RuntimeError: 0D 或 1D 目标张量预期，不支持多目标我正在训练深度学习模型，但我遇到了这个问题

Question

*My Training Model*
def train(model,criterion,optimizer,iters):
    epoch = iters
    train_loss = []
    validaion_loss = []
    train_acc = []
    validation_acc = []
    states = ['Train','Valid']
    for epoch in range(epochs):
        print("epoch : {}/{}".format(epoch+1,epochs))
        for phase in states:
            if phase == 'Train':
                model.train() *training the data if phase is train*
                dataload = train_data_loader
            else:
                model.eval()
                dataload = valid_data_loader
        
            run_loss,run_acc = 0,0 *creating variables to calculate loss and acc*
            for data in dataload:
                inputs,labels = data
                inputs = inputs.to(device)
                labels = labels.to(device)
            
                labels = labels.byte()
                optimizer.zero_grad() #Using the optimizer
            
                with torch.set_grad_enabled(phase == 'Train'):
                    outputs = model(inputs)
                    loss = criterion(outputs,labels.unsqueeze(1).float())
                
                    predict = outputs>=0.5
                    if phase == 'Train':
                        loss.backward() #backward propagation
                        optimizer.step()
                
                    acc = torch.sum(predict == labels.unsqueeze(1))
                run_loss+=loss.item()
                run_acc+=acc.item()/len(labels)
            if phase == 'Train': #calulating train loss and accucracy
                epoch_loss = run_loss/len(train_data_loader)
                train_loss.append(epoch_loss)
                epoch_acc = run_acc/len(train_data_loader)
                train_acc.append(epoch_acc)
            else: #training validation loss and accuracy
                epoch_loss = run_loss/len(valid_data_loader)
                validaion_loss.append(epoch_loss)
                epoch_acc = run_acc/len(valid_data_loader)
                validation_acc.append(epoch_acc)
        
            print("{}, loss :{},accuracy:{}".format(phase,epoch_loss,epoch_acc))
    
    history = {'Train_loss':train_loss,'Train_accuracy':train_acc,
               'Validation_loss':validaion_loss,'Validation_Accuracy':validation_acc}
    return model,history[enter image description here][1]

我遇到了错误，因为需要 0D 或 1D 目标张量，不支持多目标，您能否帮助纠正上述代码。参考了之前的相关文章，但未能得到想要的结果。我必须更改哪些代码片段才能使我的模型成功运行。任何建议都是受欢迎的。提前致谢。

Answer 1

您的问题是标签具有正确的形状来计算损失。当您添加.unsqueeze(1)到标签时，您使用此形状[32,1]制作标签，这与计算损失的要求不一致。

要解决此问题，您只需删除.unsqueeze(1)for 标签即可。

如果您阅读CrossEntropLoss的文档，则参数：

• 输入应该是（N，C）形状，这就是outputs你的情况和[32,3]。
• 目标应该是 N 形，根据labels你的情况，应该是 [32]。因此，损失函数预计labels为1D target not multi-target。