torch.nn.functional.binary_cross_entropy_with_logits — PyTorch 2.7 documentation (original) (raw)

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

Calculate 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_averageis 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_averageand 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 to be broadcasted with target. Must be a tensor with equal size along the class dimension to the number of classes. Pay close attention to PyTorch’s broadcasting semantics in order to achieve the desired operations. For a target of size [B, C, H, W] (where B is batch size) pos_weight of size [B, C, H, W] will apply different pos_weights to each element of the batch or [C, H, W] the same pos_weights across the batch. To apply the same positive weight along all spatial dimensions for a 2D multi-class target [C, H, W] use: [C, 1, 1]. Default: None

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()