plt.imshow(X_train[3].reshape(28,28).astype('float32'))
number_1 = X_train[3].reshape(1,28,28,1).astype('float32')

def get_activation(layer):
    output = model.layers[layer].output  # layer output
    activation_model = models.Model(inputs=model.input, outputs=output)
    activation = activation_model.predict(number_1)
    return (activation)

for layer in (0,2,4):  # Conv2D layers
    activation = get_activation(layer)
    #(1, height, width, num_filters)
    print(model.layers[layer].name + "  " + str(activation.shape)) 
    plot(activation)
