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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.

Questo esercizio fa parte del corso

Introduction to Deep Learning with PyTorch

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Istruzioni dell'esercizio

  • Import the required module.
  • Create a DataLoader using dataset, setting a batch size of two and enabling shuffling.
  • Iterate through the DataLoader and print each batch of inputs and labels.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

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)
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