class ERMLPE(*, embedding_dim=256, hidden_dim=None, input_dropout=0.2, hidden_dropout=None, entity_initializer=<function uniform_>, relation_initializer=None, **kwargs)[source]

Bases: ERModel

An extension of pykeen.models.ERMLP proposed by [sharifzadeh2019].

This model uses a neural network-based approach similar to ER-MLP and with slight modifications. In ER-MLP, the model is:

\[f(h, r, t) = \textbf{w}^{T} g(\textbf{W} [\textbf{h}; \textbf{r}; \textbf{t}])\]

whereas in ER-MLP (E) the model is:

\[f(h, r, t) = \textbf{t}^{T} f(\textbf{W} (g(\textbf{W} [\textbf{h}; \textbf{r}]))\]

including dropouts and batch-norms between each two hidden layers. ConvE can be seen as a special case of ER-MLP (E) that contains the unnecessary inductive bias of convolutional filters. The aim of this model is to show that lifting this bias from pykeen.models.ConvE (which simply leaves us with a modified ER-MLP model), not only reduces the number of parameters but also improves performance.

Initialize the model.

  • embedding_dim (int) – the embedding dimension (for both, entities and relations)

  • hidden_dim (Optional[int]) – the hidden dimension of the MLP; defaults to embedding_dim.

  • input_dropout (float) – the input dropout of the MLP

  • hidden_dropout (Optional[float]) – the hidden dropout of the MLP; defaults to input_dropout.

  • entity_initializer (Union[str, Callable[[FloatTensor], FloatTensor], None]) – the entity embedding initializer

  • relation_initializer (Union[str, Callable[[FloatTensor], FloatTensor], None]) – the relation embedding initializer; defaults to entity_initializer.

  • kwargs – additional keyword-based parameters passed to ERModel.__init__()

Attributes Summary


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


The default parameters for the default loss function class

Attributes Documentation

hpo_default: ClassVar[Mapping[str, Any]] = {'embedding_dim': {'high': 256, 'low': 16, 'q': 16, 'type': <class 'int'>}, 'hidden_dim': {'high': 9, 'low': 5, 'scale': 'power_two', 'type': <class 'int'>}, 'hidden_dropout': {'high': 0.5, 'low': 0.0, 'q': 0.1, 'type': <class 'float'>}, 'input_dropout': {'high': 0.5, 'low': 0.0, 'q': 0.1, 'type': <class 'float'>}}

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

loss_default_kwargs: ClassVar[Mapping[str, Any]] = {}

The default parameters for the default loss function class