Network_in_Network_bn_keras.py 文件源码

def build_model():
  model = Sequential()

  model.add(Conv2D(192, (5, 5), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal", input_shape=x_train.shape[1:]))
  model.add(BatchNormalization())
  model.add(Activation('relu'))
  model.add(Conv2D(160, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
  model.add(BatchNormalization())
  model.add(Activation('relu'))
  model.add(Conv2D(96, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
  model.add(BatchNormalization())
  model.add(Activation('relu'))
  model.add(MaxPooling2D(pool_size=(3, 3),strides=(2,2),padding = 'same'))

  model.add(Dropout(dropout))

  model.add(Conv2D(192, (5, 5), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
  model.add(BatchNormalization())
  model.add(Activation('relu'))
  model.add(Conv2D(192, (1, 1),padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
  model.add(BatchNormalization())
  model.add(Activation('relu'))
  model.add(Conv2D(192, (1, 1),padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
  model.add(BatchNormalization())
  model.add(Activation('relu'))
  model.add(MaxPooling2D(pool_size=(3, 3),strides=(2,2),padding = 'same'))

  model.add(Dropout(dropout))

  model.add(Conv2D(192, (3, 3), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
  model.add(BatchNormalization())
  model.add(Activation('relu'))
  model.add(Conv2D(192, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
  model.add(BatchNormalization())
  model.add(Activation('relu'))
  model.add(Conv2D(10, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
  model.add(BatchNormalization())
  model.add(Activation('relu'))

  model.add(GlobalAveragePooling2D())
  model.add(Activation('softmax'))

  sgd = optimizers.SGD(lr=.1, momentum=0.9, nesterov=True)
  model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
  return model