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'])

# 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])

#这是一个theano的函数

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

dense1_output = dense1(data)  #visualize these images's FC-layer feature

print(dense1_output[0])

效果应该是一样的。

来源：https://blog.csdn.net/hahajinbu/article/details/77982721