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

Model Training — Top Model
1 sbs_top = StepByStep(top_model, multi_loss_fn, optimizer_top)
2 sbs_top.set_loaders(train_preproc_loader, val_preproc_loader)
3 sbs_top.train(10)
See? That was blazing fast!
Now we can attach the trained model to the top of the full (frozen) model:
Replacing the Top Layer
1 sbs_alex.model.classifier[6] = top_model
2 print(sbs_alex.model.classifier)
Output
Sequential(
(0): Dropout(p=0.5, inplace=False)
(1): Linear(in_features=9216, out_features=4096, bias=True)
(2): ReLU(inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Linear(in_features=4096, out_features=4096, bias=True)
(5): ReLU(inplace=True)
(6): Sequential(
(0): Linear(in_features=4096, out_features=3, bias=True)
)
)
The sixth element of the classifier part corresponds to our small trained model.
Let’s see how it performs on the validation set.
We’re using the full model again, so we should use the original
dataset instead of the pre-processed one.
StepByStep.loader_apply(val_loader, sbs_alex.correct)
520 | Chapter 7: Transfer Learning