class torch.nn.BCEWithLogitsLoss(weight: Optional[torch.Tensor] = None, size_average=None, reduce=None, reduction: str = 'mean', pos_weight: Optional[torch.Tensor] = None)
This loss combines a
Sigmoid layer and the
BCELoss in one single class. This version is more numerically stable than using a plain
Sigmoid followed by a
BCELoss as, by combining the operations into one layer, we take advantage of the log-sum-exp trick for numerical stability.
The unreduced (i.e. with
reduction set to
'none') loss can be described as:
where is the batch size. If
reduction is not
This is used for measuring the error of a reconstruction in for example an auto-encoder. Note that the targets
t[i] should be numbers between 0 and 1.
It’s possible to trade off recall and precision by adding weights to positive examples. In the case of multi-label classification the loss can be described as:
where is the class number ( for multi-label binary classification, for single-label binary classification), is the number of the sample in the batch and is the weight of the positive answer for the class .
increases the recall, increases the precision.
For example, if a dataset contains 100 positive and 300 negative examples of a single class, then
pos_weight for the class should be equal to . The loss would act as if the dataset contains positive examples.
>>> target = torch.ones([10, 64], dtype=torch.float32) # 64 classes, batch size = 10 >>> output = torch.full([10, 64], 1.5) # A prediction (logit) >>> pos_weight = torch.ones() # All weights are equal to 1 >>> criterion = torch.nn.BCEWithLogitsLoss(pos_weight=pos_weight) >>> criterion(output, target) # -log(sigmoid(1.5)) tensor(0.2014)
reduction). By default, the losses are averaged over each loss element in the batch. Note that for some losses, there are multiple elements per sample. If the field
size_averageis set to
False, the losses are instead summed for each minibatch. Ignored when reduce is
reduction). By default, the losses are averaged or summed over observations for each minibatch depending on
False, returns a loss per batch element instead and ignores
'none': no reduction will be applied,
'mean': the sum of the output will be divided by the number of elements in the output,
'sum': the output will be summed. Note:
reduceare in the process of being deprecated, and in the meantime, specifying either of those two args will override
'none', then , same shape as input.
>>> loss = nn.BCEWithLogitsLoss() >>> input = torch.randn(3, requires_grad=True) >>> target = torch.empty(3).random_(2) >>> output = loss(input, target) >>> output.backward()
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