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from keras import backend as K
conv0 = K.function([model.layers[0].input], [model.layers[0].output])
out0=conv0([X])[0]
out0.shape
Out[15]: (24, 2, 363, 284)
# this is a batch of 24 samples, there are two channels of outputs, 2 feature maps

# take a look at the two channels
imshow(out0[0,0,:,:])
imshow(out0[0,1,:,:])

# take a look at the final output of the convnet layers, 
# since we use batch normalization that behaves differently between training and test,
# we must make this an argument and pass a value when we use the function:
l7fn = K.function([model.layers[0].input, K.learning_phase()],[model.layers[7].output])

convout = l7fn([X,False])[0]
convout.shape
Out[24]: (24, 6, 22, 17)
# look at one of the output channels:
imshow(convout[0,0,:,:])

# take a look at the variables so far
weights = model.weights()
# need to match up with model.summary() to see what is what
# kernel for first convolutional layer
weights[0].shape
(2,1,4,4)