import torch import torchvision import torchvision.transforms as transforms #torchvision 数据集的输出是范围在[0,1]之间的 PILImage,我们将他们转换成归一化范围为[-1,1]之间的张量 Tensors。 transform = transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))]) trainset = torchvision.datasets.CIFAR10(root='./data',train=True,download=True,transform=transform) trainloader = torch.utils.data.DataLoader(trainset,batch_size=4,shuffle=True,num_workers=2) testset = torchvision.datasets.CIFAR10(root='./data',train=False,download=True,transform=transform) testloader= torch.utils.data.DataLoader(testset,batch_size=4,shuffle=False,num_workers=2) classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck') import matplotlib.pyplot as plt import numpy as np def imshow(img): img = img/2+0.5 npimg = img.numpy() plt.imshow(np.transpose(npimg,(1,2,0))) plt.show() dataiter = iter(trainloader) images, labels = dataiter.next() imshow(torchvision.utils.make_grid(images)) print(''.join('%5s' % classes[labels[j]] for j in range(4)))
pytorch数据集
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转载自blog.csdn.net/qq_16792139/article/details/114443778
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