deep_food.py 文件源码

def build_model():
  input_tensor = Input(shape=(224, 224, 3))
  resnet_model = ResNet50(include_top=False, weights='imagenet', input_tensor=input_tensor)

  dense  = Flatten()( \
             Dense(2048, activation='relu')( \
               BN()( \
             resnet_model.layers[-1].output ) ) )
  result = Activation('sigmoid')( \
                Dense(2048, activation="linear")(\
                 dense) )

  model = Model(inputs=resnet_model.input, outputs=result)
  for layer in model.layers[:139]: # default 179
    #print(layer)
    if 'BatchNormalization' in str(layer):
      ...
    else:
      layer.trainable = False
  model.compile(loss='binary_crossentropy', optimizer='adam')
  return model