How do I resolve NaN gradients when training GANs in PyTorch

Question

With the help of code, can you explain how I resolve NaN gradients when training GANs in PyTorch?

Answer 1

In order to resolve NaN gradients when training GANs in PyTorch, you can follow the following steps below:

• Use Gradient Clipping
  • Prevent exploding gradients by clipping them.
• Add Small Epsilon to Logarithms
  • Avoid log(0) errors in loss calculations.
• Check Initialization
  • Ensure proper weight initialization to stabilize training.
• Normalize Inputs
  • Normalize input data to a standard range (e.g., [-1, 1]).
• Use Lower Learning Rates
  • Reduce learning rates for better training stability.
• Check Loss Functions
  • Avoid excessively large losses by scaling or clipping.
• Track Gradients
  • Debug gradients to identify unstable parameters.

Here are the code snippets explaining the above steps:

Hence, following these steps systematically can help identify and resolve NaN gradients in GAN training.