functional.py 文件源码

def binary_cross_entropy(input, target, weight=None, size_average=True):
    r"""Function that measures the Binary Cross Entropy
    between the target and the output.

    See :class:`~torch.nn.BCELoss` for details.

    Args:
        input: Variable of arbitrary shape
        target: Variable of the same shape as input
        weight (Variable, optional): a manual rescaling weight
                if provided it's repeated to match input tensor shape
        size_average (bool, optional): By default, the losses are averaged
                over observations for each minibatch. However, if the field
                sizeAverage is set to False, the losses are instead summed
                for each minibatch. Default: True

    Examples::

        >>> input = autograd.Variable(torch.randn(3), requires_grad=True)
        >>> target = autograd.Variable(torch.LongTensor(3).random_(2))
        >>> loss = F.binary_cross_entropy(F.sigmoid(input), target)
        >>> loss.backward()
    """
    if not (target.size() == input.size()):
        warnings.warn("Using a target size ({}) that is different to the input size ({}) is deprecated. "
                      "Please ensure they have the same size.".format(target.size(), input.size()))
    if input.nelement() != target.nelement():
        raise ValueError("Target and input must have the same number of elements. target nelement ({}) "
                         "!= input nelement ({})".format(target.nelement(), input.nelement()))

    if weight is not None:
        new_size = _infer_size(target.size(), weight.size())
        weight = weight.expand(new_size)

    return _functions.thnn.BCELoss.apply(input, target, weight, size_average)