BatchSamplers

BatchSamplers are a class of algorithms used in machine learning to efficiently sample data points from a dataset. They are particularly useful when dealing with large datasets, as they allow for the creation of mini-batches of data, which can be processed more quickly and with less memory usage than the entire dataset. This is especially important in the context of training deep learning models, where the size of the dataset can be a limiting factor.

BatchSamplers can be categorized into several types based on their sampling strategy. SimpleRandomSampler randomly selects data points from the dataset without replacement. StratifiedSampler ensures that each mini-batch contains a representative proportion of each class in the dataset, which is particularly useful for imbalanced datasets. SequentialSampler selects data points in a sequential order, which can be useful for time-series data or when the order of data points is important.

In the context of deep learning, BatchSamplers are often used in conjunction with DataLoaders, which are responsible for loading and preprocessing the data. The DataLoader uses the BatchSampler to create mini-batches of data, which are then fed into the model for training. This allows for efficient and scalable training of deep learning models on large datasets.

BatchSamplers are a crucial component of modern machine learning pipelines, enabling the efficient and scalable training of models on large datasets. They provide a flexible and customizable way to sample data points from a dataset, allowing for the creation of mini-batches that are representative of the entire dataset.