【keras】获取中间层的输出

1.使用函数模型API，新建一个model，将输入和输出定义为原来的model的输入和想要的那一层的输出，然后重新进行predict. 

import seaborn as sbn

import pylab as plt

import theano

from keras.models import Sequential

from keras.layers import Dense,Activation

from keras.models import Model

model = Sequential()

model.add(Dense(32, activation='relu', input_dim=100))

model.add(Dense(16, activation='relu',name="Dense_1"))

model.add(Dense(1, activation='sigmoid',name="Dense_2"))

model.compile(optimizer='rmsprop',

loss='binary_crossentropy',

metrics=['accuracy'])

import numpy as np

data = np.random.random((1000, 100))

labels = np.random.randint(2, size=(1000, 1))

model.fit(data, labels, epochs=10, batch_size=32)

dense1_layer_model = Model(inputs=model.input,

outputs=model.get_layer('Dense_1').output)

dense1_output = dense1_layer_model.predict(data)

print dense1_output.shape

print dense1_output[0]

2.因为我的后端是使用的theano，所以还可以考虑使用theano的函数：

dense1 = theano.function([model.layers[0].input],model.layers[1].output,allow_input_downcast=True)

dense1_output = dense1(data)

print dense1_output[0]