PyTorch DataLoader
The dataloader class provides a way to specify how many data points to sample from a dataset for each epoch in a model training loop. In addition, you can specify whether to shuffle the datapoints or not.
from torch.utils.data import DataLoader
loader = DataLoader(dataset, batch_size=10, shuffle=True)
Then , when implementing a training loop, you can iterate through the loader and it would provide the specified number of points with the given shuffle criteria.
def train(model, opt, loss, loader, epochs):
model.train()
for epoch in epochs:
for X,y in loader:
#...
pass
pass
pass