SoftMarginRankingLoss
- class SoftMarginRankingLoss(margin=1.0, reduction='mean')[source]
Bases:
pykeen.losses.MarginPairwiseLossA module for the soft pairwise hinge loss (i.e., soft margin ranking loss).
\[L(k, \bar{k}) = \log(1 + \exp(f(k) - f(\bar{k}) + \lambda))\]Where \(k\) are the positive triples, \(\bar{k}\) are the negative triples, \(f\) is the interaction function (e.g.,
pykeen.models.TransEhas \(f(h,r,t)=\mathbf{e}_h+\mathbf{r}_r-\mathbf{e}_t\)), \(g(x)=\log(1 + \exp(x))\) is the softmax activation function, and \(\lambda\) is the margin.See also
When choosing margin=0`, this loss becomes equivalent to
pykeen.losses.SoftMarginRankingLoss. It is also closely related topykeen.losses.MarginRankingLoss, only differing in that this loss uses the softmax activation andpykeen.losses.MarginRankingLossuses the ReLU activation.Initialize the margin loss instance.
- Parameters
margin (
float) – The margin by which positive and negative scores should be apart.margin_activation – A margin activation. Defaults to
'relu', i.e. \(h(\Delta) = max(0, \Delta + \lambda)\), which is the default “margin loss”. Using'softplus'leads to a “soft-margin” formulation as discussed in https://arxiv.org/abs/1703.07737.reduction (
str) – The name of the reduction operation to aggregate the individual loss values from a batch to a scalar loss value. From {‘mean’, ‘sum’}.
Attributes Summary
The default strategy for optimizing the loss's hyper-parameters
Attributes Documentation