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：

1. 导入所需的库和模型：

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

2. 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) 

3. 加载模型：

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

4. 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)) 

5. 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。