How do I calculate KL divergence for VAEs in TensorFlow

Question

With the help of Python programming, can you explain how I calculate KL divergence for VAEs in TensorFlow?

Answer 1

To calculate the KL divergence for Variational Autoencoders (VAEs) in TensorFlow, you compute the divergence between the approximate posterior q(z∣x)q(z|x)q(z∣x) (usually Gaussian) and the prior p(z)p(z)p(z) (typically a standard Gaussian). Here is the code snippet you can refer to:

Here are the steps you can follow:

• Compute μ\muμ (mean) and log⁡(σ2)\log(\sigma^2)log(σ2) (log-variance) for q(z∣x)q(z|x)q(z∣x).
• Use the formula to compute the KL divergence term.
• Reduce the sum across latent dimensions and average across the batch.

Hence, this KL divergence is typically added to the VAE's loss along with the reconstruction loss.