BinaryCrossEntropyLoss

class KGE.loss.BinaryCrossEntropyLoss

Bases: KGE.loss.Loss

An implementation of Binary Cross Entropy Loss.

Binary Cross Entropy Loss is commonly used in binary classification problem. In KGE, we can also turn the problem into a binary classification problem that classifies triplet into positive or negative \(y_i = 1~or~0\) with the triplet score as logit: \(logit_i = f\left( (h,r,t)_i \right)\)

\[ \begin{align}\begin{aligned}\begin{aligned} \mathscr{L} &= - \sum_i y_i log(\hat{y}_i) + (1-y_i) log(1-\hat{y}_i)\\ &= - \sum_i log\left[\sigma(f((h,r,t)_i^+))\right] - \sum_i log\left[1-\sigma(f((h,r,t)_i^-))\right]\\ &= - \sum_i log\left[\sigma(f((h,r,t)_i^+))\right] - \sum_i log\left[\sigma(-f((h,r,t)_i^-))\right] \end{aligned}\end{aligned}\end{align} \]

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)