From 28340e25bd28ba3838153b2a25ef155289997ac0 Mon Sep 17 00:00:00 2001 From: JunYuLiu Date: Thu, 3 Sep 2020 16:08:13 +0800 Subject: [PATCH 01/13] Add tools --- tools/link_detection/README_CN.md | 2 +- tools/pic_detection/README_CN.md | 27 ++++++ tools/pic_detection/pic_detection.py | 88 ++++++++++++++++++ .../mindinsight/images/summary_list.png | Bin 33692 -> 0 bytes ...ize_the_performance_of_data_preparation.md | 4 +- ...rder_optimizer_for_resnet50_application.md | 2 +- 6 files changed, 119 insertions(+), 4 deletions(-) create mode 100644 tools/pic_detection/README_CN.md create mode 100644 tools/pic_detection/pic_detection.py delete mode 100644 tutorials/notebook/mindinsight/images/summary_list.png diff --git a/tools/link_detection/README_CN.md b/tools/link_detection/README_CN.md index 58ab4f8d23..442726a409 100644 --- a/tools/link_detection/README_CN.md +++ b/tools/link_detection/README_CN.md @@ -21,7 +21,7 @@ cd tools/link_detection pip install requests ``` -3. 在`link_check`目录下执行如下命令，在输入需要检测目录的绝对路径后，开始进行检测，完成后会在当前目录下新建`404.txt`、`exception.txt`、`slow.txt`三个文件。 +3. 在`link_detection`目录下执行如下命令，在输入需要检测目录的绝对路径后，开始进行检测，完成后会在当前目录下新建`404.txt`、`exception.txt`、`slow.txt`三个文件。 ``` python link_detection.py ``` diff --git a/tools/pic_detection/README_CN.md b/tools/pic_detection/README_CN.md new file mode 100644 index 0000000000..c217d51929 --- /dev/null +++ b/tools/pic_detection/README_CN.md @@ -0,0 +1,27 @@ +# 图片检查工具 + +## 简介 + +此工具可以检查用户指定目录里所有图片的使用情况，会检查出没有使用的图片，并且将没有使用的图片删除。 + + +## 使用说明 + +该工具所依赖的操作系统为Windows操作系统，执行环境为Python环境，具体使用步骤如下所示： + +1. 打开Git Bash，下载MindSpore Docs仓代码。 + ``` + git clone https://gitee.com/mindspore/docs.git + ``` +2. 进入`tools/pic_detection`目录。 + ``` + cd tools/pic_detection + ``` +3. 在`pic_detection`目录下执行如下命令，在输入需要检测目录的绝对路径后，开始进行检测，最后将没有使用的图片删除。 + ``` + python pic_detection.py + ``` + > 检测目录的绝对路径全使用英文，并且使用Linux的绝对路径方式，例如：`/d/master/docs`。 + + + diff --git a/tools/pic_detection/pic_detection.py b/tools/pic_detection/pic_detection.py new file mode 100644 index 0000000000..a69cd53134 --- /dev/null +++ b/tools/pic_detection/pic_detection.py @@ -0,0 +1,88 @@ +import subprocess +import os + +def get_images_dierctory(check_path): + ''' + get all images directory. + ''' + cmd = 'find %s -type d -name images' %check_path + res = subprocess.Popen(cmd, shell=True, stderr=subprocess.PIPE, stdout=subprocess.PIPE) + dir_list = res.stdout.read().decode('utf-8').split('\n') + del dir_list[-1] + return dir_list + +def get_all_pic(dir_list): + ''' + get all the images in the images directory. + ''' + for dir in dir_list: + res = subprocess.Popen('ls %s'%dir, shell=True, stderr=subprocess.PIPE, stdout=subprocess.PIPE) + pic_list = res.stdout.read().decode('utf-8').split('\n') + del pic_list[-1] + for i in pic_list: + pic_all.add(i) + +def get_use_pic(check_path): + ''' + get all the useful pictures. + ''' + cmd1 = 'find %s -type f -name "*.md"' %check_path + cmd2 = 'find %s -type f -name "*.ipynb"' %check_path + cmd3 = [cmd1, cmd2] + for i in cmd3: + res = subprocess.Popen(i, shell=True, stderr=subprocess.PIPE, stdout=subprocess.PIPE) + file_list = res.stdout.read().decode('utf-8').split('\n') + del file_list[-1] + for j in file_list: + j = j.split('/', 1)[1].replace('/', ':/', 1) + with open(j, 'r', encoding='utf-8') as f: + data = f.read() + for k in pic_all: + if k in data: + use_pic.add(k) + +def get_use_eddx(): + ''' + get all the useful eddx files. + ''' + for i in filter_pic: + if i.endswith('eddx'): + if i.split('.')[0] in ' '.join(use_pic): + use_eddx.add(i) + +def get_useless_pic_path(check_path): + ''' + get the absolute path of all useless pictures. + ''' + for i in useless_pic: + cmd = 'find %s -type f -name %s' %(check_path,i) + res = subprocess.Popen(cmd, shell=True, stderr=subprocess.PIPE, stdout=subprocess.PIPE) + data = res.stdout.read().decode('utf-8').split('\n') + del data[-1] + for j in data: + path.append(j) + +def del_useless_pic(): + ''' + delete all useless pictures. + ''' + for i in path: + os.system('rm -rf %s' %i) + + +if __name__ == '__main__': + check_path = input('请输入您要检测的绝对路径：').strip() + pic_all = set() + use_pic = set() + use_eddx = set() + path = [] + dir_list = get_images_dierctory(check_path) + get_all_pic(dir_list) + get_use_pic(check_path) + filter_pic = pic_all.difference(use_pic) + get_use_eddx() + useless_pic = filter_pic.difference(use_eddx) + get_useless_pic_path(check_path) + print('没有用的照片：', path) + del_useless_pic() + print('删除成功') diff --git a/tutorials/notebook/mindinsight/images/summary_list.png b/tutorials/notebook/mindinsight/images/summary_list.png deleted file mode 100644 index 5b3f170433d0fee73d4d462efe6cfd6dfeb5a166..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 33692 zcmeFZcT|(v+cpZKAR?gSC?d@YDpfkt22oL|ih@W9NRy64N`NFN1E?S%E%ev`krD{K zgrF!bKoo=!0z{;Q76J(*gtRa7p7Wb=-uJ9^*7@^%>)UH(CC_H(+4sJ=_O`ve38#IIkwd`mz;L_=OTCx38JCnSelT 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b/tutorials/source_zh_cn/advanced_use/optimize_the_performance_of_data_preparation.md index e5fce293fc..05ea6013c2 100644 --- a/tutorials/source_zh_cn/advanced_use/optimize_the_performance_of_data_preparation.md +++ b/tutorials/source_zh_cn/advanced_use/optimize_the_performance_of_data_preparation.md @@ -251,9 +251,9 @@ shuffle性能优化建议如下： for data in ds2.create_dict_iterator(): print(data["data"]) ``` - ``` - 输出： + 输出： + ``` before shuffle: [0 1 2 3 4] [1 2 3 4 5] diff --git a/tutorials/source_zh_cn/advanced_use/second_order_optimizer_for_resnet50_application.md b/tutorials/source_zh_cn/advanced_use/second_order_optimizer_for_resnet50_application.md index 55371f8a0c..440e668f07 100644 --- a/tutorials/source_zh_cn/advanced_use/second_order_optimizer_for_resnet50_application.md +++ b/tutorials/source_zh_cn/advanced_use/second_order_optimizer_for_resnet50_application.md @@ -351,7 +351,7 @@ epoch: 42 step: 5004, loss is 1.6453942 其中， `*.ckpt`：指保存的模型参数文件。checkpoint文件名称具体含义：*网络名称*-*epoch数*_*step数*.ckpt。 -##### GPU +#### GPU 在GPU硬件平台上，MindSpore采用OpenMPI的`mpirun`进行分布式训练，进程创建1个目录，目录名称为`train_parallel`，用来保存日志信息和训练的checkpoint文件。下面以使用8张卡的分布式训练脚本为例，演示如何运行脚本： ``` sh run_distribute_train_gpu.sh [DATASET_PATH] [DEVICE_NUM] -- Gitee From e1fc345a65ed0384d3950656aaeb5afecd7e898a Mon Sep 17 00:00:00 2001 From: SebastianHan Date: Fri, 4 Sep 2020 10:55:17 +0800 Subject: [PATCH 02/13] add lineage video --- .../source_zh_cn/quick_start/quick_video.md | 24 +++++++++++++++++++ ...Insight_lineage_and_scalars_comparision.md | 11 +++++++++ 2 files changed, 35 insertions(+) create mode 100644 tutorials/source_zh_cn/quick_start/quick_video/mindInsight_lineage_and_scalars_comparision.md diff --git a/tutorials/source_zh_cn/quick_start/quick_video.md b/tutorials/source_zh_cn/quick_start/quick_video.md index 8c80d69900..115ef7d67e 100644 --- a/tutorials/source_zh_cn/quick_start/quick_video.md +++ b/tutorials/source_zh_cn/quick_start/quick_video.md @@ -257,6 +257,30 @@ +
+ + + + play + + + + + + + +
diff --git a/tutorials/source_zh_cn/quick_start/quick_video/mindInsight_lineage_and_scalars_comparision.md b/tutorials/source_zh_cn/quick_start/quick_video/mindInsight_lineage_and_scalars_comparision.md new file mode 100644 index 0000000000..b04ec401cc --- /dev/null +++ b/tutorials/source_zh_cn/quick_start/quick_video/mindInsight_lineage_and_scalars_comparision.md @@ -0,0 +1,11 @@ +# MindInsight溯源与对比看板 + +[comment]: <> (本文档中包含手把手系列视频，码云Gitee不支持展示，请于官方网站对应教程中查看) + + + +**立即安装**： + +**查看更多内容**： \ No newline at end of file -- Gitee From edfda3741af4faef3c3a65e2c6ebafb88e7358f1 Mon Sep 17 00:00:00 2001 From: wukesong Date: Fri, 4 Sep 2020 11:38:25 +0800 Subject: [PATCH 03/13] modify lenet --- .../source_en/quick_start/quick_start.md | 42 ++++++++++--------- .../source_zh_cn/quick_start/quick_start.md | 42 ++++++++++--------- tutorials/tutorial_code/lenet.py | 12 +++--- 3 files changed, 51 insertions(+), 45 deletions(-) diff --git a/tutorials/source_en/quick_start/quick_start.md b/tutorials/source_en/quick_start/quick_start.md index c8534c9dd5..710365a89b 100644 --- a/tutorials/source_en/quick_start/quick_start.md +++ b/tutorials/source_en/quick_start/quick_start.md @@ -200,20 +200,20 @@ Define each layer of a neural network in the `__init__` method in advance, and t ```python import mindspore.nn as nn +from mindspore.common.initializer import Normal class LeNet5(nn.Cell): """ Lenet network structure """ #define the operator required - def __init__(self, num_class=10, channel=1): + def __init__(self, num_class=10, num_channel=1): super(LeNet5, self).__init__() - self.num_class = num_class - self.conv1 = nn.Conv2d(channel, 6, 5, pad_mode='valid') + self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid') self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid') - self.fc1 = nn.Dense(16 * 5 * 5, 120) - self.fc2 = nn.Dense(120, 84) - self.fc3 = nn.Dense(84, self.num_class) + self.fc1 = nn.Dense(16 * 5 * 5, 120, weight_init=Normal(0.02)) + self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.02)) + self.fc3 = nn.Dense(84, num_class, weight_init=Normal(0.02)) self.relu = nn.ReLU() self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) self.flatten = nn.Flatten() @@ -254,7 +254,7 @@ Call the defined loss function in the `__main__` function. if __name__ == "__main__": ... #define the loss function - net_loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean') + net_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean') ... ``` @@ -344,16 +344,20 @@ The following is an example of loss values output during training: ```bash ... -epoch: 1 step: 262, loss is 1.9212162 -epoch: 1 step: 263, loss is 1.8498616 -epoch: 1 step: 264, loss is 1.7990671 -epoch: 1 step: 265, loss is 1.9492403 -epoch: 1 step: 266, loss is 2.0305142 -epoch: 1 step: 267, loss is 2.0657792 -epoch: 1 step: 268, loss is 1.9582214 -epoch: 1 step: 269, loss is 0.9459006 -epoch: 1 step: 270, loss is 0.8167224 -epoch: 1 step: 271, loss is 0.7432692 +epoch: 1 step: 1, loss is 2.3025916 +epoch: 1 step: 2, loss is 2.302577 +epoch: 1 step: 3, loss is 2.3023994 +epoch: 1 step: 4, loss is 2.303059 +epoch: 1 step: 5, loss is 2.3025753 +epoch: 1 step: 6, loss is 2.3027692 +epoch: 1 step: 7, loss is 2.3026521 +epoch: 1 step: 8, loss is 2.3014607 +... +epoch: 1 step: 1871, loss is 0.048939988 +epoch: 1 step: 1872, loss is 0.028885357 +epoch: 1 step: 1873, loss is 0.09475248 +epoch: 1 step: 1874, loss is 0.046067055 +epoch: 1 step: 1875, loss is 0.12366105 ... ``` @@ -409,7 +413,7 @@ Command output similar to the following is displayed: ``` ============== Starting Testing ============== -============== Accuracy:{'Accuracy': 0.9742588141025641} ============== +============== Accuracy:{'Accuracy': 0.9663477564102564} ============== ``` -The model accuracy data is displayed in the output content. In the example, the accuracy reaches 97.4%, indicating a good model quality. +The model accuracy data is displayed in the output content. In the example, the accuracy reaches 96.6%, indicating a good model quality. The model accuracy will be improved with more iterations `epoch_size`. diff --git a/tutorials/source_zh_cn/quick_start/quick_start.md b/tutorials/source_zh_cn/quick_start/quick_start.md index 0f583df85d..4bae411d18 100644 --- a/tutorials/source_zh_cn/quick_start/quick_start.md +++ b/tutorials/source_zh_cn/quick_start/quick_start.md @@ -203,20 +203,20 @@ MindSpore支持`TruncatedNormal`、`Normal`、`Uniform`等多种参数初始化 ```python import mindspore.nn as nn +from mindspore.common.initializer import Normal class LeNet5(nn.Cell): """ Lenet network structure """ #define the operator required - def __init__(self, num_class=10, channel=1): + def __init__(self, num_class=10, num_channel=1): super(LeNet5, self).__init__() - self.num_class = num_class - self.conv1 = nn.Conv2d(channel, 6, 5, pad_mode='valid') + self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid') self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid') - self.fc1 = nn.Dense(16 * 5 * 5, 120) - self.fc2 = nn.Dense(120, 84) - self.fc3 = nn.Dense(84, self.num_class) + self.fc1 = nn.Dense(16 * 5 * 5, 120, weight_init=Normal(0.02)) + self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.02)) + self.fc3 = nn.Dense(84, num_class, weight_init=Normal(0.02)) self.relu = nn.ReLU() self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) self.flatten = nn.Flatten() @@ -257,7 +257,7 @@ from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits if __name__ == "__main__": ... #define the loss function - net_loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean') + net_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean') ... ``` @@ -346,18 +346,20 @@ python lenet.py --device_target=CPU 训练过程中loss打印示例如下： ```bash +epoch: 1 step: 1, loss is 2.3025916 +epoch: 1 step: 2, loss is 2.302577 +epoch: 1 step: 3, loss is 2.3023994 +epoch: 1 step: 4, loss is 2.303059 +epoch: 1 step: 5, loss is 2.3025753 +epoch: 1 step: 6, loss is 2.3027692 +epoch: 1 step: 7, loss is 2.3026521 +epoch: 1 step: 8, loss is 2.3014607 ... -epoch: 1 step: 262, loss is 1.9212162 -epoch: 1 step: 263, loss is 1.8498616 -epoch: 1 step: 264, loss is 1.7990671 -epoch: 1 step: 265, loss is 1.9492403 -epoch: 1 step: 266, loss is 2.0305142 -epoch: 1 step: 267, loss is 2.0657792 -epoch: 1 step: 268, loss is 1.9582214 -epoch: 1 step: 269, loss is 0.9459006 -epoch: 1 step: 270, loss is 0.8167224 -epoch: 1 step: 271, loss is 0.7432692 -... +epoch: 1 step: 1871, loss is 0.048939988 +epoch: 1 step: 1872, loss is 0.028885357 +epoch: 1 step: 1873, loss is 0.09475248 +epoch: 1 step: 1874, loss is 0.046067055 +epoch: 1 step: 1875, loss is 0.12366105 ``` 训练完后，即保存的模型文件，示例如下: @@ -416,7 +418,7 @@ python lenet.py --device_target=CPU ``` ... ============== Starting Testing ============== -============== Accuracy:{'Accuracy': 0.9742588141025641} ============== +============== Accuracy:{'Accuracy': 0.9663477564102564} ============== ``` -可以在打印信息中看出模型精度数据，示例中精度数据达到97.4%，模型质量良好。 +可以在打印信息中看出模型精度数据，示例中精度数据达到96.6%，模型质量良好。随着网络迭代次数`epoch_size`增加，模型精度会进一步提高。 diff --git a/tutorials/tutorial_code/lenet.py b/tutorials/tutorial_code/lenet.py index e792307881..27ffc94af9 100644 --- a/tutorials/tutorial_code/lenet.py +++ b/tutorials/tutorial_code/lenet.py @@ -23,6 +23,7 @@ import argparse import mindspore.dataset as ds import mindspore.nn as nn from mindspore import context +from mindspore.common.initializer import Normal from mindspore.train.serialization import load_checkpoint, load_param_into_net from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor from mindspore.train import Model @@ -120,14 +121,13 @@ def create_dataset(data_path, batch_size=32, repeat_size=1, class LeNet5(nn.Cell): """Lenet network structure.""" # define the operator required - def __init__(self, num_class=10, channel=1): + def __init__(self, num_class=10, num_channel=1): super(LeNet5, self).__init__() - self.num_class = num_class - self.conv1 = nn.Conv2d(channel, 6, 5, pad_mode='valid') + self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid') self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid') - self.fc1 = nn.Dense(16 * 5 * 5, 120) - self.fc2 = nn.Dense(120, 84) - self.fc3 = nn.Dense(84, self.num_class) + self.fc1 = nn.Dense(16 * 5 * 5, 120, weight_init=Normal(0.02)) + self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.02)) + self.fc3 = nn.Dense(84, num_class, weight_init=Normal(0.02)) self.relu = nn.ReLU() self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) self.flatten = nn.Flatten() -- Gitee From 0d776004c2ddcbc959eded8b640359da25672837 Mon Sep 17 00:00:00 2001 From: wanyiming Date: Fri, 4 Sep 2020 11:07:01 +0800 Subject: [PATCH 04/13] Mod_SoftmaxCrossEntropyWithLogits --- docs/source_en/operator_list.md | 4 ++-- docs/source_zh_cn/operator_list.md | 4 ++-- tutorials/notebook/computer_vision_application.ipynb | 2 +- .../notebook/customized_debugging_information.ipynb | 2 +- tutorials/notebook/debugging_in_pynative_mode.ipynb | 2 +- .../mindinsight/calculate_and_datagraphic.ipynb | 2 +- .../mindinsight_image_histogram_scalar_tensor.ipynb | 10 +++++----- .../mindinsight_model_lineage_and_data_lineage.ipynb | 2 +- tutorials/notebook/mixed_precision.ipynb | 2 +- tutorials/notebook/model_security.ipynb | 4 ++-- tutorials/notebook/nlp_application.ipynb | 2 +- tutorials/notebook/quick_start.ipynb | 2 +- .../synchronization_training_and_evaluation.ipynb | 2 +- .../advanced_use/computer_vision_application.md | 2 +- .../advanced_use/debugging_in_pynative_mode.md | 2 +- .../source_en/advanced_use/differential_privacy.md | 2 +- tutorials/source_en/advanced_use/model_security.md | 2 +- tutorials/source_en/advanced_use/network_migration.md | 2 +- tutorials/source_en/advanced_use/nlp_application.md | 2 +- tutorials/source_en/advanced_use/summary_record.md | 2 +- tutorials/source_en/quick_start/quick_start.md | 2 +- tutorials/source_en/use/multi_platform_inference.md | 4 ++-- .../advanced_use/computer_vision_application.md | 2 +- .../advanced_use/debugging_in_pynative_mode.md | 2 +- .../source_zh_cn/advanced_use/differential_privacy.md | 2 +- .../source_zh_cn/advanced_use/gradient_accumulation.md | 2 +- tutorials/source_zh_cn/advanced_use/model_security.md | 2 +- tutorials/source_zh_cn/advanced_use/nlp_application.md | 2 +- tutorials/source_zh_cn/advanced_use/summary_record.md | 2 +- tutorials/source_zh_cn/quick_start/quick_start.md | 2 +- tutorials/source_zh_cn/use/multi_platform_inference.md | 4 ++-- tutorials/tutorial_code/gradient_accumulation/train.py | 2 +- tutorials/tutorial_code/lenet.py | 2 +- .../tutorial_code/model_safety/mnist_defense_nad.py | 2 +- tutorials/tutorial_code/resnet/cifar_resnet50.py | 2 +- 35 files changed, 44 insertions(+), 44 deletions(-) diff --git a/docs/source_en/operator_list.md b/docs/source_en/operator_list.md index 3a79b0a2b9..8ef84d3f5a 100644 --- a/docs/source_en/operator_list.md +++ b/docs/source_en/operator_list.md @@ -67,7 +67,7 @@ | [mindspore.nn.L1Loss](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.L1Loss) |Supported |Supported | Doing |loss/loss | [mindspore.nn.MSELoss](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.MSELoss) | Supported |Doing | Doing |loss/loss | [mindspore.nn.SmoothL1Loss](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.SmoothL1Loss) |Supported |Doing | Doing |loss/loss -| [mindspore.nn.SoftmaxCrossEntropyWithLogits](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.SoftmaxCrossEntropyWithLogits) | Supported | Supported | Doing |loss/loss +| [mindspore.nn.SoftmaxCrossEntropyWithLogits](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.SoftmaxCrossEntropyWithLogits) | Supported | Supported | Supported |loss/loss | [mindspore.nn.SoftmaxCrossEntropyExpand](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.SoftmaxCrossEntropyExpand) | Supported |Supported | Doing |loss/loss | [mindspore.nn.CosineEmbeddingLoss](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.CosineEmbeddingLoss) |Supported |Supported | Doing |loss/loss | [mindspore.nn.ProximalAdagrad](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.ProximalAdagrad) | Supported | Doing | Doing |optim/ProximalAdagrad @@ -128,7 +128,7 @@ | [mindspore.ops.operations.Conv2DBackpropInput](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Conv2DBackpropInput) | Supported | Supported |Doing | nn_ops | [mindspore.ops.operations.BiasAdd](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.BiasAdd) | Supported | Supported | Supported | nn_ops | [mindspore.ops.operations.TopK](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.TopK) | Supported | Supported |Doing | nn_ops -| [mindspore.ops.operations.SoftmaxCrossEntropyWithLogits](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.SoftmaxCrossEntropyWithLogits) | Supported | Supported |Doing | nn_ops +| [mindspore.ops.operations.SoftmaxCrossEntropyWithLogits](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.SoftmaxCrossEntropyWithLogits) | Supported | Supported |Supported | nn_ops | [mindspore.ops.operations.SparseSoftmaxCrossEntropyWithLogits](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.SparseSoftmaxCrossEntropyWithLogits) | Doing | Supported | Supported | nn_ops | [mindspore.ops.operations.ApplyMomentum](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.ApplyMomentum) | Supported | Supported | Supported | nn_ops | [mindspore.ops.operations.ApplyAddSign](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.ApplyAddSign) | Supported | Doing | Doing | nn_ops diff --git a/docs/source_zh_cn/operator_list.md b/docs/source_zh_cn/operator_list.md index db8e29e393..e5b75be664 100644 --- a/docs/source_zh_cn/operator_list.md +++ b/docs/source_zh_cn/operator_list.md @@ -67,7 +67,7 @@ | [mindspore.nn.L1Loss](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.L1Loss) |Supported |Supported | Doing |loss/loss | [mindspore.nn.MSELoss](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.MSELoss) | Supported |Doing | Doing |loss/loss | [mindspore.nn.SmoothL1Loss](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.SmoothL1Loss) | Supported |Doing | Doing |loss/loss -| [mindspore.nn.SoftmaxCrossEntropyWithLogits](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.SoftmaxCrossEntropyWithLogits) | Supported | Supported | Doing |loss/loss +| [mindspore.nn.SoftmaxCrossEntropyWithLogits](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.SoftmaxCrossEntropyWithLogits) | Supported | Supported | Supported |loss/loss | [mindspore.nn.SoftmaxCrossEntropyExpand](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.SoftmaxCrossEntropyExpand) | Supported |Supported | Doing |loss/loss | [mindspore.nn.CosineEmbeddingLoss](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.CosineEmbeddingLoss) |Supported |Supported | Doing |loss/loss | [mindspore.nn.ProximalAdagrad](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.ProximalAdagrad) | Supported |Doing | Doing |optim/ProximalAdagrad @@ -128,7 +128,7 @@ | [mindspore.ops.operations.Conv2DBackpropInput](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Conv2DBackpropInput) | Supported | Supported |Doing | nn_ops | [mindspore.ops.operations.BiasAdd](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.BiasAdd) | Supported | Supported | Supported | nn_ops | [mindspore.ops.operations.TopK](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.TopK) | Supported | Supported |Doing | nn_ops -| [mindspore.ops.operations.SoftmaxCrossEntropyWithLogits](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.SoftmaxCrossEntropyWithLogits) | Supported | Supported |Doing | nn_ops +| [mindspore.ops.operations.SoftmaxCrossEntropyWithLogits](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.SoftmaxCrossEntropyWithLogits) | Supported | Supported |Supported | nn_ops | [mindspore.ops.operations.SparseSoftmaxCrossEntropyWithLogits](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.SparseSoftmaxCrossEntropyWithLogits) | Doing | Supported | Supported | nn_ops | [mindspore.ops.operations.ApplyMomentum](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.ApplyMomentum) | Supported | Supported | Supported | nn_ops | [mindspore.ops.operations.ApplyAddSign](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.ApplyAddSign) | Supported | Doing | Doing | nn_ops diff --git a/tutorials/notebook/computer_vision_application.ipynb b/tutorials/notebook/computer_vision_application.ipynb index f6a65a867c..b9a4efc032 100644 --- a/tutorials/notebook/computer_vision_application.ipynb +++ b/tutorials/notebook/computer_vision_application.ipynb @@ -387,7 +387,7 @@ "from mindspore.nn.optim.momentum import Momentum\n", "from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits\n", "\n", - "ls = SoftmaxCrossEntropyWithLogits(sparse=True, is_grad=False, reduction=\"mean\")\n", + "ls = SoftmaxCrossEntropyWithLogits(sparse=True, reduction=\"mean\")\n", "opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 0.9)" ] }, diff --git a/tutorials/notebook/customized_debugging_information.ipynb b/tutorials/notebook/customized_debugging_information.ipynb index 7ef6762a17..44be7bd3a7 100644 --- a/tutorials/notebook/customized_debugging_information.ipynb +++ b/tutorials/notebook/customized_debugging_information.ipynb @@ -386,7 +386,7 @@ "train_data_path = \"./MNIST_Data/train\"\n", "eval_data_path = \"./MNIST_Data/train\"\n", "\n", - "net_loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')\n", + "net_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction=\"mean\")\n", "repeat_size = epoch_size\n", "network = LeNet5()\n", "\n", diff --git a/tutorials/notebook/debugging_in_pynative_mode.ipynb b/tutorials/notebook/debugging_in_pynative_mode.ipynb index b068dddd05..ce3d50557b 100644 --- a/tutorials/notebook/debugging_in_pynative_mode.ipynb +++ b/tutorials/notebook/debugging_in_pynative_mode.ipynb @@ -488,7 +488,7 @@ "\n", "net = LeNet5()\n", "optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.1, 0.9)\n", - "criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)\n", + "criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')\n", "net_with_criterion = WithLossCell(net, criterion)\n", "train_network = GradWrap(net_with_criterion)\n", "train_network.set_train()\n", diff --git a/tutorials/notebook/mindinsight/calculate_and_datagraphic.ipynb b/tutorials/notebook/mindinsight/calculate_and_datagraphic.ipynb index 2eb475d315..39bffb88e5 100644 --- a/tutorials/notebook/mindinsight/calculate_and_datagraphic.ipynb +++ b/tutorials/notebook/mindinsight/calculate_and_datagraphic.ipynb @@ -311,7 +311,7 @@ " ds_train = create_dataset(data_path=\"./MNIST_Data/train/\")\n", "\n", " network = LeNet5()\n", - " net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction=\"mean\")\n", + " net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction=\"mean\")\n", " net_opt = nn.Momentum(network.trainable_params(), learning_rate=0.01, momentum=0.9)\n", " time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())\n", " model = Model(network, net_loss, net_opt, metrics={\"Accuracy\": Accuracy()})\n", diff --git a/tutorials/notebook/mindinsight/mindinsight_image_histogram_scalar_tensor.ipynb b/tutorials/notebook/mindinsight/mindinsight_image_histogram_scalar_tensor.ipynb index 08a68bdb08..082a64b00e 100644 --- a/tutorials/notebook/mindinsight/mindinsight_image_histogram_scalar_tensor.ipynb +++ b/tutorials/notebook/mindinsight/mindinsight_image_histogram_scalar_tensor.ipynb @@ -544,7 +544,7 @@ "source": [ "\n", "network = AlexNet(num_classes=10)\n", - "net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction=\"mean\")\n", + "net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction=\"mean\")\n", "lr = Tensor(get_lr(0, 0.002, 10, ds_train.get_dataset_size()))\n", "net_opt = nn.Momentum(network.trainable_params(), learning_rate=lr, momentum=0.9)\n", "time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())\n", @@ -777,7 +777,7 @@ "\n", "lr = Tensor(get_lr(0, 0.002, 10, ds_train.get_dataset_size()))\n", "network = AlexNet(num_classes=10)\n", - "net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction=\"mean\")\n", + "net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction=\"mean\")\n", "net_opt = nn.Momentum(network.trainable_params(), learning_rate=lr, momentum=0.9)\n", "time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())\n", "config_ck = CheckpointConfig(save_checkpoint_steps=1562, keep_checkpoint_max=10)\n", @@ -873,7 +873,7 @@ "source": [ "lr = Tensor(get_lr(0, 0.002, 1, ds_train.get_dataset_size()))\n", "network = AlexNet(num_classes=10)\n", - "net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction=\"mean\")\n", + "net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction=\"mean\")\n", "net_opt = nn.Momentum(network.trainable_params(), learning_rate=lr, momentum=0.9)\n", "time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())\n", "config_ck = CheckpointConfig(save_checkpoint_steps=1562, keep_checkpoint_max=10)\n", @@ -1017,7 +1017,7 @@ "\n", "lr = Tensor(get_lr(0, 0.002, 1, ds_train.get_dataset_size()))\n", "network = AlexNet(num_classes=10)\n", - "net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction=\"mean\")\n", + "net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction=\"mean\")\n", "net_opt = nn.Momentum(network.trainable_params(), learning_rate=lr, momentum=0.9)\n", "time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())\n", "config_ck = CheckpointConfig(save_checkpoint_steps=1562, keep_checkpoint_max=10)\n", @@ -1153,7 +1153,7 @@ "\n", "lr = Tensor(get_lr(0, 0.002, 1, ds_train.get_dataset_size()))\n", "network = AlexNet(num_classes=10)\n", - "net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction=\"mean\")\n", + "net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction=\"mean\")\n", "net_opt = nn.Momentum(network.trainable_params(), learning_rate=lr, momentum=0.9)\n", "time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())\n", "config_ck = CheckpointConfig(save_checkpoint_steps=1562, keep_checkpoint_max=10)\n", diff --git a/tutorials/notebook/mindinsight/mindinsight_model_lineage_and_data_lineage.ipynb b/tutorials/notebook/mindinsight/mindinsight_model_lineage_and_data_lineage.ipynb index 3a09e78dc4..c55cbd60f4 100644 --- a/tutorials/notebook/mindinsight/mindinsight_model_lineage_and_data_lineage.ipynb +++ b/tutorials/notebook/mindinsight/mindinsight_model_lineage_and_data_lineage.ipynb @@ -313,7 +313,7 @@ " epoch_size = 10\n", " mnist_path = \"./MNIST_Data\"\n", " \n", - " net_loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')\n", + " net_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')\n", " repeat_size = 1\n", " # create the network\n", " network = LeNet5()\n", diff --git a/tutorials/notebook/mixed_precision.ipynb b/tutorials/notebook/mixed_precision.ipynb index b57154f1e1..53a7b4b7f3 100644 --- a/tutorials/notebook/mixed_precision.ipynb +++ b/tutorials/notebook/mixed_precision.ipynb @@ -859,7 +859,7 @@ " weight_decay = 1e-4\n", " \n", " # define loss, model\n", - " loss = SoftmaxCrossEntropyWithLogits(sparse=True, is_grad=False, reduction='mean')\n", + " loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')\n", " opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, momentum)\n", " model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'},amp_level=\"O2\")\n", " \n", diff --git a/tutorials/notebook/model_security.ipynb b/tutorials/notebook/model_security.ipynb index f1c00155f6..d958155df7 100644 --- a/tutorials/notebook/model_security.ipynb +++ b/tutorials/notebook/model_security.ipynb @@ -422,7 +422,7 @@ "lr = 0.01\n", "momentum = 0.9\n", "network = LeNet5()\n", - "net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction=\"mean\")\n", + "net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction=\"mean\")\n", "net_opt = nn.Momentum(network.trainable_params(), lr, momentum)\n", "time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())\n", "config_ck = CheckpointConfig(save_checkpoint_steps=1875,\n", @@ -752,7 +752,7 @@ "from mindarmour.defenses import NaturalAdversarialDefense\n", "\n", "\n", - "loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=False)\n", + "loss = SoftmaxCrossEntropyWithLogits(sparse=False, reduction='mean')\n", "opt = nn.Momentum(net.trainable_params(), 0.01, 0.09)\n", "\n", "nad = NaturalAdversarialDefense(net, loss_fn=loss, optimizer=opt,\n", diff --git a/tutorials/notebook/nlp_application.ipynb b/tutorials/notebook/nlp_application.ipynb index 8920dfef4c..02cf130217 100644 --- a/tutorials/notebook/nlp_application.ipynb +++ b/tutorials/notebook/nlp_application.ipynb @@ -821,7 +821,7 @@ "from mindspore import nn\n", "\n", "\n", - "loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)\n", + "loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')\n", "opt = nn.Momentum(network.trainable_params(), cfg.learning_rate, cfg.momentum)" ] }, diff --git a/tutorials/notebook/quick_start.ipynb b/tutorials/notebook/quick_start.ipynb index c1b390cedf..50146fffc1 100644 --- a/tutorials/notebook/quick_start.ipynb +++ b/tutorials/notebook/quick_start.ipynb @@ -858,7 +858,7 @@ "net_opt = nn.Momentum(network.trainable_params(), lr, momentum)\n", "\n", "# define the loss function\n", - "net_loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')\n", + "net_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')\n", "\n", "# define the model\n", "model = Model(network, net_loss, net_opt, metrics={\"Accuracy\": Accuracy()} )\n", diff --git a/tutorials/notebook/synchronization_training_and_evaluation.ipynb b/tutorials/notebook/synchronization_training_and_evaluation.ipynb index 236ae433c8..80f8573919 100644 --- a/tutorials/notebook/synchronization_training_and_evaluation.ipynb +++ b/tutorials/notebook/synchronization_training_and_evaluation.ipynb @@ -371,7 +371,7 @@ " eval_data = create_dataset(eval_data_path, repeat_size=repeat_size)\n", " \n", " # define the loss function\n", - " net_loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')\n", + " net_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')\n", " # define the optimizer\n", " net_opt = nn.Momentum(network.trainable_params(), learning_rate=0.01, momentum=0.9)\n", " config_ck = CheckpointConfig(save_checkpoint_steps=eval_per_epoch*1875, keep_checkpoint_max=15)\n", diff --git a/tutorials/source_en/advanced_use/computer_vision_application.md b/tutorials/source_en/advanced_use/computer_vision_application.md index 13fa54ac4c..f340d98778 100644 --- a/tutorials/source_en/advanced_use/computer_vision_application.md +++ b/tutorials/source_en/advanced_use/computer_vision_application.md @@ -167,7 +167,7 @@ An example of the code for defining the loss function and optimizer in MindSpore ```python # loss function definition -ls = SoftmaxCrossEntropyWithLogits(sparse=True, is_grad=False, reduction="mean") +ls = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") # optimization definition opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 0.9) diff --git a/tutorials/source_en/advanced_use/debugging_in_pynative_mode.md b/tutorials/source_en/advanced_use/debugging_in_pynative_mode.md index 877b07e0a6..42c3fbe928 100644 --- a/tutorials/source_en/advanced_use/debugging_in_pynative_mode.md +++ b/tutorials/source_en/advanced_use/debugging_in_pynative_mode.md @@ -361,7 +361,7 @@ class GradWrap(nn.Cell): net = LeNet5() optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.1, 0.9) -criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True) +criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean') net_with_criterion = WithLossCell(net, criterion) train_network = GradWrap(net_with_criterion) train_network.set_train() diff --git a/tutorials/source_en/advanced_use/differential_privacy.md b/tutorials/source_en/advanced_use/differential_privacy.md index 33635e67bd..746f969dbb 100644 --- a/tutorials/source_en/advanced_use/differential_privacy.md +++ b/tutorials/source_en/advanced_use/differential_privacy.md @@ -233,7 +233,7 @@ Load the LeNet network, define the loss function, configure the checkpoint param ```python network = LeNet5() -net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean") +net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps, keep_checkpoint_max=cfg.keep_checkpoint_max) ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", diff --git a/tutorials/source_en/advanced_use/model_security.md b/tutorials/source_en/advanced_use/model_security.md index 3075c95afc..1af2ab0416 100644 --- a/tutorials/source_en/advanced_use/model_security.md +++ b/tutorials/source_en/advanced_use/model_security.md @@ -185,7 +185,7 @@ The LeNet model is used as an example. You can also create and train your own mo batch_size=batch_size, repeat_size=1, sparse=False) net = LeNet5() - loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=False) + loss = SoftmaxCrossEntropyWithLogits(sparse=False) opt = nn.Momentum(net.trainable_params(), 0.01, 0.09) model = Model(net, loss, opt, metrics=None) model.train(10, ds_train, callbacks=[LossMonitor()], diff --git a/tutorials/source_en/advanced_use/network_migration.md b/tutorials/source_en/advanced_use/network_migration.md index 0e5e4fd884..71511c4356 100644 --- a/tutorials/source_en/advanced_use/network_migration.md +++ b/tutorials/source_en/advanced_use/network_migration.md @@ -223,7 +223,7 @@ The ResNet-50 network migration and training on the Ascend 910 is used as an exa After the network is defined, the loss function and optimizer need to be defined accordingly. ```python - loss = SoftmaxCrossEntropyWithLogits(sparse=True) + loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean') opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum, config.weight_decay, config.loss_scale) ``` diff --git a/tutorials/source_en/advanced_use/nlp_application.md b/tutorials/source_en/advanced_use/nlp_application.md index e42856478b..f33da2ffbb 100644 --- a/tutorials/source_en/advanced_use/nlp_application.md +++ b/tutorials/source_en/advanced_use/nlp_application.md @@ -193,7 +193,7 @@ if args.pre_trained: The sample code for defining the optimizer and loss function is as follows: ```python -loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True) +loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean') opt = nn.Momentum(network.trainable_params(), cfg.learning_rate, cfg.momentum) loss_cb = LossMonitor() ``` diff --git a/tutorials/source_en/advanced_use/summary_record.md b/tutorials/source_en/advanced_use/summary_record.md index c8e52b400e..eb80a56643 100644 --- a/tutorials/source_en/advanced_use/summary_record.md +++ b/tutorials/source_en/advanced_use/summary_record.md @@ -106,7 +106,7 @@ class AlexNet(nn.Cell): context.set_context(mode=context.GRAPH_MODE) network = AlexNet(num_classes=10) -loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean") +loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") lr = Tensor(0.1) opt = nn.Momentum(network.trainable_params(), lr, momentum=0.9) model = Model(network, loss, opt) diff --git a/tutorials/source_en/quick_start/quick_start.md b/tutorials/source_en/quick_start/quick_start.md index 4e37ef2243..4acb74f077 100644 --- a/tutorials/source_en/quick_start/quick_start.md +++ b/tutorials/source_en/quick_start/quick_start.md @@ -291,7 +291,7 @@ Call the defined loss function in the `__main__` function. if __name__ == "__main__": ... #define the loss function - net_loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean') + net_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean') ... ``` diff --git a/tutorials/source_en/use/multi_platform_inference.md b/tutorials/source_en/use/multi_platform_inference.md index 704b96d460..f18a616895 100644 --- a/tutorials/source_en/use/multi_platform_inference.md +++ b/tutorials/source_en/use/multi_platform_inference.md @@ -63,7 +63,7 @@ MindSpore supports the following inference scenarios based on the hardware platf ```python network = LeNet5(cfg.num_classes) - net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean") + net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum) model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()}) @@ -86,7 +86,7 @@ MindSpore supports the following inference scenarios based on the hardware platf ```python network = LeNet5(cfg.num_classes) - net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean") + net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum) model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()}) diff --git