pytorch实现CIFAR-10分类

PyTorch是一个流行的深度学习框架，可以用于实现CIFAR-10图像分类任务。CIFAR-10是一个常用的小型计算机视觉数据集，包含60,000张32x32像素的彩色图片，分为10个类别。

以下是使用PyTorch构建一个基础卷积神经网络(CNN)模型来对CIFAR-10进行分类的基本步骤：

1. **导入库**:

import torch
import torchvision
import torchvision.transforms as transforms

2. **数据预处理**:

transform = transforms.Compose([
    transforms.RandomHorizontalFlip(),
    transforms.RandomCrop(32, padding=4),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)

train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=128, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=128, shuffle=False)

3. **定义模型**:

model = torch.nn.Sequential(
    torch.nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1),
    torch.nn.ReLU(),
    torch.nn.MaxPool2d(kernel_size=2, stride=2),
    # 添加更多卷积层、池化层和全连接层...
    torch.nn.Linear(32 * 8 * 8, 10)  # 最后一个线性层对应于10个类别的分类
)

4. **损失函数和优化器**:

criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

5. **训练过程**:

num_epochs = 10
for epoch in range(num_epochs):
    for images, labels in train_loader:
        optimizer.zero_grad()  # 梯度清零
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

    # 训练阶段结束后的评估
    with torch.no_grad():
        correct = 0
        total = 0
        for images, labels in test_loader:
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
        print(f"Epoch {epoch+1}/{num_epochs} - Test Accuracy: {correct / total}")