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b9ba5ae3
编写于
6月 22, 2020
作者:
M
mindspore-ci-bot
提交者:
Gitee
6月 22, 2020
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差异文件
!39 Fix an issue and add a file for importing LeNet5.
Merge pull request !39 from jxlang910/master
上级
04c034fd
708e117e
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
71 addition
and
7 deletion
+71
-7
example/mnist_demo/lenet5_config.py
example/mnist_demo/lenet5_config.py
+1
-1
example/mnist_demo/lenet5_dp_model_train.py
example/mnist_demo/lenet5_dp_model_train.py
+1
-2
tests/ut/python/defenses/test_ad.py
tests/ut/python/defenses/test_ad.py
+1
-1
tests/ut/python/diff_privacy/test_model_train.py
tests/ut/python/diff_privacy/test_model_train.py
+2
-1
tests/ut/python/diff_privacy/test_monitor.py
tests/ut/python/diff_privacy/test_monitor.py
+2
-1
tests/ut/python/diff_privacy/test_network.py
tests/ut/python/diff_privacy/test_network.py
+63
-0
tests/ut/python/diff_privacy/test_optimizer.py
tests/ut/python/diff_privacy/test_optimizer.py
+1
-1
未找到文件。
example/mnist_demo/lenet5_config.py
浏览文件 @
b9ba5ae3
...
...
@@ -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
浏览文件 @
b9ba5ae3
...
...
@@ -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
浏览文件 @
b9ba5ae3
...
...
@@ -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
浏览文件 @
b9ba5ae3
...
...
@@ -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
浏览文件 @
b9ba5ae3
...
...
@@ -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 → 100644
浏览文件 @
b9ba5ae3
# 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
浏览文件 @
b9ba5ae3
...
...
@@ -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
...
...
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