ValueError: Pomocou cieľového veľkosť (horák.Veľkosť([2, 1])), ktorá je odlišná od vstupného veľkosť (horák.Veľkosť([16, 1])) je zavrhované

Snažím sa vytvoriť model pre Quora otázky pár dataset, kde výstup je binárne buď 1 alebo 0, ale ja som si to chyba. Viem, výstupný tvar môj model je odlišná od vstupného tvar, ale neviem, ako to opraviť. Dávka veľkosť nastavená na hodnotu 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)

Chyby:

/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