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

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)
But this is not enough; if we create a data loader for our custom dataset and try to
retrieve a mini-batch out of it, it will raise an error:
train_var_loader = DataLoader(
train_var_data, batch_size=16, shuffle=True
)
next(iter(train_var_loader))
Output
-----------------------------------------------------------------
RuntimeError
Traceback (most recent call last)
<ipython-input-34-596b8081f8d1> in <module>
1 train_var_loader = DataLoader(train_var_data, batch_size=16,
shuffle=True)
----> 2 next(iter(train_var_loader))
...
RuntimeError: stack expects each tensor to be equal size, but got [
3, 2] at entry 0 and [4, 2] at entry 2
It turns out, the data loader is trying to stack() together the sequences, which, as
we know, have different sizes and thus cannot be stacked together.
We could simply pad all the sequences and move on with a TensorDataset and
regular data loader. But, in that case, the final hidden states would be affected by
Variable-Length Sequences | 665