How do I deal with gradient instability when training deep generative networks

Question

Can you tell me How do I deal with gradient instability when training deep generative networks?

Answer 1

To deal with gradient instability when training deep generative networks, you can use techniques like gradient clipping, batch normalization, or better weight initialization.

Here is the code snippet you can refer to:

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

• Gradient Clipping: The function clip_gradients! Ensures that gradients do not exceed a certain magnitude, preventing them from becoming too large and causing instability.
• Batch Normalization: Adding BatchNorm layers helps stabilize activations and gradients by normalizing intermediate outputs during training.
• Weight Initialization: Use proper weight initialization techniques (e.g., Xavier or He initialization) to ensure better convergence and avoid exploding/vanishing gradients.

Hence, by referring to the above, you can deal with gradient instability when training deep generative networks