1.使用函式模型API，新建一個model，將輸入和輸出定義為原來的model的輸入和想要的那一層的輸出，然後重新進行predict.

#coding=utf-8
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
#假設訓練和測試使用同一組資料
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權重過後
#取某一層的輸出為輸出新建為model，採用函式模型
dense1_layer_model = Model(inputs=model.input,
                                     outputs=model.get_layer('Dense_1').output)
#以這個model的預測值作為輸出
dense1_output = dense1_layer_model.predict(data)

print dense1_output.shape
print dense1_output[0]
 

#這是一個theano的函式
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]

效果應該是一樣的。