PyTorch Data Loader

In this PyTorch tutorial, we will learn about the PyTorch Data Loader. The data loader is a utility provided by PyTorch that allows us to efficiently load and iterate over our data during the training process.

Creating a PyTorch Data Loader

To create a data loader in PyTorch, we first need to define a dataset that contains our training data. We can use the `torch.utils.data.Dataset` class to create our dataset. This class requires us to implement the `__len__` and `__getitem__` methods, which allow the data loader to access and iterate over our data.

    
      import torch
      from torch.utils.data import Dataset, DataLoader

      class CustomDataset(Dataset):
          def __init__(self):
              # Initialize the dataset
              pass

          def __len__(self):
              # Return the total number of samples in the dataset
              pass

          def __getitem__(self, idx):
              # Return the sample at the given index
              pass

      dataset = CustomDataset()
      dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
    
  

Using the PyTorch Data Loader

Once we have created our data loader, we can use it to iterate over our data during the training process. The data loader allows us to load batches of data, shuffle the data, and perform other useful transformations on the fly.

    
      for batch in dataloader:
          # Process the batch of data
          pass
    
  

By using the PyTorch Data Loader, we can efficiently load and iterate over our data, making the training process faster and more manageable.