BCEWithLogitsLoss with BERT ValueError: Target size (torch.Size([68, 1, 1])) must be the same as input size

Here me again ! I’m trying to implement a Bert Classifier to discriminate between 2 sequences classes (BINARY CLASSIFICATION class 0 and class 1), with AX hyperparameters tuning.
This is all my code implemented anticipated by a sample of my datasets ( I have 3 csv, train-test-val).
Now I’m trying to use a BCEwithLogitLoss

                                               0	    1
	M A T T D R P T P D G T D A I D L T T R V R R...	1
	M K K L F Q T E P L L E L F N C N E L R I I G...	0
	M L V A A A V C P H P P L L I P E L A A G A A...	1
	M I V A W G N S G S G L L I L I L S L A V S A...	0
	M V E E G R R L A A L H P N I V V K L P T T E...	1
	M G S K V S K N A L V F N V L Q A L R E G L T...	1
	M P S K E T S P A E R M A R D E Y Y M R L A M...	1
	M V K E Y A L E W I D G Y R E R L V K V S D A...	1
	M G T A A S Q D R A A M A E A A Q R V G D S F...	0
def create_data_loader(df, tokenizer, max_len, batch_size):
  ds = SequenceDataset(

  return DataLoader(


train_data_loader = create_data_loader(df_train, tokenizer, MAX_LEN, BATCH_SIZE)

val_data_loader = create_data_loader(df_val, tokenizer, MAX_LEN, BATCH_SIZE)

test_data_loader = create_data_loader(df_test, tokenizer, MAX_LEN, BATCH_SIZE)

def net_train(net, train_data_loader, parameters, dtype, device):
  net.to(dtype=dtype, device=device)

  # Define loss and optimizer
  #criterion = nn.CrossEntropyLoss()
  criterion = nn.BCEWithLogitsLoss()
  optimizer = optim.SGD(net.parameters(), # or any optimizer you prefer 
                        lr=parameters.get("lr", 0.001), # 0.001 is used if no lr is specified
                        momentum=parameters.get("momentum", 0.9)

  scheduler = optim.lr_scheduler.StepLR(
      step_size=int(parameters.get("step_size", 30)),
      gamma=parameters.get("gamma", 1.0),  # default is no learning rate decay

  num_epochs = parameters.get("num_epochs", 3) # Play around with epoch number
  # Train Network
  current_loss = 0.0
# Train Network
  for _ in range(num_epochs):
      # Your dataloader returns a dictionary
      # so access it as such
      for batch in train_data_loader:
          # move data to proper dtype and device
          labels = batch['targets'].to(device=device)
          attention_mask = batch['attention_mask'].to(device=device)
          input_ids = batch['input_ids'].to(device=device)
          labels = labels \
                  .type(torch.FloatTensor) \
                  .reshape((labels.shape[0], 1))

          #labels = labels.long()
          # zero the parameter gradients

          # forward + backward + optimize
          outputs,x= net(input_ids.long(), attention_mask,return_dict=False)
          #outputs,x= net(input_ids,atten_mask)

          loss = criterion(outputs, labels.unsqueeze(1))
  return net

#from transformers.models.bert.modeling_bert import BertForSequenceClassification,AutoModel
def init_net(parameterization):

    model = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME,return_dict=True) #pretrained ResNet50

    # The depth of unfreezing is also a hyperparameter
    for param in model.parameters():
        param.requires_grad = False # Freeze feature extractor
    Hs = 512 # Hidden layer size; you can optimize this as well
    model.fc = nn.Sequential(nn.Linear(1024, 512), # attach trainable classifier
                                 nn.Linear(512, 1))
    return model # return untrained model

def train_evaluate(parameterization):

    # constructing a new training data loader allows us to tune the batch size

    train_data_loader=create_data_loader(df_train, tokenizer, MAX_LEN, batch_size=parameterization.get("batchsize", 32))
    # Get neural net
    untrained_net = init_net(parameterization) 
    # train
    trained_net = net_train(net=untrained_net, train_data_loader=train_data_loader, 
                            parameters=parameterization, dtype=dtype, device=device)
    # return the accuracy of the model as it was trained in this run
    return evaluate(

dtype = torch.float
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

best_parameters, values, experiment, model = optimize(
        {"name": "lr", "type": "range", "bounds": [1e-6, 0.4], "log_scale": True},
        {"name": "batchsize", "type": "range", "bounds": [16, 128]},
        {"name": "momentum", "type": "range", "bounds": [0.0, 1.0]},
        #{"name": "max_epoch", "type": "range", "bounds": [1, 30]},
        #{"name": "stepsize", "type": "range", "bounds": [20, 40]},        

means, covariances = values
    raise ValueError("Target size ({}) must be the same as input size ({})".format(target.size(), input.size()))
ValueError: Target size (torch.Size([68, 1, 1])) must be the same as input size (torch.Size([68, 450, 1024]))

I now that this loss required a specific input format, but I tried everything. Probably I am not understanding the theory correctly. Thank you very much ! I really don’t now what to do

The shape of the gt must be exactly the same than the shape of the target.
It’s a element-wise loss.

And take into account that the ground truth must be binary numbers but the target must be the logits (predictions before applying a sigmoid function) as the sigmoid is fused with the loss for numerical stability.


Thank you for your answer, but I understood more or less this concept however I try a lot of edits in my code but i don’t know where to operate.

Soo soz but I’m not NLP so I’d need further context :slight_smile:
What does the BERT Classifier model output?
Aka, this shape corresponds to? batch,probabilities,seq_length¿
[68, 450, 1024]
Sounds like you have a batch of 68 elements and you have a binary score for each (which sounds ok)

The problem you seem to have is your model is returning some features which doesn’t match a binary prediction. I would say you need to add few more layers to convert those ¿features? into a binary pred

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hello ! thanks for trying to help :smile: really appreciate it!
68 is the batch size
450 is the max len
and 1024 is the hidden size of the model

So in this case I’d say you need few extra layers to convert these 450x1024 features in a single binary decision. Despite I don’t know which ones are the optimal ones.

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