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

The three units in the output layer produce three logits, one for each class (C 0 , C 1 ,
and C 2 ). We could have added an nn.LogSoftmax layer to the model, and it would
have converted the three logits to log probabilities.
Since our model produces logits, we must use the nn.CrossEntropyLoss()
function:
Model Configuration — Loss and Optimizer
1 lr = 0.1
2 multi_loss_fn = nn.CrossEntropyLoss(reduction='mean')
3 optimizer_cnn1 = optim.SGD(model_cnn1.parameters(), lr=lr)
And then we create an optimizer (SGD) with a given learning rate (0.1), as usual.
Boring, right? No worries; we’ll finally change the optimizer in the Rock Paper
Scissors classification problem in the next chapter.
Model Training
This part is completely straightforward. First, we instantiate our class and set the
loaders:
Model Training
1 sbs_cnn1 = StepByStep(model_cnn1, multi_loss_fn, optimizer_cnn1)
2 sbs_cnn1.set_loaders(train_loader, val_loader)
Then, we train it for 20 epochs and visualize the losses:
Model Training
1 sbs_cnn1.train(20)
fig = sbs_cnn1.plot_losses()
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