Learn practical skills, build real-world projects, and advance your career
import torch
import numpy as np
import torchvision
from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor # convert an image to a tensor
from torch.utils.data.sampler import SubsetRandomSampler
from torch.utils.data.dataloader import DataLoader
dataset = MNIST(root = "data/",
                download = True,
                transform = ToTensor()
)
Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to data/MNIST/raw/train-images-idx3-ubyte.gz
100.1%
Extracting data/MNIST/raw/train-images-idx3-ubyte.gz to data/MNIST/raw Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to data/MNIST/raw/train-labels-idx1-ubyte.gz
113.5%
Extracting data/MNIST/raw/train-labels-idx1-ubyte.gz to data/MNIST/raw Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to data/MNIST/raw/t10k-images-idx3-ubyte.gz
100.4%
Extracting data/MNIST/raw/t10k-images-idx3-ubyte.gz to data/MNIST/raw Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to data/MNIST/raw/t10k-labels-idx1-ubyte.gz
180.4%
Extracting data/MNIST/raw/t10k-labels-idx1-ubyte.gz to data/MNIST/raw Processing... Done!
dataset
Dataset MNIST
    Number of datapoints: 60000
    Root location: data/
    Split: Train
    StandardTransform
Transform: ToTensor()
len(dataset)
60000
img, label = dataset[0]