How do I ensure compatibility of different ML optimizers in Python?

How do I ensure compatibility of different ML optimizers in Python?

To ensure compatibility, you should check the documentation of each optimizer for required input formats and parameters.

Are there specific libraries that help with this?

Yes, libraries like TensorFlow and PyTorch provide built-in optimizers that are designed to work seamlessly with their models.

What if I want to create a custom optimizer?

When creating a custom optimizer, make sure to follow the same interface as existing optimizers and test it thoroughly with your models.

Can you give an example of a custom optimizer?

Sure! A simple custom optimizer could be a gradient descent implementation where you adjust the learning rate dynamically based on the loss.