train_ds=torchvision.datasets.CIFAR10('data',train=True,transform=torchvision.transforms.ToTensor(),# 将数据类型转化为Tensordownload=True)test_ds=torchvision.datasets.CIFAR10('data',train=False,transform=torchvision.transforms.ToTensor(),# 将数据类型转化为Tensordownload=True) 代码输出: Filesalreadydownloa...
transet = torchvision.datasets.CIFAR10(root='./data/train', train=True, download=True, transform=transform) testset = torchvision.datasets.CIFAR10(root='./data/test', train=False, download=True, transform=transform) trainloader = torch.utils.data.DataLoader(transet, batch_size=4, shuffle=True...
train_loader = torch.utils.data.DataLoader(CIFAR10(args.data_path, train=True, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize(((0.4914, 0.4822, 0.4465)), (0.2470, 0.2435, 0.2616)) ])), batch_size=args.batch_size, shuffle=True) 5、激活函数 AlexNet本身使用的是ReLU,...
0.5)) ])train_dataset = dsets.CIFAR10(root='/ml/pycifar', # 选择数据的根目录train=True, # 选择训练集transform=transform,download=True)test_dataset = dsets.CIFAR10(root='/ml/pycifar',train=False,# 选择测试集transform=transform,download=True)trainloader = DataLoader(train_dataset,batch_si...
transform=transforms.Compose([transforms.ToTensor()#归一化到(0,1),transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))])#再(x-mean)/std,归一化到(-1,1)trainset=torchvision.datasets.CIFAR10(root='./data',train=True,download=True,transform=transform)trainloader=torch.utils.data.DataLoader(train...
但是MIndSpore使用GeneratorDataset依然可以为我们提供一套相对便利的数据集加载方式。对于数据集的预处理的transform代码，研究者可以将代码直接通过transform参数传入get_item函数，十分方便；同时也可以使用mindspore语言风格，通过dataset自带的map函数，对数据集进行预处理，不过前者的语言风格更加python，推荐使用。
transform = transforms.Compose([ transforms.ToTensor(), # 转为Tensor transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), # 归一化 ]) # 训练集 trainset = tv.datasets.CIFAR10( root=r'C:\Users\wenqi\PycharmProjects\Cifar-10', ...
进行下载,然后拷贝到下方root参数指定的目录中。 trainset = torchvision.datasets.CIFAR10(root='./data/cifar10', train=True, download=False, transform=transform) trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True, num_workers=2) ...
transform_test = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(cifar_norm_mean, cifar_norm_std), ]) trainset = torchvision.datasets.CIFAR10\ (root='./data', train=True, download=True, transform=transform_train) trainloader = torch.utils.data.DataLoader\ ...