a/tutorials/source_zh_cn/advanced_use/computer_vision_application.md b/tutorials/source_zh_cn/advanced_use/computer_vision_application.md index b40d13a7fa..9d3f271063 100644 --- a/tutorials/source_zh_cn/advanced_use/computer_vision_application.md +++ b/tutorials/source_zh_cn/advanced_use/computer_vision_application.md @@ -170,7 +170,7 @@ MindSpore中定义损失函数和优化器的代码样例如下： ```python # loss function definition -ls = SoftmaxCrossEntropyWithLogits(sparse=True, is_grad=False, reduction="mean") +ls = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") # optimization definition opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 0.9) diff --git a/tutorials/source_zh_cn/advanced_use/debugging_in_pynative_mode.md b/tutorials/source_zh_cn/advanced_use/debugging_in_pynative_mode.md index a8c87f9ba8..fd4a8fed7e 100644 --- a/tutorials/source_zh_cn/advanced_use/debugging_in_pynative_mode.md +++ b/tutorials/source_zh_cn/advanced_use/debugging_in_pynative_mode.md @@ -363,7 +363,7 @@ class GradWrap(nn.Cell): net = LeNet5() optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.1, 0.9) -criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True) +criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean') net_with_criterion = WithLossCell(net, criterion) train_network = GradWrap(net_with_criterion) train_network.set_train() diff --git a/tutorials/source_zh_cn/advanced_use/differential_privacy.md b/tutorials/source_zh_cn/advanced_use/differential_privacy.md index 0f09b27154..7e9dac091d 100644 --- a/tutorials/source_zh_cn/advanced_use/differential_privacy.md +++ b/tutorials/source_zh_cn/advanced_use/differential_privacy.md @@ -233,7 +233,7 @@ class LeNet5(nn.Cell): ```python network = LeNet5() -net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean") +net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps, keep_checkpoint_max=cfg.keep_checkpoint_max) ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", diff --git a/tutorials/source_zh_cn/advanced_use/gradient_accumulation.md b/tutorials/source_zh_cn/advanced_use/gradient_accumulation.md index 574ed6e6c8..f982312405 100644 --- a/tutorials/source_zh_cn/advanced_use/gradient_accumulation.md +++ b/tutorials/source_zh_cn/advanced_use/gradient_accumulation.md @@ -218,7 +218,7 @@ if __name__ == "__main__": ds_train = create_dataset(os.path.join(args.data_path, "train"), 32) network = LeNet5(10) - net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean") + net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") net_opt = nn.Momentum(network.trainable_params(), 0.01, 0.9) model = GradientAccumulation(network, net_loss, net_opt) diff --git a/tutorials/source_zh_cn/advanced_use/model_security.md b/tutorials/source_zh_cn/advanced_use/model_security.md index 13850029cd..1d445b489b 100644 --- a/tutorials/source_zh_cn/advanced_use/model_security.md +++ b/tutorials/source_zh_cn/advanced_use/model_security.md @@ -185,7 +185,7 @@ def generate_mnist_dataset(data_path, batch_size=32, repeat_size=1, batch_size=batch_size, repeat_size=1, sparse=False) net = LeNet5() - loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=False) + loss = SoftmaxCrossEntropyWithLogits(sparse=False) opt = nn.Momentum(net.trainable_params(), 0.01, 0.09) model = Model(net, loss, opt, metrics=None) model.train(10, ds_train, callbacks=[LossMonitor()], diff --git a/tutorials/source_zh_cn/advanced_use/nlp_application.md b/tutorials/source_zh_cn/advanced_use/nlp_application.md index 3b8d9e3c48..dd6df6a34b 100644 --- a/tutorials/source_zh_cn/advanced_use/nlp_application.md +++ b/tutorials/source_zh_cn/advanced_use/nlp_application.md @@ -193,7 +193,7 @@ if args.pre_trained: 定义优化器及损失函数的示例代码如下： ```python -loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True) +loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean') opt = nn.Momentum(network.trainable_params(), cfg.learning_rate, cfg.momentum) loss_cb = LossMonitor() ``` diff --git a/tutorials/source_zh_cn/advanced_use/summary_record.md b/tutorials/source_zh_cn/advanced_use/summary_record.md index 16c459c180..8854964cdb 100644 --- a/tutorials/source_zh_cn/advanced_use/summary_record.md +++ b/tutorials/source_zh_cn/advanced_use/summary_record.md @@ -108,7 +108,7 @@ class AlexNet(nn.Cell): context.set_context(mode=context.GRAPH_MODE) network = AlexNet(num_classes=10) -loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean") +loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") lr = Tensor(0.1) opt = nn.Momentum(network.trainable_params(), lr, momentum=0.9) model = Model(network, loss, opt) diff --git a/tutorials/source_zh_cn/quick_start/quick_start.md b/tutorials/source_zh_cn/quick_start/quick_start.md index 2cc7e51cff..85a0b2da6e 100644 --- a/tutorials/source_zh_cn/quick_start/quick_start.md +++ b/tutorials/source_zh_cn/quick_start/quick_start.md @@ -291,7 +291,7 @@ from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits if __name__ == "__main__": ... #define the loss function - net_loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean') + net_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean') ... ``` diff --git a/tutorials/source_zh_cn/use/multi_platform_inference.md b/tutorials/source_zh_cn/use/multi_platform_inference.md index 77698182e5..83b588e41a 100644 --- a/tutorials/source_zh_cn/use/multi_platform_inference.md +++ b/tutorials/source_zh_cn/use/multi_platform_inference.md @@ -62,7 +62,7 @@ CPU | ONNX格式 | 支持ONNX推理的runtime/SDK，如TensorRT。 首先构建模型，然后使用`mindspore.train.serialization`模块的`load_checkpoint`和`load_param_into_net`从本地加载模型与参数，传入验证数据集后即可进行模型推理，验证数据集的处理方式与训练数据集相同。 ```python network = LeNet5(cfg.num_classes) - net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean") + net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum) model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()}) @@ -84,7 +84,7 @@ CPU | ONNX格式 | 支持ONNX推理的runtime/SDK，如TensorRT。 首先构建模型，然后使用`hub.load_weights`从云端加载模型参数，传入验证数据集后即可进行推理，验证数据集的处理方式与训练数据集相同。 ```python network = LeNet5(cfg.num_classes) - net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean") + net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum) model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()}) diff --git a/tutorials/tutorial_code/gradient_accumulation/train.py b/tutorials/tutorial_code/gradient_accumulation/train.py index e9ff0f6c1f..c52fd0d63f 100644 --- a/tutorials/tutorial_code/gradient_accumulation/train.py +++ b/tutorials/tutorial_code/gradient_accumulation/train.py @@ -139,7 +139,7 @@ if __name__ == "__main__": ds_train = create_dataset(os.path.join(args.data_path, "train"), 32) network = LeNet5(10) - net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean") + net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") net_opt = nn.Momentum(network.trainable_params(), 0.01, 0.9) model = GradientAccumulation(network, net_loss, net_opt) diff --git a/tutorials/tutorial_code/lenet.py b/tutorials/tutorial_code/lenet.py index 5f5dfffb22..dc9348c5d4 100644 --- a/tutorials/tutorial_code/lenet.py +++ b/tutorials/tutorial_code/lenet.py @@ -205,7 +205,7 @@ if __name__ == "__main__": epoch_size = 1 mnist_path = "./MNIST_Data" # define the loss function - net_loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean') + net_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean') repeat_size = 1 # create the network network = LeNet5() diff --git a/tutorials/tutorial_code/model_safety/mnist_defense_nad.py b/tutorials/tutorial_code/model_safety/mnist_defense_nad.py index a76c2a6016..d587f960ac 100644 --- a/tutorials/tutorial_code/model_safety/mnist_defense_nad.py +++ b/tutorials/tutorial_code/model_safety/mnist_defense_nad.py @@ -57,7 +57,7 @@ def test_nad_method(): load_dict = load_checkpoint(ckpt_name) load_param_into_net(net, load_dict) - loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=False) + loss = SoftmaxCrossEntropyWithLogits(sparse=False) opt = nn.Momentum(net.trainable_params(), 0.01, 0.09) nad = NaturalAdversarialDefense(net, loss_fn=loss, optimizer=opt, diff --git a/tutorials/tutorial_code/resnet/cifar_resnet50.py b/tutorials/tutorial_code/resnet/cifar_resnet50.py index 94cca8b461..cf6740e2cf 100644 --- a/tutorials/tutorial_code/resnet/cifar_resnet50.py +++ b/tutorials/tutorial_code/resnet/cifar_resnet50.py @@ -111,7 +111,7 @@ if __name__ == '__main__': epoch_size = args_opt.epoch_size net = resnet50(args_opt.batch_size, args_opt.num_classes) - ls = SoftmaxCrossEntropyWithLogits(sparse=True, is_grad=False, reduction="mean") + ls = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 0.9) model = Model(net, loss_fn=ls, optimizer=opt, metrics={'acc'}) -- Gitee From 2d16054fc404f1d8a8c9327c5441a68abc6eade2 Mon Sep 17 00:00:00 2001 From: lvmingfu <630944715@qq.com> Date: Thu, 3 Sep 2020 20:05:31 +0800 Subject: [PATCH 05/13] modify code dormats in quick_start.ipynb --- tutorials/notebook/quick_start.ipynb | 307 +++++++++++---------------- 1 file changed, 122 insertions(+), 185 deletions(-) diff --git a/tutorials/notebook/quick_start.ipynb b/tutorials/notebook/quick_start.ipynb index c1b390cedf..83b9cf02ef 100644 --- a/tutorials/notebook/quick_start.ipynb +++ b/tutorials/notebook/quick_start.ipynb @@ -58,8 +58,8 @@ "execution_count": 1, "metadata": { "ExecuteTime": { - "end_time": "2020-09-01T09:38:35.668048Z", - "start_time": "2020-09-01T09:38:35.658427Z" + "end_time": "2020-09-04T06:15:58.114167Z", + "start_time": "2020-09-04T06:15:58.105497Z" } }, "outputs": [ @@ -100,8 +100,8 @@ "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2020-09-01T09:38:35.686357Z", - "start_time": "2020-09-01T09:38:35.669064Z" + "end_time": "2020-09-04T06:15:58.130999Z", + "start_time": "2020-09-04T06:15:58.115177Z" } }, "outputs": [ @@ -119,10 +119,12 @@ "import gzip \n", "import os\n", "\n", - "def unzipfile(gzip_path):\n", - " \"\"\"unzip dataset file\n", + "def unzip_file(gzip_path):\n", + " \"\"\"\n", + " unzip dataset file\n", + " \n", " Args:\n", - " gzip_path: dataset file path\n", + " gzip_path (str): dataset file path\n", " \"\"\"\n", " open_file = open(gzip_path.replace('.gz',''), 'wb')\n", " gz_file = gzip.GzipFile(gzip_path)\n", @@ -148,7 +150,7 @@ " file_name = os.path.join(train_path,url_parse.path.split('/')[-1])\n", " if not os.path.exists(file_name.replace('.gz', '')):\n", " file = urllib.request.urlretrieve(url, file_name)\n", - " unzipfile(file_name)\n", + " unzip_file(file_name)\n", " os.remove(file_name)\n", " \n", " for url in test_url:\n", @@ -157,7 +159,7 @@ " file_name = os.path.join(test_path,url_parse.path.split('/')[-1])\n", " if not os.path.exists(file_name.replace('.gz', '')):\n", " file = urllib.request.urlretrieve(url, file_name)\n", - " unzipfile(file_name)\n", + " unzip_file(file_name)\n", " os.remove(file_name)\n", "\n", "download_dataset()" @@ -203,8 +205,8 @@ "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2020-09-01T09:38:37.018292Z", - "start_time": "2020-09-01T09:38:35.688435Z" + "end_time": "2020-09-04T06:15:59.235677Z", + "start_time": "2020-09-04T06:15:58.132025Z" } }, "outputs": [ @@ -216,12 +218,12 @@ "Number of pictures contained in the mnist_ds： 60000\n", "The item of mnist_ds: dict_keys(['image', 'label'])\n", "Tensor of image in item: (28, 28, 1)\n", - "The label of item: 9\n" + "The label of item: 1\n" ] }, { "data": { - "image/png": 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+ "image/png": "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\n", "text/plain": [ "
" ] @@ -244,7 +246,7 @@ "test_data_path = \"./MNIST_Data/test\"\n", "mnist_ds = ds.MnistDataset(train_data_path)\n", "print('The type of mnist_ds:', type(mnist_ds))\n", - "print(\"Number of pictures contained in the mnist_ds：\",mnist_ds.get_dataset_size())\n", + "print(\"Number of pictures contained in the mnist_ds：\", mnist_ds.get_dataset_size())\n", "\n", "dic_ds = mnist_ds.create_dict_iterator()\n", "item = dic_ds.get_next()\n", @@ -293,8 +295,8 @@ "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2020-09-01T09:38:37.029946Z", - "start_time": "2020-09-01T09:38:37.019757Z" + "end_time": "2020-09-04T06:15:59.246575Z", + "start_time": "2020-09-04T06:15:59.236934Z" } }, "outputs": [], @@ -307,7 +309,9 @@ "\n", "def create_dataset(data_path, batch_size=32, repeat_size=1,\n", " num_parallel_workers=1):\n", - " \"\"\" create dataset for train or test\n", + " \"\"\" \n", + " create dataset for train or test\n", + " \n", " Args:\n", " data_path (str): Data path\n", " batch_size (int): The number of data records in each group\n", @@ -377,8 +381,8 @@ "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2020-09-01T09:38:37.077669Z", - "start_time": "2020-09-01T09:38:37.030960Z" + "end_time": "2020-09-04T06:15:59.350173Z", + "start_time": "2020-09-04T06:15:59.247581Z" } }, "outputs": [ @@ -407,8 +411,8 @@ "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2020-09-01T09:38:37.443526Z", - "start_time": "2020-09-01T09:38:37.078712Z" + "end_time": "2020-09-04T06:15:59.716300Z", + "start_time": "2020-09-04T06:15:59.351186Z" }, "scrolled": false }, @@ -417,15 +421,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "dict_keys(['label', 'image'])\n", "Tensor of image: (32, 1, 32, 32)\n", - "labels: [3 4 2 2 4 9 2 3 4 8 9 1 4 0 8 1 9 0 1 2 9 4 6 5 7 4 4 5 3 9 8 5]\n" + "labels: [0 3 0 7 0 8 7 8 6 2 5 5 7 4 7 6 3 8 3 2 7 3 4 0 7 5 5 0 6 1 7 4]\n" ] } ], "source": [ "data = datas.create_dict_iterator().get_next()\n", - "print(data.keys())\n", "images = data[\"image\"] \n", "labels = data[\"label\"] \n", "print('Tensor of image:', images.shape)\n", @@ -444,14 +446,14 @@ "execution_count": 7, "metadata": { "ExecuteTime": { - "end_time": "2020-09-01T09:38:38.064277Z", - "start_time": "2020-09-01T09:38:37.444556Z" + "end_time": "2020-09-04T06:16:00.326012Z", + "start_time": "2020-09-04T06:15:59.717311Z" } }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -515,44 +517,8 @@ "source": [ "在构建LeNet5前，我们需要对全连接层以及卷积层进行初始化。\n", "\n", - "`TruncatedNormal`：参数初始化方法，MindSpore支持`TruncatedNormal`、`Normal`、`Uniform`等多种参数初始化方法，具体可以参考MindSpore API的`mindspore.common.initializer`模块说明。\n", - "\n", - "初始化示例代码如下：" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "ExecuteTime": { - "end_time": "2020-09-01T09:38:38.071875Z", - "start_time": "2020-09-01T09:38:38.066284Z" - } - }, - "outputs": [], - "source": [ - "import mindspore.nn as nn\n", - "from mindspore.common.initializer import TruncatedNormal\n", - "\n", - "# initialize 2D convolution function\n", - "def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):\n", - " \"\"\"Conv layer weight initial.\"\"\"\n", - " weight = weight_variable()\n", - " return nn.Conv2d(in_channels, out_channels,\n", - " kernel_size=kernel_size, stride=stride, padding=padding,\n", - " weight_init=weight, has_bias=False, pad_mode=\"valid\")\n", - "\n", - "# initialize full connection layer\n", - "def fc_with_initialize(input_channels, out_channels):\n", - " \"\"\"Fc layer weight initial.\"\"\"\n", - " weight = weight_variable()\n", - " bias = weight_variable()\n", - " return nn.Dense(input_channels, out_channels, weight, bias)\n", - "\n", - "# set truncated normal distribution\n", - "def weight_variable():\n", - " \"\"\"Weight initial.\"\"\"\n", - " return TruncatedNormal(0.02)" + "`Normal`：参数初始化方法，MindSpore支持`TruncatedNormal`、`Normal`、`Uniform`等多种参数初始化方法，具体可以参考MindSpore API的`mindspore.common.initializer`模块说明。\n", + "\n" ] }, { @@ -566,43 +532,39 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "ExecuteTime": { - "end_time": "2020-09-01T09:38:38.091590Z", - "start_time": "2020-09-01T09:38:38.072888Z" + "end_time": "2020-09-04T06:16:00.336989Z", + "start_time": "2020-09-04T06:16:00.328353Z" } }, "outputs": [], "source": [ + "import mindspore.nn as nn\n", + "from mindspore.common.initializer import Normal\n", + "\n", "class LeNet5(nn.Cell):\n", " \"\"\"Lenet network structure.\"\"\"\n", " # define the operator required\n", - " def __init__(self):\n", + " def __init__(self, num_class=10, num_channel=1):\n", " super(LeNet5, self).__init__()\n", - " self.batch_size = 32\n", - " self.conv1 = conv(1, 6, 5)\n", - " self.conv2 = conv(6, 16, 5)\n", - " self.fc1 = fc_with_initialize(16 * 5 * 5, 120)\n", - " self.fc2 = fc_with_initialize(120, 84)\n", - " self.fc3 = fc_with_initialize(84, 10)\n", + " self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid')\n", + " self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid')\n", + " self.fc1 = nn.Dense(16 * 5 * 5, 120, weight_init=Normal(0.02))\n", + " self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.02))\n", + " self.fc3 = nn.Dense(84, num_class, weight_init=Normal(0.02))\n", " self.relu = nn.ReLU()\n", " self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)\n", " self.flatten = nn.Flatten()\n", "\n", " # use the preceding operators to construct networks\n", " def construct(self, x):\n", - " x = self.conv1(x)\n", - " x = self.relu(x)\n", - " x = self.max_pool2d(x)\n", - " x = self.conv2(x) \n", - " x = self.relu(x)\n", - " x = self.max_pool2d(x)\n", + " x = self.max_pool2d(self.relu(self.conv1(x)))\n", + " x = self.max_pool2d(self.relu(self.conv2(x)))\n", " x = self.flatten(x)\n", - " x = self.fc1(x)\n", - " x = self.relu(x)\n", - " x = self.fc2(x)\n", - " x = self.relu(x)\n", + " x = self.relu(self.fc1(x))\n", + " x = self.relu(self.fc2(x))\n", " x = self.fc3(x) \n", " return x" ] @@ -616,11 +578,11 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "ExecuteTime": { - "end_time": "2020-09-01T09:38:38.379734Z", - "start_time": "2020-09-01T09:38:38.092597Z" + "end_time": "2020-09-04T06:16:00.652544Z", + "start_time": "2020-09-04T06:16:00.338003Z" } }, "outputs": [ @@ -628,50 +590,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "layer conv1: Conv2d, bias_init=zeros>\n", + "layer conv1: Conv2d\n", "****************************************\n", - "layer fc1: Dense\n" + " [ 0.01414873 -0.02673322 0.01534838 ... 0.00437457 -0.01688845\n", + " -0.00188475]\n", + " [ 0.01756713 -0.0201801 -0.0223504 ... 0.00682346 -0.00856738\n", + " 0.00753205]\n", + " [-0.01119993 0.01894077 -0.02048291 ... 0.03681218 -0.01461048\n", + " 0.0045935 ]]), has_bias=True, bias=Parameter (name=fc1.bias, value=[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.])