test_stats = evaluate(data_loader_val, model, device) print(f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%") max_accuracy = max(max_accuracy, test_stats["acc1"]) print(f'Max accuracy: {max_accuracy:.2f}%')在这段代码的基础添加输出在训练集上的准确率的代码怎么写

要输出在训练集上的准确率，可以在训练循环中添加代码进行计算并输出。可以先定义一个函数，在每个epoch结束时调用该函数计算训练集准确率并输出。

以下是可能的实现示例：

def evaluate_train(data_loader_train, model, device):
    model.train(False) # 将模型设置为评估模式
    total = 0
    correct = 0
    with torch.no_grad():
        for data in data_loader_train:
            inputs, labels = data
            inputs = inputs.to(device)
            labels = labels.to(device)
            outputs = model(inputs)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    accuracy = 100 * correct / total
    print(f"Accuracy of the network on the {total} training images: {accuracy:.1f}%")
    return accuracy

然后在每个epoch结束时调用该函数，并记录最大准确率：

max_accuracy_train = 0
for epoch in range(num_epochs):
    # 训练模型
    # ...

    # 在训练集上评估模型
    train_accuracy = evaluate_train(data_loader_train, model, device) 
    max_accuracy_train = max(max_accuracy_train, train_accuracy)
    print(f'Max training accuracy: {max_accuracy_train:.2f}%')

    # 在验证集上评估模型
    test_stats = evaluate(data_loader_val, model, device) 
    print(f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%")
    max_accuracy = max(max_accuracy, test_stats["acc1"]) 
    print(f'Max accuracy: {max_accuracy:.2f}%')

这样就可以在训练过程中同时输出训练集和验证集的准确率，并记录最大准确率。