Hands-On Image Processing with Python

Visualizing the weights in the intermediate layers 

Now, let's visualize the weights learned in the intermediate layers. The following Python code visualizes the weights learned for the first 200 hidden units at the first dense layer:

from keras.models import Modelimport matplotlib.pylab as pylabimport numpy as npW = model.get_layer('dense_1').get_weights()print(W[0].shape)print(W[1].shape)fig = pylab.figure(figsize=(20,20))fig.subplots_adjust(left=0, right=1, bottom=0, top=0.95, hspace=0.05, wspace=0.05) pylab.gray()for i in range(200):    pylab.subplot(15, 14, i+1), pylab.imshow(np.reshape(W[0][:, i], (28,28))), pylab.axis('off')pylab.suptitle('Dense_1 Weights (200 hidden units)', size=20)pylab.show()

This results in the following output: ...