TPR、FPR、precision、recall、accuracy、ROC、AUC

好的,以下是给出GPU上训练CIFAR10并计算指标的PyTorch代码: ```python import torch import torch.nn as nn import torch.optim as optim import torchvision import torchvision.transforms as transforms from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_curve, auc # 定义网络模型 class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(3, 32, 5) self.pool = nn.MaxPool2d(2, 2) self.conv2 = nn.Conv2d(32, 64, 5) self.fc1 = nn.Linear(64 * 5 * 5, 256) self.fc2 = nn.Linear(256, 10) 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 * 5 * 5) x = nn.functional.relu(self.fc1(x)) x = self.fc2(x) return x # 加载数据集并进行数据增强 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)) ]) trainset = torchvision.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 = torchvision.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) # 定义损失函数优化器 net = Net() criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9) # 将模型移动到GPU上进行训练 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") net.to(device) # 训练模型 for epoch in range(20): running_loss = 0.0 for i, data in enumerate(trainloader, 0): inputs, labels = data inputs, labels = inputs.to(device), labels.to(device) optimizer.zero_grad() outputs = net(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() running_loss += loss.item() if i % 100 == 99: print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100)) running_loss = 0.0 # 在测试集上进行预测并计算各项指标 y_true = [] y_pred = [] y_score = [] net.eval() with torch.no_grad(): for data in testloader: images, labels = data images, labels = images.to(device), labels.to(device) outputs = net(images) _, predicted = torch.max(outputs.data, 1) y_true += labels.cpu().numpy().tolist() y_pred += predicted.cpu().numpy().tolist() y_score += nn.functional.softmax(outputs, dim=1).cpu().numpy().tolist() acc = accuracy_score(y_true, y_pred) precision = precision_score(y_true, y_pred, average='macro') recall = recall_score(y_true, y_pred, average='macro') f1 = f1_score(y_true, y_pred, average='macro') fpr = {} tpr = {} roc_auc = {} for i in range(10): fpr[i], tpr[i], _ = roc_curve([1 if j == i else 0 for j in y_true], [score[i] for score in y_score]) roc_auc[i] = auc(fpr[i], tpr[i]) print("Accuracy:", acc) print("Precision:", precision) print("Recall:", recall) print("F1 score:", f1) for i in range(10): print("ROC AUC of class", i, ":", roc_auc[i]) ``` 这段代码会输出ACC PRECISION F1 RECALL AUC以及每一类ROC曲线的值。
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