>\n" ] } ], @@ -710,11 +647,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "ExecuteTime": { - "end_time": "2020-09-01T09:38:38.387303Z", - "start_time": "2020-09-01T09:38:38.381254Z" + "end_time": "2020-09-04T06:16:00.660140Z", + "start_time": "2020-09-04T06:16:00.653553Z" } }, "outputs": [], @@ -735,7 +672,7 @@ " cur_step = (cur_epoch-1)*1875 + cb_params.cur_step_num\n", " self.step_loss[\"loss_value\"].append(str(cb_params.net_outputs))\n", " self.step_loss[\"step\"].append(str(cur_step))\n", - " if cur_step % 25 == 0:\n", + " if cur_step % 125 == 0:\n", " acc = self.model.eval(self.eval_dataset, dataset_sink_mode=False)\n", " self.steps_eval[\"step\"].append(cur_step)\n", " self.steps_eval[\"acc\"].append(acc[\"Accuracy\"])\n", @@ -762,11 +699,11 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": { "ExecuteTime": { - "end_time": "2020-09-01T09:38:38.408697Z", - "start_time": "2020-09-01T09:38:38.388305Z" + "end_time": "2020-09-04T06:16:00.675920Z", + "start_time": "2020-09-04T06:16:00.661139Z" } }, "outputs": [], @@ -805,11 +742,11 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": { "ExecuteTime": { - "end_time": "2020-09-01T09:39:52.156166Z", - "start_time": "2020-09-01T09:38:38.409703Z" + "end_time": "2020-09-04T06:16:24.850821Z", + "start_time": "2020-09-04T06:16:00.676924Z" }, "scrolled": true }, @@ -819,21 +756,21 @@ "output_type": "stream", "text": [ "============== Starting Training ==============\n", - "epoch: 1 step: 125, loss is 2.2996988\n", - "epoch: 1 step: 250, loss is 2.2885375\n", - "epoch: 1 step: 375, loss is 2.2842615\n", - "epoch: 1 step: 500, loss is 2.2915533\n", - "epoch: 1 step: 625, loss is 1.0949335\n", - "epoch: 1 step: 750, loss is 0.09757905\n", - "epoch: 1 step: 875, loss is 0.16094187\n", - "epoch: 1 step: 1000, loss is 0.1573071\n", - "epoch: 1 step: 1125, loss is 0.14192748\n", - "epoch: 1 step: 1250, loss is 0.017596576\n", - "epoch: 1 step: 1375, loss is 0.0800842\n", - "epoch: 1 step: 1500, loss is 0.26227137\n", - "epoch: 1 step: 1625, loss is 0.1203058\n", - "epoch: 1 step: 1750, loss is 0.06940367\n", - "epoch: 1 step: 1875, loss is 0.021781247\n" + "epoch: 1 step: 125, loss is 2.3081794\n", + "epoch: 1 step: 250, loss is 2.2945735\n", + "epoch: 1 step: 375, loss is 2.3107677\n", + "epoch: 1 step: 500, loss is 2.3018627\n", + "epoch: 1 step: 625, loss is 2.3044233\n", + "epoch: 1 step: 750, loss is 2.3034055\n", + "epoch: 1 step: 875, loss is 1.1475224\n", + "epoch: 1 step: 1000, loss is 0.20896824\n", + "epoch: 1 step: 1125, loss is 0.35238677\n", + "epoch: 1 step: 1250, loss is 0.1871425\n", + "epoch: 1 step: 1375, loss is 0.071077615\n", + "epoch: 1 step: 1500, loss is 0.07669073\n", + "epoch: 1 step: 1625, loss is 0.12473262\n", + "epoch: 1 step: 1750, loss is 0.010296674\n", + "epoch: 1 step: 1875, loss is 0.11679248\n" ] } ], @@ -896,18 +833,18 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": { "ExecuteTime": { - "end_time": "2020-09-01T09:39:52.302283Z", - "start_time": "2020-09-01T09:39:52.158174Z" + "end_time": "2020-09-04T06:16:24.980730Z", + "start_time": "2020-09-04T06:16:24.850821Z" }, "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -959,18 +896,18 @@ "1. 载入模型`.cptk`文件中的参数`param`；\n", "2. 将参数`param`载入到神经网络LeNet5中；\n", "3. 载入测试数据集；\n", - "4. 调用函数`model.eval`传入参数测试数据集`ds_eval`，就生成模型`checkpoint_lenet-1_1875.ckpt`的精度值。\n", + "4. 调用函数`model.eval`传入参数测试数据集`ds_eval`，就生成模型`checkpoint_lenet-{epoch}_1875.ckpt`的精度值。\n", "\n", "> `dataset_sink_mode`表示数据集下沉模式，不支持CPU，所以这里设置成`False`。" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": { "ExecuteTime": { - "end_time": "2020-09-01T09:39:53.278595Z", - "start_time": "2020-09-01T09:39:52.303292Z" + "end_time": "2020-09-04T06:16:25.898285Z", + "start_time": "2020-09-04T06:16:24.981730Z" } }, "outputs": [ @@ -979,7 +916,7 @@ "output_type": "stream", "text": [ "============== Starting Testing ==============\n", - "============== Accuracy:{'Accuracy': 0.9716546474358975} ==============\n" + "============== Accuracy:{'Accuracy': 0.9665464743589743} ==============\n" ] } ], @@ -1017,17 +954,17 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": { "ExecuteTime": { - "end_time": "2020-09-01T09:39:53.421939Z", - "start_time": "2020-09-01T09:39:53.295258Z" + "end_time": "2020-09-04T06:16:26.021313Z", + "start_time": "2020-09-04T06:16:25.899301Z" } }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1089,11 +1026,11 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": { "ExecuteTime": { - "end_time": "2020-09-01T09:39:54.411207Z", - "start_time": "2020-09-01T09:39:53.422944Z" + "end_time": "2020-09-04T06:16:26.982886Z", + "start_time": "2020-09-04T06:16:26.022325Z" } }, "outputs": [ @@ -1102,13 +1039,13 @@ "output_type": "stream", "text": [ "All the figures in this group are predicted correctly!\n", - "[3 3 0 5 2 9 8 9 0 7 0 1 0 9 0 1 0 1 7 9 6 6 0 8 6 3 2 3 2 5 3 6] <--Predicted figures\n", - "[3 3 0 5 2 9 8 9 0 7 0 1 0 9 0 1 0 1 7 9 6 6 0 8 6 3 2 3 2 5 3 6] <--The right number\n" + "[4 4 2 4 3 0 0 4 4 0 6 8 2 1 7 7 5 9 2 6 7 4 3 3 3 5 1 6 6 2 5 9] <--Predicted figures\n", + "[4 4 2 4 3 0 0 4 4 0 6 8 2 1 7 7 5 9 2 6 7 4 3 3 3 5 1 6 6 2 5 9] <--The right number\n" ] }, { "data": { - "image/png": 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gmOQ7isnbMJnCAIIjnKm21fF8+TjKRlwELraaXg+8PXd5+yaHza3tkjG6xtRU8rZ+y63xlYBv1yZ/zOzyIwO3ngzZOTpU3q4cxMYbM1Xj7wPbiMG8/Yd36G+K01zPsdtimt2oDszqD/iOnPpm8nwqHlSN2oO7IucY7w1HGck2q0aquxHcA2j6m+L4r6+WYhUKU1+YTecV32mqae0vf8eFZwzc/uUsAPXzdsRgFqxsab7rbTKAzi5X4eLe4nsw3FdLtBhcPQi70c1cUgbA2ELXoAhvDue6YlDINp8iRvEdXHCosZpHZsymw3m15uAeyBGjmLk1vpL9G0t0CTjwxkOluZyZ2Qtx2v+5hUnx6o+sBc6BAgPfUaM4xn6l3v+GTmaWLXyreeyFNGNH0oxq2i7xvgMUtKY0fyQPT9josi7GUMKt8ZV+y4hRMTTn6+wXV1H8bMtASR9uHx325rJeJjXf/njbQgA2/HsIaeYNQQXJ6EXc7FiufGVSUMFPb/f/hMkfTSX5Dv3Dqx0UZK/kk39f3byc1+VfaBn5F3aj62ivc4wWZkzqzLFl6eTG7yeSIYy2qnNMfWE2s19c1dyY7xiVq2qFOpiIsVGQtOH75k/Gbk1DyZ45BYB9Y5YD6BZw4Iuj1UnEtGLsG8bn0POlAy7rMj99guzPKjXvBHS//3ExMUx6fi5Ws9LsxO9gXv/PmbfhTipr4uj5SLkuTQ3Fi3Pok3GGF/v48hkP/Cvo/oRz4NQx9LeMQWFQ6L2sOvx1h8d4b8PP3jyl+bejrOqNbc+PnK/ICWqfLHNHVg5Zyn2fPa5bGVDeTGXssxOa28GzzB3dmhNa7JTDRihvphJoX0BraNq8YByYyb6XOrPj6ndaDbvVGlFfT+cV3/FqkuqgDS2Dfid/1tJz6dwCqmzZhWGs3U9zjE5CQ6B24nAAYv7zuEfnW8oOg26eC8448h3gG+tIpk7v2Ow1khtno3DwZ+rAPDHaRnm1FnDQGu7BHuDdlzscOJfVw096P4cjSMdYpzbVNCXZuP7a/w27lnDgrNVbnmWZO1J49YfkLHyK7F+f03zksQ7rd1AbF1jgy4HSbqrGMA6VGrDRrZ04nNNDW5I7nJ59YbjSQtG0RA7dVIB7u5jD0btiQzrpYXp7BIo/zdGMvzyrmjKS85kKXUceB1qGeKy01pCz6Sls9UYGFkfuU95B0vJv2WMayVT76F0O45tiFByY1Z+B73TQxLPB1LsX+5/qzXd3znfp4HPk6Tdn+rv0/jvjSLPm41wy3Ny1llePZ5GloXk5oahDWMtz2sKtLI91PYcDpc5Idv6e5giu4g9zXBz5I0HVlJHkXu76FeYcUGS7cIHl1eOpebjQI5CjizGegzctZewHUzHs115r3GfbqUWtpPSboI7OZoixsuOGd5rHhZhaNobULW3r//GHDI6QSCQSHQm4pntkgo1Dt7b0mH5S3bnZ6bnrFweoHdaH6vSWt0JFjo1Dd77vcRxnR+90L47e4caU2ZfzQ7u1yVE8WpzjQQ3/hBbneOcZGdzbSh2csEL2nGOauQw5NCSvPxiwN0fykm/5rre9yeYJtWaWZuxI8ZR3GfuVNjOHNGR2tXtTtNRy3zjbn48229uMFMDiuV9rZVWP8uvvHHoG6QRC3EPHPWrb7gEF6fO3soZc4h9R+020aJ4JFMdcgll2LzdjtzRObIMu9o7e774aQsaS8N/jNrfpOjs9D4uZxmVTirw68TsTiKN3uHCMtF8yJZ1ZD3wWtKO4r1Hw60Ujb1cOwnixEe09dX1oS0ykJtfid0YGrR3OnTXck/AM3f9MQIbXODCTuh6umiKRp4u+uRlM6tm8DdupZ1nVgmgJOEgzn8d2w0iXdd3/WcuiATcD8MydkTO6kSIsHWkOx3d/HGqs5q2XnyRthT6FuORRdRZixzQ8uX94h/9z61SoqPLt1O7kHG97+wK7B33pedzGRjbe3B9xUl93MYejvrFbms+ZIyqt6uhIJ6zw1kltHc6bhg1o1rDzxXe5yjid7ksvYrOP0OQNx4wdajt/C5HIU3/jI+tdVrWgoPLqqAg4yOtcTl6Qs3b81NEtOOKRGbNVdyy9TuhGIE7tzs7x0eZw7hhp/8Q2fI5yn7PpKUBtUlBrOPo9cGt/+TvGd3qWXq/6NlTtZcaOSJdVyU+boGaOGLtcdXg/GsREjo6Ag/jCImwah0w64wjSsCjTKcpb5Nep3UFrzvEF59JZ8/g4DBX6B0U4elQd7U3u9Ps8D8ubaoSaXnOiOdPLlMCCxz5g+yQ1Is0RKGBMSabnunr6xFVwf+fWZ+zQAtPOA9zwRJ5LkIYzD5XmcvK5fs3L8Tv1LauhUrw4xz5rRPh72oOhLcERlyJBzRzhcOVIyBzpPzEw5/gw/rFwRHPAgd6F2OF6lHC4t8c2d6d2V7wX3DnHh7E9/xrit2yLqhqQY0aGQTurdJ1Z1bz3KFflT2ftL3/XHDU1Lr6RcfGqk/noG4uZt+FOYk2NLOi5jgRDLO7BMS5lpMr7LBLhwHbhAnEbdjcHabiTUN5Ih00tfpjR1kHVGn0yzrTJ9zjc2Pb8SLc3PAOKWsN51pauew9EbExtvWhT80LqziosBdP9pulUIkhergYdRNJIpe6sot/neW2e48wxu8TeRYNJ+kvrw7+Fkz8X3MTK1Jv8pnHMyKC3obCePk33pRcZ3+lZbDGegQKOwAcV12Ya5zzVq4w4B2lItEPZsoveZnUiA0uRfxvhwHnWlmgyuNfe+gOFGWqEXUJRh7B5q7TJ6Np2F4V3iEANse0uwvJmJv3Mj7dpf4dztMM46EkggRyR7Ju21dQ0t+G6Bwr4I5J5eimw8kIKH6+9kb5EJn+Nhar7YkZhRE4fNpZmbObXcWqo+Jqi3LAdVwZHSCQSiY5cEkM7WveXkPWYfu2dlyJ6BApIvHNsZw9ymeiynBnATAgSN+rreXDXYy6j31VsSAfCW74vCaMrkfyUyfyVq4HN1Hk8k58K1qpzHuM7azE2jGxekEgkEh1RhA7TzkgkEolERdZ0JRKJREek0ZVIJBIdkUZXIpFIdEQaXYlEItERaXQlEolER6LC6CoKyYrCp4rCRUXhsKLws0hr8oWiUO32Z1UUfh9pXd5oZ/k6Q1HYoSjUKwrLIq3HF+0sTy2Kwt8VhXOKwgFF4a5Ia/JFO8vXkMpq2IMjFAWTEEEPCfAO0AB0A4YCXyoKu4VA0+lN26JViJaBbBWFjsBJYHW4tbnzU89XoBx4BRgPxIVflSc/5TxVFEzAfwPvAWOBG4C1isJVQlCskczmc/9U89VOaGVVCBHQH4hSEM+B2AuiEsRSELEgckEcBfFLECdAfGxPfweIXSCqQGwFcYWP43YE0QAiy2ndxyBeC1SbXlq9nOfnIEpAKNGmtb3mK4hXQCxrq0aZp83HHQyi2rlsgtgA4jdRqLXd5Gs4ymqwF7EHRG8QySC22E+aC6IJxOsgYkDEgRgG4hSIESCMqMapFESM/ViLQCyy/74KRK3bueaCWBtihoddq5fz/B3EvLbqlPnqma9tLcgyTz20DsHT6H4N4tMo1Npu8jUcZTXYi3jSafk2EAftF9EAItZp27u4vVFB7ANxg5fjjgFxwm3dL0AUhpjhYdfqliYDhBVEv7bqlPnq9RzhMrqXdJ6CMKN+hT1r/z3Ofrz1Uai13eRrOMpqsB1pR5x+HwbS7b9PC0Gd07Y+wBxFocrxB/R2Su9MNdDJbV0n4EKQ2vTQ6swU4BshOBSiTq20ttd8DReXdJ4KQSMwEbgdOAHMAT4BQp3j/pLO13AQbEea89w3GagNyoDHwP9HgHwhyA/gmMWASVEYKAT2CYG4EkJuQNdCqzNTgNfaqM0dma/h55LPUyH4N2oHGgCKwlbgoxB0gszX0Amyuv4DiF6obSSbQfzWXl0/6pY2B8QR1DYSBbWh/HYQiT6O/UcQq+zprgNxDsTlIX5aaKLVvs8oEBf9pYkGre0pX0GYUDs6XkXtRIkFYYpCne0pT6+w52M8ahvpIeztlFGotT3la0hlNdiLcPQGVoH4yH4zPS7Cnv4WEP+0pz0OYrXjIkC8B+I9p7TJID5DNWRlIH7W1szWWqt93fvYez1D/ZP52px2Hqgl0ulvXhTqbE95+gZqz301iK9ADIhire0pX0MqqwEP7agolAKPC8HfAtohgkit2tBetLYXnSC1akU0a42KiDSJRCK5VJBGVyKRSHREzhwhkUgkOiJruhKJRKIj0uhKJBKJjvgNjhhruC+q2h6+tq1WfG2TWtuOL63tRSdIraHwU9DaXnSCrOlKJBKJrkijK5FIJDoS9kHMJRJ/1E4czumhrsXOUA8Z/28XtpqaCKnyTnvSKgkfp2aMoi7VdV2nEkHS8m/DcnxZ05VIJBIdkTVdiW40jM8h5j+PU2RZ67K+qKGGOUsmQpTVHo9MsHHo1kUu66JVa3tCXDeUyizvs9x0/eIA1tOndVbkyj15f+eF1B9d1uXumQjLw3P8S9romnr3AqAhs2vraStqse35sdV0WtGa1g4lp2k6EupQqdoi5pzhazeDG60YBg+iU8pFj/Wxio2Lw/sSX1iL7UKow716RzGZsI0YjDD57AD3oD3cfyVnMNaOZk7NqmP38GVe0wyLmUZKUS/dr8eQmEjTsAEApJnXa3quS9boGhIT2f+UOtxm8ZR3W02fu2ciMeO0VuWJMakzxMS0qjVr+TQGLGhQF+rrsVad00tiQBhTkok1NUZaRkAYU5KxvX2B3YO+9NjWz5zApvcLGDtpKoZN34f93IrJhCErk7f/8A5Z5o4B75e1fBr989V7rtXLoM0oCsbUVG5c9i3PJB/0m3Tni2r51vt6moYN4OtVSz3WV1rVL5rKmji6h+lcl6zR3ffaZXx353z7UuCFW2+OLUvnj0M/JMXocEP0rvWbyfOpeFCtGT246zG6T4wuo9tzXT0Leq4DYiMtpVUiqdU2YjBv/+Ed+puCm2T2m8nzuTZxNgADn9qmhbQ2Y0xNJW/rt9waXwmYA9onWq4nZ9NTAGTPKMEapmNqZnRr7h7B+HmbAkp7uDaFY7fFYK04q5UcF4oX5/CnmxeSZnQ1YIcaq3lkxmw6nG+k26uHWNG3UBc9/ugSX4ulQ3yr6dKMHUkzqr9XX7WYT/59te756o8+cRUkGDyNWMG5dNY8Pg5DxZ4IqHLFmJJMz3X15KdvIMEQmRexMCkeNdx+n+cxYJXrV0JDJzPLFr5FP3MCoN7/P962EIAHFk8j6/Ed+gj2g8MGxBhKuDW+khglMIML6vWI2HCZubZjq1cfqnB+OYbd6FZNGQnA6Ke3eTRG+6LaVse9q+/BMHOQLu2mfTLOMDzGtQB8Ut2Zt15+kqQN3yPq6zla3VdzHa1RvDiHpf2XuKybc3wY69ZeS1HeIh97QZa5Iy+k/qh7vnrDmNSZY8vSub/zYtxr6XOOD2N7/jXEb9nmMX+K3hgGD8L29gUW9FzXbHCzN09p3r5vTJh6UfzQMD6Hni8d8Fgfe9yEYdN2l3VxMTFMen4us19cxf0JqkFwlOmlNy5h6uJHI2Z4vduAwA2u3vjKd60Im9EtnzuKaksDuZerNZY3e+wMeN8EQyx/HfQlV74yifMVOfReayDus+2t79gGSvNH8mKfT1zWvXG2P394bzxpK7ZG/OEHtb1532uX8aebFzY/SFPLxgCwd9FgMjeWYWG6x35dRx4H4B9DPgVc87XbG0NRtuzS6QqciInhj0M/9No++a+KDOL/EvlPYXHdUMqfqbe34aq18ezNU+j9roljufba+RjtdVSnm12+rvp9nkfsCRM9N9V5pBX19XRe8R2vJk3m8JPrXdpKc+NsPDb8GzZHoHnk7KMjGZwXnA3YUGNmxprH+WbyfI+vTz1wz3etCcnoGuLjKXt6KLYYePTe9a02krfG7uGrALAcmU7GZyEdygOH1t8/8AHj4tVPtV+fvgyANR/nkr5wa3hP2EZMvXux/6nefHdnSwGcWjaGPQWDAUhe/i1NQMY8z57d2onDAeg34XE6pVxszs/dw1eRPXMKvc3DMBYG/jIMFce1tLRHqzjyvWJDOumU6qbHHWvuMACOTGtinz2vAK7cPok+bxtQtuyE3FG66Qa+TnwAACAASURBVDh/i6u3xIBVjR41XHfSFm7lyzuHhPzshYOqKarBXZqxudW0jjKwbMcoOpwwM+Dtg1Q8qDQ3kUUDU8vGkLol/DV0GRwhkUgkOtLmmq6xa1eO3z+Q9dN+Ry9TQjg1ocU3vpKYwJon5rt0Si3bodZisuZHSS03sy8lU9IpnrIIR/vnnOPD2LtoMMkBhCA6mmSyPlPbKG95+3bWZP2ZBEMs+8Ysx7J3OhmFGl6AGw2ZXe0ubq6fjNGQ7+K6oRyZ1gR4ttemLYhF2WJ3B9OhvenYDbF2Hb7b6f3uv7MHK/ukMDmxIpyygqLm7hGMfnqb3yaFh0pzOVqdBKhfOeBUBrqlaa4xWL77aggZS8JfRttkdI1du3LingF8//wiIMwGVwMMiYlcHN6XWMXWvG5DjZkOJ6Krcf/09T1cOsgKzqWzPf8akv4SfMy3bc+PGO9LZta6m8lP36B7W5mxa1dOW6LXPWz/oyYOjVnssq5eNPJ25SCMFxsRgHFgJnU9mvym0YJgz5H5q295oetdTL615XrSzOcR112LYdseRFOTn71DwxHI8XD+WvI6l3tN47ieMzN7EbNDbe+NZLOSA0fAUXWfwINQwnLeYBIb4tVa4vH7B9oNbvCcsl6kwqpeZKLBFv5asheahg1g0/sFOF4QR5uqmfXhs/R9NTpquI58bezoevNXPzme+E1t72iynq3k2B2pfLTxCt3b/M7cPqDZ0T2aMKYkg8mEIcbVHaleNPJVTRc23piJOK0ahqJfJXPo1gKXdCWNjWy8uT/ipHYuboebGth4/9VQHJjHiTGps8f15HUuZ8zKRcwZMRHryVNayAwokMNbvno7DilJGBV9u7FLHs0A8OsJpAVBGd2yp4cCsH7a72hrDXf0yrkMWKAagOP3Dmiz8Q6FCa8/S8bSXdhaT6oL4chXb7TFKf2nTs919czu9gXdjQAtTU1vVw5i442ZWM+ciZg2B/1NcfzXV0uZ8bOnAvI4ObYsnR1Xv4Pz9ehBIIEcgeRrWwNC2itBGV1bB/V/MLVT54ADgIElRyi/T41xfm7mymBO32ZMOw9wwxN5LFv4Fve8+gw9/rwfq58BS85+kcXK7KU4t0UWZK/ULODAPV8deRa/syi0F4NBIdt8ihhFv4fx1Ay1vdbXve33eR6WN9UHMBKu733iKjyCTR4qzeXMzF4uNbHixTn86aZ3icTLyqgYyDJ35N7FG/j4+QmtutV1ia+li9HzHmeazdz4Pwf5n0dGInaEv2buLZADVO8PUNvGjRcbfdZw/R0n0uXEUjCdzCVlaNEwo1lE2pzjw/jHwhEYG0VzwAFA2dxRTHlEHVDC4dStNbYLF4jbsJtJz8+l+3rPUYwaxucg5rS8iVdmL/UoBI6Ag6KGGuaYJmqqt04Y6Li9FGsbY84d1xNraqS3SV8HlboU9b+vext73IR1f4mOilrnaHVSc1sj2INSblziO4CmKrxjLvT9rBKAzLQnKLnr/eb1eZ3L2ff8P/nXk3397j+v/+de18coZp5JPsiGjteH3U3JV0BB9uYp9HlbPZuy5ftW26T9BYRoWU7K547i0ft8D2yTcFhoNuBOcEY3gPZmZyd+R4+7I+PL547i3ocL/bYvZm+eQm8vzuCh4nAm9/bWrE4388/Bzo7BOjtoh6kd3+GnG/Ofx51G83LqzNK4v+DsoyO59tYffG7X6t4GgiPg5DeJC3HUXh1ltXZFD2KcOnYeG/4NuXGu3xhaBtDYdhcBkF1goZ8xj0N3trQjv9ljJwQRaKQXvgIKDEUJKFta7ytxLqvux9GjnFRbGvzaoaSHjlJbOVyTIK2w1nTdnfidOTVDreH6u1AXp/QoY+WFFF745i6UOiPZNXs1O8/2+kYeXDcr4HM4j3LviEjzNnzi2KIJdN2lXS92a47xkb63SnwcX96+wKVpofB/swHIspdVRwDN8PgPPPb/snwIaRoH0Nh2F2F5M5N+5scB+NNN73rUtr0xtmgCB0q7kXv5PoCAghMiSe3E4TBN/dp0L6uRLicOvras5fppd1GL+nIIp/GVwRESiUSiI8HVdP18V/ly4ldiYqi67yqem7nSZztfta2Oe4vvIf0FgW2P/uMDJJQ38lBprtfPpZUX1EbKeZ/eT9av1GsLu9eDU76WNqaS9q2CaGjwmdzYtSun72jpjGytbfyh0lyafteNuPXajGcBEPfQcb81LJeAgyjEV7CP4/4f29mDTB18S637S8h6TG3LfGDxNPpkqH0Nt6erzTbXxB1i3sE7XfZR3kwla/0OvnvJHrL8RARruq20vThmD3Gu4RY3XiRv32SAiNkAb/xjyKf0m6B+dWSFcViCsDUvbP79CLp4iZoyJHVm6Stv+Rye8JT1Is+Xj8NwXy3WimPhkhMUHdbv4GTdVbCq0GX9hhozL6+5H4DM58MzKV1r3J9wjmvy5/PIuRaPD3dOW2Jb9YE9Zb1IQeXVAJyZ2YsOOyIz4pQewQSt4Ss4hkb1Q892w1WctsR6BPtE4v474zxK2B9mjAdg0dAGL6OHleonyo4vl9q6Hk0YB2YCNHeEOc+E0fOlAx6Vm0/OXU3MuFJAgwpNFBJ27wWHo7+SaC+8fpyeq211PF8+jrIRFwHPqVEizdMf/4K+v9Y/gMIxO0Fbcc1XgMiNVatHMEFruAfHAMxY83izM9jXqzzzOtoCaBztydEXLOvKoTsL6J8wFYDsOXa1KUk+AyiqbXUcrk0hGp//U9aLKHXhH4En7EbX4ei/5gl1VgajInw6Pd9bfA+G+2qJxgxvz8h8bZ1vJs93WvI0BtEWQNOe2HHDOwCcsLsXt1cbMHrlXLLz1cpCOMtBUEY3c0kZAGMLp3psS917ACvQd4WaZubWp1o/eUXkmhSiCX/52hZkvrriHBzjPNOCL4a9PK3VAJpoozR/JL9/wNXrImxBNl5I/fIAw2KneW3mcgRqdPFTSXQEUKS/ICJSVi2vnaVfYx6Ai4seqPc/paiOgSVHaNJgfragjK7DWdjgxWnYGkAad2QtQiWYPAuEaMlXrYIJgsV24QIdt5dSJ/w76xxtqmbC68+qBjfC04AHS0P3xuZxoh2EGmTjD+vp06QU9Qp6v8xPnyBlh4FuxbUAEes0s+4vIfa496kmU4rqMGz6XpNoNLiEJ6Z0Rlw3lFOzvDhj6zv4ULumdkUPLJmus1nEniFqZuMQF6q59/252GJ8pzHUQ8bSXe2qhhtJOpScJmv5tIBnfOj3eR6DCs43B4NEGseMHBbFtdxmlmgT/utAGl2gMiuO3cOXuazTOpjgp0bS8m9JirQIP9hqaugVQKdYtHwlhEqwQTZtoenIUfrnn+PaxNkBTSJpefNMVIWAO2ZRcR9jWuunXgZHSCQSiY7Imi5qcETuHtdBbJQ3UzUNJpBIwklCUQdye7eU4cNlqWQ9tU3zmrvtwgUGPhXYmM+Rn1A9OpBGFzU4Ao8Bh0ojoEQiaRvp87eCkxdcliy/UYtsXpBIJBIdUYSIhr5liUQiuTSQNV2JRCLREWl0JRKJREek0ZVIJBIdkUZXIpFIdEQaXYlEItERaXQlEolER6LC6CoKyYrCp4rCRUXhsKLws0hr8oWiMENR2KEo1CsKyyKtxx+KQrXbn1VR+H2kdXlDUbAoCn9XFM4pCgcUhbsirckXisKDikKRvbweVBTGRFqTL9qL1vZiAxSFvorCOkWhUlE4oSgsVJTggszCbnSDFWDnHaAB6AZMBt5VFC4PqzAvtFFrOfAKsCTMcvzSFq1CkOD4Q83bWmB12MW5EaxWe/r/Br4AkoE8YIWikKWBPPfzBrvPWOB1YCqQCFwPaD6Ky09dKxGwAW3UuQg4BfQAhgI3ANP97uGOECKgPxClIJ4DsRdEJYilIGJB5II4CuKXIE6A+Nie/g4Qu0BUgdgK4gofx+0IogFEltO6j0G8Fqg2vbS6neMVEMvaqlFPrfb9fg6iBIQSbVpBDAZR7awNxAYQv4kmnfa0W0E8Fup9l1q1swEa52kRiNuclt8A8X5Q+oK8kD0geoNIBrHFbnhyQTSBeB1EDIg4EMNAnAIxAoTR/sCXgoixH2sRiEX231eBqHU711wQa0MsHGHX6naOcBpdTbXat/0dxLxo1ApiCJ5G92sQn0aZTiOqcfgViAOoD/BCEHFRmKftSWtYbYCWzxSIJ0EsBxEPoqf9PHcFpS/IC3nSafk2EAftF9IAItZp27u41VJA7ANxg5fjjgFxwm3dL0AUhlg4wq7VLU04ja7WWjNAWEH0i0atIMyotfBn7b/H2Y+3Psp0poMQIHaA6AEi1f5A50dhnrYnrWG1AVo+UyAsIP6FarwFiGUE+fUYbJvuEaffh4F0++/TQuA89UIfYI6iUOX4A3o7pXemGujktq4TEOocI1po1QqttU4BvhGCQ9GoVQgagYnA7cAJYA7wCRDK/EVa5Gmt/f/vheC4EJwB3gJuC0Gn1KqNDQi7TkXBgDoe4V9QZzNNBbqgtpsHTLANyb2dfmegdioBHjOyHAHyhSA/gGMWAyZFYaAQ7LevuxL43yC1uaOFVq3QWusU4LU2anNHE61C8G/UTgkAFIWtwEfRpFMIKhWFo16OESqXtFa0sQFa6Ey2H3ehENQD9YrCUtSO9WcDVhZklf0HEL1Q20k2g/itvcp+1C1tDogjqO0kCmpD+e0gEn0c+48gVtnTXQfiHIjLQ/wM0kqrCbVR/lXUxv5YEKZo1GrfZxSIi/7SRINWEFfY8zIetT3vEPZ2tSjT+TKIf4JIA9HFfuw2dfhJrdrYAI11lqC2k5tAJIH4FMTKoPQFeSGOHsEqEB/ZHxCPC7Gnv8V+w6tAHAex2nEhIN4D8Z5T2mQQn9mNQxmIn7W1YOigdR6oOef0Ny8atdrXvY+9lzbUP43z9Q3UnuZqEF+BGBClOs2onStVqD3gb+PURii1Rt4GaKxzKIhCe1k9Y0+bFoy+gMfTVRRKgceF4G8B7RBBpFZtaC9a24tOkFq1INp1RkVEmkQikVwqSKMrkUgkOiKn65FIJBIdkTVdiUQi0RG/frpjDfdFVTX4a9tqxdc2qbXt+NLaXnSC1BoKPwWt7UUnyJquRCKR6Io0uhKJRKIjbRlPUiL5yVM+dxTVlgaXdR1OmOn7/LcRUhQ44rqh7H+05dG2vHYW637Nh9H1iSExkX2vXYaItbaaNtJa9UAzo2u40kLpXV38pulUIkhaHv2FWHJpYIiPp+zpodhi4NF71/NM8kGX7a+cGcTm52MjpC5wKrPiOHTru83L/RrzGPRuDLbdRRHRo8TH8eXtC7B0iG81baS16oFsXpBIJBIdCWtNV1w3lMqsOAAqcmyU3LXIb/o5x4fxD+NIjI2CpNXfI+rrwyknLBiutABwZliSx7a0TcdpKinVWZEk3Jgy+3Lqhh40xSusn/Y7epkSIi2pTTjKakWOzWX9dVcUc6z7ADrsjoSq4Dh0ZwHXbJtGss5ajV27cvqOAT63J5Q30mH9jrCcK2xG1zB4EOXP1LN7+LKA93mzx07I38mhxmoeOTeb+MIibBdCHUY3vJROVJtIip7wfIEM/7/T6CKNbsAYBg+iKSXObxrz3qNYT5/WRY+pdy8ASqakU5TnuL/t0+AaB2ZSNC0RgEN3vu+y7eRz/eiwKTwGo000NfHWybH0iavwujnNfJ68zuXNy4qGzl9KzmCsHc0e609bYtn54rte9lDJ3TNRHUk3DITF6BpTkrG9fYHdg750WV8vGilpbPRIn2JUczXN2BGAfuYENr1fwNhJUzFs+j4cksKCITERa1x0uP8Z4tX2MCXR1SjYKs5iSOgIMTGeO9kE1jNnIAqiDh1l5OtBf/Sbbvj/nUaXZdobXUNiIvufUodcLZ7i+4us0loDwAkrHK5NAS5qri1YjEmd2fdSZw7dVOCy3ipsHGyqRWmK7P23VpylbAQcS0kHkwlxoRpbTU3zdtsNI8lbtbR5ubGjgiE+3iVNKCgmE4aUZABuXPatR1u93oTF6PZcV8+CnusA106GtysHsfHm/h7pD8xS1xVP8f1miQb2vXYZ3905377UMaJayp4eCsCaJ+a7rH968nSOP1PPH4d+6LHPvsY0CkaN1K3m6A9fZSRSBHpvczY9BUD2nGPQ1EQ0Gt1jy9LZcfU7gGtH1cGmWv7PrVMxFO8J+0jmbaHnunpmd/uCe9+fS69Xt/pMt/aXv2N8p2f9pgkG24jBLFipvlgzzWbAs6arJ2Exun3iKkgwqA/TldsnAZC2IBbjxUbEyT0e6Qe+0wEAS910p886uO+99Xz8/ATi/7ItHLJConhxDn+6eWFzbTySOh4b/g3D4z8A8OgBvnfxBnLj95Nl9qbzFBh8BsZoSs3dIxg/b1Pzcl6XLSQYIpuXDgK5t5aC6fTaWEt2+TkArCdP6SUvKM5+kcXqIYvp4nYtBefSWfP4ONXgNjVFSJ2KMSWZnuvqyU/fQJqxI7YOrttNOw9wwxN5LFv4Fv3MCfQyJXikCQVhUrx6TjjbKm+cmlXH7uGrwifETthdxs5XqDe/+6YdPt+uTUfUqa8yl5uw0GJ48zqX80FnA607lmhL8eIclt64hOExnm/Eo03VTHj9WUw1grR/HEfr4twn4wwvpP7oc7vaFhYdxgygaspIAEY/vc1Nt2+NkcjT1u5t5qZymkpKad2zNDIYkzpzbFk6q4cs9njhzjk+jO351xC/ZZvuNdzyuaNIGdfSPnu4LBXL82XM7vaF75dcn57EzTpGV6O+YQPPWjYA8MHzYwCorImj5yPlWKvOUfLaSOZZPtfkvCFdpcPp+TeJC2lLlb2ppJReG7tAXigqws9jw78hN87msX5DjZlZHz5LxtJd2GpqNDcO7Y2zj45kcJ76ZfNmj50B73fBZqDHmgNYT57SPE9L80fyYp9PPNa3p3trHJjJvpc6s+Pqd1xquFPLVOOxd9Fgkv6ir/97af5IGro38khOIS913du8/pXug9hyIZl735+Lzd7t0HNTncu+TSlx9rZ+pxqnDh9okxPVjr3Jgz8D1Pb7nIVPYas38sroT5q3h5uQjK670/PUsjGkbgnO+JrLz9H/71PZccM7dDFGro5r6t2LkscyAJo/5d3ZXtOfXq9uxdMca0fFhnT6HXncb5pXRn/qUUC21zfy4LpZZNfs9bFXeKmaohrcpRmbdTlfW3l4wkaPvFp5IYWX19xPX/u9PTVjFHWpnvvGnoG0heFpZ2wrhistFE1LtHeatTwvU8vGsKdgMADJEQg4enjCRp9fZLaamrC1z7YFc/k5+n3V8gx5e14AuhjjOXjTUo/14UYGR0gkEomOhLUR5buvhpCxJLg3mnV/Cdlz0jixDboYVcfu1J0WXcMATZl93Xw1PSmsNfDh9tFkoa+/Y/r81vPzgw1jmj+RHGy4MISBT23TpVZec/cIRj+9zWuTQrWtjnuL76Guycy8/p97bbbRAyUmhqr7riIr1rNj5IPDY+j/Xwc4PVVtj35u5kruTzjnke6T6s68VTUpIoE8jsCHfXmdPPxw5xwfxt5FgyNSw/WXr4E8M6bMvpTc6MV3O4yN0db9JWQ91jKew7zX7ueDYcebl69OKQuqOSxU2mx0DYmJXBzel1glvA9RyV3vYzk1nQwdIlK8O8e3sKHGzPYa1b3tw+2jyXo8gg7mPlByBtMr4ajLuuLGi/x32RUkU6yLhvHzNnn9tDxlvcjLJ26ibn465otNLH71enL7FnqkeevkOLs7lnYYkjqz9JW3XHqxt9erPuQnqhKJv6eHX+d4gPsTznFN/nzdA3n8BT4UnEtne/41urfhOvCXr1M3Tmv1mTl9fQ+PZ++T6s7EatOcCkDmr1zzavvdIyjIP+ESoOFOr4QqzuQMRuzw9MYKljYb3aZhA9j0fgHtNYIHoORRtQ3XVw336Y9/Qcav1Zqm3jXcQBm9ZIeHwcvbN5nkO3QwuIqCMTWVGIP3UaE+OncFJT/vQ8FX/+XDpQ0KKq+mbMRFIuH/OmmL2oPb6ds4dr7oP2TdQSQCeYp+lcyhW70HPqx5fBzxW1QXS5cAmqYmrBVnNdWlmEyQkoTRLYTsgb9PA/BrcB1aGzt69pjN/+3PSFumXxtw/F+2sbpivEuAhjsr+hbyxrL+bLwxM+SAIzm0o6TNGFNTydv6LbfGV+LNe2Vmlx/5j6/+TX+T/9DfSLHjhncAuHi9jfZWefAW+OAcQPPWybGUjdBWg23EYN7+wzttur8Oreun/Y72kvczu/zIwK0nQw44ijqjaymYTuaSMl1ch37/gHcvhfaAw+E8r8sWHD6wDmfv9BeEPh4WBoVs8yliFO9eJzGKmSxzZKN//OHwlvE1AGm/z1t8GQ/dWeAjlbYUL87hTze9i/NLzT3wwVsATX76Bp7fNo5jt8VoUuOtuXsED+ev9fiC6fd5HpY3zwD49XF2BD+4Dy407OVpdP/ygK7+0Y5rccZSMJ0OQytdgiNiFDPZ5tADjsJmdDM/fYLszyqDetgbxucg5pwh1tRIb5PqSJFwWDQHT2hJQ/dGxsV7jgvhoC3XoxeGwYOwvX2BBT3XuUR5NQem7InOphB3mp34iXwEIrgGRwAM2lnVvC3T+gQld73va1dN8BWkc6qxE8qWXQinNO4dlGnGjszu9jVzTBPDqsk5+MW9DTTz0ycYVHDe7yDkJa+NpOew4zya7n30mJSiOt3D1us6G1yuxVIwnczl5TR+nUT2zCkA7BuzHIDeJgO2VWYMMwdh2+M7aMkfYTO6/QeVc7FfD+IC6ACrnTgcgJj/PM7XFscbRr+Y/PK5o3gkp9Bj/SnrRUavnIuxTiH708qoHUjZmzP59T/cRe+17csD8F8VGVER8g2ewRGAyws3ZcdIuKtl+dSsOro1DUXZskszTc5BOo7Ah+/+OgSbWdDwoVph+NNN73qNrtOK85lqLc9bb3/KDoPfZ6Y0fyTz7vIMOthQY+bpFb8AILNE+69cXzie/4EfltF05ChKCRjGjlI3qtlPgiGWvw76krEpU9vsbxs2o/u1ZS2WodPJ+Mx/utqJw2Ha6eZ9nLly+yS6FdeGS5JPUsaVu0TNOKiwKgxYcBDryVNRWcMF1XWoeJKXoem+7UHGZ/o6oIuaWm7/chYi1urT4dwbvz59GaAGfqRTqqHCwHAPjnDGmjsMgPO3uHb07R6+imuyppG8Jfx6HDNY+ArSaUixcujWxfYlz7Kw8kIKL3xzF0qdUbfgGH/PruN6fv/AB16/LrfX9Cdjnlp2IxkJ6Hj+m5zG2ei6S1U0tmiCh71qK+2raiSRSCTtnPB2pAXgRXFkgo1DQz71ui1tQSzKFm3dcGruHsHwlH9qeg4tOTMsiUN3uvqTzjk+jE4l+g/eZ7twgYFPqc0DgTqcv3G2P2s+zgUCC/wIG/X1PLjrMVZf5TlAzM7qPnQ6AOceutYl8EFcN5Qj09SajqNNz8FDpbkklPvuEwgFJTGBNU/Md/F9bQ6vzvMeZv3G2f58WT4EgGM7e5Bl90UN+xebj2KWtiCWxgQj1fYAE2f8zcgRqaAj8D3ThjNxn20H4GyXkZAfnvO22egaLzbyxtn+zOzyIzGK+olT16MJ2w1XqQeuqIXDx2ga5joFRqcU18+0U9aLFFRe3XxMrUyHYjJhGzGYh/PXenWC1stJv604Ajmq+7j2nEbaOd6Bu8P55kdGwm89je6S1ePJ0NPY2rFWnaP7xHN88u+rPfya3Wcw6XBeNaanZtWxz8fQfhGfjcGJT6o784f3xjePC5EZgSabCkssl00pYoVb8EsLrgY3mAAKrXDMClNy1yKfz7+v5y4U2mx0xY49bLy5P3ds+wFLB9XoHrqzAO5Ut9/y4+3ULrDYAyi8U22r4/nycXbneIDQoz18YUhJZsHKRV7H1XTVEX2DVIPvQI7VT44nflN0dEaBf6f3o03VGBo8VuvK4doUqm11zeM/O+MIfIg4NsG+xjQyzZXNFRpfHGqs5q2XnyRtRWQH4mktms/B0aZqLtgMPLhuFgBZT0VH2f3o3BUcuyMe69kzLutbC6BqC5r56a7J+jOnFzbhz/H53uJ7MNxXS6QNXbTo+Cngz+l9wut274AI6HJw7LYY7l19D391m1oqmrCeOUPBqJHs31jS6tQyj8yYTdKG76NiZohAmPD6s/RYc6C5gy9aOqzDFfgQCCEZXVvFWZ6ePJ17F2/w+GRPMMSS4KObztmJ31pxLBQJYaGuyUxMFOjwhbdAjkON1TwyYzbxO4uipuCCb6d3APNFEbZ5r9qKteIshpmDuPKVSW2aFUCXfBcC6+nT/M8jI9nQ8XoADkxy+pp01lFYhE3HwXcyl5QBYFGmB1T7KziXzuonxzcv99i7Pyqmj3InRjFza3wl+zeWUG9r+brw5UESCiEZXdHUhLJlFx8/P4F9z/8zoJF6sjdPoc/bqjW27dHOx/GnhHsgxyfVnXnr5SdJ2vC9rg9ca5TPHcWj93l3ercUTCfzH+VRMTi4bc+PdHtjKNdkTQt6X2Oj0C3fxY49ze5Fg6rUTp9rtk3TXYczzrO+XHO49fyLPWdzaf6Kppk4+n5WCUBmmhr4EqOY/X5ZfFLdmTde/RkAXfe2PWouLM0L8X/ZxjcJI7FkXttq2t6FdShb9BtGzR8Oh/PaFT2IiQJ/UW94C+QorutB5xXfRd0nZbWlwaPQujucRwvKll1t9rGNRL47gg6SnYKPInn/m0pKSS4pjaCC0HHkqXvgizfeONtf7axcqradh/LyCFubbtLyb0kK18E0wNmR34FjlotIjEMaKL4COdoD6uwVs8nO30OTTsMgSiTB0qW41mVmCW8kFHUgPUyzhsjgCIlEItGRqBtlTCucHfnbM5F0Jm+NhKIO5PZuGWDlcFkqWTrNXiGRtBVlyy6yNAjn9sUlY3TbK4fLUnml+6Dm5WidwQLsEWbzW5azorSdXCKJJNLoRjlZj+9gMLQH/wAAFa5JREFUs9NoYtFYw5VIJIGjiBCmnZBIJBJJcMiONIlEItERaXQlEolER6TRlUgkEh2RRlcikUh0RBpdiUQi0ZGoMbqKwoOKQpGicFFROKgojqngogtFYYWicFxROK8oFCsK/uMHI4SiEKMofKgoHFYULigK3ysKt0Zaly/aQ762wzwtVBTqFIVq+9++SGvyRXu4/86EZK+EEGH9A2Fqwz5jQRwGcS0IA4ieIHqGW1uYtF4OIsb+exCIEyCujjatIDqCmAeirz1P7wBxAUTfaNMaqXy9BPK0EMTjWmtrr/c/BK0h2atgTlQK4jkQe0FUglgKIhZELoijIH5pz6iP7envALELRBWIrSCu8HPsrSAeC2NGaqbV7TzZII6DuD/atdr3/TeIe6Jda6j5KvO0+dhhNbrt5f7rkK8h2atgL2IPiN4gkkFsAfGK/SKaQLwOIgZEHIhhIE6BGAHCCOLn9v0db7JFIBbZfxtBNID4FYgD9gxZCCIuxAwPu1an4y8CUQNCgNgJIiFatTqdpxuIOhCDolVruPJV5mnzsQtBnAZxxn7c3LbqbE/3P9rtVbAX8aTT8m0gDtovogFErNO2d0H8xm3/fSBu8HLcdHsm7wDRA0SqPYPyQ8zwsGt1S2MEMRrECyDMUa7VDOJvIN4Pw0MX9fkq87R52wgQiXbj8nPUppD+0ag1nPdfS63hsFfBdqQdcfp9GEi3/z4tBHVO2/oAcxSFKscf0NspvTO19v+/F4LjQnAGeAu4LUhtemhtRgisQvAN0AsIfgoCnbQqCgbgY6ABmBGiTk21Qljz9ZLPUyHYJgQXhKBeCD4CtiCfq1C1hmyvgh3wprfT7wzAMTGacEt3BMgXovWZ4oWgUlE46uUYoRJ2rT4wAf3buK8DTbQqCgrwIdANuE0IGlvZJRDaS77KPPVEAKHOJd5e7j9Eq70Ksrr+A4heqG0km0H81l5dP+qWNgfEEfvnjYLa63s7iEQfx34ZxD9BpIHoYj/2bwLVppdWu74HQSTYP4PGg7gI4j+iTas9/XsgviOEtrH2mK8yTwUgkuz5GAvCBGKyPU+zo1Bre3uuQrJXwV6EozewCsRHIOK9XYQ9/S12YVWoPZGrHRdhL7jvOaU1ozZWV6H2KL6NU5tLGzM87FpBdAWxyZ7uvP2m/iIMD50WWvuAEKgdPdVOf5OjUGtY81XmaXOe/hO1HbcK9UUxNkrLart5ruzLIdmrgId2VBRKgceF4G8B7RBBpFZtaC9a24tOkFq1Ipq1Rk1EmkQikVwKSKMrkUgkOiJnjpBIJBIdkTVdiUQi0RFpdCUSiURH/AZHjDXcF1VtD1/bVvt07JZa244vre1FJ0itofBT0NpedIKs6UokEomuBBsGLJFIJD85DIMHUftWncu62hU9SFr+bdjPpZvRLc0fSUN3NUw9dYsZgOQl4b8giX4YEhPZ99pliFhrq2ktr53Fur9EB1USSXCI64ZS/kw9uwd/5rLekjmdJA3Op7nRNcTHU/b0UH7/wAeMi1eNbj/7TBzJS7Q+u3eqpozkfGbr4370LKzDWLhTB0XeOTVjFHWpgafvuqsJgLjPtmukyBUlPo4vb1+ApUN8q2nHLp+KYb8OosJEIGWk76eV2HYX6aSofVA7cTinh/o3K5F+rtypzIpj9/BlLuvGFk1ofp7CjWzTlUgkEh3RvKarJCaw5on5AdWG9KDm7hGMfnobb/Zo/U2bbZlCp34jAUgob6TD+h1ay3Phnry/80LqjwGnH1s0AYCG2hzdtbbG0RvjyDzSl6aS0khL8Yqxa1dO3zGgefn6Ga2XkWsOTyN5t7a6GsbnUJ1uDmqfSJTVmrtHUNfZQNJDRymyrPWbNtsyhT6NQwFQtuzSQ55XDFdaAKjIsbmsf6g0l6bfdSNuvTZfjJoZXWPXrjRe1ou6TmZilZaL2lBjpsOJ4ApROBk/b1PAhmzfmOU45vh8qDSXMxWDETv2aKhORTGZsI0YTJp5Q1D7fW0v7A+9pJPWpibeOjmWPnEVHpuyYo9zf8K55uWivEUML51GlygyuqbevQBoyOzKaUssO198N6j9FR2clMScM/zTra2xNXL3TIT1Gglyw1FWH85fS17n8tZ3QH2u+lWrTYxZW7RU55/SiV0AKLlrkcv6k8/1o8Mm7V5amhndM7cPYPtvHYU4oXn90x//gr6/3qrVaTVjRd9C3ljWn403ZmI9c0Yd4U8jDCnJLFi5qM1fByv6FvLKkkFsviI2zMpcsVacpWwElOF5ns8f+Rn3/zY4I6Y3JY9mAFD0xCKfaSqtNQCcsEKsYqOfOcFn2nBjTEkm1hSOMdK1w19ZrbbVcaTJhlER9DfFYVSipzXTkJiINc71GbYKGwebalGatH2bSpexIJjZ5UcGbj1JwaiRWE+fjrQciQ7kbHoKgOw5x7g4vC+b3i/Q7dw919WzoOc68PJSaw/cW3wPhkmNkJLEf321lCxzx0hLambfa5fx3Z3z7UuqroNNtfyfW6diKN4T9mlsnNHV6FoKppO5pAxt+gQD45tHcxjbcWRAaU/NqmP38FXNyzGKmWzzKTCEOuPJT5dTM0YB8NzMlS7rh708je5fHqB15zL9yFxSBoBFmU5Rnvfarq3eCID15Ck6nO+pmzaAPnEVJBgCN7hXbp8EQPoLAlsrabWi3+d5DFil1s5NFbVYT/6IEbCK6HpmRKyVNGPLS6DgXDprHh+nGtwmbS1U2I1u1RTVoI35z20e2xIOC5qOHA33KYNC7NgTsMvG+Sk5mmrxha3qHFNfmM3sF1dxf8I5sjdPodNfXWsJTfEKa3/5O3qZ9PvcDYS6FPW/c3suQEpRXdR9HTjKYsLh3i7rLQXTSTgsSHrIf1nV0444OkmrVvTymaZbsTpnom2PPp1ThsGDsL19gd6mlicq9rgJwya1AypShr81Sl4bySujP3FZd6qxE8qWXZrWcB2E3eg6fBu99fwmPXSU4tE5MjiiFUR9PZ1XfMerSZN5KRV6F9ZhLHTNK2O3NC48Ez1tZM1EV4UmIFJ3VmEpmN68nPlhGU1HjlJbOZzefvbTCkfQyW8SFwLqs3KgtBsAA4tr2f9oy2MbyaATw7lq9m/PoGaglQQge/MUem9yjeoyDsxk30ud6W6MiESv9Bx2nMmJLZ2/b5ztz58LbiINffqadG1e+NqyFiww9XLVJWBv00hNwuzCQdWUkeRe7tr7v72+kQfXzSK7Zq8uGtIWei8Ept692P9Ub1KM3t/LKy+k8PHaG+mL/nnrcCgfWzSh2ZsC4MAkM4OqLFEZTGDbXUSGk+uX4+PSOcjEcKWF4kn6eN14CzrJvXwfAFtn9uPQmMXN6/s15hF7ojugf3BM05Gj9M8/x7WJsxGxVgYuaULZ0lLZMlxpoWhaIoduKgBcO9oc17N3SuRtwJflQ3w+a1oQhVUliUQi+ekS/ppuAI0iSzM2A5D7UAosD7uCgHE4R58Z5hlh7c05fsOFIQx8altE26pMmX3///bOPaipK4/jXxIiECLyVFFQiEuAGW3VIhafaAddZ1tGt66ta9dCZbAo6qi1a8ed8TFltqNVZ9xqV9Titlrr2123trqzFeubYdVOHRXUBKHCgjxEEpENuXf/uNyb3OSGhHgfUc/nH01uSE7uPeeXc3/n+/0dGOcOQOXcbWBXXZ3ZcW88ElYpM3tgZ1nNERlAkf15U3Yx9Lb5SC72v9luoD4BDRNjec/1PVOHTmMV10cq8sNgyt7OHX+nKhO6WvnkXOyYAftvF6Zsu5pCCXMM1daGpIX89Rt358wR9vssX2JBmXk0tEdc14D8AecYobbSCD94FXRHh8/vKWrQVSfp8SRWSW2Cd6iGpqAzKoS7XTRle9aTVlot+Hv1S4hEpdTN65YHE2LdrrSXdTBB4F51NAyokrFVrgS3Uvi4MQVBKisWR9xCUIAGxhnbkajOR+pGPQAol4vsuv4sxkkhLuc0tXgB4k5HOPQRfvCQWkDvC2w6J3O5fOYIIdyZDoTYGHsFxUX/xcGmqQCAwCt3QLW1Sda2gLShiNMxC6TdjRd3McJkNSOndRl6PbJCc+MXnxaHRQ26N1dGwjRNWMdospoRow7skQRGCtRRkaC2tOFfKd/06O8W352FmHcbFZM8qbRMTswaKrxS1WCz4O0TywAAhoXKzxq0Ry7j7JFgqGPikXShHtO0LQgK0MCUXYwhulwAQHJhH9getnp4J3Hx9vrfzN8G5Ls+L5eA3hlHk4Yz8YEq3rgKDrRCFRUJW1OzXM3zCpPVjCe0Cv3VQITanuPN71OL/H0lAICs2blQnbkqWRvGfVHOOVLf+qEAAGDI4/94dtdHEjU6Tqs9omgB+pdYQD1+3KM2yLaQllO4DCFL7+P7lG/l+khBfBWcHzIcxtITr6F6tDTt8kT1EsarfrJgPRwdfizj9n6A5CJm4c+fpDq2xkYUj8nA7dNGrIi8CwAon7gVAJC5ex76T5c36D6t4UAuAb0zjiYNZ6h9Gt64UrqvuiOncBlCy6pQsXEg7k4uUbo5bvG2jxz/43pMDfsQcX/u2SKcaEG3cmca9k/+HKzEhcVkNSOncBm0pTfRvihKrI/zmZ4Kzll0qmAMDmkStLxKTVVRBv7y1g4AcKvLVbcHSHpb5jM0DduDB/h3TgZ+2hKPPQml3Czn4IidmPPPXES+Ll3K5vFvR2PqmjPc4/yI89CpfHdG2egAoOmhpAJ6qqkZS+YswMydp7B5/3TEnW5Hci3z42Srb3B5vWpxCl7+eDZn5FGyrwJ200lWaS7vee2Vm7C1tSF5rQ5ZO5hjvwikdpRCHRWJgSc6UDTglFd9JC5QB6pXzz9HtKA7eFAj0oP4AfeAuQ82rXsf4aeu4tbWYSgZolABXQ8srxuJE8df9XjxZ/X5Dw4ey8PAnFrZbouNn2RgzYwDXC1iIfRH5yP5WItiM9zKnWkYPKix20r7dPl13F+bhqwPe3O5R4MmFHuHleB3EpxT1qQzbsllpwJHzGASMpywrPjoaxdzh5zQnZ0IOH8NX616A/prteg0VnWb1qKu38KjJmWMPEKwphOVkxGK7Z+220autrJO7507VGpYo8fmgSd4AdfZlCKGKUnS9ELlk1j02XMJNIB56eeQGaL8je/h4snYGz2Z91yYkYb+dDVSYRfIvzfzJHc7zGLQhOKb4buwPGi6LG0FXIXcziT+Ix8pxY94igB67HCegJ4l/rhKVA0nK+Lf/9pnSA/SeKy03+tkOdpD0jGhYAYA4MdhR2HQhKL0lV1I+2whkte2ira41p1JJ/nsXMR/HuhiOGG/T4KmEc53bEqgPXLZa8t8/HEVJsTZz+uU3j/ji62FSF55Q9I7IKHdQ54V81NOGpMW2L1rDMKiLPgp5VuwKYXks3OhuqnjtM+Rx5jv4mJK8sEMJNvOEenaHVJ/lFe4E0F3Ahi0xv7L/KV5Kh7/oRSrY+QxQgjRUDgGvx8gvAzdYnuMtDMLkbqxEbbbRl7FfirVzBPQs6TWLMCgnlUJ7BZnEX9MRh3ap6cDcC/QDzlWhnYwr8laxBgoItRa3J1cgqwd4uwu0T49HTEZdYLHXi6bjcFbVC4i/qoZEbAF07iU/SnPk/+sEHKsDNXDmboXGAakB2nw7W82Y/m66YCUagABI4c3O8PYMkcCAB792iJZ24T46vgkJM58gDm9m7ixvXqa6xgP+z4UkSUXuHZWr2HOrS2Y5kxJvu4uQcwRBAKBICOSznQNwXU4nDsZJwv4OZC9bcyC2v0rsdArrCd1R/TP/8O5xiGAgjPdN/N/cElxsFhoCmEXQ/BgXDAwrp9XFfulXm7/cdhRZC1icmDNERkIbqUERe/uDBRi7S5R8wYF07Cj3ONKqwX5FXMAdFXg6ioI4yjit2tK7bPcDc1DuP+z16G3ikLdzF8h9gDtVwV86LHDQaWauceVVgvevpaHgR3eFRaXE3rscNQUMDPEivGMO8pMPcHMyjcR2NQu6dpEwqqL2DFqPOZ4KAzP7CaRwc3EK8Y7rvcwfeThnjgu7dATRAu696qjUZZk5S2mzdK1YtaqbXCWOP3pHJN7MqyUL+fDVrinA71LwgxcfQd7Ekp5zzXYLNhUPwWQuPSbN8QF6nB1lfervgfMfRDsPjUsGly9hSKmXB4rehfCPJh/LaTYXaLSasGcn+0KCTptKKiJI5hjbowPAHO+vv7rVK5q2oquQufsec+6ngvVGf8JurffC+SllEofJ6HfhiBQZnlv3wEgLIr5TPY8O9Ow9AkqHEqmAkBNJwXVbCts9d5vT+UrQrHKGeOM7cAM9+9R3DoAwa2+/TyIFnQNeeV4a1cBTNNcc4mONNgsCHgif8mhp92NAQCKW15B9WgLAPk7ck/poK0wWu2Kh03r3kffPSIX9aBoVFj7AmhwEegDfNG7N5isZqhENh0svjsLMfMeAv36AgAm7b7o9u7BsR3s+apePUbU9vgKa44J6K0DOjtha26BOjqaq+2sCuLrG94Nu4e+uw8pUnCfq0G9r/vXsXTQVqYfUfIonw155Xh7ayEuZW8CAK9z+I5j6lDeFGjP+2ZCkn3nCH8V8T9vbGlJwenX7LfH4Q+vip5dYI0PUAW4CPR9IadwGcJPidvOQ4bDqLls72l6jQaelAlStONpYc0xh+Z/ik31Wbj/ejTyL1xkiuoDXaUT7ROKLS0p9q2l/Bwl2pq88gbGtX0AAKic6922Uo5jStXkuzlG1KCb+kkzEq2Md9KxEAd3vHgBknZVo9MfRfweeKcqE42L4wBIvzHl08K2la6XuK20Pa/pLNDvCY4GGuopCokIoVMFI9WDgP2dqkzUf5TIPdZesbfDndA/8ModWScNrAg/tZcWRQNO4W+nX+qyVgvfuXVQGr/KObuD66sP5B1XVFsbkrbWAACyvsv18GoGtcUqypgSNejabhuR8nkQAGDU5QKX4/oztYrtHMHuxmDTBHi1vTbAF9Draq3oVe5fBU5Y9EfnI6rcLkRRoq3U9Vvot2E4Rhlcr7sn1FYa4aeuih5wPcEK3zvX9+MVr3EMpp6E/krQVx3alSIRnrEvrxuJsqJR0EL6Ghy+jCvA6dwrNK7cXVt3iHXnI3p6gRXpR/7kekzJ5Sd2NwYAOGfLQKr+VY9/I7Rjg5wIGTmESD7a4hflEgPOX0Okj1tqi3krH39chdSaBR5fxxX9PilP0e+nwov139zqrs0Btg1F+BF5+q0v4wp4xs69yLyQuwGHf3mxW+eUv+BtNXuSG+cTcqxMVBOIP8AGqcTv8ty+hnOCKbQTw7MyrpSGmCMIBAJBRl7ImS6B8KzBGkoMz9kM/kWEzHQJBAJBRkjQJRAIBBkJoGl/koATCATC8w2Z6RIIBIKMkKBLIBAIMkKCLoFAIMgICboEAoEgIyToEggEgoyQoEsgEAgy8n8wrVUstfIKhgAAAABJRU5ErkJggg==\n", 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\n", 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" ] @@ -1125,7 +1062,7 @@ "images = data[\"image\"]\n", "labels = data[\"label\"]\n", "\n", - "output =model.predict(Tensor(data['image']))\n", + "output = model.predict(Tensor(data['image']))\n", "prb = output.asnumpy()\n", "pred = np.argmax(output.asnumpy(), axis=1)\n", "err_num = []\n", @@ -1150,18 +1087,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "构建一个概率分析的饼图函数。\n", + "构建一个概率分析的饼图函数，本例展示了当前`batch`中的前两张图片的分析饼图。\n", "\n", "备注：`prb`为上一段代码中，存储这组数对应的数字概率。" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": { "ExecuteTime": { - "end_time": "2020-09-01T09:39:54.582155Z", - "start_time": "2020-09-01T09:39:54.412242Z" + "end_time": "2020-09-04T06:16:27.131596Z", + "start_time": "2020-09-04T06:16:26.984263Z" } }, "outputs": [ @@ -1170,13 +1107,13 @@ "output_type": "stream", "text": [ "Figure 1 probability of corresponding numbers [0-9]:\n", - " [-3.4309022 -0.5834117 0.01877569 6.612137 -2.295098 2.562793\n", - " -4.9184275 -0.60120285 0.77347684 2.361938 ]\n" + " [-2.2434433 -5.460074 -0.27407748 -3.74839 13.366689 -1.8540848\n", + " 1.6585187 -1.8376697 0.1426354 0.26712573]\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1189,13 +1126,13 @@ "output_type": "stream", "text": [ "Figure 2 probability of corresponding numbers [0-9]:\n", - " [-4.255555 -2.4440086 -3.5128884 7.8171043 -3.3812468 6.995471\n", - " -4.468177 -2.909104 2.3818579 4.859018 ]\n" + " [-3.8317444 1.3081287 -1.564763 -0.72753066 4.7249494 -0.22273807\n", + " -3.6146772 0.6123574 -1.3609397 4.722361 ]\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] -- Gitee From ad9b0f747374685353f8670360dd2f8d46132a44 Mon Sep 17 00:00:00 2001 From: yao_yf Date: Fri, 4 Sep 2020 14:45:28 +0800 Subject: [PATCH 06/13] parallel interface change --- tutorials/notebook/computer_vision_application.ipynb | 3 ++- .../advanced_use/distributed_training_ascend.md | 7 ++++--- .../advanced_use/distributed_training_ascend.md | 7 ++++--- .../source_zh_cn/advanced_use/use_on_the_cloud.md | 12 ++++++------ .../resnet50_distributed_training.py | 5 +++-- tutorials/tutorial_code/resnet/cifar_resnet50.py | 3 ++- .../tutorial_code/sample_for_cloud/resnet50_train.py | 5 +++-- 7 files changed, 24 insertions(+), 18 deletions(-) diff --git a/tutorials/notebook/computer_vision_application.ipynb b/tutorials/notebook/computer_vision_application.ipynb index f6a65a867c..ec72dad8f8 100644 --- a/tutorials/notebook/computer_vision_application.ipynb +++ b/tutorials/notebook/computer_vision_application.ipynb @@ -345,7 +345,8 @@ "outputs": [], "source": [ "from mindspore.communication.management import init\n", - "from mindspore.train.model import Model, ParallelMode\n", + "from mindspore.train.model import Model\n", + "from mindspore.context import ParallelMode\n", "from resnet import resnet50\n", "from mindspore.parallel._auto_parallel_context import auto_parallel_context\n", "\n", diff --git a/tutorials/source_en/advanced_use/distributed_training_ascend.md b/tutorials/source_en/advanced_use/distributed_training_ascend.md index 6cfb3db242..79e37f45fe 100644 --- a/tutorials/source_en/advanced_use/distributed_training_ascend.md +++ b/tutorials/source_en/advanced_use/distributed_training_ascend.md @@ -215,7 +215,7 @@ The `Momentum` optimizer is used as the parameter update tool. The definition is `context.set_auto_parallel_context` is an API for users to set parallel training parameters and must be called before the initialization of networks. The related parameters are as follows: - `parallel_mode`: parallel distributed mode. The default value is `ParallelMode.STAND_ALONE`. The options are `ParallelMode.DATA_PARALLEL` and `ParallelMode.AUTO_PARALLEL`. -- `mirror_mean`: During backward computation, the framework collects gradients of parameters in data parallel mode across multiple hosts, obtains the global gradient value, and transfers the global gradient value to the optimizer for update. The default value is `False`, which indicates that the `allreduce_sum` operation is applied. The value `True` indicates that the `allreduce_mean` operation is applied. +- `gradients_mean`: During backward computation, the framework collects gradients of parameters in data parallel mode across multiple hosts, obtains the global gradient value, and transfers the global gradient value to the optimizer for update. The default value is `False`, which indicates that the `allreduce_sum` operation is applied. The value `True` indicates that the `allreduce_mean` operation is applied. - `enable_parallel_optimizer`: a developing feature. Whether to use optimizer model parallel, which improves performance by distributing the parameters to be updated to each worker, and applying Broadcast among workers to share updated parameters. This feature can be used only in data parallel mode and when the number of parameters is larger than the number of devices. > You are advised to set `device_num` and `global_rank` to their default values. The framework calls the HCCL API to obtain the values. @@ -228,7 +228,8 @@ In the following sample code, the automatic parallel mode is specified. To switc from mindspore import context from mindspore.nn.optim.momentum import Momentum from mindspore.train.callback import LossMonitor -from mindspore.train.model import Model, ParallelMode +from mindspore.train.model import Model +from mindspore.context import ParallelMode from resnet import resnet50 device_id = int(os.getenv('DEVICE_ID')) @@ -236,7 +237,7 @@ context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") context.set_context(device_id=device_id) # set device_id def test_train_cifar(epoch_size=10): - context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL, mirror_mean=True) + context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL, gradients_mean=True) loss_cb = LossMonitor() dataset = create_dataset(data_path) batch_size = 32 diff --git a/tutorials/source_zh_cn/advanced_use/distributed_training_ascend.md b/tutorials/source_zh_cn/advanced_use/distributed_training_ascend.md index d02d36a460..a0e4e8864f 100644 --- a/tutorials/source_zh_cn/advanced_use/distributed_training_ascend.md +++ b/tutorials/source_zh_cn/advanced_use/distributed_training_ascend.md @@ -218,7 +218,7 @@ class SoftmaxCrossEntropyExpand(nn.Cell): `context.set_auto_parallel_context`是配置并行训练参数的接口，必须在初始化网络之前调用。主要参数包括： - `parallel_mode`：分布式并行模式，默认为单机模式`ParallelMode.STAND_ALONE`。可选数据并行`ParallelMode.DATA_PARALLEL`及自动并行`ParallelMode.AUTO_PARALLEL`。 -- `mirror_mean`：反向计算时，框架内部会将数据并行参数分散在多台机器的梯度值进行收集，得到全局梯度值后再传入优化器中更新。默认值为`False`，设置为True对应`allreduce_mean`操作，False对应`allreduce_sum`操作。 +- `gradients_mean`：反向计算时，框架内部会将数据并行参数分散在多台机器的梯度值进行收集，得到全局梯度值后再传入优化器中更新。默认值为`False`，设置为True对应`allreduce_mean`操作，False对应`allreduce_sum`操作。 - `enable_parallel_optimizer`：开发中特性。打开优化器模型并行开关，通过拆分权重到各卡分别进行更新再同步的方式以提升性能。该参数目前只在数据并行模式和参数量大于机器数时有效，支持`Lamb`和`Adam`优化器。 > `device_num`和`global_rank`建议采用默认值，框架内会调用HCCL接口获取。 @@ -231,7 +231,8 @@ class SoftmaxCrossEntropyExpand(nn.Cell): from mindspore import context from mindspore.nn.optim.momentum import Momentum from mindspore.train.callback import LossMonitor -from mindspore.train.model import Model, ParallelMode +from mindspore.train.model import Model +from mindspore.context import ParallelMode from resnet import resnet50 device_id = int(os.getenv('DEVICE_ID')) @@ -239,7 +240,7 @@ context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") context.set_context(device_id=device_id) # set device_id def test_train_cifar(epoch_size=10): - context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL, mirror_mean=True) + context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL, gradients_mean=True) loss_cb = LossMonitor() dataset = create_dataset(data_path) batch_size = 32 diff --git a/tutorials/source_zh_cn/advanced_use/use_on_the_cloud.md b/tutorials/source_zh_cn/advanced_use/use_on_the_cloud.md index 6fbc1722b6..4f88abb2f4 100644 --- a/tutorials/source_zh_cn/advanced_use/use_on_the_cloud.md +++ b/tutorials/source_zh_cn/advanced_use/use_on_the_cloud.md @@ -164,13 +164,13 @@ MindSpore暂时没有提供直接访问OBS数据的接口，需要通过MoXing ```python import os from mindspore import context - from mindspore.train.model import ParallelMode + from mindspore.context import ParallelMode device_num = int(os.getenv('RANK_SIZE')) if device_num > 1: context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL, - mirror_mean=True) + gradients_mean=True) ``` ### 示例代码 @@ -183,7 +183,7 @@ MindSpore暂时没有提供直接访问OBS数据的接口，需要通过MoXing import os import argparse from mindspore import context -from mindspore.train.model import ParallelMode +from mindspore.context import ParallelMode import mindspore.dataset.engine as de device_id = int(os.getenv('DEVICE_ID')) @@ -201,7 +201,7 @@ def resnet50_train(args_opt): if device_num > 1: context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL, - mirror_mean=True) + gradients_mean=True) train_dataset = create_dataset(local_data_path) if __name__ == '__main__': @@ -220,7 +220,7 @@ if __name__ == '__main__': import os import argparse from mindspore import context -from mindspore.train.model import ParallelMode +from mindspore.context import ParallelMode import mindspore.dataset.engine as de # adapt to cloud: used for downloading data @@ -244,7 +244,7 @@ def resnet50_train(args_opt): if device_num > 1: context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL, - mirror_mean=True) + gradients_mean=True) # adapt to cloud: define distributed local data path local_data_path = os.path.join(local_data_path, str(device_id)) diff --git a/tutorials/tutorial_code/distributed_training/resnet50_distributed_training.py b/tutorials/tutorial_code/distributed_training/resnet50_distributed_training.py index a8a42109f9..4a3e56e566 100644 --- a/tutorials/tutorial_code/distributed_training/resnet50_distributed_training.py +++ b/tutorials/tutorial_code/distributed_training/resnet50_distributed_training.py @@ -28,7 +28,8 @@ from mindspore.communication.management import init, get_rank, get_group_size from mindspore import Tensor from mindspore.ops import operations as P from mindspore.nn.optim.momentum import Momentum -from mindspore.train.model import Model, ParallelMode +from mindspore.train.model import Model +from mindspore.context import ParallelMode from mindspore import context from mindspore.train.callback import LossMonitor from resnet import resnet50 @@ -117,7 +118,7 @@ class SoftmaxCrossEntropyExpand(nn.Cell): def test_train_cifar(epoch_size=10): - context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL, mirror_mean=True) + context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL, gradients_mean=True) loss_cb = LossMonitor() data_path = os.getenv('DATA_PATH') dataset = create_dataset(data_path) diff --git a/tutorials/tutorial_code/resnet/cifar_resnet50.py b/tutorials/tutorial_code/resnet/cifar_resnet50.py index 94cca8b461..61dfbd8cc8 100644 --- a/tutorials/tutorial_code/resnet/cifar_resnet50.py +++ b/tutorials/tutorial_code/resnet/cifar_resnet50.py @@ -29,7 +29,8 @@ from mindspore.communication.management