text: # Copyright 2019 The FastEstimator Authors. All Rights Reserved.
  #
  # 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 tensorflow.python.keras import layers, Sequential
  
  
  def LeNet(input_shape=(28, 28, 1), classes=10):
      """Creates a LeNet model with 3 convolutional and 2 dense layers.
      
      Args:
          input_shape (tuple, optional): shape of the input data. Defaults to (28, 28, 1).
          classes (int, optional): number of classes. Defaults to 10.
      
      Returns:
          'Model' object: LeNet model.
      """
      model = Sequential()
      model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))
      model.add(layers.MaxPooling2D((2, 2)))
      model.add(layers.Conv2D(64, (3, 3), activation='relu'))
      model.add(layers.MaxPooling2D((2, 2)))
      model.add(layers.Conv2D(64, (3, 3), activation='relu'))
      model.add(layers.Flatten())
      model.add(layers.Dense(64, activation='relu'))
      model.add(layers.Dense(classes, activation='softmax'))
      return model
  