Sequential

from keras import backend as K

# with a Sequential model
get_3rd_layer_output = K.function([model.layers[0].input], [model.layers[3].output])

layer_output = get_3rd_layer_output([X])[0]

注意，如果你的模型在训练和测试两种模式下不完全一致，例如你的模型中含有Dropout层，批规范化（BatchNormalization）层等组件，你需要在函数中传递一个learning_phase的标记，像这样：

from keras import backend as K

# with a Sequential model
get_3rd_layer_output = K.function([model.layers[0].input, K.learning_phase()], [model.layers[3].output])

# output in test mode = 0
layer_output = get_3rd_layer_output([X, 0])[0]

# output in train mode = 1
layer_output = get_3rd_layer_output([X, 1])[0]

Functional

每个model都是一个可调用对象