text: # 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.
  # ============================================================================
  import mindspore.nn as nn
  from mindspore.ops import operations as P
  
  
  class LeNet(nn.Cell):
      def __init__(self):
          super(LeNet, self).__init__()
          self.relu = P.ReLU()
          self.batch_size = 32
  
          self.conv1 = nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid')
          self.conv2 = nn.Conv2d(6, 16, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid')
          self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
          self.reshape = P.Reshape()
          self.fc1 = nn.Dense(400, 120)
          self.fc2 = nn.Dense(120, 84)
          self.fc3 = nn.Dense(84, 10)
  
      def construct(self, input_x):
          output = self.conv1(input_x)
          output = self.relu(output)
          output = self.pool(output)
          output = self.conv2(output)
          output = self.relu(output)
          output = self.pool(output)
          output = self.reshape(output, (self.batch_size, -1))
          output = self.fc1(output)
          output = self.relu(output)
          output = self.fc2(output)
          output = self.relu(output)
          output = self.fc3(output)
          return output
  