TransF

class TransF(embedding_dim=128, entity_initializer=None, entity_initializer_kwargs=None, entity_normalizer=None, entity_normalizer_kwargs=None, relation_initializer=None, relation_initializer_kwargs=None, **kwargs)

Bases: ERModel

An implementation of TransF from [feng2016].

Initialize TransF via the pykeen.nn.modules.TransFInteraction interaction.

Parameters:

• embedding_dim (int) – The entity embedding dimension \(d\).

• entity_initializer (Union[str, Callable[[FloatTensor], FloatTensor], None]) – Entity initializer function. Defaults to torch.nn.init.uniform_()

• entity_initializer_kwargs (Optional[Mapping[str, Any]]) – Keyword arguments to be used when calling the entity initializer

• entity_normalizer (Union[str, Callable[[FloatTensor], FloatTensor], None]) – Entity normalizer function. Defaults to torch.nn.functional.normalize()

• entity_normalizer_kwargs (Optional[Mapping[str, Any]]) – Keyword arguments to be used when calling the entity normalizer

• relation_initializer (Union[str, Callable[[FloatTensor], FloatTensor], None]) – Relation initializer function. Defaults to torch.nn.init.uniform_()

• relation_initializer_kwargs (Optional[Mapping[str, Any]]) – Keyword arguments to be used when calling the relation initializer

• kwargs – Remaining keyword arguments passed through to pykeen.models.ERModel.

Attributes Summary

hpo_default

The default strategy for optimizing the model's hyper-parameters

Attributes Documentation

hpo_default: ClassVar[Mapping[str, Any]] = {'embedding_dim': {'high': 256, 'low': 16, 'q': 16, 'type': <class 'int'>}}

The default strategy for optimizing the model’s hyper-parameters