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.
#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)
从定义的网络的其中一层输出：
#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)
然后进行预测
#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]
