对CIFAR10 数据进行训练的代码 无需自己标注数据

 ## 运行

 新建文件夹CIFAR10.py   粘贴后  python CIFAR10.py 运行即可

使用的环境为  

PyTorch  2.0.0

Python  3.8(ubuntu20.04)

Cuda  11.8

识别率 70.1%

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms

# 定义简单的卷积神经网络模型
class CifarClassifier(nn.Module):
    def __init__(self, num_classes):
        super(CifarClassifier, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(64 * 8 * 8, 128)
        self.fc2 = nn.Linear(128, num_classes)

    def forward(self, x):
        x = self.pool(nn.functional.relu(self.conv1(x)))
        x = self.pool(nn.functional.relu(self.conv2(x)))
        x = x.view(-1, 64 * 8 * 8)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 设置参数
batch_size = 64
learning_rate = 0.001
num_epochs = 10
num_classes = 10

# 数据预处理
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

# 加载 CIFAR-10 数据集
train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
test_dataset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)

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

# 实例化模型
model = CifarClassifier(num_classes)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
for epoch in range(num_epochs):
    for batch_idx, (data, targets) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(data)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()

        if batch_idx % 100 == 0:
            print(f'Epoch [{epoch + 1}/{num_epochs}], Step [{batch_idx + 1}/{len(train_loader)}], Loss: {loss.item()}')

torch.save(model.state_dict(), 'model.pth')
# 测试模型
model.eval()
with torch.no_grad():
    correct = 0
    total = 0
    for data, targets in test_loader:
        outputs = model(data)
        _, predicted = torch.max(outputs.data, 1)
        total += targets.size(0)
        correct += (predicted == targets).sum().item()

    print(f'Accuracy on test set: {100 * correct / total}%')