binary_cross_entropy_with_logits(input, target, weight=None, size_average=None, reduce=None, reduction='mean', pos_weight=None)¶
Function that measures Binary Cross Entropy between target and input logits.
input – Tensor of arbitrary shape as unnormalized scores (often referred to as logits).
target – Tensor of the same shape as input with values between 0 and 1
weight (Tensor, optional) – a manual rescaling weight if provided it’s repeated to match input tensor shape
size_average (bool, optional) – Deprecated (see
reduction). By default, the losses are averaged over each loss element in the batch. Note that for some losses, there multiple elements per sample. If the field
size_averageis set to
False, the losses are instead summed for each minibatch. Ignored when reduce is
reduce (bool, optional) – Deprecated (see
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
reduction (string, optional) – Specifies the reduction to apply to the output:
'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
pos_weight (Tensor, optional) – a weight of positive examples. Must be a vector with length equal to the number of classes.
>>> input = torch.randn(3, requires_grad=True) >>> target = torch.empty(3).random_(2) >>> loss = F.binary_cross_entropy_with_logits(input, target) >>> loss.backward()