How to Create a Custom Pytorch Dataloader (opens in new tab)

First, create a custom dataset class. from torch.utils.data import Dataset, DataLoader class CustomDataset(Dataset): def __init__(self, features, labels): assert len(features) == len(labels) self.features = features self.labels = labels def __len__(self): return len(self.features) def __getitem__(self, idx): return self.features[idx], self.labels[idx] Next, create a custom dataloader where we specify the batch size. features, labels = load_data() # features & labels must have equal lengths # ...