Advanced Deep Learning with Keras

Chapter 8
Implementing CVAE requires a few modifications in the code of the VAE. For the
CVAE, the VAE CNN implementation is used. Listing 8.2.1 highlights the changes
made to the original code of VAE for MNIST digits. The encoder input is now a
concatenation of original input image and its one-hot label. The decoder input is
now a combination of the latent space sampling and the one-hot label of the image it
should generate. The total number of parameters is 174, 437. The codes related to β
-VAE will be discussed in the next section of this chapter.
There are no changes in the loss function. However, the one-hot labels are supplied
during training, testing, and plotting of results. Figures 8.2.1 to 8.2.3 show us the
encoder, decoder, and CVAE models. The role of the conditioning label in the form
of a one-hot vector is indicated.
Figure 8.2.4: The latent vector mean values for the test dataset (CVAE CNN). The colorbar shows the
corresponding MNIST digit as a function of z. Color images can be found on the book GitHub repository:
https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/tree/master/chapter8-vae.
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