for keys, val in loss_dict.items():
+        summaries.append(tf.summary.scalar(keys, val))
+    # add learning rate
+    summaries.append(tf.summary.scalar('leraning_rate', lr))
+    summary_op = tf.summary.merge(summaries)
+
+    # Create a saver.
+    saver = tf.train.Saver(tf.global_variables())
+    # init all variables
+    sess.run(tf.global_variables_initializer())
+    # begin iteration
+    count = 0
+    for i in range(args.epoch):
+        sess.run(iterator.initializer)
+        while True:
+            try:
+                images_train, labels_train = sess.run(next_element)
+                feed_dict = {images: images_train, labels: labels_train, dropout_rate: 0.4}
+                start = time.time()
+                _, _, inference_loss_val_gpu_1, wd_loss_val_gpu_1, total_loss_gpu_1, inference_loss_val_gpu_2, \
+                wd_loss_val_gpu_2, total_loss_gpu_2, acc_val = sess.run([train_op, inc_op, loss_dict[loss_keys[0]],
+                                                                         loss_dict[loss_keys[1]],
+                                                                         loss_dict[loss_keys[2]],
+                                                                         loss_dict[loss_keys[3]],
+                                                                         loss_dict[loss_keys[4]],
+                                                                         loss_dict[loss_keys[5]], acc],
+                                                                         feed_dict=feed_dict)
+                end = time.time()
+                pre_sec = args.batch_size/(end - start)
+                # print training information
+                if count > 0 and count % args.show_info_interval == 0:
+                    # print('epoch %d, total_step %d, total loss gpu 1 is %.2f , inference loss gpu 1 is %.2f, weight deacy '
+                    #       'loss gpu 1 is %.2f, total loss gpu 2 is %.2f , inference loss gpu 2 is %.2f, weight deacy '
+                    #       'loss gpu 2 is %.2f, training accuracy is %.6f, time %.3f samples/sec' %
+                    #       (i, count, total_loss_gpu_1, inference_loss_val_gpu_1, wd_loss_val_gpu_1, total_loss_gpu_2,
+                    #        inference_loss_val_gpu_2, wd_loss_val_gpu_2, acc_val, pre_sec))
+
+                    print('epoch %d, total_step %d, total loss: [%.2f, %.2f], inference loss: [%.2f, %.2f], weight deacy '
+                          'loss: [%.2f, %.2f], training accuracy is %.6f, time %.3f samples/sec' %
+                          (i, count, total_loss_gpu_1, total_loss_gpu_2, inference_loss_val_gpu_1, inference_loss_val_gpu_2,
+                           wd_loss_val_gpu_1, wd_loss_val_gpu_2, acc_val, pre_sec))
+                count += 1
+
+                # save summary
+                if count > 0 and count % args.summary_interval == 0:
+                    feed_dict = {images: images_train, labels: labels_train, dropout_rate: 0.4}
+                    summary_op_val = sess.run(summary_op, feed_dict=feed_dict)
+                    summary.add_summary(summary_op_val, count)
+
+                # save ckpt files
+                if count > 0 and count % args.ckpt_interval == 0:
+                    filename = 'InsightFace_iter_{:d}'.format(count) + '.ckpt'
+                    filename = os.path.join(args.ckpt_path, filename)
+                    saver.save(sess, filename)
+                # # validate
+                if count >= 0 and count % args.validate_interval == 0:
+                    feed_dict_test ={dropout_rate: 1.0}
+                    results = ver_test(ver_list=ver_list, ver_name_list=ver_name_list, nbatch=count, sess=sess,
+                             embedding_tensor=embedding_tensor, batch_size=args.batch_size//args.num_gpus, feed_dict=feed_dict_test,
+                             input_placeholder=images_test)
+                    if max(results) > 0.998:
+                        print('best accuracy is %.5f' % max(results))
+                        filename = 'InsightFace_iter_best_{:d}'.format(count) + '.ckpt'
+                        filename = os.path.join(args.ckpt_path, filename)
+                        saver.save(sess, filename)
+            except tf.errors.OutOfRangeError:
+                print("End of epoch %d" % i)
+                break
+
diff --git a/TensorFlow/contrib/cv/InsightFace_TF/verification.py b/TensorFlow/contrib/cv/InsightFace_TF/verification.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d1fa2e99a605633ab4db30c6c97bec90b235499
--- /dev/null
+++ b/TensorFlow/contrib/cv/InsightFace_TF/verification.py
@@ -0,0 +1,252 @@
+"""Helper for evaluation on the Labeled Faces in the Wild dataset
+"""
+from npu_bridge.npu_init import *
+
+# MIT License
+#
+# Copyright (c) 2016 David Sandberg
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+import tensorflow as tf
+import numpy as np
+from sklearn.model_selection import KFold
+from sklearn.decomposition import PCA
+import sklearn
+from scipy import interpolate
+import datetime
+import mxnet as mx
+
+
+def calculate_roc(thresholds, embeddings1, embeddings2, actual_issame, nrof_folds=10, pca=0):
+    assert (embeddings1.shape[0] == embeddings2.shape[0])
+    assert (embeddings1.shape[1] == embeddings2.shape[1])
+    nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
+    nrof_thresholds = len(thresholds)
+    k_fold = KFold(n_splits=nrof_folds, shuffle=False)
+
+    tprs = np.zeros((nrof_folds, nrof_thresholds))
+    fprs = np.zeros((nrof_folds, nrof_thresholds))
+    accuracy = np.zeros((nrof_folds))
+    indices = np.arange(nrof_pairs)
+    # print('pca', pca)
+
+    if pca == 0:
