How can I avoid exploding gradients in large-scale generative models

Question

With the help of code and explanation, can you tell me how I can avoid exploding gradients in large-scale generative models?

Answer 1

In order to avoid exploding gradients in large-scale generative models, especially for large models. You can refer to the code snippet below.

Here is the code showing how:

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

• Gradient Clipping: clip_grad_norm_ prevents gradients from exploding.
• Spectral Normalization: Regularizes the discriminator’s weights.
• Learning Rate Scheduling: Dynamically adjusts learning rates.
• Proper Optimizers: Adam optimizer with tuned betas.
• Stable Initialization: Default PyTorch initialization is robust for GANs.

Hence, these strategies collectively ensure stable training for large-scale generative models.