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

In the __init__() method, we create an attribute that contains our nested Linear
model.
In the forward() method, we call the nested model itself to perform the forward
pass (notice, we are not calling self.linear.forward(x)!).
Now, if we call the parameters() method of this model, PyTorch will figure out the
parameters of its attributes recursively.
torch.manual_seed(42)
dummy = MyLinearRegression().to(device)
list(dummy.parameters())
Output
[Parameter containing:
tensor([[0.7645]], device='cuda:0', requires_grad=True),
Parameter containing:
tensor([0.8300], device='cuda:0', requires_grad=True)]
You can also add extra Linear attributes, and, even if you don’t use them at all in
the forward pass, they will still be listed under parameters().
If you prefer, you can also use state_dict() to get the parameter values, together
with their names:
dummy.state_dict()
Output
OrderedDict([('linear.weight',
tensor([[0.7645]], device='cuda:0')),
('linear.bias',
tensor([0.8300], device='cuda:0'))])
Notice that both bias and weight have a prefix with the attribute name: linear, from
the self.linear in the __init__() method.
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