def __init__(self, reduction: Optional[Union[nn.Module, str]] = 'sum'):
"""
Initialize BayesianPersonalizedRankingLoss
Args:
reduction Union[nn.Module, str], optional): reduction method to calculate loss. Defaults to sum
"""
super().__init__()
self.reduction = get_reduction(reduction)
def __init__(self,
margin: float = 1.0,
reduction: Optional[nn.Module] = torch.sum):
"""
Initialize HingeLoss
Args:
margin (float, optional): Margin size of loss. Defaults to 1.0
reduction (nn.Module, optional): Reduction method to calculate loss. Defaults to sum
"""
super().__init__()
self.margin = margin
self.reduction = get_reduction(reduction)
def __init__(self,
reduction: Union[Callable[[torch.Tensor], torch.Tensor], str] = "sum"):
r"""Initialize BayesianPersonalizedRankingLoss
Args:
reduction Union[Callable[T, T], str], optional): reduction method to calculate loss.
Defaults to torch.sum.
Attributes:
reduction (Union[Callable[T, T], str]): reduction method to calculate loss.
"""
# Refer to parent class
super(BayesianPersonalizedRankingLoss, self).__init__()
# Bind reduction to reduction
self.reduction = get_reduction(reduction)
def __init__(self,
margin: float = 1.0,
reduction: Callable[[torch.Tensor], torch.Tensor] = torch.sum):
r"""Initialize HingeLoss
Args:
margin (float, optional): Margin size of loss. Defaults to 1.0.
reduction (Callable[T, T], optional): Reduction method to calculate loss.
Defaults to torch.sum.
Attributes:
margin (float): Margin size of loss.
reduction (Callable[T, T]): Reduction method to calculate loss.
"""
# Refer to parent class
super(HingeLoss, self).__init__()
# Bind margin and reduction to margin and reduction
self.margin = margin
self.reduction = get_reduction(reduction)