pytorch无法洗牌数据集

Question

我试图用torchvision的mnist数据集制作ai，并用pytorch制作ai，但当我键入一些对数据进行洗牌的代码并运行时，它会说：

    trainset = torch.utils.data.Dataloader(train, batch_size=10, shuffle=True)
AttributeError: module 'torch.utils.data' has no attribute 'Dataloader'

我尝试了difrent方法，但仍然不起作用，它说：

    trainset = torch.autograd.Variable.DataLoader(train, batch_size=10, shuffle=True) 
AttributeError: type object 'Variable' has no attribute 'DataLoader'

我使用的代码是：

import torch
import numpy as np
import torchvision
from torchvision import transforms, datasets


train = datasets.MNIST("", train=True, download=True,
                       transform = transforms.Compose([transforms.ToTensor()]))

test = datasets.MNIST("", train=False, download=True,
                       transform = transforms.Compose([transforms.ToTensor()]))

trainset = torch.utils.data.Dataloader(train, batch_size=10, shuffle=True)
testset = torch.utils.data.Dataloader(test, batch_size=10, shuffle=True)

for data in trainset:
    print(data)
    break

此代码中的eror：

    trainset = torch.utils.data.Dataloader(train, batch_size=10, shuffle=True)
AttributeError: module 'torch.utils.data' has no attribute 'Dataloader'

我尝试了一个新版本，但仍然不起作用：

import torch
import numpy as np 
import torchvision
from torchvision import transforms, datasets


train = datasets.MNIST("", train=True, download=True,
                       transform = transforms.Compose([transforms.ToTensor()]))

test = datasets.MNIST("", train=False, download=True,
                       transform = transforms.Compose([transforms.ToTensor()])

trainset = torch.autograd.Variable.DataLoader(train, batch_size=10, shuffle=True)
testset = torch.autograd.Variable.DataLoader(test, batch_size=10, shuffle=True)

for data in trainset:
    print(data)
    break

此代码中的eror：

    trainset = torch.autograd.Variable.DataLoader(train, batch_size=10, shuffle=True) 
AttributeError: type object 'Variable' has no attribute 'DataLoader'

我仍然很困惑为什么它不起作用，我一直在遵循一个教程，但它不起作用

Answer 1

您有一个简单的打字错误：Dataloader->^{}（大写{}）

尝试：

 trainset = torch.utils.data.DataLoader(train, batch_size=10, shuffle=True)