ERMLP
- class ERMLP(*, embedding_dim: int = 64, hidden_dim: int | None = None, activation: str | ~torch.nn.modules.module.Module | type[~torch.nn.modules.module.Module] | None = <class 'torch.nn.modules.activation.ReLU'>, activation_kwargs: ~collections.abc.Mapping[str, ~typing.Any] | None = None, entity_initializer: str | ~collections.abc.Callable[[~torch.Tensor], ~torch.Tensor] | None = <function uniform_>, relation_initializer: str | ~collections.abc.Callable[[~torch.Tensor], ~torch.Tensor] | None = <function uniform_>, **kwargs)[source]
Bases:
ERModel
[Tensor
,Tensor
,Tensor
]An implementation of ERMLP from [dong2014].
This model represents both entities and relations as \(d\)-dimensional vectors stored in an
Embedding
matrix. The representations are then passed to theERMLPInteraction
function to obtain scores.Initialize the model.
- Parameters:
embedding_dim (int) – The embedding vector dimension for entities and relations.
hidden_dim (int | None) – The hidden dimension of the MLP. Defaults to embedding_dim.
activation (str | Module | type[Module] | None) – The activation function or a hint thereof.
activation_kwargs (Mapping[str, Any] | None) – Additional keyword-based parameters passed to the activation’s constructor, if the activation is not pre-instantiated.
entity_initializer (str | Callable[[Tensor], Tensor] | None) – the method to initialize the entity embeddings
relation_initializer (str | Callable[[Tensor], Tensor] | None) – the method to initialize the entity embeddings
kwargs – additional keyword-based parameters passed to
pykeen.models.ERModel
Note
The parameter pair
(activation, activation_kwargs)
is used forclass_resolver.contrib.torch.activation_resolver
An explanation of resolvers and how to use them is given in https://class-resolver.readthedocs.io/en/latest/.
Attributes Summary
The default strategy for optimizing the model's hyper-parameters
Attributes Documentation