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 Model
import matplotlib.pylab as pylab
import numpy as np
W = 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:

The following screenshot shows what the neural network sees in the output layer, the code being left as an exercise to the reader: