pytorch训练2个类别的cifar10数据集的python代码

以下是一个使用 PyTorch 训练 CIFAR10 数据集的代码示例，其中仅包含两个类别。

import torch
import torchvision
import torchvision.transforms as transforms

# 定义数据集转换
transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

# 加载 CIFAR10 数据集
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)

# 将数据集分成两个类别（飞机和汽车）
classes = ('plane', 'car')
trainset.targets = [1 if target == 1 else 0 for target in trainset.targets]

# 定义数据加载器
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)

# 定义神经网络模型
import torch.nn as nn
import torch.nn.functional as F


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 2)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


net = Net()

# 定义损失函数和优化器
import torch.optim as optim

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

# 训练神经网络
for epoch in range(2):  # 多次迭代数据集

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # 获取输入数据
        inputs, labels = data

        # 清零梯度缓存、前向传播、计算损失、反向传播、更新权重
        optimizer.zero_grad()

        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # 打印统计信息
        running_loss += loss.item()
        if i % 2000 == 1999:    # 每 2000 个小批量数据打印一次
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')

希望这个代码示例能够帮到你！