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

Data Generation
1 test_points, test_directions = generate_sequences(seed=19)
Data Preparation
There is nothing special about it: typical data preparation using a tensor dataset
and data loaders that will yield batches of sequences with shape (N=16, L=4,
F=2).
Data Preparation
1 train_data = TensorDataset(
2 torch.as_tensor(points).float(),
3 torch.as_tensor(directions).view(-1, 1).float()
4 )
5 test_data = TensorDataset(
6 torch.as_tensor(test_points).float(),
7 torch.as_tensor(test_directions).view(-1, 1).float()
8 )
9 train_loader = DataLoader(
10 train_data, batch_size=16, shuffle=True
11 )
12 test_loader = DataLoader(test_data, batch_size=16)
Model Configuration
The main structure behind the SquareModel is fairly simple: a simple RNN layer
followed by a linear layer that works as a classifier producing logits. Then, in the
forward() method, the linear layer takes the last output of the recurrent layer as
its input.
616 | Chapter 8: Sequences