How do you implement an adaptive learning rate for large generative models

Question

I am confused about how to implement an adaptive learning rate for large generative models. Can you explain with the help of Python programming?

Answer 1

In order to implement an adaptive learning rate for large generative models, you can refer to the following:

In the code above, adaptive optimizers like Adam with learning rate schedules are applied using Pytorch.

• Adam: it is used to adjust the learning rate per parameter.
• Cosine annealingLR: Helping to avoid overshooting as training progresses.

By using above code technique you can implement an adaptive  learning rate for large generative models.