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

Output
Downloading: "https://download.pytorch.org/models/alexnet-owt-
4df8aa71.pth" to ./pretrained/alexnet-owt-4df8aa71.pth
From now on, it works as if we had saved a model to disk. To load the model’s state
dictionary, we can use its load_state_dict() method:
Loading Model
1 alex.load_state_dict(state_dict)
Output
<All keys matched successfully>
There we go! We have a fully trained AlexNet to play with! Now what?
Model Freezing
In most cases, you don’t want to continue training the whole model. I mean, in
theory, you could pick it up where it was left off by the original authors and resume
training using your own dataset. That’s a lot of work, and you’d need a lot of data to
make any kind of meaningful progress. There must be a better way! Of course,
there is: We can freeze the model.
Freezing the model means it won’t learn anymore; that is, its
parameters / weights will not be updated anymore.
What best characterizes a tensor representing a learnable parameter? It requires
gradients. So, if we’d like to make them stop learning anything, we need to change
exactly that:
Helper Function #6 — Model freezing
1 def freeze_model(model):
2 for parameter in model.parameters():
3 parameter.requires_grad = False
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