Python PyTorch 如何獲取 MNIST 數(shù)據(jù)

更新時間：2024年04月27日 12:07:57 作者：深色風信子

這篇文章主要介紹了Python PyTorch 如何獲取 MNIST 數(shù)據(jù),通過示例代碼介紹了PyTorch 保存 MNIST 數(shù)據(jù),PyTorch 顯示 MNIST 數(shù)據(jù)的操作方法,感興趣的朋友跟隨小編一起看看吧

1 PyTorch 獲取 MNIST 數(shù)據(jù)

import torch
import numpy as np
import matplotlib.pyplot as plt # type: ignore
from torchvision import datasets, transforms
def mnist_get():
    print(torch.__version__)
    # 定義數(shù)據(jù)轉換
    transform = transforms.Compose([
        transforms.ToTensor(),  # 將圖像轉換為張量
        transforms.Normalize((0.5,), (0.5,))  # 歸一化圖像數(shù)據(jù)
    ])
    # 獲取數(shù)據(jù)
    train_data = datasets.MNIST(root='./data', train=False, download=True, transform=transform)
    test_data = datasets.MNIST(root='./data', train=False, download=True, transform=transform)
    # 訓練數(shù)據(jù)
    train_image = train_data.data.numpy()
    train_label = train_data.targets.numpy()
    # 測試數(shù)據(jù)
    test_image = test_data.data.numpy()
    test_label = test_data.targets.numpy()

2 PyTorch 保存 MNIST 數(shù)據(jù)

import torch
import numpy as np
import matplotlib.pyplot as plt # type: ignore
from torchvision import datasets, transforms
def mnist_save(mnist_path):
    print(torch.__version__)
    # 定義數(shù)據(jù)轉換
    transform = transforms.Compose([
        transforms.ToTensor(),  # 將圖像轉換為張量
        transforms.Normalize((0.5,), (0.5,))  # 歸一化圖像數(shù)據(jù)
    ])
    # 獲取數(shù)據(jù)
    train_data = datasets.MNIST(root='./data', train=False, download=True, transform=transform)
    test_data = datasets.MNIST(root='./data', train=False, download=True, transform=transform)
    # 訓練數(shù)據(jù)
    train_image = train_data.data.numpy()
    train_label = train_data.targets.numpy()
    # 測試數(shù)據(jù)
    test_image = test_data.data.numpy()
    test_label = test_data.targets.numpy()
    np.savez(mnist_path, train_data=train_image, train_label=train_label, test_data=test_image, test_label=test_label)
mnist_path = 'C:\\Users\\Hyacinth\\Desktop\\mnist.npz'
mnist_save(mnist_path)

3 PyTorch 顯示 MNIST 數(shù)據(jù)

import torch
import numpy as np
import matplotlib.pyplot as plt # type: ignore
from torchvision import datasets, transforms
def mnist_show(mnist_path):
    data = np.load(mnist_path)
    image = data['train_data'][0:100]
    label = data['train_label'].reshape(-1, )
    plt.figure(figsize = (10, 10))
    for i in range(100):
        print('%f, %f' % (i, label[i]))
        plt.subplot(10, 10, i + 1)
        plt.imshow(image[i])
    plt.show()
mnist_path = 'C:\\Users\\Hyacinth\\Desktop\\mnist.npz'
mnist_show(mnist_path)

在這里插入圖片描述