pytorch训练cifar10并输出 ROC曲线的python代码

以下是使用PyTorch训练cifar10并输出ROC曲线的Python代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt

# 定义数据增强和预处理
transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
transform_test = 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 = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)
testset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=128, shuffle=False, num_workers=2)

# 构建卷积神经网络模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
        self.bn1 = nn.BatchNorm2d(32)
        self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
        self.bn2 = nn.BatchNorm2d(64)
        self.conv3 = nn.Conv2d(64, 128, 3, padding=1)
        self.bn3 = nn.BatchNorm2d(128)
        self.fc1 = nn.Linear(4 * 4 * 128, 512)
        self.bn4 = nn.BatchNorm1d(512)
        self.fc2 = nn.Linear(512, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = nn.functional.relu(x)
        x = nn.functional.max_pool2d(x, 2)
        x = self.conv2(x)
        x = self.bn2(x)
        x = nn.functional.relu(x)
        x = nn.functional.max_pool2d(x, 2)
        x = self.conv3(x)
        x = self.bn3(x)
        x = nn.functional.relu(x)
        x = x.view(-1, 4 * 4 * 128)
        x = self.fc1(x)
        x = self.bn4(x)
        x = nn.functional.relu(x)
        x = self.fc2(x)
        return x

# 实例化模型、损失函数和优化器
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)

# 训练模型
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print('[Epoch %d] loss: %.3f' % (epoch + 1, running_loss / len(trainloader)))

# 使用测试集评估模型
correct = 0
total = 0
pred_prob = []
true_labels = []
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
        pred_prob.append(outputs.numpy())
        true_labels.append(labels.numpy())
test_acc = 100 * correct / total
pred_prob = torch.cat(pred_prob, 0)
true_labels = torch.cat(true_labels, 0)

# 计算ROC曲线和AUC值
fpr, tpr, thresholds = roc_curve(true_labels, pred_prob[:, 1])
roc_auc = auc(fpr, tpr)

# 绘制ROC曲线
plt.figure()
plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic')
plt.legend(loc="lower right")
plt.show()

需要安装的Python库有：PyTorch、scikit-learn和matplotlib。代码中的卷积神经网络模型和训练参数可以根据需求进行修改。