ERMLP
- class ERMLP(*, embedding_dim=64, hidden_dim=None, entity_initializer=<function uniform_>, relation_initializer=<function uniform_>, **kwargs)[source]
Bases:
ERModel
An implementation of ERMLP from [dong2014].
ERMLP is a multi-layer perceptron based approach that uses a single hidden layer and represents entities and relations as vectors. In the input-layer, for each triple the embeddings of head, relation, and tail are concatenated and passed to the hidden layer. The output-layer consists of a single neuron that computes the plausibility score of the triple:
\[f(h,r,t) = \textbf{w}^{T} g(\textbf{W} [\textbf{h}; \textbf{r}; \textbf{t}]),\]where \(\textbf{W} \in \mathbb{R}^{k \times 3d}\) represents the weight matrix of the hidden layer, \(\textbf{w} \in \mathbb{R}^{k}\), the weights of the output layer, and \(g\) denotes an activation function such as the hyperbolic tangent.
Initialize the model.
Attributes Summary
The default strategy for optimizing the model's hyper-parameters
Attributes Documentation
- Parameters: