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

and variable-length sequences.
Model Configuration
1 torch.manual_seed(21)
2 model = SquareModelOne(n_features=2, hidden_dim=2, n_outputs=1,
3 rnn_layer=nn.LSTM, num_layers=1,
4 bidirectional=True)
5 loss = nn.BCEWithLogitsLoss()
6 optimizer = optim.Adam(model.parameters(), lr=0.01)
Model Training
1 sbs_one = StepByStep(model, loss, optimizer)
2 #sbs_one.set_loaders(train_loader)
3 sbs_one.set_loaders(train_var_loader)
4 sbs_one.train(100)
#StepByStep.loader_apply(train_loader, sbs_one.correct)
StepByStep.loader_apply(train_var_loader, sbs_one.correct)
Output
tensor([[66, 66],
[62, 62]])
Recap
In this chapter, we’ve learned about sequential data and how to use recurrent
neural networks to perform a classification task. We followed the journey of a
hidden state through all the transformations happening inside of different
recurrent layers: RNN, GRU, and LSTM. We learned the difference between
padding and packing variable-length sequences, and how to build a data loader for
packed sequences. We also brought back convolutions, using the one-dimensional
version to process sequential data as well. This is what we’ve covered:
• understanding the importance of order in sequential data
• generating a synthetic two-dimensional dataset so we can visualize what’s
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