Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

print(model.state_dict())
Output
OrderedDict([('0.weight', tensor([[1.9690]], device='cuda:0')),
('0.bias', tensor([1.0235], device='cuda:0'))])
So, I guess we could say all these lines of code (7-23) perform a training step. For a
given combination of model, loss, and optimizer, it takes the features and
corresponding labels as arguments. Right?
How about writing a function that takes a model, a loss, and an optimizer and
returns another function that performs a training step? The latter would then
take the features and corresponding labels as arguments and return the
corresponding loss.
"Wait; what?! A function that returns another function?"
Sounds complicated, right? It is not as bad as it sounds, though; that’s called a
higher-order function, and it is very useful for reducing boilerplate.
If you’re familiar with the concept of higher-order functions, feel free to skip the
aside.
126 | Chapter 2: Rethinking the Training Loop