from torchvision import datasets, transforms

train_data = datasets.MNIST(root="./MNIST", 
                            train=True, 
                            transform=transforms.ToTensor(), 
                            download=False)

test_data = datasets.MNIST(root="./MNIST", 
                           train=False, 
                           transform=transforms.ToTensor(), 
                           download=False)

print(train_data)
print(test_data)

输出结果：

Dataset MNIST
    Number of datapoints: 60000
    Root location: ./MNIST
    Split: Train
    StandardTransform
Transform: ToTensor()

Dataset MNIST
    Number of datapoints: 10000
    Root location: ./MNIST
    Split: Test
    StandardTransform
Transform: ToTensor()

完整的数据集读取代码：

from torchvision import datasets, transforms
from torch.utils.data import DataLoader

train_data = datasets.MNIST(root="./MNIST",
                            train=True,
                            transform=transforms.ToTensor(),
                            download=False)

test_data = datasets.MNIST(root="./MNIST",
                           train=False,
                           transform=transforms.ToTensor(),
                           download=False)

train_loader = DataLoader(dataset=train_data,
                          batch_size=64,
                          shuffle=True)

test_loader = DataLoader(dataset=test_data,
                         batch_size=64,
                         shuffle=True)

数据可视化

以训练集为例：

import torchvision
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import numpy as np
import matplotlib.pyplot as plt

train_data = datasets.MNIST(root="./MNIST",
                            train=True,
                            transform=transforms.ToTensor(),
                            download=False)

train_loader = DataLoader(dataset=train_data,
                          batch_size=64,
                          shuffle=True)

for num, (image, label) in enumerate(train_loader):
    image_batch = torchvision.utils.make_grid(image, padding=2)
    plt.imshow(np.transpose(image_batch.numpy(), (1, 2, 0)), vmin=0, vmax=255)
    plt.show()
    print(label)

1）image :
[ 数据集 ] MINIST 数据集介绍

2）label ：

tensor([1, 8, 9, 6, 8, 9, 9, 9, 4, 0, 4, 9, 0, 1, 6, 5, 2, 6, 1, 6, 4, 2, 8, 5,
        1, 7, 7, 8, 9, 3, 5, 0, 8, 9, 3, 6, 5, 4, 0, 2, 4, 2, 4, 5, 8, 7, 1, 5,
        9, 8, 6, 8, 6, 8, 3, 8, 7, 7, 3, 0, 8, 6, 2, 0])

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[ 数据集 ] MINIST 数据集介绍

文章目录

MINIST

数据集读取

数据可视化

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