Valueerror异常:使用一个目标的大小(火炬。大小([2,1]))，这是不同于输入的大小(火炬。大小([16,1]))被弃用

我在试图建立一个模型，用于表问题对数据集输出是二进制的1或0,但是我得到这一错误。 我知道的输出形状的我的模型是不同的输入的形状，但我不知道怎么修它。 批的大小设置为16

    class Bert_model (nn.Module):
      def __init__(self) :
        super(Bert_model,self).__init__()
        self.bert =  BertModel.from_pretrained('bert-base-uncased', return_dict=False)
        self.drop_layer = nn.Dropout(.25)
        self.output = nn.Linear(self.bert.config.hidden_size,1)
    
      def forward(self,input_ids,attention_mask):
        _,o2 = self.bert (input_ids =input_ids , attention_mask = attention_mask )
        o2 = self.drop_layer(o2)
        return self.output(o2)

    model = Bert_model()
    
    loss_fn = nn.BCELoss().to(device)

    def train_epoch(
      model, 
      data_loader, 
      loss_fn, 
      optimizer, 
      device, 
      n_examples
    ):
      model = model.train()
    
      losses = []
      correct_predictions = 0
      
      for d in data_loader:
        input_ids = d["input_ids"].to(device)
        attention_mask = d["attention_mask"].to(device)
        targets = d["target"].to(device)
    
        input_ids = input_ids.view(BATCH_SIZE,-1)
        attention_mask = attention_mask.view(BATCH_SIZE,-1)
    
        outputs = model(
          input_ids=input_ids,
          attention_mask=attention_mask
        )
    
        _, preds = torch.max(outputs, dim=1)
    
        targets = targets.unsqueeze(-1)
        loss = loss_fn(F.softmax(outputs,dim=1), targets)
    
        correct_predictions += torch.sum(preds == targets)
        losses.append(loss.item())
    
        loss.backward()
        nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
        optimizer.step()
        optimizer.zero_grad()
    
      return correct_predictions.double() / n_examples, np.mean(losses)

错误：

/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py in
binary_cross_entropy(input, target, weight, size_average, reduce,
reduction)    2913         weight = weight.expand(new_size)    2914 
-> 2915     return torch._C._nn.binary_cross_entropy(input, target, weight, reduction_enum)    2916     2917  ValueError: Using a target
size (torch.Size([2, 1])) that is different to the input size
(torch.Size([16, 1])) is deprecated