Manual loss weights adaptation in TF2.0

[haison19952013]

What?

• Supported and tested backend: tensorflow
• Details:
  • Provide a way to change the loss weights dynamically via callbacks without the need to recompile the model
  • Currently works for non-gradient-based adaptive loss weight scheme

Why?

• Motivation
• Help deepxde users can formulate their non-gradient-based adaptive loss weights scheme

How?

• In model.py:
  • Add loss_weights as instances for functions that work for tensorflow backend
• In callbacks.py, give some examples on how to define a calback to change the loss_weights
  • Add ManualDynamicLossWeight: to change the loss weights based on the specified index
  • Add PrintLossWeight: to display the loss weights based on the specified period

Testing?

• A working example is given in deepxde\examples\pinn_inverse\elliptic_inverse_field_manual_dynamic_loss_weights.py

Future work

• Work on gradient-based adaptive loss weight scheme

[lululxvi]

Format the code via black https://github.com/psf/black

[haison19952013]

Format the code via black https://github.com/psf/black

Updated

[lululxvi]

• How about loss weights is None?
• Using tf here will break other backends.

[pescap]

@haison19952013, do you plan to keep working on this PR? If not, I will continue the work.