Keras Theano 输出中间层结果

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Keras & Theano get output of an intermediate layer

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,Activationfrom keras.models import Modelmodel = 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'])# Generate dummy data
import numpy as np
#假设训练和测试使用同一组数据
data = np.random.random((1000, 100))
labels = np.random.randint(2, size=(1000, 1))# Train the model, iterating on the data in batches of 32 samples
model.fit(data, labels, epochs=10, batch_size=32)
#已有的model在load权重过后
#取某一层的输出为输出新建为model，采用函数模型
dense1_layer_model = Model(inputs=model.input,outputs=model.get_layer('Dense_1').output)
#以这个model的预测值作为输出
dense1_output = dense1_layer_model.predict(data)print(dense1_output.shape)
print(dense1_output[0])