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

Notebook Cell 2.3 - Building a data loader for our training data
train_loader = DataLoader(
dataset=train_data,
batch_size=16,
shuffle=True,
)
To retrieve a mini-batch, one can simply run the command below—it will return a
list containing two tensors, one for the features, another one for the labels:
next(iter(train_loader))
Output
[tensor([[0.1196],
[0.1395],
...
[0.8155],
[0.5979]]), tensor([[1.3214],
[1.3051],
...
[2.6606],
[2.0407]])]
"Why not use a list instead?"
If you call list(train_loader), you’ll get, as a result, a list of five elements; that is,
all five mini-batches. Then you could take the first element of that list to obtain a
single mini-batch as in the example above. It would defeat the purpose of using the
iterable provided by the DataLoader; that is, to iterate over the elements (minibatches,
in that case) one at a time.
To learn more about it, check RealPython’s material on iterables [57] and iterators [58] .
138 | Chapter 2: Rethinking the Training Loop