+        diff = np.subtract(embeddings1, embeddings2)
+        dist = np.sum(np.square(diff), 1)
+
+    for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):
+        # print('train_set', train_set)
+        # print('test_set', test_set)
+        if pca > 0:
+            print('doing pca on', fold_idx)
+            embed1_train = embeddings1[train_set]
+            embed2_train = embeddings2[train_set]
+            _embed_train = np.concatenate((embed1_train, embed2_train), axis=0)
+            # print(_embed_train.shape)
+            pca_model = PCA(n_components=pca)
+            pca_model.fit(_embed_train)
+            embed1 = pca_model.transform(embeddings1)
+            embed2 = pca_model.transform(embeddings2)
+            embed1 = sklearn.preprocessing.normalize(embed1)
+            embed2 = sklearn.preprocessing.normalize(embed2)
+            # print(embed1.shape, embed2.shape)
+            diff = np.subtract(embed1, embed2)
+            dist = np.sum(np.square(diff), 1)
+
+        # Find the best threshold for the fold
+        acc_train = np.zeros((nrof_thresholds))
+        for threshold_idx, threshold in enumerate(thresholds):
+            _, _, acc_train[threshold_idx] = calculate_accuracy(threshold, dist[train_set], actual_issame[train_set])
+        best_threshold_index = np.argmax(acc_train)
+        print('best_threshold_index', best_threshold_index, acc_train[best_threshold_index])
+        for threshold_idx, threshold in enumerate(thresholds):
+            tprs[fold_idx, threshold_idx], fprs[fold_idx, threshold_idx], _ = calculate_accuracy(threshold,
+                                                                                                 dist[test_set],
+                                                                                                 actual_issame[
+                                                                                                     test_set])
+        _, _, accuracy[fold_idx] = calculate_accuracy(thresholds[best_threshold_index], dist[test_set],
+                                                      actual_issame[test_set])
+
+    tpr = np.mean(tprs, 0)
+    fpr = np.mean(fprs, 0)
+    return tpr, fpr, accuracy
+
+
+def calculate_accuracy(threshold, dist, actual_issame):
+    predict_issame = np.less(dist, threshold)
+    tp = np.sum(np.logical_and(predict_issame, actual_issame))
+    fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))
+    tn = np.sum(np.logical_and(np.logical_not(predict_issame), np.logical_not(actual_issame)))
+    fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame))
+
+    tpr = 0 if (tp + fn == 0) else float(tp) / float(tp + fn)
+    fpr = 0 if (fp + tn == 0) else float(fp) / float(fp + tn)
+    acc = float(tp + tn) / dist.size
+    return tpr, fpr, acc
+
+
+def calculate_val(thresholds, embeddings1, embeddings2, actual_issame, far_target, nrof_folds=10):
+    '''
+    Copy from [insightface](https://github.com/deepinsight/insightface)
+    :param thresholds:
+    :param embeddings1:
+    :param embeddings2:
+    :param actual_issame:
+    :param far_target:
+    :param nrof_folds:
+    :return:
+    '''
+    assert (embeddings1.shape[0] == embeddings2.shape[0])
+    assert (embeddings1.shape[1] == embeddings2.shape[1])
+    nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
+    nrof_thresholds = len(thresholds)
+    k_fold = KFold(n_splits=nrof_folds, shuffle=False)
+
+    val = np.zeros(nrof_folds)
+    far = np.zeros(nrof_folds)
+
+    diff = np.subtract(embeddings1, embeddings2)
+    dist = np.sum(np.square(diff), 1)
+    indices = np.arange(nrof_pairs)
+
+    for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):
+
+        # Find the threshold that gives FAR = far_target
+        far_train = np.zeros(nrof_thresholds)
+        for threshold_idx, threshold in enumerate(thresholds):
+            _, far_train[threshold_idx] = calculate_val_far(threshold, dist[train_set], actual_issame[train_set])
+        if np.max(far_train) >= far_target:
+            f = interpolate.interp1d(far_train, thresholds, kind='slinear')
+            threshold = f(far_target)
+        else:
+            threshold = 0.0
+
+        val[fold_idx], far[fold_idx] = calculate_val_far(threshold, dist[test_set], actual_issame[test_set])
+
+    val_mean = np.mean(val)
+    far_mean = np.mean(far)
+    val_std = np.std(val)
+    return val_mean, val_std, far_mean
+
+
+def calculate_val_far(threshold, dist, actual_issame):
+    predict_issame = np.less(dist, threshold)
+    true_accept = np.sum(np.logical_and(predict_issame, actual_issame))
+    false_accept = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))
+    n_same = np.sum(actual_issame)
+    n_diff = np.sum(np.logical_not(actual_issame))
+    val = float(true_accept) / float(n_same)
+    far = float(false_accept) / float(n_diff)
+    return val, far
+
+
+def evaluate(embeddings, actual_issame, nrof_folds=10, pca=0):
+    # Calculate evaluation metrics
+    thresholds = np.arange(0, 4, 0.01)
+    embeddings1 = embeddings[0::2]
+    embeddings2 = embeddings[1::2]
+    tpr, fpr, accuracy = calculate_roc(thresholds, embeddings1, embeddings2,
+                                       np.asarray(actual_issame), nrof_folds=nrof_folds, pca=pca)
+    thresholds = np.arange(0, 4, 0.001)
+    val, val_std, far = calculate_val(thresholds, embeddings1, embeddings2,
+                                      np.asarray(actual_issame), 1e-3, nrof_folds=nrof_folds)
+    return tpr, fpr, accuracy, val, val_std, far
+
+
+def data_iter(datasets, batch_size):
+    data_num = datasets.shape[0]
+    for i in range(0, data_num, batch_size):
+        yield datasets[i:min(i+batch_size, data_num), ...]
