Using DataLoader
The DataLoader class is essential for efficiently handling large datasets. It speeds up training, optimizes memory usage, and stabilizes gradient updates, making deep learning models more effective.
Now, you'll create a PyTorch DataLoader using the dataset from the previous exercise and see it in action.
Deze oefening maakt deel uit van de cursus
Introduction to Deep Learning with PyTorch
Oefeninstructies
- Import the required module.
- Create a
DataLoaderusingdataset, setting a batch size of two and enabling shuffling. - Iterate through the
DataLoaderand print each batch of inputs and labels.
Praktische interactieve oefening
Probeer deze oefening eens door deze voorbeeldcode in te vullen.
from torch.utils.data import ____
# Create a DataLoader
dataloader = ____
# Iterate over the dataloader
for batch_inputs, batch_labels in dataloader:
print('batch_inputs:', batch_inputs)
print('batch_labels:', batch_labels)