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

Output
[Parameter containing:
tensor([0.3367], requires_grad=True), Parameter containing:
tensor([0.1288], requires_grad=True)]
state_dict
Moreover, we can get the current values of all parameters using our model’s
state_dict() method.
dummy.state_dict()
Output
OrderedDict([('b', tensor([0.3367])), ('w', tensor([0.1288]))])
The state_dict() of a given model is simply a Python dictionary that maps each
attribute / parameter to its corresponding tensor. But only learnable parameters
are included, as its purpose is to keep track of parameters that are going to be
updated by the optimizer.
By the way, the optimizer itself has a state_dict() too, which contains its internal
state, as well as other hyper-parameters. Let’s take a quick look at it:
optimizer.state_dict()
Output
{'state': {},
'param_groups': [{'lr': 0.1,
'momentum': 0,
'dampening': 0,
'weight_decay': 0,
'nesterov': False,
'params': [140535747664704, 140535747688560]}]}
106 | Chapter 1: A Simple Regression Problem