How do you implement stochastic weight averaging for robust generative model training

Question

With the help of code, can you tell me how do you implement stochastic weight averaging for robust generative model training?

Answer 1

You can implement stochastic weight averaging for robust generative model training. Weight Averaging (SWA) improves model robustness by averaging weights over multiple checkpoints during training.

Here is the code snippet which you can refer to:

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

• AveragedModel: Maintains the average of model weights.
• SWALR: Adjusts the learning rate for SWA-specific training.
• update_bn: Updates batch normalization statistics after SWA training.

Hence, by referring to the above,  you can implement stochastic weight averaging for robust generative model training.