为以下代码添加注释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) # Compute prediction error pred = model(X) loss = loss_fn(pred, y) # Backpropagation loss.backward() optimizer.step() optimizer.zero_grad() if batch % 100 == 0: loss, current = loss.item(), (batch + 1) * len(X) print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]")

# 定义模型训练函数
def train(dataloader, model, loss_fn, optimizer):
# 获取数据集大小
size = len(dataloader.dataset)
# 将模型设置为训练模式
model.train()

# 遍历数据集中的每个批次
for batch, (X, y) in enumerate(dataloader):
# 将数据移动到GPU上
X, y = X.to(device), y.to(device)

# 计算预测误差
pred = model(X)
loss = loss_fn(pred, y)

# 反向传播
loss.backward()
optimizer.step()
optimizer.zero_grad()

# 打印每100批次的损失值
if batch % 100 == 0:
loss, current = loss.item(), (batch + 1) * len(X)
print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]")