import init from mindspore import Tensor from mindspore.ops import operations as P from mindspore.nn.optim.momentum import Momentum -from mindspore.train.model import Model, ParallelMode +from mindspore.train.model import Model +from mindspore.context import ParallelMode from mindspore import context from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor from mindspore.train.serialization import load_checkpoint, load_param_into_net diff --git a/tutorials/tutorial_code/sample_for_cloud/resnet50_train.py b/tutorials/tutorial_code/sample_for_cloud/resnet50_train.py index 0fbd103914..183f3ba739 100644 --- a/tutorials/tutorial_code/sample_for_cloud/resnet50_train.py +++ b/tutorials/tutorial_code/sample_for_cloud/resnet50_train.py @@ -24,7 +24,8 @@ from mindspore import context from mindspore import Tensor from mindspore.nn.optim.momentum import Momentum from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits -from mindspore.train.model import Model, ParallelMode +from mindspore.train.model import Model +from mindspore.context import ParallelMode from mindspore.train.callback import Callback, LossMonitor from mindspore.train.loss_scale_manager import FixedLossScaleManager from mindspore.communication.management import init @@ -121,7 +122,7 @@ def resnet50_train(args_opt): if device_num > 1: context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL, - mirror_mean=True) + gradients_mean=True) init() local_data_path = os.path.join(local_data_path, str(device_id)) -- Gitee From 734c920966cee11433b137abe64a7de52ceb433e Mon Sep 17 00:00:00 2001 From: lvmingfu <630944715@qq.com> Date: Fri, 4 Sep 2020 14:50:14 +0800 Subject: [PATCH 07/13] modify code formats in quick_start.ipynb --- tutorials/notebook/quick_start.ipynb | 137 ++++++++++++++------------- 1 file changed, 70 insertions(+), 67 deletions(-) diff --git a/tutorials/notebook/quick_start.ipynb b/tutorials/notebook/quick_start.ipynb index 83b9cf02ef..b60f82f784 100644 --- a/tutorials/notebook/quick_start.ipynb +++ b/tutorials/notebook/quick_start.ipynb @@ -58,8 +58,8 @@ "execution_count": 1, "metadata": { "ExecuteTime": { - "end_time": "2020-09-04T06:15:58.114167Z", - "start_time": "2020-09-04T06:15:58.105497Z" + "end_time": "2020-09-04T06:46:31.241068Z", + "start_time": "2020-09-04T06:46:31.232345Z" } }, "outputs": [ @@ -100,8 +100,8 @@ "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2020-09-04T06:15:58.130999Z", - "start_time": "2020-09-04T06:15:58.115177Z" + "end_time": "2020-09-04T06:46:31.263831Z", + "start_time": "2020-09-04T06:46:31.242077Z" } }, "outputs": [ @@ -205,8 +205,8 @@ "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2020-09-04T06:15:59.235677Z", - "start_time": "2020-09-04T06:15:58.132025Z" + "end_time": "2020-09-04T06:46:32.448830Z", + "start_time": "2020-09-04T06:46:31.265357Z" } }, "outputs": [ @@ -218,12 +218,12 @@ "Number of pictures contained in the mnist_ds： 60000\n", "The item of mnist_ds: dict_keys(['image', 'label'])\n", "Tensor of image in item: (28, 28, 1)\n", - "The label of item: 1\n" + "The label of item: 5\n" ] }, { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "
" ] @@ -295,8 +295,8 @@ "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2020-09-04T06:15:59.246575Z", - "start_time": "2020-09-04T06:15:59.236934Z" + "end_time": "2020-09-04T06:46:32.459913Z", + "start_time": "2020-09-04T06:46:32.450854Z" } }, "outputs": [], @@ -381,8 +381,8 @@ "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2020-09-04T06:15:59.350173Z", - "start_time": "2020-09-04T06:15:59.247581Z" + "end_time": "2020-09-04T06:46:32.488908Z", + "start_time": "2020-09-04T06:46:32.460923Z" } }, "outputs": [ @@ -411,8 +411,8 @@ "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2020-09-04T06:15:59.716300Z", - "start_time": "2020-09-04T06:15:59.351186Z" + "end_time": "2020-09-04T06:46:32.855325Z", + "start_time": "2020-09-04T06:46:32.489911Z" }, "scrolled": false }, @@ -422,7 +422,7 @@ "output_type": "stream", "text": [ "Tensor of image: (32, 1, 32, 32)\n", - "labels: [0 3 0 7 0 8 7 8 6 2 5 5 7 4 7 6 3 8 3 2 7 3 4 0 7 5 5 0 6 1 7 4]\n" + "labels: [2 4 1 7 2 5 7 2 4 7 2 0 9 1 5 0 8 1 8 2 7 8 9 3 7 2 3 9 2 9 3 1]\n" ] } ], @@ -446,14 +446,14 @@ "execution_count": 7, "metadata": { "ExecuteTime": { - "end_time": "2020-09-04T06:16:00.326012Z", - "start_time": "2020-09-04T06:15:59.717311Z" + "end_time": "2020-09-04T06:46:33.459883Z", + "start_time": "2020-09-04T06:46:32.856341Z" } }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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DOQdPt4+qGz8BuPGLO+ixxVj5sPBiFst33uwV4LK46wFsmT/0OdYVWNK74FRYAxxcK8iIajN9nvwSR1kZVWu7Ysuc7j6mXYEk1s9gkNj1GaZRw7SN4WEUFSQJb+4lYMBxgwAogMknNZFVa7v6/T16oAUEPUH6qs+wB+Hh4rmyRsdd2ss8eWXwrfUWGWDTNTby7wm9pvhU0ZUArN4/LOAxrh8VLmRlFbe98xgyTmtY3D9ot5eTvaL5OCor6f5M4IEKfwEwRXu6kr7ZWKs1vFN4FbbFF+hlnQLA3370AoNjrXQaetbn2GK74IolR6k7dz6sGkqy4zl2ywuagS+bRe/nT5G0Zg9JYb2LMdj+nz4AZIdxhYlw4IiF40/0D+pYz+CjyT/QXiifpA8LejUSY1VBFAqF4ntEi2rAFwYmcez2F0I+31XzzX4wen2AjrIyrykFN80ayYejs93b3RMv8WL6PxmffydLszZ4NZmMTPEgBx0P2Nxx7UbCbwBM5Kf8bTbXppxk31XXkf2gVj62fX4Vg2O/4uOr3oq6llRzG/InvYCtejqZayzBB9kYMF3NnTpxdvwVtDXpHYrVNE2tiNEY7vDrnB3aog3LmvawaFENWLQgs7dVWon5RvvTk7RFu4kdfdz9d+GR7jx25mZMd1WxofRav+ekWr9FXt8fYdHPXc1cUcvCi1nUSK35U3DHSxwf10E3PYEwd+pEsc34E9eA1t9734ItOEYMwDFiAKnWb/0epwXijIpIIE7DZyovZzkFk9LcmhwjBmDq19fnPFO/vjhGDKC6ax3VXY0RIOSi9srufDpnudfAt27Pv3NFjPN2Y/jwR92CHKstp1qaeGzFLDIa6TfUC7n/ECeHAFS4V01INHkbkJz2hQxft5yZQ8ZhD3MfYHN0fnhzFj/Z+4WX+57RuHDbFe45aY1GSWU8JfZKtxsiaHmbs35Vo+dFMhDHVCc5VlvusyoHOfXH/Pir2zDfk+p1nmNpGe/39R0Mt0sHR+uqEHXGqho/+tp/k/FU9J//aKyI0ZzAkqgb4PtnPE6bfcdJL/tM97khmsK1akJDB3fFd4Nu9xcyaNlDHP1R4wY3mgRalcOTTdlvcGqv99PTw2LCX1DB0boqfnXLZEz5h4zYO/GdpDmBJVExwLmlaWz8pTZ9XsKBPOw6rKsVCvbii9TUZegtI2j+fN/LPByjjeBHMnClpdhyp5O58mTU5qcNhP1SKX2eKqVXuVa9bGx+ZVfQSOqSOMwVtUBkghoCrcrhSaIpDltM09fKLU1j05TRmvHVad4S14oYRqOlK2Icqy3n/hmPE/OtbzizpbgKe5CBJS0ywJ0+Pst1c6Y1eVxcqYOEj7SBDaPXehsiFndk1BNjfSbt6GEx4VhvxfRIXxyH9F/cD7QBBJdPolFoGCBiy51O5ppCXVcZ9sR+pIC+L2gOzNftDVyWO+dXASB2fRrxmqTnqhyu4Iake083OXGMJzPPDmTfgutI2LVX15pvdXuTzwsk862p9NlcoqstCLQiRrCYayVJ2z71O19Nc35XiwxwXcFxkg00A1ckiNm6n6r4wdw4TZvdyzUinmiK459932FUymTdfPkaBkAA9PhM73qlRtW4wQAk3ettaLt/WKXbtJmBcHmOJB/UWYgfXMENVSWDsfWf3uTxLtoVSJLeNGYrKGW/yVDeOqHmfzhebGrWmSCI37yPKjSD0mvsFK/vbIUXozqFnidNBUDoiWuVgabmWlUER/zmfaRvbvo4RetCBWIoFAqFTqgacJDEb94HQHaDWohetV/Do4bcFYomUQZYERHiirX/8y94Bw2YK2qVbf4eEVfq8CkDcaWtbSg+cigDrIgIrolIfMMxI79EjsI4JLy5lx1vepeBBHznV/6+ImQElypRKBQKRWDUIJxCoVDohDLACoVCoRPKACsUCoVOKAOsUCgUOqEMsEKhUOiE4Q2wEKK3EKJaCLFWby2BEEKUN/izCyH+oreuhgghYoUQK4QQJ4QQZUKIT4UQt+itKxBCiGQhxFtCiAqn5p/rrckfQgibEOIDIUSpEOJrIcQdemsKhBBihhBivxCiRgixWm89jSGE2O589l3P1WG9NQUi1LJqeAMMPA/8r94iGkNKmej6AzoDVcBGnWX5wwKcAkYA7YH/C2wQQmToqKkxngcuo6XpROAFIcQP9JXkjRDCAvwdeBtIRps6fa0QIrvRE/WjEJgPrNRbSJDM8Hi++ugtphFCK6tSykb/gOPALOBzoBT4G9rMz/cDOxscK4ErnJ9XA8uB94ByYBfQBfgTUAJ8BQxo4t53AxuAecBaI2v1uO4vgAKcPtZG1uq83ufAnUbTiraA3GUg22Pfa8CzBtPZz3mO8Ni3Dfi90dK0wTXnA6uDLCO6aAW2A1OCLcutqay6/oKtAU8Afgz0Aq52/phgz5sLdARqgD3AAef2JuA514FCiOVCiOUe2+2Ap4GZQd5LN60N+AWwRjpzwchahRCdgWzgPwbUmg3YpZT5Htc6CDRVq4i2zgYzHrv39QvynnqW1eagl9ZnhBAXhBC7hBAjDao11LIatAFeKqUslFJeBLYA/YM87y0p5b+llNXAW0C1lHKNlNKO9mYa4DpQSjldSuk54envgRVSylNB3ktPrQAIIdLRmvevtgKtVmAd8KqUMpgZ5aOtNRGtBuNJKdDWYDq/As4Ds4UQViHEaLQykEDT6Jb/IaCH1l8DmUA3IBfYIoTIMqDWUMtq0Ab4G4/Plc4bBsM5j89Vfrb9XkcI0R/4P8CSIO/jSVS1NmASWjPnWJD31EWrEMKE1kS6DMwI8p7R1loOtGuwrx3Q1HpWUdUppawFxgG3Oe89E63bLJglP/Qsq80l6lqllHullGVSyhop5ato3QK3GlBrqGW1RZPxVODxlhdCdGnBtRoyEsgATgohQPvhZiHElVLKgSFcL5JaPZkEPNvCa0RUq9ASdAXaYMGtTgMSKpHUmg9YhBC9pZRHnPuuIbjukoZENE2llJ+j1Xpd199N8K2ghkSrrIaDaGuV+O/yCQZDltWWeEEcBH4ghOgvhIhDGygLF7lAFlrToT/wIvAOMCbE60VSKwBCiGFoTaWWej9EWusLgA0YK6WsauG1IqZVSlkBvAk8LYRoI4S4HvgpWs3dMDoBhBBXCyHihBAJQohZQFe0QZ1QiLRWi/O6ZrRKTZzTkyMUIqZVCJEkhBjj0ieEmAjcCGw1mtaWlNWQDbCzw/lp4H+AI8DOUK8FIIR4UQjxovPalVLKb1x/aFX8aillkdG0evAL4E0pZYuWfI6kViFET2Aq2kvtGw//yolG0+pkOhCP1se6HpgmpWx2DTgKOu8Dzjp13gyMklL6rtZoDK1z0ZrTTwL3Oj/PNaBWK5qnRhFwAXgYGCelDMkX2KhlVU1HqVAoFDrRGgIxFAqF4juJMsAKhUKhE8oAKxQKhU4oA6xQKBQ6oQywQqFQ6ERQ/n+jTHcZylXifcfGgM7YrUVra9EJSmtLUPkffr4rWkHVgBUKhUI3lAFWKBQKnQgpBNE+UpuO4czIOOIuQOqy3S0SYbrGxvE7Ori3M1ecpO5UMHOZfPc5P2MY1R2bPi7jrRIcB/MiLygIqsYNpqi/d9Ey1UD6nz/DUVnpc3zD/Adj/R4jYunRnYIH0wFjPy/B5K2/YwC6ba/GvP1ARPX5K6suopGuqgasUCgUOhFSDbgsPRaAzsMKOXGyI6ktFHFhYBJ5OfXzMI/6cDImg77Ro82dOR8wt2PT0/VmdppKyv6hdMjX5tcRuz6LtLSAlFxhofOwQu99lfGIGCtUwuUxgyhPs7q/Kx7koOAO73m4rzsxjeSDUZHbKrmc2cn9zBj5eWn4bEN9WXXhL/8B+tgm0bNWm8o3UuX51FgHx27xP1+9jekknuhBxwOXItYaC8kAJ63Zo31YA9kcd+8Xg/phb2MlpqAoqKq7pUd3AMp7hjrDnMJFwR0vwR1wzb57AEi1DMBcUYvcfyjqWtIW7YZF3vu6WizUDemHtAi6/e5r1mZsb/QawlBj2cam2BZHly87YS8Kaa6qiNDYs+0qq01xePgaepVPASB7V1jlBYXrxdHrHznYFmcCYD9SENZ7tGQ+4HqEwNyxIzet3sPs5KPYXppO+lNNG+CCB7Q+rIZvSD0RFgumlOTAB9TUYL/UcPL7yHGiKoVyRzWJpjj3vnJHNUX2OnpZfeeHPjh4vfZhPcy/0JcdV8f5HBNthMWCKTuTpa8/T7a1jd5yAiJiYzEltW/8oCjnfzAc+O0LDK6eRofVxjHAoTzbdungaF0VdllvtEW1OezaAhHouTp2ey5ZiZMB6DOjfVjzPywG2NyxIzm793BLQgnaLHKtF8eQfixZF7jQ3P3Zg3QZF70H8MytsYzfeCf/7PuOe9/4/DupWtKNj17KjZqOluAY0o+lrz9PliVebymNcumuAaya/1yjx0Q7/79PHK2r4le3TIbiS+59fSq/BMARhfs39lztH/E8ACNXhzf/w1MDNgn6WM8TK7QJ5/9838s8HKM1HTLm7An6Msdqy7l/xuMkHMiLSoIDHF8wlPvGfujeTrVuwxYTeDmvDgktncO8ediLL2J6pC/XzL/HvS9triThRB6j7pns3tf5mWNNNuv1oPJnQ7hvwRa/NV93d8mSOIptcRz47QvRlgdoniYAv3lkXaN5D9HP/9bI8QVD+ct/vdzoMb3+kcMV670XYxF1ElP+IWRdXSTleWF79iK9anMA6Jl5noTteYyYmsPqZc951YQ7mLVysXHAK0x8ezLJP8n3e73m0mIDbOrXF8fSMnpY6h0qRifUcrlL81e6qZYm2uw7jr2sRXOaB03Bs0OZd8cGJrYt9vu9LXc6t479hMVdI+sK0xSOQ1/ReWF/j21tQML00afufafLM6Itq1EuTdIGWW54dC857Qt9vu+zYxI9l2plRuz6lJT69Q4BkFEaFiicNYxJ92uLLExI1Go2p+vKGfuHJ7BUah3RSfee5n3blugICpFopVdTuJ6p0QmBn//Mt6bSN/dbvwNb0e76P/XTLky/Qcv/6+KPMXnJA/R56CD3zJmF3Sq4ccZer+c/29qGn6Z/zg7C07XXIgMsr+9P4ewaDvZ9BzwE3fjFHfTY0riH28UHhvLDW75oye1DwtS2LYefvRIZZ2f+Db7Gd1ullUfX/jcA1V3qGJh4AoDJJ4cDULW2K7EeA4/RoqlR4Kq1XZk8fTir0ndESVFgLj4wlH452uBfw5dXr3/kEPeNhR7bqxG7Ar/Yku49TVXJYOI374uYzsJZwxh/33ZmJx9179tWaeWxFU+QvkrzWS6cNYwbOh5t5CoKT7oNPNvoMwXQx0A+3uW2y175/+DgneyoiaP92k8A2GkfyuTpbSL2XLXIAJdkx3Nw8Gqf/UV7upK+OXBwxqVJ2gOqh7EQCfG8c9sSv03NdWUpPL1pAhnzNO3DP692F6bt/+kDQPaa4LtUvo8EytsSeyWDPnoI2+ILQY0kv2/bgq3/dNI3R0oppIwu5HedvnRvu/P/md040LomJt2/1esBVQTm/Ixh/DzNe8m2hs8URKc/NxguTRrKyB807iWUtGYP20cMAo/yPDjhKOt/898BA4uagwrEUCgUCp0IqQZsusYGaA7UDZl5diDtChrvyYm/96xPDSm/toK7P5tCtxrf/sJIsvBiFu8UXgXAmQNdyWzGoKFRqPzZEG54dK/ufdWBdOTXVnDXp1PoM6PArwuPJTODgpu8PSSCKUfh5uUTw8mYswcRG8uluwbwm0fWufuF15WlAM4yokMXVFMUD3LQ8YBN16b9nTkf+LQWXGlqRBraoe1VJlbsu4Fs9nsdl5gXw8IhWQDMTj7K6IRaNk1dxMyV46CFNeCQDPDxcVrctr/olR1/GUKHEJrpG0qvpcu4POyhCGoOdXU8d24UPeO1roU3cn/knsvCiA9WMIyZ91FQ0XJ66MivrWDiF5MbzduiG7v6+IuGWo5aQvfES5wbMYDL7aysX7DIaxR87k4tciD7SWMak4I7XsJ2fjrpBoke3FejDcKdONnRK1jLyMw7ejvZU/b77E9btJuVbcYAMHtqeGMWwuOG5kFtG4G5c+PByXEW3xHSWFMt5s7pXvtkWXmL+1gaYtxeEDQAACAASURBVC++yMkhcNI5aJhKyyYS0hVnAEysKbzROeHUkXN4YthcdsJNSWU8JfZKt4vR2oztsH6781vfIBcjc7quHNNlvVXUc88uzbXLNuckdE4Fh8R+4QKoVdi9CLsB3vLrP1I2u/GuZc1lzduN45EOX/GTvd5eEeNfmkX3Z1qxgYwwRgmAMYqO5tLt/kIGLXuIoz9apbeUFjP2D07PDb2FOHEFLnyzV9s+XJtK7rChhgqXNgIhGeDMlScBsInpPk3H7pbQag6xwootxvvhdcSEdKkWY05Jptu7NeR02AUYM3RWDOrHTas1oxcrfI3evcdHcuGR7kAU5oJoEIgD9UEWaXNlQKPgGQChB/ZLpfR5qpRRL0/2+/1dL27168NsRKwVMuytxZbgalV0cEYSZ1pLOPJhATUOrax6dv19nwnJALsm2kk80SOsYgyDxcLjnd8m1awZ3z47JtF7ZfSic4LB3sbqHPDwNr6j8sYCUPfHzsTs9+3PihbfFmtp1+WQfw3+AiBc2HKnk/lxIdFIcfuRAkxH/H93vrYd0DoMsBECMbbOG0HJnAS/g8Gxwuo1QBf7y1reuf0q9/aJkx399r9GA9czIxZ3hCj3V7eoC6LjgUvYcqe7t/9878uNRsA0GwMUKgBTXiJil/5v66pxgwFtCr12KRU+39/4xR3wQicA4rdGLoDBE3PvTA7/rj1dPOZMCSYQp6EDPMB5ewU3rJtFbwNPMK4ITMKbe9mZOBRb5g+99ld3qePY7d7zK8xOPuqV//t613L38zPo8+SXOKIUCevi6+OdAcje2sgLIEK2qEUG2HEwz2vU9WHrFL8hyO1SKupn6fLgqaIrAVi9f5jf6/f4zFi1Tr24NGko32YKOg09C8Cxq97SWVE9tWntnX2o9d0PwQTi+HOAL7YLrlhylLpz5yMhVREFktbsIanBPnPvTHpZp7i359/wlk+03OBYK+/ctoSZT4+DKBvgkT84DMCXk4bWT7XrQdW4we5nL9yoQAyFQqHQibB6QfhzuLZkZlAwKQ0Ge+9feDGLTa+NBCB7kf7N+0bRwXPGFQzgb0KQQHx81VuMeljrz7rYYShxpQ4S3twbaam++Emvpn6PXoE4wbDwYhaJeTqNCH8HsB8pIPtBzUWx8mdDONC/Z8AJsPTAFYwx89EK9pUP8XlmTo11eLU6w1lWw+6G5r6wc0b8gklpPp4SG8rb8/qLY0hTo6ABMSW1Z9X855qcHrEh7lm7FkBuaRobizUHcsuBr6PetwZg7tSJ2iu7+w1ucBFMsEY0ERYLjiH9SLVuA2DlxjGkG7ySYNQVRFz57+K+BVv8epact1fw3LnREMWpKBuyuOsBchd8w6ZzozHtDTwtZjiDxiJmgBubEX/R//s5qauNXaD1RFgskJKEuYmnquEKAl3M9e4/ADntC8lZr/m4jpiaQ/y2g8iamsgJd+mKl+5gnLPjr+DTOa4y4G18S+ya29Rdn06hyzhjzI4FYEpJZsm65c1++X1vcQbiYPIdqfLOf/+UO6qZUziak0MqAN/B5UjRMBAHtGdm5OvPe00Mb4qNXLUgYgZYETrBriDRcAWBw4u7BQwqWL3sOe6ZM8s9zV4k2TlxEcV3aw9jW5ODQFFlgz56CECbIyLiqhSRwhWI08fqO3jaWP67GJ9/J6a7qoim8YXAgThZlnj+9N4qr4qN5yBzOIm6AR749DS6vPO1euAaQVpEk2un5ZamsWnKaK8VBPo8legVVOC5ykQvayIOS3T8+lLNbUhtZCkvW+50un9YRZ9Czf/XaGuseWLLnU7mypNR8UlutTgDcUJtMVTXWYktPhNmUU1jv1SKo8a3oJqFKeDzF+4Ap6gb4JS8ahWO2ATWL08zYMF0tvz6j34jC2eeHci+BdeRsGuv13hXw6CCLl924rqaae7t1I/Pht2QNKW1Ibbc6WSuKaSu4LghX8INV3hJPCFbhU+ynoEYjkulTJ77eNADxplvTSVlf70DVmJhLdEOgHDRe2Ud1+3UnpHiQQ5txeZGOF2eRGwYVxqPmAHu9lE1oIUre5JZ0AprE1Eu3PaiIrqsqmBMuydwxPp+365AkvRm07Ny2YuKSF5V/7KLRLo3pbUhmQYPsqhLief9vn+l4VwlRiOmoMgrCCrjQIlu80DImhqvFSQaBmI0xEgrYohdn5HsXPK+4wEbtqLpjR7frkCGdUWciBlg83btLZi+3Xt/qzO+OuGorGw1ExE1R2tryv9r9t1D53xjLsJZd+o06fPqX2RGmYTHXyBGQ4yitSENA8uigQrEUCgUCp1QBjgYDOpjqYgsqUvimlwMVaFoCcoNzR8NVs2IM07QjiLCmCtqmX+hr/uzevcqIokywH74Tq2aoWgWcv8hdlztGoCLwlzKiu81QqolQhQKhUIXVB+wQqFQ6IQywAqFQqETygArFAqFTigDrFAoFDqhDLBCoVDohDLACoVCoROGNcBCiAwhxLtCiBIhxDdCiGVCCEP6LQshZggh9gshaoQQq/XW0xitJV2FELFCiBVCiBNCiDIhxKdCiFv01uUPIUR5gz+7EOIveuvyR2vJfwAhhE0I8YEQolQI8bUQ4g69NQVCCLFWCHFWCPGtECJfCDGl6bMMbICB5cB5oCvQHxgBND5VkX4UAvOBlXoLCYLWkq4W4BSavvbA/wU2CCEydNTkFyllousP6AxUARt1lhWIVpH/zpfC34G3gWQgB1grhMjWVVhgngEypJTtgNuB+UKIa5s8S0rZ6B/aRJ2zgM+BUuBvaHP13Q/sbHCsBK5wfl6NltnvAeXALqAL8CegBPgKGNDIffOAWz22FwIvGVGrxzXnA6ubOk6la/PS1ePanwN3Glkn8AugAGeQk9G0tpb8B/o5zxEe+7YBvzeaVj8a+gBngQlNHRtsDXgC8GOgF3C188cEe95coCNQA+wBDji3NwHPuQ4UQiwXQnguHvVn4G4hRIIQohtwC/BPg2oNFZWuQaarEKIzkA38x8g60QzwGul8Eg2otbXkv79ZuAWaYTaaVs99lWjG+izwblM3DNYAL5VSFkopLwJb0JouwfCWlPLfUspq4C2gWkq5RkppR3szDXAdKKWcLqX0bAp9BPwA+BY4DewHNhtUa6iodA0iXYUQVmAd8KqU8isD60xHa9K/GuQ9Vf4H1voVWlfJbCGEVQgxGi1tg1n3SJcy4NxuCwwH3kQz4o0SrAH+xuNzJU2tslfPOY/PVX62/V5HCGECtqL9iDZob6AOwB+MprWFqHRt4jpOza8Bl4EZQdxPz/yfhNbMPRbkPVX+B7iOlLIWGAfc5rz3TGAD2kvDUFo9kVLapZQ7ge7AtKaOb8kgXAUebyMhRJcWXKshyUAPYJmUskZKWQysAm4N8XqR1BpuVLrWX08AK9AGtu50PpShEK38n0Twtd9AqPx3IqX8XEo5QkqZIqUcA2QC+0K8XLRtgAXIauqglhjgg8APhBD9hRBxwLwWXMsLKeUF4BgwTQhhEUIkofWthbpgSMS0gjZi67yuGTALIeJa4Nqj0rWeFwAbMFZK2ZK1gSKtEyHEMKAbLfd+UPnvRAhxtfNZShBCzELz3Fgd4uUiplUIkSqEuFsIkSiEMAshxgD3AB80dW7IBlhKmQ88DfwPcATYGeq1AIQQLwohXvTY9TO0jvQi4Gu05cQeM6jWuWhNlCeBe52f5xpUa6tIVyFET2AqWv/dNx4+thONpNODXwBvSinLWnJtlf9eWu9DG8w6D9wMjJJSNtmvqoNWidbdcBrNY2IR8Csp5d+bvE5wg7UKhUKhCDdGDsRQKBSK7zTKACsUCoVOKAOsUCgUOqEMsEKhUOhEUK5So0x3GWqk7n3HRn9hikDr0dpadILS2hJU/oef74pWUDVghUKh0A1lgBUKhUInlAFWKBQKnTDkTPjRxj5yIABnRsb5fJfxVgmOg3nRlvSdwXSNjeN3dGjyuLgLkLpsdxQUKRT1VI0bTFH/xs1gt+3VmLcfiMj9VQ1YoVAodMJQNWARG8uluwZgt2oDhx0PXIpK7fPMCK3mm5fjO8f2dSemkRzqVCURpmF6+cNcK0na+CmyJqQQ+pCQ1/enJDsegOJBDgruaHru+vkX+rJjmW8LRFGP6RobABcGJgU8pkO+NmeR2PVZVDS1Zi6PGUTsw2fJs21p9Lg+tkn0rNWmFA53ukbNAAuLBceQfkhLYGNxuZ2V9QsW0cuqTbtpe2k66TobP2EgpxZLj+4AXM7spP1vkF7+yLtcycz3x2E/dz4qGk39+lI4u4aDg1c367xU67fI63+Iae8hZF1dZMS1Ukz9+lKXEk/+PVYAjt3+QsBjr9l3DwCpFm3ucFEndUnThmW10WOLq3Acamqe/fDgaYe6/e5r1mZsb/Kcw8PX0KtcW2Mze1d49UTEAJvatkUkxHvvTEli6evPk21t08TZkZj3vHVjTmoPsbEceagHAPmTPB9ALb1O15VT5jCRYtbeGKlmLZ3NQkJKEmbAcak0MjVhITB37AgmgWNpGQf7vtPsS+S0L2T4uuXMHBK9l4UrXZtFTQ32S6WREeQHc0oyjqVlvN/3r0Edf3Dweu2D819+bQW/umUyFF+KXP47Mackg0UzKf7Lqn9+/NVtmO9Kxl58MWLaQDO+puxMHztUYq8E4Bu79/GZViuxwhpRTRExwIefvZJ3blvitc8sJFmW+ABnKBrjzOo0/tp/hdu4aosZeDP2D0/QddPXfP2YNge0q+BnWeL503ursEvB5LmP037tJ2HXZ+7YkZzde+hjPU8PiwltDUTj40rX5nD3Zw/SZVz0DHC3d2tY0u1dQk3TaOS/i27v1vB457cBGi2rDdmU/QaPvXszJ4dETBoAjiH9WPr68z52aNBHDwHQZ+YZr/03/esos5OPRlRTWA3wxbez+Wn65/y+7TJsMcEs3RQYW+50MleeRDVGoUNCVaPpOfDpaXR94whHf3UFy8a/4vWdWZjcb3tHI90/LcIk6GM936jGY7Xl3D/jcWK+raXzM8f8Nv0yrVZu+tdR/nX/UOT+Q5HR6kFT6eqPjQNeYeLbk0n+SX6EVGmYU5Lp9m4NC9K2kWhq2ogFvE4U8t9Tq6vl1RwSTXH0jC/mZIRf3NIivGq+ttzpdP+wij6F2gvV1fJy/Z5ftP+cYF4gLSEsBtic1J4zq9PYeNUrzh/Y8mp74glJ3algln+KLMMf3stOx1CS1uzRW4qbmWcH8vGy+upCl7e/5sjMK5h3xwZGJ3iv2nO6rpyxf3gCS6Uk9eOzurzQNpS357mnf0nSNm0w8HR5ht/jYoWV2clH2dbmxoi75+S/MohVWSubfV62tQ0/Tf+cHZGu5VssPN757aANWuZbU8nqW8j7jQwoyQi9f5vSuqG8PQuf+bl7+8YZe1nc1duta0L7f7Nx8xS63V8Y0S4ez+ch86NC6gqOY294UDPTviW02ACbe2dy+Hft2X/t83RohuAbv7jD/fnjq95qqYyQufjAUH54yxcBv1/c9QC2zB8SeNw5+vy7OJ3kVfUvhKMLhjLvjg1MbFvs3rfwYhYrN43BVAPpqz7DUVkZEePryv8uZu/9k08OB+CTf16l+fiu3Y2BxjN5cPBORsY79JbRIs7bK7hh3SyWjX+FrL6F3NAxss3l5uCT/6vqfbx32ocyeXobVqXvcO/Ltrbhr/1XMDN2XMQ0xRQUMeaFJxp9HgKV50jRYgNcm9aeoz9aRcPVop8quhKA1fuH+T3PfMlCWr9zfr+LJheur/UqCEakeFsaT3W+kt91+hKA29K+4PUZY9yBC/eN/dDL+AK8U3gV6fO07yNpZvzl/+STwzmU2w+A9JXGDK54bctN9Bpf5JNunlyz7x6+LW7D/YN2u9PeCKwrS2HuzjsQ1Wb6LDjEvluzGq35Go2kNXs4ZBnK5Bxt2/X8pZglXz+WRe/nYyLS+q07dZruz5wO+DyYrrGRN60tx36US0N7NvIHhwH4clJ4W8MqEEOhUCh0ImJ+wK6ab/aD+32+uzxmEJYnzgR8a997fCSJhaGuQN48EvNiWDhE8xwIOOKpc9s5bdFuVtuG8btbtFrY7OSj1OR8wM6X23PprgFkx613H7uuLAWAMwe6kslxPeTyyXtXGbbm6yJjzh7mmSfw8sCzAY9JmytJ7gY7M7LAQDXgl08MJ/vB/YjYWEoa5L9RcLcqc3ZofcAVWh9wp7e/xl5URPLKPXzSw9k6nqodm2puQ/6kFxj13mRMURz/cQW4HM5px7HbX/J7jOv3zHy0go/NQ8MW4NTyPuCKWhZezOKRDl95+cy1S6kAwDFiAOaKWveothjUr0kH6HO/6UXMR76GOxKkLdrNyjZjAJg9temILaOQav2WqtE3egVibKu08vSmCQBkzonOoKG5opb5F/p67YsL3KpvlBpZy9KSvlp5CYO2psh8svE0cgDyuQzDNe+7J17i3IgBTQbinLdXkFtyrXs7rlSfPu8JiaVMWKC5RQ6MnUZKnhakUd3V/6hEsS2OLl92wl5UFHFt5t6Z5E1rCxDQ+HqyuOsBWHBA8+opfZyE7Xk4ykJfCLvFBljuP8SHN2fxk71fYIupN8CeDuELL2bx4c1aLfOm1Xv81jRrZC0FtVqtV9RFt8ppuqz9P11XTneLb2G2x0tMbbVMaklitwRRbea8XXuppZrbkNO+kJyXcvEMxHhsxRNkPBPdmqfcf4gdV3t7BKSyG1OC1ocm2iZCXV1QTvYFtbV8eHMW8lzkXdCCwZySTJzFuyVW7qjmRFUKUBHZmzskh2tTybSW+AQDrM3YDuu3O7cCBy7lllzrlTcJ7A2/TnBrBc0PPNEU2EPkwG+bDsw48NsXGFw9jQ6rI2+A855M5tgtuV777NLB0boq7B5uIw0DnHpZE/nopVxG3TMZ00efhnz/qIQiP9LhK36yV/M0yLRa8eemtrSkr9tIm4oPRbXVn/5nLb577LdP8Okc31rwzomL+GHbxwHo/VCECnET9HnyS24omwX4jy4a+wfn6G60hQXg5KNa7PymqYt47tyoiDvZRwJ/QRDj8+/EdFcVkTbA9gsXyB02lCMfFkQ8GKCluLRiEjjWW/lnCJGQRuJoXZU7etBFwwCncBEWA+wovsijE6cz/pVt5LQv9Pk+Vli9ascNuff4SC480l23mo+jUgtFtFb4N/up5jbIOB9vwajiKCvDXBXYkdNaId2/I9q4HNd7xtf3PQxOeBkAW0wCC9K2kft5fVN4Qvt1NHRwzy1NY9OU0ZiK9a/9NhYEUV1nJbb4TIAzw4iU2IuK+Nf9Qzm4tEdQcxZ44nqmIArp6dQKYHqkL6NSJnt9XWyLC6rmawTc5TDfe/6M3s/HAGCrnu530q5QCYsBlnV1iF2f8dqcsRye878+TtZNcbo8idgoRD41RcQc1aOArtqdjuuBospSzW2Y29FzshVff/Hzte0Quz7Te7xTw48j/qi8sQCIxR0hioObcv+hgIEr/nDprPtjZ2L2R2ccxRPHoa98XKu6fNmJ62qmATD7N68zIdF/oIXeQUMzzw5k34LrSNi116ccutziEk/0COs9w9oFkfDmXnYmDsWW+UM6DdVGl/UMsgiWqnGDAUi6V//Iu0ZpxMgm3XuaqpLBxG/eFz093yO+Pt4ZgOyt0TdqVWu7Mnn68KD81fXUGQh7UREdN1Vz+NkrybBewF8X5LZKK4+taDxIIhL02GLCdmo6AO0KJElv7sHUti2Hn72Svn8qwn6kIKL3D3sfcNKaPSQBJ+c5XUyuCvcdwo9rRvym5gU1Mu/btmDrP530zXorad1YenTnyEM9PCaTUbQE14oo9jjJJ7cv8hveu64shac3TSDjmd1RH8OI37yPbpfqV8T5dt4wt9aJG2ZgOhLZ+6tADIVCodCJiHhByOv747CVR+LSEaFdgVbbmXl2YMD+6ysytLDpy2MGEaND865hmubXVvDI0Qlsyn7D7fbjsJUjr4/MzP3NxRUQ8vKJ4XRPvNTkIFJ23FnevveeqK/e4YklM4OCSWnkT1qOq5965tmB/Ls4ncS8GF00AcTfe9bw4fIN8QxuqF8Rxbv26yoj896aEDW/9YbI6/tzaprW4XF4uKaz3FHN+PwJ2kTxzuNcv6d4UHjr6GE3wK4VEQ4PNl50TiBcsd37yoeQu+Abv54cLmf8kTPHwdaoygPgyAMWjg3XpprMr61g4heT6fSLCzz27s3uaQAPD1/DNbHaighppX2jtsqAP/6YNxqA1CVxfGnrCr/d3ujxExJLuW7BIu4v1aastH55OiqO+J4U3djVZ4R7x1+G0GH1HtJ0iipsLq4AKFM//fI/2OCGuTu1CbmymwiIiQRiUD/sbaycf6zay1adt1cwp3A0pruqsBef8bMaifZ7whU0FFYD7Jq9P5QVEYxAwpt72Vg8hpz1q/SW4oU5qT2m2Ho3uJzDE0n+ST524OQQmLN3NEu6/YtEU5w7AObHS6OzykAgGq7MEAwu53aAAQum02VVRVRc61xBI7VtWrEbjBNXuo98bhyxo6N7b9eKGId/1945oY1/XKu3iOooTTnmhxtW7m/gmaPxaunVnLm9DVjA3Dk14Gok4QoaCqsBbuns/Qr/nFmdxv5rn6fhDE3u72+NZfzGO70c4KO1ykCk2PLrPzKm3RN0j0JknytoZOu0P6KWxAod14oY2lSOgSe6d63e0qdSm1/DKMFD4B00BkR8hZewGuCe8cWNhiEqQqNDQhUdzAnuBRfT5kqvQmsvvkhNXYbXOdFaZSAUXCsRfO1u1vnWlrpbElny4Ms8nKgthpgRwT5CR0z9PT0Z+PQ0urzzte+E3QovglkRw3NFlK5fHol691KwNBU0BuENGtJtWfo+OybR7p9aZmkznx3XS0qr4QnbNgDm3TuBzCd1FuOB41Ipk+c+zuO/XR/Qyd6FLXc6mWu0lQj6XtIGNjLtUym4w7evcHRCLZe7RHZWvMJZw3jgLu9OfVdAQNc3jGEoxOKOjHpirOEmBQJnX/PSMpZ0e9fv0kmu1VvMtdK9IooRXmhb542gZE5C0EFjrgCXS2u7E1fq8BusEQpRN8C9/pFD3DcWemyvxrzdOMv8uIgpKMKWOz3g9+0KJLE6vSxck4c3NoWii4UXs3gj90ekEvkmvKypof3aT3gmaSK/69j4sZkrTrqjihwH8wDok2vDVuQ/zXt8FjmX/MJZwxh/33afuRbKHCa6bvraEMYXIGbrfqriB3PjNG3QyijBTfL6/hTOrnGO+Xi3tFwrYny5vB/JzkFuI3lWewaNBUMnZzlM3hxemxVWA/zalptY0eWGRo+xLb4Q8eiSllB36jTp84wVEVe8LY1ep6a4txPzYnxG5f0esyy6M6OlBnE/f+bUcTCP9IPh19MUKaMLfVa62FdTy93vPubunzQK8Zv3UYUWsdlr7JQmjvZfRsJNSXY8Bwev9to3Km8sXx/vTMddWjM+2UBrKTbEFTSmJyoQQ6FQKHQirDXgYAZKjND/09pIW9R0zTKYYxRNs63sKno/tNdQI/MuXPN8ZBsk3DyxsJaRh7wX0RSLOxpqHgqjo9sgnEKhNydOdmR+F+/VPP5+8mqSyddJUesiZut+P0FJx3VQ0npRBljxvSV7yn52NBg8UsZXEU2ElEYam1QoFIrvD2oQTqFQKHRCGWCFQqHQCWWAFQqFQieUAVYoFAqdUAZYoVAodMKwBlgIUd7gzy6E+IveuhoihIgVQqwQQpwQQpQJIT4VQtyit65ACCHWCiHOCiG+FULkCyGajmvVCSGETQjxgRCiVAjxtRDiDr01NaQV5r/h09RFKyurM4QQ+4UQNUKI1cGeZ1gDLKVMdP0BnYEqYKPOsvxhAU4BI4D2wP8FNgghMnTU1BjPABlSynbA7cB8IcS1OmvyQQhhAf4OvA0kAznAWiFEtq7CfGk1+d+K0tRFqyirTgqB+cDKZp0lpWz0Dy20ZRbwOVAK/A1t6qP7gZ0NjpXAFc7Pq4HlwHtAObAL6AL8CSgBvgIGNHV/57V+ARTg9Fs2slbn9T4H7jS6VqAPcBaYYDStQD/nOcJj3zbg90bS2ZryP5Q0NUq6GrmsNrjmfGB1sGUl2BrwBODHQC/gauePCfa8uUBHoAbYAxxwbm8CnnMdKIRYLoRY7u8iaAZ4jXT+QiNrFUJ0BrKB/xhVq3NfJVqhOgu8a0Ct/tYHEmhGxEg6vQUaO/9DTVM9tHruM3pZDZlgDfBSKWWhlPIisAXoH+R5b0kp/y2lrAbeAqqllGuklHa0N9MA14FSyulSSp9JYYUQ6WjNu1dbgVYrsA54VUoZzIqIumh1brcFhgNvohU2o2n9CjgPzBZCWIUQo9HKQeC1bvTR6aYV5H+oaaqHVvc+jF9WQyZYA/yNx+dKgl8465zH5yo/28FcZxJa0+FYkPfURasQwgS8BlwGZgR5T93SVUppl1LuBLoD04K4Z1S1SilrgXHAbc57zwQ2AE1N1qzyP/xpGnWtnhi9rLaElkzGU4HHm1MI0aXlcvwyCXi2hdeIqFYhhABWoA0W3uos6KESrXR1YQGyQjw3olqllJ+j1dBc199N8C0hT1T+OwljmoIqqy2mJV4QB4EfCCH6CyHigHnhkVSPEGIY0I2Wez9EWusLgA0YK6WsauG1IqZVCJEqhLhbCJEohDALIcYA9wAfGE0rgBDiaiFEnBAiQQgxC+iKNljSXFT+OwljmoIqq26EEBbndc2A2ZnGTVZwQzbAUsp84Gngf4AjwM5QrwUghHhRCPFig92/AN6UUpa15NqR1CqE6AlMRetn+kbU+y1PNJpWtFHfaWhNzhJgEfArKeXfDagV4D60gZfzwM3AKCllMH2AUdPZyvIfwpSmUdDa2srqXLRuiieBe52f5zZ5neAcCxQKhUIRbgwbiKFQKBTfdZQBVigUCp1QBlihUCh0QhlghUKh0AllgBUKhUInggrEGGW6y1CuEu879+VZ3QAAEK1JREFUNvqLaQdaj9bWohOU1pag8j/8fFe0gqoBKxQKhW4oA6xQKBQ60ZK5IBSK7zTHFwzlcpdaemwxEb95n95yFN9BlAE2OOdnDKO6Y3DHdttejXn7gcgK+o5RNW4wRf39PwZ/+a+XGZ1Qi+3UdNI3R1mY4nuB6oJQKBQKnVA1YA8smRmcH9HVa1/qR2epKziujyDgzpwPmNsxmLm9oY9tEu16DQUgsbCWmK37IyntO8GpsQ6O3dLEwgaGGldvHZiusQFwYWBS0OeYayVJGz9F1oQ0N1DEkNf3pyQ73mtfuLRGzABbenQH4HJmJwBEncS09xCyri5St2wWYlA/7G2sXvsKboonL8f7YbTlTqf7hx289pkrapH7D0VcY3M5PHyNtm4AcO/xkVwo7mc4nf7SHfRJU1O/vrRLqWjyuOqudZh7ZwJgP1IQaVkBafhMNSSmoIi6U8HMrR55jo/Tnpm8qcGv2pN3uZKZ74/Dfu58pGSFxJEHLBy75QWvfeHSGpIBNiVo8xqLtoEniD/yUA8A8idpwvNrK/jVLZOh+JLvwTU12C+VhiKlWQiLBVNKMgA3rd7D7OSjTZ6Tl7NcWzvWg4UXs/jwpkzsFy5AlGaTO2+voNju36Uw02olVngbtbUZ25m/si87ro6LhrxGCSbd51+Ivtaq56o52K/pzt1jt+fSy6qtiJ79oD4G2NS2rc8z1ZDsNdPIWqA9R46yFs3gGnbs0sHRuirsUivDXczQwVy/ElKNrOVwbSo4jNXcMCe1xxRrj9j1QzLAJx/VlljaNHVRwGNSzK6EbANAliWeP723yp0Bntz92YN0GRd5A+wY0o8l67Q3cqbVCvjWxILhkQ5f0Xv3OXKHDcVeVBRGhYG5Yd0srlji/4Vx07+OBvUy0Ytwpfv3mcPPXsknt7uetzZ+j9k5cRE/bPs4AL0f2hslZcFxtK7KqwJ2eHE3jv5olfv7pSV96ys1BuLM6jT2X/s8wS2b13xCMsCOGO2/LSZ4UWZhItvqv+BsHPAKE9+eTPJP8kORExSVPxvCfQu2NKr53uMjOfebXl77Oj9zjLUZ2732xQorfaznwdRokEtY2PnAIEa1GUrvglPUBWju/Ov+oRxc2sNHpxEIJt0Bcjr8mxN7R3Pm1ljsxRejpM6be4+P5Ms1Ng781n8NUy/yXxnE325eRqrZ//PjItXcBhkXudpaqOSWprFpymhM+VoXZP4rg/jb9S/g+SKucVijVplpDh0Sqrxq6uHGEINw2dY2/DT9c3YQuSZodXsTOe0LOV1Xztg/PIGl0repk1hYS8xH3gNXZ54axHVpNr79sdZXeHj4mohp9IfcfwgT0FjPudx/iNPlGVFSFByXJmmDgTc8upec9oVNHp9qbsPjnd9npmVcpKUFZErnj3llkm6390v+K4NYddNKBsfWG6uZZwfy8bIh7u3Zv3mdCYmRb0GGwsyzA9m34DoSdu1FEvj37FtwHQkYq9ae/8ogVmWt9Nm/obw9zz39S5Iufdrie4RmgJtR8Vt4MYuVm8Z47Xtg/NaoN5k7HriELXc6phpIX/UZjsrKoM6L2bofHhjKsJ7BLsocfY4vGMpve27w2rfwYhZv5P6IVHZHXc/FB4bSL0cbUFvc1dsvudc/coj7xsIPf/wFq9J3RF2bJ1VruzJ5+nC3jpHxDkYarBXx4OCdjIx3ADD5pDbC+uXyfiSv2eM+Jv+xrmAwA9zto2oAdh4bQtKbezC1bcvhZ6/kbzcvcxtfz9+T9OaegNeKNv60ulh4MYvXXxxD6trdYXGOCckAd/pMq4/1em9Kk8cm5sWQvqjeCJyfMSyUW7YYx8E80g86PzfjvEuTNGPiaSz21dRy97uP0afyy/CKbCamhAROPtrfHTDgyTuFV5G6LPrGF+DC9bUBjesV62v5tqfZ73cpZsnXj2XR+/mYqIzmJ63Zw/YRg0DnF4E/XHk7OOFlQDNWh3L7AXgZX6PiCghyOaGJhHjeuW2JuyvKyL+noVaAp4quBGDTayNJC+NzpQIxFAqFQidCqgG74uKzQwjPvDPnA5/uh+1VJlbsu4FsjBU4UPmzIdzw6F6vZvT2KhOTP5xG9kN7m1WTDieugJG6BMHWaX+ku6XeHXBdWQoAZw50JZPjUdd2ecwgrsg457O/3FHN+Pw7OXdtPKMmfuLTNQFaP3D+pBcY9d5kTFHyZ03Mi2HhkCyAgN1iCy9mkZgXExU9LkTbRDZNXeSuhX3y3lWkr9SnRRMJjPp7zJ06cXb8FbQ11T/dCy9msem1kQCkLQqvZkMMws07ejvZU4xjfIXFgmNIP+5bsMU9gLSvRmviT/5wmm5aXY74BZPSPAJGvH2x/5g3GoAr1pbo8oKQMy/wvm2Lezu/toLtlb0ZlZBP1ZJuzPjjZp9BufzaCjaUXuveNlfURi34LG3Rbl6v1sYoanI+INX6rY++lRvHeHWjGRlXYImpX18ch4KLoFTUU3tldz6dsxzP5yqS+W8IA2wkhMWCKTuTpa8/73abO2+v4O53Nf/KbB39KwseSAfwidbz5ODg9QD8eOltmO9KjqpLlzklmTiLd1/0I0cnULWkGzkv5fLRS7l+z8s5PJHY0cc99kQ3Is7VV75jWRyOEUPJWb+qiTPqMackg8WCLCsPemA3HHgGt8Sa6mvueuZ/ozgkh2tTybSWECus2OMlprZtta8MEjQiYmO53M570O10XTmmy5G7p+oDboBjSD/+9N4qsiz1sd83rJtFnye/pM+T+g66NYdN2W/Q7d3oxtR3e7eGTdlv+OhYvey5qOqIJt3erWHx3s3u4KRo4RjSj8V7N7N472Ye6eBb09Uj/xvDfuECucOGsrSkL6AFjRx+9koOP3ulzsrquXTXAJ+yOvYPT5D+588idk9VA6beM+POnA9ItW5z13wHPj2NlLxqLQjCIG/pYEk0xdEzvpiTEfStbkjP+GISTd73SzTFkRjgNX/NvnsASJsrdetP98QVNNIcesYXY4tJYMmDL/NwouYVlDEn/KP6f77vZfbdmeXeTrVuazS4RY/8bxQpsRcVUePQaphGChpxPf+/eWQdvazeXXrWChnRlk1YDXD+K4Pomd54KOGE9utoGEo5L+sfzNt2u9c+sbhjVGbzKpw1jEn3bwXqB2FcwRpd3ziCvaio0SCIaJKxuQSA605O8/nuxhl7/Q5sRZOt80ZQMichaB3fFmvloMshY/T/u4J1PMl8ayp9Nvv2p5uT2nNmdRr/v71zj2nqDMP405ZS6Apyq1bkWi9QNjdlijPxgiZizILRTVlQYjQ6L1NMlMVscYlO53AZ02zTJWNDFo1hc0Y2GWZhiVeEmaHTaYYCK6gI3hgghQKlZX8cT+npaeUA5+b4fgkhOT20h9PTt9/5vud5n7QR3wJ4AZmX0xF9yirYsaVobUjRkjldIeik1q1ZZhZT7jswnm8Q9PPPawFenVTKoXUi206Z7O/AWbemKPO2pcLqnyR4EoHF1M1Y/S7pUGNL3jZE5V+FXcQ5PS44rlUCAEKusR+7nB4DSFyAtScu4fJ6bscx6/piRBbJYwbMuigJABCUwVRexJ5ci/jcJ87zzkCjwfeT8px3S8pKHRQX+Vuo6W2zYMnX72L/arbG2x05mVueS7wYyyLOWAVvRctrAT5SNAexSx5heUDTkJ/rN1MRTJOETSJoWTEdyS/2LfgcbQvFruNpiMkuG/AtMf0hBiBJfE1TSTg+HJWAHXpp56mbSsIRe5dt0FFq7KiYfdDpq39UPhpRP4mrLPCJjIB5dRRru356IwAw1BsAZRrxVHx9IiNQvTHSpeEU/zg6OhCRXYZM3Rp0G55dgE2fPYa92oyzkUxTSZL2HxS8/zaiPufu/BQKd2OJXLAuSnK+/zTN9g5MObcRcQ2tEHqSRB5DEAKBQBiG8DoCjtlejm+mzsRyDj1WPUGL9Tt7qIn6QLOwalD/jEbkR11wmhd2FqbByHEBRaHRoGXpZNjV1P1LUEY9aupGARicQcUbHW9MQ+DVB/3eCoVd70bp47GAxCNgT0J1lV6PxrTxaJ/lgLO1vchtX32MMW766f6pn+MP490Y9JjrGAkPlmgFqlZ8BXo6LasxUbBrlcuCnrdRWorWhuPrcpB1aBEg4QiYfv9dTUNSmFs8cTfVgdqJhYxt9+1AXNY9URrD866CuH0nDB8Z4lnbNUobNgffZDUOB/qE+LetoVAutULTdI/6GwGdXIopLyFCR835fVC6GAAw4b3+L3aVXg9bQgS6A9Uo2JPjXDUt6VAj8/f+e2MMlPk7z+HoibmIOBMMdQO1SGCvNkMZEICexHHO/cbsqGG0o6yytePnOy8jBMK1+OSKu7j9mGUE/IY+SzUgHs0aPaDiC1B6axOoRJSqdOq6rV3IblV54ctpCJZZPwO5oNLrcf/NcaKaG7jiKRHlob0d+x6kACIl9/BegCesqfDYVlKlj8T4sgdYoG1mFeE+IX770x/hmXGognPWmjIgAAotpQtuXEJfTAB9QdX3WLAlbxtisnm8oBQKqMLCoFGanakcY0+vAgDEZY1Ee1KMV2MDQJ1TIfsrc8WTuD3n42UY+d3z4SzzlIjiSq3NAmWPvFIcpMb754VCaHMDF1ShIXB80YZr8cWM7bnNr+LONPHqkGg6YFqIXX3GLOv0Bk/c2puA4tf3A8BTjzhTK5j6CaWa4FPLqgoLw9qycizQNoNuXF0x+yAA4P4lwE/BPg450rJ0Mgr25OB5ONbBsHLTVgSV/ElyO12Q4vMyUMac6sL+MacAiXXS4hkx3ITYcuKHudRtZclfEz0+vjvgAEv0XmuzYOWmrfB9YsPov6v5l6wpFYhTP4RG0fe6tIIg2HM3R8SeXItxBdSKuX+TVRbmBoePgiFuT9y1AYbiGsFXlwcD10SM3NZw/Lie6h+hvVIJh0xSfE17/0WsjRqu1y70fnckJHR6hzeTSOKuDZS+XmJVhifTUEZdMh5vjoCYVvhh64RzNQ3QTZeTNN6mJNhpBCpbLzXy6eqSRTExFq5jaFalLr50IsbMTGbvjNDKTkmiZ/TnGzF1O9vA4oquwQbDlRpM7Xr2fn6tDmjPUf+X1OfZFXu1GX6NBsa2SB8lHAVqKDfz35yne/4U9GYxjVf5Yw+xmpgfs4zAp9nLAACGX2pkGT0EAPWWIGhETuYetgVYe+ISSnXTYTK+5tQBnndbDaUF7q4EmnudDaSFvO2khfgODbf94wqbPRsGJOKJkVKHSO3Oo+kx1yGEg6jeDiAkX54Fggt0EkVcwgrcmnkYOqUffo0vxrzQVbxrTi3havzRj+LJmSCRT835Sz1YodMudgccgBzCYUUvwEeK5iDPMIOxTVfpi3AJetcGHS5HEPpMFLGpTBUDLXCXAlqIz3l/AY+FD8QUtw9n6CSKaNskvKJJd3ZHq0lXI77FxOuXdHCVtd9UHF2lL68JEnzx1mn2XY4UdYgYMQgEAkEiRB8BC9Epaqh4S/ggI7XBQxsTkm9QKcfNHf6I22SGvUVe4ZH/VxQXryK8NR7J+6jzH218iE5DGHw99BEZymtMuMjf84mBo60N4yXs6e3OsJ0DJghLEG1MOEz9MoB8oYmN48ZNaFJct9RJdCQEb5ApCAKBQJAIRW8vkZATCASCFJARMIFAIEgEKcAEAoEgEaQAEwgEgkSQAkwgEAgSQQowgUAgSAQpwAQCgSAR/wFy70xSbi9HmgAAAABJRU5ErkJggg==\n", 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" ] @@ -535,8 +535,8 @@ "execution_count": 8, "metadata": { "ExecuteTime": { - "end_time": "2020-09-04T06:16:00.336989Z", - "start_time": "2020-09-04T06:16:00.328353Z" + "end_time": "2020-09-04T06:46:33.471087Z", + "start_time": "2020-09-04T06:46:33.461899Z" } }, "outputs": [], @@ -581,8 +581,8 @@ "execution_count": 9, "metadata": { "ExecuteTime": { - "end_time": "2020-09-04T06:16:00.652544Z", - "start_time": "2020-09-04T06:16:00.338003Z" + "end_time": "2020-09-04T06:46:33.787344Z", + "start_time": "2020-09-04T06:46:33.472097Z" } }, "outputs": [ @@ -650,8 +650,8 @@ "execution_count": 10, "metadata": { "ExecuteTime": { - "end_time": "2020-09-04T06:16:00.660140Z", - "start_time": "2020-09-04T06:16:00.653553Z" + "end_time": "2020-09-04T06:46:33.795001Z", + "start_time": "2020-09-04T06:46:33.788382Z" } }, "outputs": [], @@ -702,8 +702,8 @@ "execution_count": 11, "metadata": { "ExecuteTime": { - "end_time": "2020-09-04T06:16:00.675920Z", - "start_time": "2020-09-04T06:16:00.661139Z" + "end_time": "2020-09-04T06:46:33.810658Z", + "start_time": "2020-09-04T06:46:33.796009Z" } }, "outputs": [], @@ -745,8 +745,8 @@ "execution_count": 12, "metadata": { "ExecuteTime": { - "end_time": "2020-09-04T06:16:24.850821Z", - "start_time": "2020-09-04T06:16:00.676924Z" + "end_time": "2020-09-04T06:46:57.649137Z", + "start_time": "2020-09-04T06:46:33.811666Z" }, "scrolled": true }, @@ -756,21 +756,21 @@ "output_type": "stream", "text": [ "============== Starting Training ==============\n", - "epoch: 1 step: 125, loss is 2.3081794\n", - "epoch: 1 step: 250, loss is 2.2945735\n", - "epoch: 1 step: 375, loss is 2.3107677\n", - "epoch: 1 step: 500, loss is 2.3018627\n", - "epoch: 1 step: 625, loss is 2.3044233\n", - "epoch: 1 step: 750, loss is 2.3034055\n", - "epoch: 1 step: 875, loss is 1.1475224\n", - "epoch: 1 step: 1000, loss is 0.20896824\n", - "epoch: 1 step: 1125, loss is 0.35238677\n", - "epoch: 1 step: 1250, loss is 0.1871425\n", - "epoch: 1 step: 1375, loss is 0.071077615\n", - "epoch: 1 step: 1500, loss is 0.07669073\n", - "epoch: 1 step: 1625, loss is 0.12473262\n", - "epoch: 1 step: 1750, loss is 0.010296674\n", - "epoch: 1 step: 1875, loss is 0.11679248\n" + "epoch: 1 step: 125, loss is 2.3096159\n", + "epoch: 1 step: 250, loss is 2.3002408\n", + "epoch: 1 step: 375, loss is 2.3008525\n", + "epoch: 1 step: 500, loss is 2.3079991\n", + "epoch: 1 step: 625, loss is 2.2878244\n", + "epoch: 1 step: 750, loss is 2.3090718\n", + "epoch: 1 step: 875, loss is 1.6479633\n", + "epoch: 1 step: 1000, loss is 0.19777162\n", + "epoch: 1 step: 1125, loss is 0.17173\n", + "epoch: 1 step: 1250, loss is 0.22985725\n", + "epoch: 1 step: 1375, loss is 0.16031101\n", + "epoch: 1 step: 1500, loss is 0.12752411\n", + "epoch: 1 step: 1625, loss is 0.03572363\n", + "epoch: 1 step: 1750, loss is 0.15765305\n", + "epoch: 1 step: 1875, loss is 0.20735049\n" ] } ], @@ -795,7 +795,7 @@ "net_opt = nn.Momentum(network.trainable_params(), lr, momentum)\n", "\n", "# define the loss function\n", - "net_loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')\n", + "net_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')\n", "\n", "# define the model\n", "model = Model(network, net_loss, net_opt, metrics={\"Accuracy\": Accuracy()} )\n", @@ -836,15 +836,15 @@ "execution_count": 13, "metadata": { "ExecuteTime": { - "end_time": "2020-09-04T06:16:24.980730Z", - "start_time": "2020-09-04T06:16:24.850821Z" + "end_time": "2020-09-04T06:46:57.780213Z", + "start_time": "2020-09-04T06:46:57.649137Z" }, "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -906,8 +906,8 @@ "execution_count": 14, "metadata": { "ExecuteTime": { - "end_time": "2020-09-04T06:16:25.898285Z", - "start_time": "2020-09-04T06:16:24.981730Z" + "end_time": "2020-09-04T06:46:58.668334Z", + "start_time": "2020-09-04T06:46:57.781766Z" } }, "outputs": [ @@ -916,7 +916,7 @@ "output_type": "stream", "text": [ "============== Starting Testing ==============\n", - "============== Accuracy:{'Accuracy': 0.9665464743589743} ==============\n" + "============== Accuracy:{'Accuracy': 0.9653445512820513} ==============\n" ] } ], @@ -957,14 +957,14 @@ "execution_count": 15, "metadata": { "ExecuteTime": { - "end_time": "2020-09-04T06:16:26.021313Z", - "start_time": "2020-09-04T06:16:25.899301Z" + "end_time": "2020-09-04T06:46:58.821007Z", + "start_time": "2020-09-04T06:46:58.671515Z" } }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1029,8 +1029,8 @@ "execution_count": 16, "metadata": { "ExecuteTime": { - "end_time": "2020-09-04T06:16:26.982886Z", - "start_time": "2020-09-04T06:16:26.022325Z" + "end_time": "2020-09-04T06:46:59.801973Z", + "start_time": "2020-09-04T06:46:58.822022Z" } }, "outputs": [ @@ -1038,14 +1038,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "All the figures in this group are predicted correctly!\n", - "[4 4 2 4 3 0 0 4 4 0 6 8 2 1 7 7 5 9 2 6 7 4 3 3 3 5 1 6 6 2 5 9] <--Predicted figures\n", - "[4 4 2 4 3 0 0 4 4 0 6 8 2 1 7 7 5 9 2 6 7 4 3 3 3 5 1 6 6 2 5 9] <--The right number\n" + "Row 2, column 4 is incorrectly identified as 2, the correct value should be 4 \n", + "\n", + "Row 4, column 4 is incorrectly identified as 3, the correct value should be 7 \n", + "\n", + "[0 1 2 5 3 7 0 8 3 3 9 2 8 0 2 6 8 5 0 2 9 4 3 3 6 0 2 3 3 4 7 6] <--Predicted figures\n", + "[0 1 2 5 3 7 0 8 3 3 9 4 8 0 2 6 8 5 0 2 9 4 3 3 6 0 2 7 3 4 7 6] <--The right number\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1097,8 +1100,8 @@ "execution_count": 17, "metadata": { "ExecuteTime": { - "end_time": "2020-09-04T06:16:27.131596Z", - "start_time": "2020-09-04T06:16:26.984263Z" + "end_time": "2020-09-04T06:46:59.943697Z", + "start_time": "2020-09-04T06:46:59.803412Z" } }, "outputs": [ @@ -1107,13 +1110,13 @@ "output_type": "stream", "text": [ "Figure 1 probability of corresponding numbers [0-9]:\n", - " [-2.2434433 -5.460074 -0.27407748 -3.74839 13.366689 -1.8540848\n", - " 1.6585187 -1.8376697 0.1426354 0.26712573]\n" + " [10.731268 -8.178983 0.7688376 -1.5208994 -3.4331348 -0.6836271\n", + " 3.7032425 -2.7914028 0.43636245 -0.42814386]\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1126,13 +1129,13 @@ "output_type": "stream", "text": [ "Figure 2 probability of corresponding numbers [0-9]:\n", - " [-3.8317444 1.3081287 -1.564763 -0.72753066 4.7249494 -0.22273807\n", - " -3.6146772 0.6123574 -1.3609397 4.722361 ]\n" + " [-5.470294 8.598144 -0.29099795 -1.5832896 -0.88322777 -1.8221556\n", + " -0.637848 -0.71524227 2.496806 -0.6640963 ]\n" ] }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] -- Gitee From 152f87bccc513f38afe099e74d003162183d357b Mon Sep 17 00:00:00 2001 From: SebastianHan Date: Fri, 4 Sep 2020 15:25:07 +0800 Subject: [PATCH 08/13] add dataset video en --- .../source_en/quick_start/quick_video.md | 24 +++++++++++++++++++ ..._the_dataset_and_converting_data_format.md | 14 +++++++++++ 2 files changed, 38 insertions(+) create mode 100644 tutorials/source_en/quick_start/quick_video/loading_the_dataset_and_converting_data_format.md diff --git a/tutorials/source_en/quick_start/quick_video.md b/tutorials/source_en/quick_start/quick_video.md index d31b4343a7..84d8945edb 100644 --- a/tutorials/source_en/quick_start/quick_video.md +++ b/tutorials/source_en/quick_start/quick_video.md @@ -137,6 +137,30 @@ Provides video tutorials from installation to try-on, helping you quickly use Mi +
+ + + + play + + + + + + + +
(This document contains Hands-on Tutorial Series. Gitee does not support display. Please check tutorials on the official website) + + + +**See More**： + + + + + \ No newline at end of file -- Gitee From 54a380b2bfceb9010887a3b6deb7222171c0dbd8 Mon Sep 17 00:00:00 2001 From: wanyiming Date: Fri, 4 Sep 2020 15:25:40 +0800 Subject: [PATCH 09/13] Mod_SoftmaxCrossEntropyWithLogits --- docs/source_en/operator_list.md | 2 +- docs/source_zh_cn/operator_list.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source_en/operator_list.md b/docs/source_en/operator_list.md index 8ef84d3f5a..43a3acbcb4 100644 --- a/docs/source_en/operator_list.md +++ b/docs/source_en/operator_list.md @@ -128,7 +128,7 @@ | [mindspore.ops.operations.Conv2DBackpropInput](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Conv2DBackpropInput) | Supported | Supported |Doing | nn_ops | [mindspore.ops.operations.BiasAdd](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.BiasAdd) | Supported | Supported | Supported | nn_ops | [mindspore.ops.operations.TopK](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.TopK) | Supported | Supported |Doing | nn_ops -| [mindspore.ops.operations.SoftmaxCrossEntropyWithLogits](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.SoftmaxCrossEntropyWithLogits) | Supported | Supported |Supported | nn_ops +| [mindspore.ops.operations.SoftmaxCrossEntropyWithLogits](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.SoftmaxCrossEntropyWithLogits) | Supported | Supported |Doing | nn_ops | [mindspore.ops.operations.SparseSoftmaxCrossEntropyWithLogits](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.SparseSoftmaxCrossEntropyWithLogits) | Doing | Supported | Supported | nn_ops | [mindspore.ops.operations.ApplyMomentum](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.ApplyMomentum) | Supported | Supported | Supported | nn_ops | [mindspore.ops.operations.ApplyAddSign](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.ApplyAddSign) | Supported | Doing | Doing | nn_ops diff --git a/docs/source_zh_cn/operator_list.md b/docs/source_zh_cn/operator_list.md index e5b75be664..74b6137433 100644 --- a/docs/source_zh_cn/operator_list.md +++ b/docs/source_zh_cn/operator_list.md @@ -128,7 +128,7 @@ | [mindspore.ops.operations.Conv2DBackpropInput](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Conv2DBackpropInput) | Supported | Supported |Doing | nn_ops | [mindspore.ops.operations.BiasAdd](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.BiasAdd) | Supported | Supported | Supported | nn_ops | [mindspore.ops.operations.TopK](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.TopK) | Supported | Supported |Doing | nn_ops -| [mindspore.ops.operations.SoftmaxCrossEntropyWithLogits](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.SoftmaxCrossEntropyWithLogits) | Supported | Supported |Supported | nn_ops +| [mindspore.ops.operations.SoftmaxCrossEntropyWithLogits](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.SoftmaxCrossEntropyWithLogits) | Supported | Supported |Doing | nn_ops | [mindspore.ops.operations.SparseSoftmaxCrossEntropyWithLogits](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.SparseSoftmaxCrossEntropyWithLogits) | Doing | Supported | Supported | nn_ops | [mindspore.ops.operations.ApplyMomentum](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.ApplyMomentum) | Supported | Supported | Supported | nn_ops | [mindspore.ops.operations.ApplyAddSign](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.ApplyAddSign) | Supported | Doing | Doing | nn_ops -- Gitee From 097910cac4ab71e8d97bb0282f2e656fac8b9e1e Mon Sep 17 00:00:00 2001 From: Li Hongzhang Date: Fri, 4 Sep 2020 16:25:02 +0800 Subject: [PATCH 10/13] remove mindinsight APIs --- .../api/python/mindinsight/mindinsight.lineagemgr.rst | 5 ----- api/source_en/conf.py | 2 -- api/source_en/index.rst | 6 ------ .../api/python/mindinsight/mindinsight.lineagemgr.rst | 5 ----- api/source_zh_cn/conf.py | 2 -- api/source_zh_cn/index.rst | 6 ------ 6 files changed, 26 deletions(-) delete mode 100644 api/source_en/api/python/mindinsight/mindinsight.lineagemgr.rst delete mode 100644 api/source_zh_cn/api/python/mindinsight/mindinsight.lineagemgr.rst diff --git a/api/source_en/api/python/mindinsight/mindinsight.lineagemgr.rst b/api/source_en/api/python/mindinsight/mindinsight.lineagemgr.rst deleted file mode 100644 index f63acde354..0000000000 --- a/api/source_en/api/python/mindinsight/mindinsight.lineagemgr.rst +++ /dev/null @@ -1,5 +0,0 @@ -mindinsight.lineagemgr -====================== - -.. automodule:: mindinsight.lineagemgr - :members: \ No newline at end of file diff --git a/api/source_en/conf.py b/api/source_en/conf.py index f08d1d521a..dc2ef84ebe 100644 --- a/api/source_en/conf.py +++ b/api/source_en/conf.py @@ -16,8 +16,6 @@ import os # sys.path.insert(0, os.path.abspath('.')) import mindspore -# If you don't want to generate MindInsight APIs, comment this line. -import mindinsight # If you don't want to generate MindArmour APIs, comment this line. import mindarmour diff --git a/api/source_en/index.rst b/api/source_en/index.rst index c5053627a3..36b995c3e8 100644 --- a/api/source_en/index.rst +++ b/api/source_en/index.rst @@ -33,12 +33,6 @@ MindSpore API api/python/mindspore/mindspore.mindrecord api/python/mindspore/mindspore.profiler -.. toctree:: - :maxdepth: 1 - :caption: MindInsight Python API - - api/python/mindinsight/mindinsight.lineagemgr - .. toctree:: :maxdepth: 1 :caption: MindArmour Python API diff --git a/api/source_zh_cn/api/python/mindinsight/mindinsight.lineagemgr.rst b/api/source_zh_cn/api/python/mindinsight/mindinsight.lineagemgr.rst deleted file mode 100644 index f63acde354..0000000000 --- a/api/source_zh_cn/api/python/mindinsight/mindinsight.lineagemgr.rst +++ /dev/null @@ -1,5 +0,0 @@ -mindinsight.lineagemgr -====================== - -.. automodule:: mindinsight.lineagemgr - :members: \ No newline at end of file diff --git a/api/source_zh_cn/conf.py b/api/source_zh_cn/conf.py index 21de9d481a..474f585971 100644 --- a/api/source_zh_cn/conf.py +++ b/api/source_zh_cn/conf.py @@ -16,8 +16,6 @@ import os # sys.path.insert(0, os.path.abspath('.')) import mindspore -# If you don't want to generate MindInsight APIs, comment this line. -import mindinsight # If you don't want to generate MindArmour APIs, comment this line. import mindarmour diff --git a/api/source_zh_cn/index.rst b/api/source_zh_cn/index.rst index 63b9597aef..d5014c4f39 100644 --- a/api/source_zh_cn/index.rst +++ b/api/source_zh_cn/index.rst @@ -39,12 +39,6 @@ MindSpore API api/python/mindspore/mindspore.mindrecord api/python/mindspore/mindspore.profiler -.. toctree:: - :maxdepth: 1 - :caption: MindInsight Python API - - api/python/mindinsight/mindinsight.lineagemgr - .. toctree:: :maxdepth: 1 :caption: MindArmour Python API -- Gitee From e47dd52eec7611231e083e9e0df9170cdf6c1779 Mon Sep 17 00:00:00 2001 From: JunYuLiu Date: Fri, 4 Sep 2020 16:36:46 +0800 Subject: [PATCH 11/13] fix links --- lite/tutorials/source_en/use/timeprofiler_tool.md | 2 +- tutorials/notebook/computer_vision_application.ipynb | 2 +- .../convert_dataset_to_mindspore_data_format.ipynb | 4 ++-- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/lite/tutorials/source_en/use/timeprofiler_tool.md b/lite/tutorials/source_en/use/timeprofiler_tool.md index d7abe79fb7..b0e3d35860 100644 --- a/lite/tutorials/source_en/use/timeprofiler_tool.md +++ b/lite/tutorials/source_en/use/timeprofiler_tool.md @@ -20,7 +20,7 @@ After model conversion and before inference, you can use the TimeProfiler tool t To use the TimeProfiler tool, you need to prepare the environment as follows: -- Compilation: Install build dependencies and perform build. The code of the TimeProfiler tool is stored in the `mindspore/lite/tools/time_profile` directory of the MindSpore source code. For details about the build operations, see the [Environment Requirements](https://www.mindspore.cn/lite/tutorial/en/master/compile.html#environment-requirements) and [Compilation Example](https://www.mindspore.cn/lite/tutorial/en/master/compile.html#compilation-example) in the build document. +- Compilation: Install build dependencies and perform build. The code of the TimeProfiler tool is stored in the `mindspore/lite/tools/time_profile` directory of the MindSpore source code. For details about the build operations, see the [Environment Requirements](https://www.mindspore.cn/lite/tutorial/en/master/build.html#environment-requirements) and [Compilation Example](https://www.mindspore.cn/lite/tutorial/en/master/build.html#compilation-example) in the build document. - Run: Obtain the `timeprofile` tool and configure environment variables by referring to [Output Description](https://www.mindspore.cn/lite/tutorial/en/master/build.html#output-description) in the build document. diff --git a/tutorials/notebook/computer_vision_application.ipynb b/tutorials/notebook/computer_vision_application.ipynb index b9a4efc032..4525696f8b 100644 --- a/tutorials/notebook/computer_vision_application.ipynb +++ b/tutorials/notebook/computer_vision_application.ipynb @@ -71,7 +71,7 @@ "metadata": {}, "source": [ "本次面向Ascend 910 AI处理器硬件平台,将卷积神经网络ResNet加入到案例中，你可以在这里下载完整的样例代码案例作为基础用例：\n", - "https://gitee.com/mindspore/docs/blob/master/tutorials/tutorial_code/resnet" + "https://gitee.com/mindspore/docs/tree/master/tutorials/tutorial_code/resnet" ] }, { diff --git a/tutorials/notebook/convert_dataset_to_mindspore_data_format/convert_dataset_to_mindspore_data_format.ipynb b/tutorials/notebook/convert_dataset_to_mindspore_data_format/convert_dataset_to_mindspore_data_format.ipynb index 60f0ffe0c6..f34bc0c817 100644 --- a/tutorials/notebook/convert_dataset_to_mindspore_data_format/convert_dataset_to_mindspore_data_format.ipynb +++ b/tutorials/notebook/convert_dataset_to_mindspore_data_format/convert_dataset_to_mindspore_data_format.ipynb @@ -281,7 +281,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "- 本例中需要的数据位置在https://gitee.com/mindspore/docs/tree/master/tutorials/notebook/convert_dataset_to_mindspore_data_format/csv_data/data.csv\n", + "- 本例中需要的数据位置在https://gitee.com/mindspore/docs/blob/master/tutorials/notebook/convert_dataset_to_mindspore_data_format/csv_data/data.csv\n", "中，使用过程中可以在此路径下找到文件并下载，并且保存在`jupyter工作目录/dataset/`下，如图所示：" ] }, @@ -838,7 +838,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "3. 准备需要写入的数据，按照用户定义的Schema形式，准备需要写入的样本列表，本例中需要的数据位置在https://gitee.com/mindspore/docs/tree/master/tutorials/notebook/convert_dataset_to_mindspore_data_format/images/transform.jpg\n", + "3. 准备需要写入的数据，按照用户定义的Schema形式，准备需要写入的样本列表，本例中需要的数据位置在https://gitee.com/mindspore/docs/blob/master/tutorials/notebook/convert_dataset_to_mindspore_data_format/images/transform.jpg\n", "中，使用过程中可以在此路径下找到图片并下载，并且保存在`jupyter工作目录/dataset/`下。" ] }, -- Gitee From cc92374c5a906aede4c5b0d4c8b5d2b5327de586 Mon Sep 17 00:00:00 2001 From: JunYuLiu Date: Fri, 4 Sep 2020 17:24:23 +0800 Subject: [PATCH 12/13] fix links --- tutorials/notebook/mixed_precision.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials/notebook/mixed_precision.ipynb b/tutorials/notebook/mixed_precision.ipynb index 53a7b4b7f3..1455e1710d 100644 --- a/tutorials/notebook/mixed_precision.ipynb +++ b/tutorials/notebook/mixed_precision.ipynb @@ -952,7 +952,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "当然，如果你想参考单步训练或者手动设置混合精度训练，可以参考官网教程。" + "当然，如果你想参考单步训练或者手动设置混合精度训练，可以参考官网教程。" ] }, { -- Gitee From 32651744cfc4633ed436cf7152962b8f9d4b0846 Mon Sep 17 00:00:00 2001 From: Payne Date: Mon, 31 Aug 2020 20:42:39 +0800 Subject: [PATCH 13/13] add mobilenetv2_incremental_learn.md --- .../mobilenetv2_incremental_learn.md | 396 ++++++++++++++++++ 1 file changed, 396 insertions(+) create mode 100644 tutorials/source_zh_cn/advanced_use/mobilenetv2_incremental_learn.md diff --git a/tutorials/source_zh_cn/advanced_use/mobilenetv2_incremental_learn.md b/tutorials/source_zh_cn/advanced_use/mobilenetv2_incremental_learn.md new file mode 100644 index 0000000000..f47fd4ad6c --- /dev/null +++ b/tutorials/source_zh_cn/advanced_use/mobilenetv2_incremental_learn.md @@ -0,0 +1,396 @@ +# MobileNetV2 增量学习 +`CPU` `Ascend` `GPU` `模型开发` `中级` `高级` + + + +- [增量学习](#增量学习) + - [概述](#概述) + - [任务描述及准备](#任务描述及准备) + - [环境配置](#环境配置) + - [下载代码](#下载代码) + - [准备预训练模型](#准备预训练模型) + - [准备数据](#准备数据) + - [预训练模型加载代码详解](#预训练模型加载代码详解) + - [参数简介](#参数简介) + - [运行Python文件](#运行python文件) + - [运行Shell脚本](#运行shell脚本) + - [加载增量学习训练](#加载增量学习训练) + - [CPU加载训练](#cpu加载训练) + - [GPU加载训练](#gpu加载训练) + - [Ascend加载训练](#ascend加载训练) + - [增量学习训练结果](#增量学习训练结果) + - [验证增量学习训练模型](#验证增量学习训练模型) + - [验证模型](#验证模型) + - [验证结果](#验证结果) + + +   + +## 概述 + +计算机视觉任务中，从头开始训练一个网络耗时巨大，需要大量计算能力。预训练模型选择的常见的OpenImage、ImageNet、VOC、COCO等公开大型数据集，规模达到几十万甚至超过上百万张。大部分任务数据规模较大，训练网络模型时，如果不使用预训练模型，从头开始训练网络，需要消耗大量的时间与计算能力，模型容易陷入局部极小值和过拟合。因此大部分任务都会选择预训练模型，在其上做增量学习。 + +MindSpore是一个多元化的机器学习框架。既可以在手机等端侧和PC等设备上运行，也可以在云上的服务器集群上运行。目前MobileNetV2支持在Windows系统中使用单核CPU做增量学习，在EulerOS、Ubuntu系统中使用单个或者多个Ascend AI处理器或GPU中做增量学习，本教程将会介绍如何在不同系统与处理器下的MindSpore框架中做增量学习的训练与验证。 + +目前，Window上暂只支持支持CPU，Ubuntu与EulerOS上支持CPU、GPU与Ascend AI处理器三种处理器。 + +>你可以在这里找到完整可运行的样例代码：https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/mobilenetv2 + +## 任务描述及准备 + +### 环境配置 + +若在本地环境运行，需要安装MindSpore框架，配置CPU、GPU或Ascend AI处理器。若在华为云环境上运行，不需要安装MindSpore框架，不需要配置Ascend AI处理器、CPU与GPU，可以跳过本小节。 + +1. 安装MindSpore框架 + 在EulerOS、Ubuntu或者Windows等系统上需要根据系统和处理器架构[安装对应版本MindSpore框架](https://www.mindspore.cn/install)。 + +2. 配置CPU环境 + 使用CPU时，在代码中，需要在调用CPU开始训练或测试前，按照如下代码设置： + + ```Python + if config.platform == "CPU": + context.set_context(mode=context.GRAPH_MODE, device_target=config.platform, \ + save_graphs=False) + ``` + +3. 配置GPU环境 + 使用GPU时，在代码中，需要在调用GPU开始训练或测试前，按照如下代码设置： + + ```Python + elif config.platform == "GPU": + context.set_context(mode=context.GRAPH_MODE, device_target=config.platform, \ + save_graphs=False) + init("nccl") + context.set_auto_parallel_context(device_num=get_group_size(), + parallel_mode=ParallelMode.DATA_PARALLEL, + mirror_mean=True) + ``` + +4. 配置Ascend环境 + 以Ascend 910 AI处理器为例，1个8个处理器环境的json配置文件`hccl_config.json`示例如下。单/多处理器环境可以根据以下示例调整`"server_count"`与`device`： + + ```json + { + "version": "1.0", + "server_count": "1", + "server_list": [ + { + "server_id": "10.155.111.140", + "device": [ba + {"device_id": "0","device_ip": "192.1.27.6","rank_id": "0"}, + {"device_id": "1","device_ip": "192.2.27.6","rank_id": "1"}, + {"device_id": "2","device_ip": "192.3.27.6","rank_id": "2"}, + {"device_id": "3","device_ip": "192.4.27.6","rank_id": "3"}, + {"device_id": "4","device_ip": "192.1.27.7","rank_id": "4"}, + {"device_id": "5","device_ip": "192.2.27.7","rank_id": "5"}, + {"device_id": "6","device_ip": "192.3.27.7","rank_id": "6"}, + {"device_id": "7","device_ip": "192.4.27.7","rank_id": "7"}], + "host_nic_ip": "reserve" + } + ], + "status": "completed" + } + ``` + + 使用Ascend AI处理器时，在代码中，需要在调用Ascend AI处理器开始训练或测试前，按照如下代码设置： + + ```Python + elif config.platform == "Ascend": + context.set_context(mode=context.GRAPH_MODE, device_target=config.platform, \ + device_id=config.device_id, save_graphs=False) + if config.run_distribute: + context.set_auto_parallel_context(device_num=config.rank_size, + parallel_mode=ParallelMode.DATA_PARALLEL, + parameter_broadcast=True, mirror_mean=True) + auto_parallel_context().set_all_reduce_fusion_split_indices([140]) + init() + ... + ``` + +### 下载代码 + +在Gitee中克隆[MindSpore开源项目仓库](https://gitee.com/mindspore/mindspore.git)，进入`./model_zoo/official/cv/mobilenetv2/`。 + +```bash +git clone https://gitee.com/mindspore/mindspore/pulls/5766 +cd ./mindspore/model_zoo/official/cv/mobilenetv2 +``` + +代码结构如下： + +``` +├─MobileNetV2 + ├─README.md # descriptions about MobileNetV2 + ├─scripts + │ run_train.sh # Shell script for train with Ascend or GPU + │ run_eval.sh # Shell script for evaluation with Ascend or GPU + ├─src + │ config.py # parameter configuration + │ dataset.py # creating dataset + │ launch.py # start Python script + │ lr_generator.py # learning rate config + │ mobilenetV2.py # MobileNetV2 architecture + │ models.py # net utils to load ckpt_file, define_net... + │ utils.py # net utils to switch precision, set_context and so on + ├─train.py # training script + └─eval.py # evaluation script +``` + +运行增量学习训练与测试时，Windows、Ubuntu与EulersOS上可以使用Python文件`train.py`与`eval.py`，Ubuntu与EulerOS上还可以使用Shell脚本文件`run_train.sh`与`run_eval.sh`。 + +使用脚本文件`run_train.sh`时，该文件会将运行`launch.py`并且将参数传入`launch.py`，`launch.py`根据分配的CPU、GPU或Ascend AI处理器数量，启动单个/多个进程运行`train.py`，每一个进程分配对应的一个处理器。 + +### 准备预训练模型 + +[下载预训练模型](https://download.mindspore.cn/model_zoo/official/lite/mobilenetv2_openimage_lite/mobilenetV2.ckpt)到以下目录： +`./pretrain_checkpoint/[pretrain_checkpoint_file]` + +```Python +mkdir pretrain_checkpoint +wget -P ./pretrain_checkpoint https://download.mindspore.cn/model_zoo/official/lite/mobilenetv2_openimage_lite/mobilenetV2.ckpt +``` + +### 准备数据 + +准备ImageFolder格式管理的数据集，运行`run_train.sh`时加入`[dataset_path]`参数，运行`train.py`时加入`--dataset_path [dataset_path]`参数： + +数据集结构如下： + +``` +└─ImageFolder + ├─train + │ class1Folder + │ class2Folder + │ ...... + └─eval + class1Folder + class2Folder + ...... +``` + +## 预训练模型加载代码详解 + +在增量学习时，需要加载预训练模型。不同数据集和任务中特征提取层（卷积层）分布趋于一致，但是特征向量的组合（全连接层）不相同，分类数量（全连接层output_size)通常也不一致。在增量学习时，只加载与训练特征提取层参数，不加载与训练全连接层参数；在微调与初始训练时，加载与训练特征提取层参数与全连接层参数。 + +在训练与测试之前，首先按照代码第1行，构建MobileNetV2的backbone网络，head网络，并且构建包含这两个子网络的MobileNetV2网络。代码第4-11行展示了如何在`fine_tune`训练模式下，将预训练模型加载入`net`(MobileNetV2)；在`incremental_learn`训练模式下，将预训练模型分别加载入backbone_net子网络，并且冻结backbone_net中的参数，不参与训练。代码第22-24行展示了如何冻结网络参数。 + +```Python + 1: backbone_net, head_net, net = define_net(args_opt, config) + 2: ... + 3: def define_net(args, config): + 4: backbone_net = MobileNetV2Backbone(platform=args.platform) + 5: head_net = MobileNetV2Head(input_channel=backbone_net.out_channels, num_classes=config.num_classes) + 6: net = mobilenet_v2(backbone_net, head_net) + 7: if args.pretrain_ckpt: + 8: if args.train_method == "fine_tune": + 9: load_ckpt(net, args.pretrain_ckpt) +10: elif args.train_method == "incremental_learn": +11: load_ckpt(backbone_net, args.pretrain_ckpt, trainable=False) +12: elif args.train_method == "train": +13: pass +14: else: +15: raise ValueError("must input the usage of pretrain_ckpt when the pretrain_ckpt isn't None") +16: return backbone_net, head_net, net +17: ... +18: def load_ckpt(network, pretrain_ckpt_path, trainable=True): +19: """load the pretrain checkpoint and with the param trainable or not""" +20: param_dict = load_checkpoint(pretrain_ckpt_path) +21: load_param_into_net(network, param_dict) +22: if not trainable: +23: for param in network.get_parameters(): +24: param.requires_grad = False +``` + +## 参数简介 + +### 运行Python文件 +在Windows与Linux系统上训练时，运行`train.py`时需要传入`dataset_path`、`platform`、`train_method`与`pretrain_ckpt`四个参数。验证时，运行`eval.py`并且传入`dataset_path`、`platform`、`pretrain_ckpt`与`head_ckpt`四个参数。 + +```Shell +# Windows/Linux train with Python file +python train.py --dataset_path [dataset_path] --platform [platform] --pretrain_ckpt [pretrain_checkpoint_path] --train_method[("train", "fine_tune", "incremental_learn")] + +# Windows/Linux eval with Python file +python eval.py --dataset_path [dataset_path] --platform [platform] --pretrain_ckpt [pretrain_checkpoint_path] --head_ckpt [head_ckpt_path] +``` + +- `--dataset_path`：训练与验证数据集地址，无默认值，用户训练/验证时必须输入。 +- `--platform`：处理器类型，默认为“Ascend”，可以设置为“CPU”或"GPU"。 +- `--train_method`：训练方法，必须输入“train"、"fine_tune"和incremental_learn"其中一个。 +- `--pretrain_ckpt`：增量训练或调优时，需要传入pretrain_checkpoint文件路径以加载预训练好的模型参数权重。 +- `--head_ckpt`：增量训练模型验证时，需要传入head_net预训练模型路径以加载预训练好的模型参数权重。 + + +### 运行Shell脚本 +在Linux系统上时，可以选择运行Shell脚本文件`./scripts/run_train.sh`与`./scripts/run_eval.sh`。运行时需要在交互界面中同时传入参数。 + +```Shell +# Windows doesn't support Shell +# Linux train with Shell script +sh run_train.sh [PLATFORM] [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [RANK_TABLE_FILE] [DATASET_PATH] [TRAIN_METHOD] [CKPT_PATH] + +# Linux eval with Shell script for incremental learn +sh run_eval.sh [PLATFORM] [DATASET_PATH] [PRETRAIN_CKPT_PATH] [HEAD_CKPT_PATH] +``` + +- `[PLATFORM]`：处理器类型，默认为“Ascend”，可以设置为“GPU”。 +- `[DEVICE_NUM]`：每个节点（一台服务器/PC相当于一个节点）进程数量，建议设置为机器上Ascend AI处理器数量或GPU数量。 +- `[VISIABLE_DEVICES(0,1,2,3,4,5,6,7)]`：字符串格式的的设备ID，训练将会根据`[VISIABLE_DEVICES]`将进程绑定到对应ID的设备上，多个设备ID之间使用','分隔，建议ID数量与进程数量相同。 +- `[RANK_TABLE_FILE]`：platform选择Ascend时，需要配置Ascend的配置Json文件,。 +- `[DATASET_PATH]`：训练与验证数据集地址，无默认值，用户训练/验证时必须输入。 +- `[CKPT_PATH]`：增量训练或调优时，需要传入checkpoint文件路径以加载预训练好的模型参数权重。 +- `[TRAIN_METHOD]`：训练方法，必须输入`train`、`fine_tune`和`incremental_learn`其中一个。 +- `[PRETRAIN_CKPT_PATH]`：针对增量学习的模型做验证时，需要输入主干网络层保存模型路径。 +- `[HEAD_CKPT_PATH]`：针对增量学习的模型做验证时，需要输入全连接层保存模型路径。 + +## 加载增量学习训练 + +Windows系统上，MobileNetV2做增量学习训练时，只能运行`train.py`。Linux系统上，使用MobileNetV2做增量学习训练时，可以选择运行`run_train.sh`， 并在运行Shell脚本文件时传入[参数](#参数简介)。 + +Windows系统输出信息到交互式命令行，Linux系统环境下运行`run_train.sh`时，命令行结尾使用`&> [log_file_path]`将标准输出与错误输出写入log文件。 增量学习成功开始训练，`./train/device*/log*.log`中会持续写入每一个epoch的训练时间与Loss等信息。若未成功，上述log文件会写入失败报错信息。 + +### CPU加载训练 + +- 设置节点数量 + + 目前运行`train.py`时仅支持单处理器，不需要调整处理器数量。运行`run_train.sh`文件时，`CPU`设备默认为单处理器，目前暂不支持修改CPU数量。 + +- 开始增量训练 + + 使用样例1：通过Python文件调用1个CPU处理器。 + + ```Shell + # Windows or Linux with Python + python train.py --platform CPU --dataset_path /store/dataset/OpenImage/train/ -- train_method incremental_learn --pretrain_ckpt ./pretrain_checkpoint/mobilenetV2.ckpt + ``` + + 使用样例2：通过Shell文件调用1个CPU处理器。 + + ```Shell + # Linux with Shell + sh run_train.sh CPU /store/dataset/OpenImage/train/ incremental_learn ../pretrain_checkpoint/mobilenetV2.ckpt + ``` + +### GPU加载训练 + +- 设置节点数量 + + 目前运行`train.py`时仅支持单处理器，不需要调整节点数量。运行`run_train.sh`文件时，设置`[nproc_per_node]`为GPU数量， `[visible_devices]`为可使用的处理器编号，即GPU的ID，可以选择一个或多个设备ID，使用`,`隔开。 + +- 开始增量训练 + + - 使用样例1：通过Python文件调用1个GPU处理器。 + + ```Shell + # Windows or Linux with Python + python train.py --platform GPU --dataset_path /store/dataset/OpenImage/train/ --pretrain_ckpt ./pretrain_checkpoint/mobilenetV2.ckpt --train_method incremental_learn + ``` + + - 使用样例2：通过Shell脚本调用1个GPU处理器，设备ID为`“0”`。 + + ```Shell + # Linux with Shell + sh run_train.sh GPU 1 0 /store/dataset/OpenImage/train/ incremental_learn ../pretrain_checkpoint/mobilenetV2.ckpt + ``` + + - 使用样例3：通过Shell脚本调用8个GPU处理器，设备ID为`“0,1,2,3,4,5,6,7”`。 + + ```Shell + # Linux with Shell + sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 /store/dataset/OpenImage/train/ incremental_learn ../pretrain_checkpoint/mobilenetV2.ckpt + ``` + +### Ascend加载训练 + +- 设置节点数量 + + 目前运行`train.py`时仅支持单处理器，不需要调整节点数量。运行`run_train.sh`文件时，设置`[nproc_per_node]`为Ascend AI处理器数量， `[visible_devices]`为可使用的处理器编号，即Ascend AI处理器的ID，8卡服务器可以选择0-7中一个或多个设备ID，使用`,`隔开。Ascend节点处理器数量目前只能设置为1或者8。 + +- 开始增量训练 + + - 使用样例1：通过Python文件调用1个Ascend处理器。 + + ```Shell + # Windows or Linux with Python + python train.py --platform Ascend --dataset_path /store/dataset/OpenImage/train/ --train_method incremental_learn --pretrain_ckpt ./pretrain_checkpoint/mobilenetV2.ckpt + ``` + + - 使用样例2：通过Shell脚本调用1个Ascend AI处理器，设备ID为“0”。 + + ```Shell + # Linux with Shell + sh run_train.sh Ascend 1 0 ~/rank_table.json /store/dataset/OpenImage/train/ incremental_learn ../pretrain_checkpoint/mobilenetV2.ckpt + ``` + + - 使用样例3：通过Shell脚本调用8个Ascend AI处理器，设备ID为”0,1,2,3,4,5,6,7“。 + + ```Shell + # Linux with Shell + sh run_train.sh Ascend 8 0,1,2,3,4,5,6,7 ~/rank_table.json /store/dataset/OpenImage/train/ incremental_learn ../pretrain_checkpoint/mobilenetV2.ckpt + ``` + +### 增量学习训练结果 + +- 查看运行结果。 + + - 运行Python文件时在交互式命令行中查看打印信息，`Linux`上运行Shell脚本运行后使用`cat ./train/device0/log0.log`中查看打印信息，输出结果如下： + + ```Shell + train args: Namespace(dataset_path='.\\dataset\\train', platform='CPU', \ + pretrain_ckpt='.\\pretrain_checkpoint\\mobilenetV2.ckpt', train_method='incremental_learn') + cfg: {'num_classes': 26, 'image_height': 224, 'image_width': 224, 'batch_size': 150, \ + 'epoch_size': 15, 'warmup_epochs': 0, 'lr_max': 0.03, 'lr_end': 0.03, 'momentum': 0.9, \ + 'weight_decay': 4e-05, 'label_smooth': 0.1, 'loss_scale': 1024, 'save_checkpoint': True, \ + 'save_checkpoint_epochs': 1, 'keep_checkpoint_max': 20, 'save_checkpoint_path': './checkpoint', \ + 'platform': 'CPU'} + Processing batch: 16: 100%|███████████████████████████████████████████ █████████████████████| 16/16 [00:00