Loss Functions

Loss functions integrated in PyKEEN.

Functions

get_loss_cls(query)

Get the loss class.

Classes

PointwiseLoss([size_average, reduce, reduction])	Pointwise loss functions compute an independent loss term for each triple-label pair.
PairwiseLoss([size_average, reduce, reduction])	Pairwise loss functions compare the scores of a positive triple and a negative triple.
SetwiseLoss([size_average, reduce, reduction])	Setwise loss functions compare the scores of several triples.
BCEAfterSigmoidLoss([size_average, reduce, …])	A loss function which uses the numerically unstable version of explicit Sigmoid + BCE.
BCEWithLogitsLoss([size_average, reduce, …])	A wrapper around the numeric stable version of the PyTorch binary cross entropy loss.
CrossEntropyLoss([size_average, reduce, …])	Evaluate cross entropy after softmax output.
MarginRankingLoss([margin, size_average, …])	A wrapper around the PyTorch margin ranking loss.
MSELoss([size_average, reduce, reduction])	A wrapper around the PyTorch mean square error loss.
NSSALoss([margin, adversarial_temperature, …])	An implementation of the self-adversarial negative sampling loss function proposed by [sun2019].
SoftplusLoss([reduction])	A loss function for the softplus.

Class Inheritance Diagram