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

Data Generation & Preparation
1 points, directions = generate_sequences(n=128, seed=13)
2 train_data = TensorDataset(
3 torch.as_tensor(points).float(),
4 torch.as_tensor(directions).view(-1, 1).float()
5 )
6 train_loader = DataLoader(
7 train_data, batch_size=16, shuffle=True
8 )
Variable-Length Dataset
For variable-length sequences, though, we built a custom dataset and a collate
function to pack the sequences:
Data Generation
1 var_points, var_directions = generate_sequences(variable_len=True)
Data Preparation
1 class CustomDataset(Dataset):
2 def __init__(self, x, y):
3 self.x = [torch.as_tensor(s).float() for s in x]
4 self.y = torch.as_tensor(y).float().view(-1, 1)
5
6 def __getitem__(self, index):
7 return (self.x[index], self.y[index])
8
9 def __len__(self):
10 return len(self.x)
11
12 train_var_data = CustomDataset(var_points, var_directions)
680 | Chapter 8: Sequences