Keras, How to get the output of each layer?

Question

I have trained a binary classification model with CNN, and here is my code

model = Sequential()

model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1],border_mode='valid',input_shape=input_shape))

model.add(Activation('relu'))

model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1]))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=pool_size))

# (16, 16, 32)

model.add(Convolution2D(nb_filters*2, kernel_size[0], kernel_size[1]))

model.add(Activation('relu'))

model.add(Convolution2D(nb_filters*2, kernel_size[0], kernel_size[1]))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=pool_size))

# (8, 8, 64) = (2048)

model.add(Flatten())

model.add(Dense(1024))

model.add(Activation('relu'))

model.add(Dropout(0.5))

model.add(Dense(2))  # define a binary classification problem

model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy,optimizer='adadelta',

metrics=['accuracy'])

model.fit(x_train,y_train,batch_size=batch_size,nb_epoch=nb_epoch,verbose=1,validation_data=(x_test, y_test))

And here, I wanna get the output of each layer just like TensorFlow, how can I do that?

Answer 1

You can easily get the output of any layer in Keras by using the following syntax:

Model.layers[index].output

For all layers refer the following piece of code:

from keras import backend as K

input1 = model.input               # input placeholder

output1 = [layer.output for layer in model.layers]# all layer outputs

fun = K.function([input1, K.learning_phase()],output1)# evaluation function

# Testing

t = np.random.random(input_shape)[np.newaxis,...]

layer_outputs = fun([t, 1.])

print layer_outputs// printing the outputs of layers

K.function creates theano/TensorFlow tensor functions which are later used to get the output from the symbolic graph given the input. The model builds the predict function using K.function.

Now K.learning_phase() is required as an input as many Keras layers like Dropout/Batchnomalization depend on it to change behavior during training and test time.

 You can take reference from the following link:

the output of an intermediate layer in Keras.

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