text: #  Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
  #
  #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 paddle.fluid as fluid
  
  from paddle.fluid.dygraph.nn import Conv2D, BatchNorm, Pool2D, Linear
  from paddle.fluid.dygraph.container import Sequential
  from hapi.model import Model
  
  __all__ = ['LeNet']
  
  
  class LeNet(Model):
      """LeNet model from
      `"LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.`_
  
      Args:
          num_classes (int): output dim of last fc layer. If num_classes <=0, last fc layer 
                              will not be defined. Default: 10.
          classifier_activation (str): activation for the last fc layer. Default: 'softmax'.
  
      Examples:
          .. code-block:: python
  
              from hapi.vision.models import LeNet
  
              model = LeNet()
      """
  
      def __init__(self, num_classes=10, classifier_activation='softmax'):
          super(LeNet, self).__init__()
          self.num_classes = num_classes
          self.features = Sequential(
              Conv2D(
                  1, 6, 3, stride=1, padding=1),
              Pool2D(2, 'max', 2),
              Conv2D(
                  6, 16, 5, stride=1, padding=0),
              Pool2D(2, 'max', 2))
  
          if num_classes > 0:
              self.fc = Sequential(
                  Linear(400, 120),
                  Linear(120, 84),
                  Linear(
                      84, 10, act=classifier_activation))
  
      def forward(self, inputs):
          x = self.features(inputs)
  
          if self.num_classes > 0:
              x = fluid.layers.flatten(x, 1)
              x = self.fc(x)
          return x
  