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# 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)

  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 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 spacial 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()

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