How can I fix the vanishing gradient problem in TensorFlow for GANs

Question

With the help of a proper code explanation, can you tell me How I can fix the vanishing gradient problem in TensorFlow for GANs?

Answer 1

To fix the vanishing gradient problem in TensorFlow for GANs, use techniques like replacing the loss function with Wasserstein loss, adding gradient penalty, or using spectral normalization.

Here is the code snippet you can refer to:

In the above code, we are using the following key points:

• Wasserstein Loss: Replaces standard GAN loss to mitigate vanishing gradients.
• Gradient Penalty: Enforces the Lipschitz constraint required for WGANs, improving stability.
• Optimization: Use Adam with tuned learning rates and momentum parameters for convergence.

Hence, by referring to the above, you can fix the vanishing gradient problem in TensorFlow for GANs.