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

previous ones (6,823 parameters against 213 parameters for the last chapter’s
model). After it finishes, the computed losses should look like this:
fig = sbs_cnn2.plot_losses()
Figure 6.10 - Losses
Accuracy
We can also check the model’s accuracy for each class:
StepByStep.loader_apply(val_loader, sbs_cnn2.correct)
Output
tensor([[ 92, 124],
[106, 124],
[115, 124]])
The model got 313 out of 372 right. That’s 84.1% accuracy on the validation
set—not bad!
Regularizing Effect
Dropout layers are used for regularizing; that is, they should reduce overfitting
and improve generalization. Or so they say :-)
Let’s (empirically) verify this claim by training a model identical in every way BUT the
dropout, and compare its losses and accuracy to the original model.
440 | Chapter 6: Rock, Paper, Scissors