PyTorch Basics Tutorial: Practical Data Loading with Dataset and DataLoader

Data loading is a crucial step in machine learning training, and PyTorch's `Dataset` and `DataLoader` are core tools for efficient data management. As an abstract base class for data storage, `Dataset` requires inheriting to implement `__getitem__` (to read a single sample) and `__len__` (to get the total number of samples). Alternatively, `TensorDataset` can be directly used to wrap tensor data. `DataLoader`, on the other hand, handles batch processing and supports parameters such as `batch_size` (batch size), `shuffle` (shuffling order), and `num_workers` (multithreaded loading) to optimize training efficiency. In practice, taking MNIST as an example, image data can be loaded via `torchvision`, and combined with `Dataset` and `DataLoader` to achieve efficient iteration. It should be noted that under Windows, `num_workers` is defaulted to 0 to avoid memory issues. During training, `shuffle=True` should be used to shuffle the data, while `shuffle=False` is set for the validation/test sets to ensure reproducibility. Key steps: 1. Define a `Dataset` to store data; 2. Create a `DataLoader` with specified parameters; 3. Iterate over the `DataLoader` to input data into the model for training. These two components are the cornerstones of data processing. Once mastered, they can be flexibly applied to various data loading requirements.