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Chapter 9
plt.imshow(x_test[index].reshape(28, 28), cmap='gray')
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# display reconstruction
ax = plt.subplot(2, number, index + 1 + number)
plt.imshow(autoencoder(x_test)[index].numpy().reshape(28, 28),
cmap='gray')
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
It is interesting to note that in the preceding code we reduced the dimensions of the
input from 784 to 128 and our network could still reconstruct the original image. This
should give you an idea of the power of the autoencoder for dimensionality reduction.
One advantage of autoencoders over PCA for dimensionality reduction is that while
PCA can only represent linear transformations, we can use non-linear activation
functions in autoencoders, thus introducing non-linearities in our encodings:
Figure 3: Comparison between the result of a PCA
The preceding figure is reproduced from the Hinton paper Reducing the dimensionality
of data with Neural Networks. It compares the result of a PCA (A) with that of stacked
autoencoders with architecture consisting of 784-1000-500-250-2.
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