The model.parameter() feed into optimizer when backpropagation

[Nikronic (Nikan Doosti)]

Hi,

The answer is yes, you can and there won’t be any differences in result.
Please see this thread for more details:

Passing to the optimizers frozen parameters

When I want to do transfer learning, I set the require_grad = False and only pass the other parameters to the optimizer. But what happens if I only do one of these two steps? What happens if I set several modules’ reguire_grad = False but then I pass all net.parameters() to the optimize. What happens if I keep the require_grad = True but only pass a subset of the parameters to the optimizer (the subset I want to fine tune, for example).

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