二元分类回归模型的准确度计算

如何解决二元分类回归模型的准确度计算

谁能告诉我为什么我的交叉熵损失函数给出这个错误：

我的准确率方法：

def accuracy(outputs,labels):
    _,preds = torch.max(outputs,dim=1)
    return torch.tensor(torch.sum(preds == labels).item() / len(preds))

我的类定义模型：

class PulsarLogisticRegression(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear= nn.Linear(input_size,output_size)
    def forward(self,xb):
        xb = xb.view(xb.size(0),-1)
        out= self.linear(xb)
        return out
    def training_step(self,batch):
        inputs,targets = batch 
        # Generate predictions
        out = self(inputs)          
        # Calcuate loss
        loss = F.cross_entropy(out,targets)                         
        return loss
    def validation_step(self,targets = batch
        # Generate predictions
        out = self(inputs)
        # Calculate loss
        loss = F.cross_entropy(out,targets)
        acc = accuracy(out,targets)        # Calculate accuracy
        return {'val_loss': loss,'val_acc': acc} # fill this    
    def validation_epoch_end(self,outputs):
        batch_losses = [x['val_loss'] for x in outputs]
        epoch_loss = torch.stack(batch_losses).mean()   # Combine losses
        batch_accs = [x['val_acc'] for x in outputs]
        epoch_acc = torch.stack(batch_accs).mean()      # Combine accuracies
        return {'val_loss': epoch_loss.item(),'val_acc': epoch_acc.item()}
   def epoch_end(self,epoch,result,num_epochs):
        # Print result every 20th epoch
        print("Epoch [{}],val_loss: {:.4f},val_acc: {:.4f}".format(epoch,result['val_loss'],result['val_acc']))

错误：交叉熵错误期望一维目标张量，但多目标被分配给它。

 RuntimeError                              Traceback (most recent call last)
<ipython-input-88-cd9b8a9a3b02> in <module>()
----> 1 result = evaluate(model,val_loader) # Use the the evaluate function
      2 print(result) 4 frames
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in nll_loss(input,target,weight,size_average,ignore_index,reduce,reduction)
   2262                          .format(input.size(0),target.size(0)))
   2263     if dim == 2:
-> 2264         ret = torch._C._nn.nll_loss(input,_Reduction.get_enum(reduction),ignore_index)
   2265     elif dim == 4:
   2266         ret = torch._C._nn.nll_loss2d(input,ignore_index)
 

RuntimeError: 1D target tensor expected,multi-target not supported

我对机器学习非常陌生，我正在尝试制作一个模型，该模型基于 5 列预测一列数据。列中的值是0和1。所以它基本上是一个二元分类模型。

我尝试过的：正如我所说，我对这个领域相当陌生，一些解释建议使用挤压函数以某种方式将目标张量的形状减少到一维，但这似乎会在该类的其他方法中引发一些其他错误。

我正在寻找可以帮助我获得正确准确度的误差函数。