TransE

class ampligraph.latent_features.layers.scoring.TransE(*args, **kwargs)

Translating Embeddings (TransE) scoring layer.

The model as described in [BUGD+13].

The scoring function of TransE computes a similarity between the embedding of the subject \(\mathbf{e}_{sub}\) translated by the embedding of the predicate \(\mathbf{e}_{pred}\), and the embedding of the object \(\mathbf{e}_{obj}\), using the \(L_1\) or \(L_2\) norm \(||\cdot||\) (default: \(L_1\)):

\[f_{TransE}=-||\mathbf{e}_{sub} + \mathbf{e}_{pred} - \mathbf{e}_{obj}||\]

Such scoring function is then used on positive and negative triples \(t^+, t^-\) in the loss function.

Attributes

class_params
external_params
name

Methods

__init__(k)	Initializes the scoring layer.
get_config()	Returns the config of the layer.

__init__(k)

Initializes the scoring layer.

Parameters:

k (int) – Embedding size.

get_config()

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Note that get_config() does not guarantee to return a fresh copy of dict every time it is called. The callers should make a copy of the returned dict if they want to modify it.

Returns:

Python dictionary.