Hands-On Convolutional Neural Networks with TensorFlow

Training the VAE

In order for us to train the VAE and use the KL divergence loss, we will first need to play around with how we generate the latent vectors. Rather than having the encoder produce a latent vector exactly directly, we will make the encoder produce two vectors. The first will be a vector μ of mean values, and the second will be a vector σ of standard deviation values. From these, we can create a third vector, where the ith element is sampled from a Gaussian distribution using the ith values of our μ and σ vectors as the mean and standard deviation for that Gaussian distribution. This third sampled vector is then sent to the decoder.

Our model now looks something like this:

The mean and standard deviation blocks in the preceding ...