PairwiseLogisticLoss

class KGE.loss.PairwiseLogisticLoss

Bases: KGE.loss.Loss

An implementation of Pairwise Logistic Loss.

Described in Loss Functions in Knowledge Graph Embedding Models.

For each pair of postive triplet \((h,r,t)_i^+\) and negative triplet \((h,r,t)_i^-\), Pairwise Logistic Loss compare the difference of scores between postivie triplet and negative triplet:

\[\Delta_i = f\left( (h,r,t)_i^- \right) - f\left( (h,r,t)_i^+ \right)\]

and define the Pairwise Logistic Loss as:

\[\mathscr{L} = \sum_i log(1+exp(\Delta_i))\]

Pairwise Logistic Loss is a smooth version of Pairwise Hinge Loss while \(\gamma = 0\), you can view function graph here to campare these two functions.

Methods Summary

__call__(pos_score, neg_score)

Calculate loss.

Methods Documentation

__call__(pos_score, neg_score)

Calculate loss.

Parameters

• pos_score (tf.Tensor) – score of postive triplets, with shape (n,)

• neg_score (tf.Tensor) – score of negative triplets, with shape (n,)

__init__()

Initialize loss

__new__(*args, **kwargs)