Advanced Deep Learning with Keras

Chapter 8
Figure 8.1.7: The digits generated as a function of latent vector mean values (VAE MLP).
For ease of interpretation, the range of values for the mean is similar to Figure 8.1.6.
Using CNNs for VAEs
In the original paper Auto-encoding Variational Bayes [1], the VAE network was
implemented using MLP, which is similar to what we covered in the previous
section. In this section, we'll demonstrate that using a CNN will result in a significant
improvement in the quality of the digits produced and a remarkable reduction in the
number of parameters down to 134,165.
Listing 8.1.3 shows the encoder, decoder, and VAE network. This code was also
contributed to the official Keras GitHub repository. For conciseness, some lines
of code that are similar to the MLP are no longer shown. The encoder is made of two
layers of CNNs and two layers of MLPs in order to generate the latent code. The
encoder output structure is similar to the MLP implementation seen in the previous
section. The decoder is made up of one layer of MLP and three layers of transposed
CNNs. Figures 8.1.8 to 8.1.10 show the encoder, decoder, and VAE models. For VAE
CNN, RMSprop will result in a lower loss than Adam.
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