import torch from torchvision import datasets, transforms torch.manual_seed(44)

transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=(0.5), std=(0.5)), ])

trainset = datasets.FashionMNIST(root='./data', download=True, train=True, transform=transform) trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True, num_workers=0) testset = datasets.FashionMNIST(root='./data', download=True, train=False, transform=transform) testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=True, num_workers=0)

print("Train shape:", trainloader.dataset.data.shape) print("Test shape:", testloader.dataset.data.shape)

print("Train batch size:", trainloader.batch_size) print("Test batch size:", testloader.batch_size)

print("Sampler:", trainloader.sampler) print("Collate function:", trainloader.collate_fn)

import matplotlib.pyplot as plt

images, labels = next(iter(trainloader)) # Gets a batch of 64 images in the training set first_image = images[0] # Get the first image out of the 64 images. plt.imshow(first_image.numpy().squeeze(), cmap='Greys_r') plt.show()