Python打卡第38天

import numpy as np

import torch

import torch.nn as nn

import torch.optim as optim

from torch.utils.data import DataLoader, Dataset

from torchvision import datasets, transforms

import matplotlib.pyplot as plt

# 设置随机种子确保结果可复现

torch.manual_seed(42)

# 定义数据预处理

transform = transforms.Compose([

    transforms.ToTensor(), # 将图像转换为Tensor

    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) # 归一化处理

])

# 加载数据集

train_dataset = datasets.CIFAR10(

    root='./data',

    train=True,

    download=True,

    transform=transform

)

test_dataset = datasets.CIFAR10(

    root='./data',

    train=False,

    transform=transform

)

# 类别名称

class_names = ('plane', 'car', 'bird', 'cat', 'deer', 

               'dog', 'frog', 'horse', 'ship', 'truck')

# 可视化函数

def imshow(img, title=None):

    """显示反归一化后的图像"""

    img = img * 0.5 + 0.5 # 反归一化 [-1,1] -> [0,1]

    np_img = img.numpy()

    plt.figure(figsize=(4, 4))

    plt.imshow(np.transpose(np_img, (1, 2, 0))) # CHW -> HWC

    if title:

        plt.title(title)

    plt.axis('off')

    plt.show()

# 随机选择并显示样本

sample_idx = torch.randint(0, len(train_dataset), (1,)).item()

image, label = train_dataset[sample_idx]

print(f"Label: {label} ({class_names[label]})")

imshow(image, f"Label: {class_names[label]}")