def train(dataloader, model, loss_fn, optimizer): size = len(dataloader.dataset) model.train() for batch, (X, y) in enumerate(dataloader): X, y = X.to(device), y.to(device) y=y.long() # Compute prediction error pred = model(X) loss = loss_fn(pred, y) # Backpropagation optimizer.zero_grad() loss.backward() optimizer.step() if batch % 50 == 0: loss, current = loss.item(), (batch + 1) * len(X) print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]") return Losslist 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) y=y.long() 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") return LossList,acclist

这段代码是一个简单的 PyTorch 训练和测试循环。训练循环使用给定的数据加载器、模型、损失函数和优化器来训练模型；测试循环使用给定的数据加载器、模型和损失函数来评估模型在测试集上的性能。

具体来说，训练循环会遍历数据加载器中的每一个 batch，将输入数据和标签移动到指定的设备上（例如 GPU），然后将输入数据喂给模型，得到模型的输出。接下来，计算模型输出和标签之间的损失，使用反向传播更新模型的权重。每经过 50 个 batch，训练循环会输出当前的损失和已经处理的样本数量。

测试循环会将模型设置为评估模式，然后遍历数据加载器中的每一个 batch。对于每一个 batch，测试循环会将输入数据和标签移动到指定的设备上，然后将输入数据喂给模型，得到模型的输出。接下来，计算模型输出和标签之间的损失，并统计模型在该 batch 中正确预测的样本数量。最后，测试循环会输出模型在测试集上的平均损失和准确率。

在代码中，LossList 和 AccList 都是用来记录每个 epoch 中的损失和准确率的列表。但是，在函数中并没有直接对它们进行操作，所以这段代码需要进行一些修改才能正确地记录每个 epoch 的损失和准确率。

相关问题

def train(dataloader, model, loss_fn, optimizer): size = len(dataloader.dataset) model.train() LossList = [] for batch, (X, y) in enumerate(dataloader): X, y = X.to(device), y.to(device) y=y.long() # Compute prediction error pred = model(X) loss = loss_fn(pred, y) # Backpropagation optimizer.zero_grad() loss.backward() optimizer.step() if batch % 50 == 0: loss, current = loss.item(), (batch + 1) * len(X) print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]") LossList.append(loss) return Losslist def test(dataloader, model, loss_fn): size = len(dataloader.dataset) num_batches = len(dataloader) model.eval() LossList = [] AccList = [] test_loss, correct = 0, 0 with torch.no_grad(): for X, y in dataloader: X, y = X.to(device), y.to(device) y=y.long() 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 LossList.append(test_loss) AccList.append(correct) print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n") return LossList,acclist报错显示lostlist未定义

在这段代码中，`losslist` 和 `acclist` 都没有被定义。你需要在函数内部定义这两个变量，或者在函数调用前将它们定义并且传入函数。例如，你可以在函数内部这样定义它们：

def train(dataloader, model, loss_fn, optimizer):
    size = len(dataloader.dataset)
    model.train()
    losslist = []  # 在这里定义 losslist
    for batch, (X, y) in enumerate(dataloader):
        X, y = X.to(device), y.to(device)
        y = y.long()
        # Compute prediction error
        pred = model(X)
        loss = loss_fn(pred, y)
        # Backpropagation
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        if batch % 50 == 0:
            loss, current = loss.item(), (batch + 1) * len(X)
            print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")
            losslist.append(loss)  # 将 loss 添加到 losslist 中
    return losslist

def test(dataloader, model, loss_fn):
    size = len(dataloader.dataset)
    num_batches = len(dataloader)
    model.eval()
    losslist = []  # 在这里定义 losslist
    acclist = []  # 在这里定义 acclist
    test_loss, correct = 0, 0
    with torch.no_grad():
        for X, y in dataloader:
            X, y = X.to(device), y.to(device)
            y = y.long()
            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
    losslist.append(test_loss)  # 将 test_loss 添加到 losslist 中
    acclist.append(correct)  # 将 correct 添加到 acclist 中
    print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")
    return losslist, acclist

def train(dataloader, model, loss_fn, optimizer): size = len(dataloader.dataset) model.train() for batch, (X, y) in enumerate(dataloader): X, y = X.to(device), y.to(device) y=y.long() # Compute prediction error pred = model(X) loss = loss_fn(pred, y) # Backpropagation optimizer.zero_grad() loss.backward() optimizer.step() if batch % 50 == 0: loss, current = loss.item(), (batch + 1) * len(X) print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]") return Losslist 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) y=y.long() 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") return LossList,acclist如何修改

如果你想要在这段代码中记录每个 epoch 的损失和准确率，你需要做以下修改：

1. 在 train 和 test 函数中定义 LossList 和 AccList 两个列表，用于记录每个 epoch 的损失和准确率。可以在函数开始时定义它们，比如：

def train(dataloader, model, loss_fn, optimizer):
    LossList = []
    ...

def test(dataloader, model, loss_fn):
    LossList = []
    AccList = []
    ...

2. 在 train 循环中，每 50 个 batch 输出当前的损失和已经处理的样本数量的同时，将损失添加到 LossList 中，比如：

if batch % 50 == 0:
    loss, current = loss.item(), (batch + 1) * len(X)
    print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")
    LossList.append(loss)

3. 在 test 循环中，将每个 batch 的损失和准确率记录到对应的列表中，比如：

for X, y in dataloader:
    ...
    test_loss += loss_fn(pred, y).item()
    correct += (pred.argmax(1) == y).type(torch.float).sum().item()

test_loss /= num_batches
correct /= size
LossList.append(test_loss)
AccList.append(correct)

4. 最后，在 train 和 test 函数的返回值中添加 LossList 和 AccList，以便在之后的分析和可视化中使用，比如：

return LossList

...

return LossList, AccList

这些修改会让 train 和 test 函数记录每个 epoch 的损失和准确率，并将它们存储在 LossList 和 AccList 中。你可以在每个 epoch 结束时保存这些列表，然后使用它们进行可视化或其他分析。