目录
  • 1 PyTorch 获取 MNIST 数据
  • 2 PyTorch 保存 MNIST 数据
  • 3 PyTorch 显示 MNIST 数据

1 PyTorch 获取 MNIST 数据

import torch
import numpy as np
import matplotlib.pyplot as plt # type: ignore
from torchvision import datasets, transforms
def mnist_get():
    print(torch.__version__)
    # 定义数据转换
    transform = transforms.Compose([
        transforms.ToTensor(),  # 将图像转换为张量
        transforms.Normalize((0.5,), (0.5,))  # 归一化图像数据
    ])
    # 获取数据
    train_data = datasets.MNIST(root='./data', train=False, download=True, transform=transform)
    test_data = datasets.MNIST(root='./data', train=False, download=True, transform=transform)
    # 训练数据
    train_image = train_data.data.numpy()
    train_label = train_data.targets.numpy()
    # 测试数据
    test_image = test_data.data.numpy()
    test_label = test_data.targets.numpy()

2 PyTorch 保存 MNIST 数据

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__)
    # 定义数据转换
    transform = transforms.Compose([
        transforms.ToTensor(),  # 将图像转换为张量
        transforms.Normalize((0.5,), (0.5,))  # 归一化图像数据
    ])
    # 获取数据
    train_data = datasets.MNIST(root='./data', train=False, download=True, transform=transform)
    test_data = datasets.MNIST(root='./data', train=False, download=True, transform=transform)
    # 训练数据
    train_image = train_data.data.numpy()
    train_label = train_data.targets.numpy()
    # 测试数据
    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 显示 MNIST 数据

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)

Python PyTorch 如何获取 MNIST 数据

声明：本站所有文章，如无特殊说明或标注，均为本站原创发布。任何个人或组织，在未征得本站同意时，禁止复制、盗用、采集、发布本站内容到任何网站、书籍等各类媒体平台。如若本站内容侵犯了原著者的合法权益，可联系我们进行处理。