What are the key parameters of PyTorch DataLoader?
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What are the key parameters of PyTorch DataLoader?
The key parameters of PyTorch DataLoader include dataset, batch_size, shuffle, num_workers, and collate_fn.
Can you explain what each of these parameters does?
Sure! 'dataset' is the dataset to load, 'batch_size' defines how many samples per batch, 'shuffle' randomizes the data, 'num_workers' sets how many subprocesses to use for data loading, and 'collate_fn' is a function to merge samples.
How does the 'num_workers' parameter affect performance?
Increasing 'num_workers' can speed up data loading by utilizing multiple CPU cores, but too many can lead to diminishing returns or overhead.
What is the default value for 'batch_size'?
The default value for 'batch_size' is 1, meaning one sample will be loaded at a time unless specified otherwise.
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