From 7dc81b4b99f96a2b2edd0279c7e61122d62c93e0 Mon Sep 17 00:00:00 2001 From: pkuliuliu Date: Fri, 14 Aug 2020 11:23:30 +0800 Subject: [PATCH] Fix issue of DP for tutorial --- .../source_en/advanced_use/differential_privacy.md | 11 ++++++----- .../source_zh_cn/advanced_use/differential_privacy.md | 11 ++++++----- 2 files changed, 12 insertions(+), 10 deletions(-) diff --git a/tutorials/source_en/advanced_use/differential_privacy.md b/tutorials/source_en/advanced_use/differential_privacy.md index 4245802235..7d140ae155 100644 --- a/tutorials/source_en/advanced_use/differential_privacy.md +++ b/tutorials/source_en/advanced_use/differential_privacy.md @@ -69,13 +69,14 @@ from mindspore.dataset.transforms.vision import Inter import mindspore.common.dtype as mstype from mindarmour.diff_privacy import DPModel -from mindarmour.diff_privacy import DPOptimizerClassFactory from mindarmour.diff_privacy import PrivacyMonitorFactory +from mindarmour.diff_privacy import NoiseMechanismsFacotry +from mindarmour.diff_privacy import ClipMechanismsFactory from mindarmour.utils.logger import LogUtil from lenet5_net import LeNet5 from lenet5_config import mnist_cfg as cfg -LOGGER = LogUtil.get_instances() +LOGGER = LogUtil.get_instance() LOGGER.set_level('INFO') TAG = 'Lenet5_train' ``` @@ -131,7 +132,7 @@ def generate_mnist_dataset(data_path, batch_size=32, repeat_size=1, create dataset for training or testing """ # define dataset - ds1 = ds.Dataset(data_path) + ds1 = ds.MnistDataset(data_path) # define operation parameters resize_height, resize_width = 32, 32 @@ -334,13 +335,13 @@ ds_train = generate_mnist_dataset(os.path.join(cfg.data_path, "train"), 5. Display the result. - The accuracy of the LeNet model without differential privacy is 99%, and the accuracy of the LeNet model with Gaussian noise and adaptive clip differential privacy is 97%. + The accuracy of the LeNet model without differential privacy is 99%, and the accuracy of the LeNet model with Gaussian noise and adaptive clip differential privacy is mostly more than 95%. ``` ============== Starting Training ============== ... ============== Starting Testing ============== ... - ============== Accuracy: 0.9767 ============== + ============== Accuracy: 0.9698 ============== ``` ### References diff --git a/tutorials/source_zh_cn/advanced_use/differential_privacy.md b/tutorials/source_zh_cn/advanced_use/differential_privacy.md index 49b3eef3cf..635a5a81df 100644 --- a/tutorials/source_zh_cn/advanced_use/differential_privacy.md +++ b/tutorials/source_zh_cn/advanced_use/differential_privacy.md @@ -55,13 +55,14 @@ from mindspore.dataset.transforms.vision import Inter import mindspore.common.dtype as mstype from mindarmour.diff_privacy import DPModel -from mindarmour.diff_privacy import DPOptimizerClassFactory +from mindarmour.diff_privacy import NoiseMechanismsFacotry +from mindarmour.diff_privacy import ClipMechanismsFactory from mindarmour.diff_privacy import PrivacyMonitorFactory from mindarmour.utils.logger import LogUtil from lenet5_net import LeNet5 from lenet5_config import mnist_cfg as cfg -LOGGER = LogUtil.get_instances() +LOGGER = LogUtil.get_instance() LOGGER.set_level('INFO') TAG = 'Lenet5_train' ``` @@ -117,7 +118,7 @@ def generate_mnist_dataset(data_path, batch_size=32, repeat_size=1, create dataset for training or testing """ # define dataset - ds1 = ds.Dataset(data_path) + ds1 = ds.MnistDataset(data_path) # define operation parameters resize_height, resize_width = 32, 32 @@ -320,13 +321,13 @@ ds_train = generate_mnist_dataset(os.path.join(cfg.data_path, "train"), 5. 结果展示。 - 不加差分隐私的LeNet模型精度稳定在99%，加了Gaussian噪声，自适应Clip的差分隐私LeNet模型收敛，精度稳定在97.6%。 + 不加差分隐私的LeNet模型精度稳定在99%，加了Gaussian噪声，自适应Clip的差分隐私LeNet模型收敛，精度稳定在95%左右。 ``` ============== Starting Training ============== ... ============== Starting Testing ============== ... - ============== Accuracy: 0.9767 ============== + ============== Accuracy: 0.9698 ============== ``` ### 引用 -- Gitee