Keras - 训练卷积网络，获得自动编码器输出

我想要做什么：我想在两个类上训练cifar10数据集上的卷积神经网络。然后，一旦我得到我的拟合模型，我想要采取所有的图层和重现输入图像。所以我想从网络中取回图像而不是分类。Keras - 训练卷积网络，获得自动编码器输出

我迄今所做的：

def copy_freeze_model(model, nlayers = 1): 
    new_model = Sequential() 
    for l in model.layers[:nlayers]: 
     l.trainable = False 
     new_model.add(l) 
    return new_model 

numClasses = 2 
(X_train, Y_train, X_test, Y_test) = load_data(numClasses) 
#Part 1 
rms = RMSprop() 
model = Sequential() 
#input shape: channels, rows, columns 
model.add(Convolution2D(32, 3, 3, border_mode='same', 
         input_shape=(3, 32, 32))) 
model.add(Activation("relu")) 
model.add(MaxPooling2D(pool_size=(2, 2))) 
model.add(Dropout(0.5)) 

model.add(Flatten()) 
model.add(Dense(512)) 
model.add(Activation("relu")) 
model.add(Dropout(0.5)) 
#output layer 
model.add(Dense(numClasses)) 
model.add(Activation('softmax')) 
model.compile(loss='categorical_crossentropy', optimizer=rms,metrics=["accuracy"]) 

model.fit(X_train,Y_train, batch_size=32, nb_epoch=25, 
      verbose=1, validation_split=0.2, 
      callbacks=[EarlyStopping(monitor='val_loss', patience=2)]) 
print('Classifcation rate %02.3f' % model.evaluate(X_test, Y_test)[1]) 

##pull the layers and try to get an output from the network that is image. 

newModel = copy_freeze_model(model, nlayers = 8) 
newModel.add(Dense(1024)) 

newModel.compile(loss='mean_squared_error', optimizer=rms,metrics=["accuracy"]) 
newModel.fit(X_train,X_train, batch_size=32, nb_epoch=25, 
      verbose=1, validation_split=0.2, 
      callbacks=[EarlyStopping(monitor='val_loss', patience=2)]) 
preds = newModel.predict(X_test)

而且当我这样做：