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

Data Generation — Train
1 points, directions = generate_sequences(n=256, seed=13)
2 full_train = torch.as_tensor(points).float()
3 target_train = full_train[:, 2:]
For the test set, though, we only need the source sequences as features (X) and the
target sequences as labels (y):
Data Generation — Test
1 test_points, test_directions = generate_sequences(seed=19)
2 full_test = torch.as_tensor(test_points).float()
3 source_test = full_test[:, :2]
4 target_test = full_test[:, 2:]
These are all simple tensors, so we can use TensorDatasets and simple data
loaders:
Data Preparation
1 train_data = TensorDataset(full_train, target_train)
2 test_data = TensorDataset(source_test, target_test)
3 generator = torch.Generator()
4 train_loader = DataLoader(train_data, batch_size=16,
5 shuffle=True, generator=generator)
6 test_loader = DataLoader(test_data, batch_size=16)
In version 1.7, PyTorch introduced a modification to the random
sampler in the DataLoader that’s responsible for shuffling the
data. In order to ensure reproducibility, we need to assign a
Generator to the DataLoader (we did something similar in
Chapter 4 when we used other samplers). Luckily, our StepByStep
class already sets a seed to the generator, if there is one, in its
set_seed() method, so you don’t need to worry about that.
By the way, we didn’t use the directions to build the datasets this time.
We have everything we need to train our first sequence-to-sequence model now!
702 | Chapter 9 — Part I: Sequence-to-Sequence