How can I debug vanishing gradients in GANs during training

Question

With the help of Python programming, can you tell me how I can debug vanishing gradients in GANs during training?

Answer 1

To debug vanishing gradients in GANs, you can follow the following steps below:

• Loss Function: Use Wasserstein loss (WGAN) or hinge loss to stabilize gradients.
• Activation Functions: Replace saturating activations (e.g., sigmoid) with non-saturating ones (e.g., ReLU or LeakyReLU).
• Batch Normalization: Add BatchNorm layers in the generator.
• Gradient Monitoring: Check gradient values during training.

Here is the code snippet given below:

In the above code snippet, we are using the following approaches:

• Monitor Gradient Values: Use param.grad to check if gradients vanish.
• Loss Function: Use stable losses like Wasserstein.
• Adjust Architectures: Use BatchNorm, LeakyReLU, or spectral normalization.

Hence, by referring to the above, you can debug vanishing gradients in GANs during training.