binary_cross_entropy_with_logits(input, target, weight=None, reduction='mean', pos_weight=None)¶
The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.binary_cross_entropy_with_logits.html.
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
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.
>>> import oneflow as flow >>> import oneflow.nn.functional as F >>> input = flow.randn(3, requires_grad=True) >>> target = flow.randn(3) >>> target[target >= 0] = 1 >>> target[target < 0] = 0 >>> loss = F.binary_cross_entropy_with_logits(input, target) >>> loss.backward()