+
+
+def test(data_set, sess, embedding_tensor, batch_size, label_shape=None, feed_dict=None, input_placeholder=None):
+    '''
+    referenc official implementation [insightface](https://github.com/deepinsight/insightface)
+    :param data_set:
+    :param sess:
+    :param embedding_tensor:
+    :param batch_size:
+    :param label_shape:
+    :param feed_dict:
+    :param input_placeholder:
+    :return:
+    '''
+    print('testing verification..')
+    data_list = data_set[0]
+    issame_list = data_set[1]
+    embeddings_list = []
+    time_consumed = 0.0
+    for i in range(len(data_list)):
+        datas = data_list[i]
+        embeddings = None
+        feed_dict.setdefault(input_placeholder, None)
+        for idx, data in enumerate(data_iter(datas, batch_size)):
+            data_tmp = data.copy()    # fix issues #4
+            data_tmp -= 127.5
+            data_tmp *= 0.0078125
+            feed_dict[input_placeholder] = data_tmp
+            time0 = datetime.datetime.now()
+            _embeddings = sess.run(embedding_tensor, feed_dict)
+            time_now = datetime.datetime.now()
+            diff = time_now - time0
+            time_consumed += diff.total_seconds()
+            if embeddings is None:
+                embeddings = np.zeros((datas.shape[0], _embeddings.shape[1]))
+            try:
+                embeddings[idx*batch_size:min((idx+1)*batch_size, datas.shape[0]), ...] = _embeddings
+            except ValueError:
+                print('idx*batch_size value is %d min((idx+1)*batch_size, datas.shape[0]) %d, batch_size %d, data.shape[0] %d' %
+                      (idx*batch_size, min((idx+1)*batch_size, datas.shape[0]), batch_size, datas.shape[0]))
+                print('embedding shape is ', _embeddings.shape)
+        embeddings_list.append(embeddings)
+
+    _xnorm = 0.0
+    _xnorm_cnt = 0
+    for embed in embeddings_list:
+        for i in range(embed.shape[0]):
+            _em = embed[i]
+            _norm = np.linalg.norm(_em)
+            # print(_em.shape, _norm)
+            _xnorm += _norm
+            _xnorm_cnt += 1
+    _xnorm /= _xnorm_cnt
+
+    acc1 = 0.0
+    std1 = 0.0
+    embeddings = embeddings_list[0] + embeddings_list[1]
+    embeddings = sklearn.preprocessing.normalize(embeddings)
+    print(embeddings.shape)
+    print('infer time', time_consumed)
+    _, _, accuracy, val, val_std, far = evaluate(embeddings, issame_list, nrof_folds=10)
+    acc2, std2 = np.mean(accuracy), np.std(accuracy)
+    return acc1, std1, acc2, std2, _xnorm, embeddings_list
+
+
+def ver_test(ver_list, ver_name_list, nbatch, sess, embedding_tensor, batch_size, feed_dict, input_placeholder):
+    results = []
+    for i in range(len(ver_list)):
+        acc1, std1, acc2, std2, xnorm, embeddings_list = test(data_set=ver_list[i], sess=sess, embedding_tensor=embedding_tensor,
+                                                              batch_size=batch_size, feed_dict=feed_dict,
+                                                              input_placeholder=input_placeholder)
+        print('[%s][%d]XNorm: %f' % (ver_name_list[i], nbatch, xnorm))
+        print('[%s][%d]Accuracy-Flip: %1.5f+-%1.5f' % (ver_name_list[i], nbatch, acc2, std2))
+        results.append(acc2)
+    return results
+