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- # 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
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