torch_geometric.nn.kge.DistMult
- class DistMult(num_nodes: int, num_relations: int, hidden_channels: int, margin: float = 1.0, sparse: bool = False)[source]
Bases:
KGEModel
The DistMult model from the “Embedding Entities and Relations for Learning and Inference in Knowledge Bases” paper.
DistMult
models relations as diagonal matrices, which simplifies the bi-linear interaction between the head and tail entities to the score function:\[d(h, r, t) = < \mathbf{e}_h, \mathbf{e}_r, \mathbf{e}_t >\]Note
For an example of using the
DistMult
model, see examples/kge_fb15k_237.py.- Parameters:
num_nodes (int) – The number of nodes/entities in the graph.
num_relations (int) – The number of relations in the graph.
hidden_channels (int) – The hidden embedding size.
margin (float, optional) – The margin of the ranking loss. (default:
1.0
)sparse (bool, optional) – If set to
True
, gradients w.r.t. to the embedding matrices will be sparse. (default:False
)
- forward(head_index: Tensor, rel_type: Tensor, tail_index: Tensor) Tensor [source]
Returns the score for the given triplet.
- Parameters:
head_index (torch.Tensor) – The head indices.
rel_type (torch.Tensor) – The relation type.
tail_index (torch.Tensor) – The tail indices.
- Return type:
- loss(head_index: Tensor, rel_type: Tensor, tail_index: Tensor) Tensor [source]
Returns the loss value for the given triplet.
- Parameters:
head_index (torch.Tensor) – The head indices.
rel_type (torch.Tensor) – The relation type.
tail_index (torch.Tensor) – The tail indices.
- Return type: