为以下代码添加注释def test(dataloader, model, loss_fn): size = len(dataloader.dataset) num_batches = len(dataloader) model.eval() test_loss, correct = 0, 0 with torch.no_grad(): for X, y in dataloader: X, y = X.to(device), y.to(device) pred = model(X) test_loss += loss_fn(pred, y).item() correct += (pred.argmax(1) == y).type(torch.float).sum().item() test_loss /= num_batches correct /= size print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")

# 定义一个测试函数，用于测试模型
def test(dataloader, model, loss_fn):
# 获取数据集大小和批次数
size = len(dataloader.dataset)
num_batches = len(dataloader)
# 将模型设为评估模式
model.eval()
# 初始化测试损失和正确率
test_loss, correct = 0, 0
# 关闭梯度计算
with torch.no_grad():
# 遍历数据集
for X, y in dataloader:
# 将数据和标签移动到设备上
X, y = X.to(device), y.to(device)
# 前向传播计算预测值
pred = model(X)
# 计算损失
test_loss += loss_fn(pred, y).item()
# 统计正确率
correct += (pred.argmax(1) == y).type(torch.float).sum().item()
# 计算平均测试损失和正确率
test_loss /= num_batches
correct /= size
# 输出测试结果
print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")