Python v0.3.0.dev+c1b8f91
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    • BCEWithLogitsLoss

    class dragon.vm.torch.nn.BCEWithLogitsLoss(
      weight=None,
      size_average=None,
      reduce=None,
      reduction='valid',
      pos_weight=None
    )[source]

    Compute the sigmoid cross entropy with contiguous targets.

    Examples:

    m = torch.nn.BCEWithLogitsLoss()
loss = m(torch.randn(2, 1), torch.tensor([0., 1.], 'float32'))

    __init__

    BCEWithLogitsLoss.__init__(
      weight=None,
      size_average=None,
      reduce=None,
      reduction='valid',
      pos_weight=None
    )[source]

    Create a BCEWithLogitsLoss module.

    Parameters:
    • weight (dragon.vm.torch.Tensor, optional) – The weight for each class.
    • size_average (bool, optional) – True to set the reduction to ‘mean’.
    • reduce (bool, optional) – True to set the reduction to ‘sum’ or ‘mean’.
    • reduction ({'none', 'mean', 'sum', 'valid'}, optional) – The reduce method.