# 模型训练 for epoch in range(num_epochs): total_loss = 0.0 for batch_idx, (head, tail, relation) in enumerate(train_loader): head, tail, relation = head.to(device), tail.to(device), relation.to(device) optimizer.zero_grad() loss = model(head, tail, relation) loss.backward() optimizer.step() total_loss += loss.item() print("Epoch {}, Loss {:.4f}".format(epoch+1, total_loss/len(train_loader)))报错AttributeError: 'tuple' object has no attribute 'to'
时间: 2024-03-07 21:48:54 浏览: 23
这个错误是因为你的train_loader返回的是一个元组tuple类型的(head, tail, relation),而元组类型没有to()方法,不能直接转移到GPU上。你需要将元组中的每个张量都转移到GPU上,可以通过以下代码进行修改:
```python
for batch_idx, batch_data in enumerate(train_loader):
head, tail, relation = batch_data[0].to(device), batch_data[1].to(device), batch_data[2].to(device)
```
这样就可以将元组中的每个张量都转移到GPU上,避免了这个错误。
相关问题
for epoch in range(8): # train net.train() running_loss = 0.0
这段代码用于训练模型,训练过程中将会遍历所有的训练数据,每遍历一次称为一个epoch。
首先,我们使用for循环遍历8个epoch,即对训练数据进行8次完整的遍历。在每个epoch中,我们需要将模型设置为训练模式,即net.train(),以便启用训练时特有的模块,如Dropout、BatchNorm等。
接着,我们将running_loss初始化为0.0,用于累计当前epoch中所有mini-batch的损失函数值之和。在每个mini-batch中,我们将输入数据和标签数据传入模型,计算模型输出和损失函数值,然后通过反向传播更新模型参数。
在训练过程中,我们可以计算每个mini-batch的损失函数值,并累加到running_loss中,以便后续计算平均损失。
for epoch in range(num_epochs): model.train() train_loss = 0 train_mae = 0 for batch_data, batch_target in train_dataloader: optimizer.zero_grad() output = model(batch
_data) # 前向传播
loss = loss_fn(output, batch_target) # 计算loss
train_loss += loss.item()
mae = mean_absolute_error(output.detach().cpu().numpy(), batch_target.detach().cpu().numpy()) # 计算MAE
train_mae += mae
loss.backward() # 反向传播
optimizer.step() # 更新梯度
train_loss /= len(train_dataloader)
train_mae /= len(train_dataloader)
model.eval() # 切换到评估模式
eval_loss = 0
eval_mae = 0
with torch.no_grad():
for batch_data, batch_target in eval_dataloader:
output = model(batch_data)
loss = loss_fn(output, batch_target)
eval_loss += loss.item()
mae = mean_absolute_error(output.detach().cpu().numpy(), batch_target.detach().cpu().numpy())
eval_mae += mae
eval_loss /= len(eval_dataloader)
eval_mae /= len(eval_dataloader)
print(f"Epoch {epoch+1}/{num_epochs}, Train Loss: {train_loss:.4f}, Train MAE: {train_mae:.4f}, Eval Loss: {eval_loss:.4f}, Eval MAE: {eval_mae:.4f}")
if eval_loss < best_eval_loss:
best_eval_loss = eval_loss
torch.save(model.state_dict(), save_path)
print("Save model successfully!")