Deep Learning with PyTorch

MNIST – getting data

The MNIST dataset contains 60,000 handwritten digits from 0 to 9 for training, and 10,000 images for a test set. The PyTorch torchvision library provides us with an MNIST dataset, which downloads the data and provides it in a readily-usable format. Let's use the dataset MNIST function to pull the dataset to our local machine, and then wrap it around a DataLoader. We will use torchvision transformations to convert the data into PyTorch tensors and do data normalization. The following code takes care of downloading, wrapping around the DataLoader and normalizing the data:

transformation =   transforms.Compose([transforms.ToTensor(),  transforms.Normalize((0.1307,), (0.3081,))])train_dataset =  datasets.MNIST('data/',train=True,transform=transformation, ...