How do you fix unstable gradients in the VAE decoder when working with non-linear data

Question

With the help of code, can you tell me How do you fix unstable gradients in the VAE decoder when working with non-linear data?

Answer 1

To fix unstable gradients in the VAE decoder when working with non-linear data, you can follow the following :

• Gradient Clipping: Clip gradients during backpropagation to prevent exploding gradients.
• Use Stable Activation Functions: Replace activation functions like ReLU with LeakyReLU or ELU to handle non-linear data better.
• Weight Initialization: Use proper initialization methods, such as Xavier or He initialization.
• Reduce Learning Rate: Use a smaller learning rate to stabilize updates.
• Batch Normalization: Apply batch normalization in the decoder to smoothen learning.

Here is the code snippet you can refer to:

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

• Gradient Clipping: Prevents gradient explosion by limiting their magnitude.
• LeakyReLU Activation: Improves stability for non-linear data.
• Proper Initialization: Ensures weights are set to appropriate starting values.
• Batch Normalization: Stabilizes training dynamics in the decoder.

Hence, these techniques address unstable gradients in the decoder when working with non-linear data in VAEs.