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Updated 4 years ago
import torch
import numpy as np
import torchvision
from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor
from torch.utils.data.sampler import SubsetRandomSampler
from torch.utils.data.dataloader import DataLoaderdataset = MNIST(root='data/',
download = True,
transform=ToTensor())0it [00:00, ?it/s]Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to data/MNIST/raw/train-images-idx3-ubyte.gz
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0it [00:00, ?it/s]Extracting data/MNIST/raw/train-labels-idx1-ubyte.gz
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0it [00:00, ?it/s]Extracting data/MNIST/raw/t10k-images-idx3-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to data/MNIST/raw/t10k-labels-idx1-ubyte.gz
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Processing...
Done!
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1654784it [00:24, 376406.82it/s] def split_indices(n, val_pct):
n_val = int(val_pct*n)
idxs = np.random.permutation(n)
return idxs[n_val:], idxs[:n_val]train_indices, val_indices = split_indices(len(dataset), val_pct=0.2)print(len(train_indices), len(val_indices))48000 12000