如何在PyTorch中进行模型评估

在PyTorch中进行模型评估通常需要以下步骤：

导入所需的库和模型：

import torchimport torch.nn as nnimport torch.optim as optimimport torchvisionfrom torchvision import transforms, datasets

加载测试数据集：

transform = transforms.Compose([    transforms.ToTensor(),    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])test_dataset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=False)

加载模型：

model = YourModel()model.load_state_dict(torch.load('model.pth'))model.eval()

定义评估函数：

def evaluate_model(model, test_loader):    correct = 0    total = 0    with torch.no_grad():        for images, labels in test_loader:            outputs = model(images)            _, predicted = torch.max(outputs.data, 1)            total += labels.size(0)            correct += (predicted == labels).sum().item()    accuracy = correct / total    print('Accuracy of the model on the test set: {:.2f}%'.format(accuracy * 100))

调用评估函数：

evaluate_model(model, test_loader)

这样你就可以在PyTorch中对模型进行评估了。