126 optimizer.step() --> 127 loss = torch.tensor() 128 loss+=loss.item() 129 current+=cur_acc.item() TypeError: tensor() missing 1 required positional arguments: "data"

这个错误通常是由于 `torch.tensor()` 函数没有传入正确的参数导致的。 `torch.tensor()` 要求传入一个数据类型的参数，例如一个列表或者一个numpy数组。下面是一个例子：

import torch
import numpy as np

# 创建一个numpy数组
my_array = np.array([1, 2, 3])

# 使用numpy数组创建一个张量
my_tensor = torch.tensor(my_array)

print(my_tensor)

请检查你的代码，确保你传入了正确的参数。

相关问题

ttributeError Traceback (most recent call last) Cell In[3], line 160 158 print(f"epoch{t+1}\n----------") 159 # 训练模型 --> 160 train_loss, train_acc = train(train_dataloader, model, loss_fn, optimizer) 161 # 验证模型 162 val_loss, val_acc = val(val_dataloader, model, loss_fn) Cell In[3], line 125, in train(dataloader, model, loss_fn, optimizer) 122 cur_loss.backward() 123 optimizer.step() --> 125 loss+=loss.item() 126 current+=cur_acc.item() 127 n=n+1 AttributeError: 'int' object has no attribute 'item'

这个错误是因为你试图将一个Python整数转换为PyTorch张量，以便在计算损失时使用。请确保你将输入数据转换为PyTorch张量，以便进行计算。你可以使用`torch.tensor()`方法将Python对象转换为张量。在这种情况下，您可以尝试将`loss`变量初始化为一个张量，而不是整数，如下所示：

loss = torch.tensor(0.0)

这样就会将`loss`变量初始化为一个浮点数张量，并且可以使用`.item()`方法获得其值。

def the_loop(net, optimizer, train_loader, val_loader=None, epochs=None, swa_model=None, swa_start=5): if epochs is None: raise Exception("a training duration must be given: set epochs") log_iterval = 1 running_mean = 0. loss = torch.Tensor([0.]).cuda() losses = [] val_losses = [] states = [] i, j = 0, 0 pbar = tqdm(train_loader, desc=f"epoch {i}", postfix={"loss": loss.item(), "step": j}) for i in range(epochs): running_mean = 0. j = 0 pbar.set_description(f"epoch {i}") pbar.refresh() pbar.reset() for j, batch in enumerate(train_loader): # implement training step by # - appending the current states to `states` # - doing a training_step # - appending the current loss to the `losses` list # - update the running_mean for logging states.append(net.state_dict()) optimizer.zero_grad() output = net(batch) batch_loss = loss_function(output, batch.target) batch_loss.backward() optimizer.step() losses.append(batch_loss.item()) running_mean = (running_mean * j + batch_loss.item()) / (j + 1) if j % log_iterval == 0 and j != 0: pbar.set_postfix({"loss": running_mean, "step": j}) running_mean = 0. pbar.update() if i > swa_start and swa_model is not None: swa_model.update_parameters(net) if val_loader is not None: val_loss = 0. with torch.no_grad(): for val_batch in val_loader: val_output = net(val_batch) val_loss += loss_function(val_output, val_batch.target).item() val_loss /= len(val_loader) val_losses.append(val_loss) pbar.refresh() if val_loader is not None: return losses, states, val_losses return losses, states net = get_OneFCNet() epochs = 10 optimizer = GD(net.parameters(), 0.002) loss_fn = nn.CrossEntropyLoss() losses, states = the_loop(net, optimizer, gd_data_loader, epochs=epochs) fig = plot_losses(losses) iplot(fig)这是之前的代码怎么修改这段代码的错误？

根据错误提示，您的模型在CPU和GPU之间切换，因此您需要确保模型和数据都在同一个设备上。您可以将数据加载器的设备设为与模型相同的设备，例如：

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=2, pin_memory=True)
train_loader.to(device)

将类似的代码放在数据加载器的定义之后，确保数据和模型都在同一个设备上。如果您的模型已经在GPU上，可以使用`.cuda()`方法将数据加载器移到GPU上，如下所示：

train_loader = train_loader.cuda()