fix issue.

example/mnist_demo/lenet5_config.py

@@ -31,7 +31,7 @@ mnist_cfg = edict({
     'device_target': 'Ascend',  # device used
     'data_path': './MNIST_unzip',  # the path of training and testing data set
     'dataset_sink_mode': False,  # whether deliver all training data to device one time　
-    'micro_batches': 32,  # the number of small batches split from an original batch
+    'micro_batches': 16,  # the number of small batches split from an original batch
     'l2_norm_bound': 1.0,  # the clip bound of the gradients of model's training parameters
     'initial_noise_multiplier': 1.5,  # the initial multiplication coefficient of the noise added to training
     # parameters' gradients

example/mnist_demo/lenet5_dp_model_train.py

@@ -124,8 +124,7 @@ if __name__ == "__main__":
     rdp_monitor = PrivacyMonitorFactory.create('rdp',
                                                num_samples=60000,
                                                batch_size=cfg.batch_size,
-                                               initial_noise_multiplier=cfg.initial_noise_multiplier*
-                                               cfg.l2_norm_bound,
+                                               initial_noise_multiplier=cfg.initial_noise_multiplier,
                                                per_print_times=50)
 
     # Create the DP model for training.

tests/ut/python/defenses/test_ad.py

@@ -54,7 +54,7 @@ def test_ad():
     net = Net()
     loss_fn = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=sparse)
     optimizer = Momentum(learning_rate=Tensor(np.array([0.001], np.float32)),
-                         momentum=Tensor(np.array([0.9], np.float32)),
+                         momentum=0.9,
                          params=net.trainable_params())
 
     ad_defense = AdversarialDefense(net, loss_fn=loss_fn, optimizer=optimizer)

tests/ut/python/diff_privacy/test_model_train.py

@@ -18,13 +18,14 @@ import pytest
 import numpy as np
 
 from mindspore import nn
-from mindspore.model_zoo.lenet import LeNet5
 from mindspore import context
 import mindspore.dataset as ds
 
 from mindarmour.diff_privacy import DPOptimizerClassFactory
 from mindarmour.diff_privacy import DPModel
 
+from test_network import LeNet5
+
 
 def dataset_generator(batch_size, batches):
     data = np.random.random((batches * batch_size, 1, 32, 32)).astype(np.float32)

tests/ut/python/diff_privacy/test_monitor.py

@@ -21,11 +21,12 @@ import mindspore.nn as nn
 import mindspore.dataset as ds
 from mindspore.train import Model
 import mindspore.context as context
-from mindspore.model_zoo.lenet import LeNet5
 
 from mindarmour.diff_privacy import PrivacyMonitorFactory
 from mindarmour.utils.logger import LogUtil
 
+from test_network import LeNet5
+
 LOGGER = LogUtil.get_instance()
 TAG = 'DP-Monitor Test'
 

tests/ut/python/diff_privacy/test_network.py

@@ -0,0 +1,63 @@
+# Copyright 2019 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from mindspore import nn
+from mindspore.common.initializer import TruncatedNormal
+
+
+def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
+    weight = weight_variable()
+    return nn.Conv2d(in_channels, out_channels,
+                     kernel_size=kernel_size, stride=stride, padding=padding,
+                     weight_init=weight, has_bias=False, pad_mode="valid")
+
+
+def fc_with_initialize(input_channels, out_channels):
+    weight = weight_variable()
+    bias = weight_variable()
+    return nn.Dense(input_channels, out_channels, weight, bias)
+
+
+def weight_variable():
+    return TruncatedNormal(0.05)
+
+
+class LeNet5(nn.Cell):
+    """
+    Lenet network
+    """
+    def __init__(self):
+        super(LeNet5, self).__init__()
+        self.conv1 = conv(1, 6, 5)
+        self.conv2 = conv(6, 16, 5)
+        self.fc1 = fc_with_initialize(16*5*5, 120)
+        self.fc2 = fc_with_initialize(120, 84)
+        self.fc3 = fc_with_initialize(84, 10)
+        self.relu = nn.ReLU()
+        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.flatten = nn.Flatten()
+
+    def construct(self, x):
+        x = self.conv1(x)
+        x = self.relu(x)
+        x = self.max_pool2d(x)
+        x = self.conv2(x)
+        x = self.relu(x)
+        x = self.max_pool2d(x)
+        x = self.flatten(x)
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        x = self.relu(x)
+        x = self.fc3(x)
+        return x

tests/ut/python/diff_privacy/test_optimizer.py

@@ -15,11 +15,11 @@ import pytest
 
 from mindspore import nn
 from mindspore import context
-from mindspore.model_zoo.lenet import LeNet5
 from mindspore.train.model import Model
 
 from mindarmour.diff_privacy import DPOptimizerClassFactory
 
+from test_network import LeNet5
 
 @pytest.mark.level0
 @pytest.mark.platform_arm_ascend_training