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

In the next chapter, we’ll be using the full output, that is, the full
sequence of hidden states, for encoder-decoder models.
Next, we create an instance of the model, the corresponding loss function for a
binary classification problem, and an optimizer:
Model Configuration
1 torch.manual_seed(21)
2 model = SquareModel(n_features=2, hidden_dim=2, n_outputs=1)
3 loss = nn.BCEWithLogitsLoss()
4 optimizer = optim.Adam(model.parameters(), lr=0.01)
Model Training
Then, we train our SquareModel over 100 epochs, as usual, visualize the losses, and
evaluate its accuracy on the test data:
Model Training
1 sbs_rnn = StepByStep(model, loss, optimizer)
2 sbs_rnn.set_loaders(train_loader, test_loader)
3 sbs_rnn.train(100)
fig = sbs_rnn.plot_losses()
Figure 8.12 - Losses—SquareModel
618 | Chapter 8: Sequences