torch.nn.functional.binary_cross_entropy_with_logits¶

torch.nn.functional.binary_cross_entropy_with_logits(input, target, weight=None, size_average=None, reduce=None, reduction='mean', pos_weight=None)[source]

Function that measures Binary Cross Entropy between target and input logits.

See BCEWithLogitsLoss for details.

Parameters:
• input (Tensor) – Tensor of arbitrary shape as unnormalized scores (often referred to as logits).

• target (Tensor) – 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_average is set to False, the losses are instead summed for each minibatch. Ignored when reduce is False. Default: True

• reduce (bool, optional) – Deprecated (see reduction). By default, the losses are averaged or summed over observations for each minibatch depending on size_average. When reduce is False, returns a loss per batch element instead and ignores size_average. Default: True

• reduction (str, optional) – Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'. '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: size_average and reduce are in the process of being deprecated, and in the meantime, specifying either of those two args will override reduction. Default: 'mean'

• pos_weight (Tensor, optional) – a weight of positive examples. Must be a vector with length equal to the number of classes.

Return type:

Tensor

Examples:

>>> input = torch.randn(3, requires_grad=True)
>>> target = torch.empty(3).random_(2)
>>> loss = F.binary_cross_entropy_with_logits(input, target)
>>> loss.backward()