# Keras, How to get the output of each layer?

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?

You can easily get the outputs of any layer by using: `model.layers[index].output`

For all layers use this:

```
from keras import backend as K
inp = model.input # input placeholder
outputs = [layer.output for layer in model.layers] # all layer outputs
functors = [K.function([inp, K.learning_phase()], [out]) for out in outputs] # evaluation functions
# Testing
test = np.random.random(input_shape)[np.newaxis,...]
layer_outs = [func([test, 1.]) for func in functors]
print layer_outs
```

Note: To simulate Dropout use `learning_phase`

as `1.`

in `layer_outs`

otherwise use `0.`

**Edit:** (based on comments)

`K.function`

creates theano/tensorflow tensor functions which is later used to get the output from the symbolic graph given the input.

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.

So if you remove the dropout layer in your code you can simply use:

```
from keras import backend as K
inp = model.input # input placeholder
outputs = [layer.output for layer in model.layers] # all layer outputs
functors = [K.function([inp], [out]) for out in outputs] # evaluation functions
# Testing
test = np.random.random(input_shape)[np.newaxis,...]
layer_outs = [func([test]) for func in functors]
print layer_outs
```

**Edit 2: More optimized**

I just realized that the previous answer is not that optimized as for each function evaluation the data will be transferred CPU->GPU memory and also the tensor calculations needs to be done for the lower layers over-n-over.

Instead this is a much better way as you don't need multiple functions but a single function giving you the list of all outputs:

```
from keras import backend as K
inp = model.input # input placeholder
outputs = [layer.output for layer in model.layers] # all layer outputs
functor = K.function([inp, K.learning_phase()], outputs ) # evaluation function
# Testing
test = np.random.random(input_shape)[np.newaxis,...]
layer_outs = functor([test, 1.])
print layer_outs
```

From: stackoverflow.com/q/41711190

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