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

来自“知识库中嵌入实体和关系以进行学习和推理”论文的DistMult模型。

DistMult 将关系建模为对角矩阵，这简化了头实体和尾实体之间的双线性交互，得分函数为：

\[d(h, r, t) = < \mathbf{e}_h, \mathbf{e}_r, \mathbf{e}_t >\]

注意

有关使用DistMult模型的示例，请参见 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) – 排名损失的边距。 (默认值: 1.0)

  • sparse (bool, optional) – If set to True, gradients w.r.t. to the embedding matrices will be sparse. (default: False)

reset_parameters()[source]

重置模块的所有可学习参数。

forward(head_index: Tensor, rel_type: Tensor, tail_index: Tensor) → Tensor[source]

返回给定三元组的分数。

Parameters:
Return type:

Tensor

loss(head_index: Tensor, rel_type: Tensor, tail_index: Tensor) → Tensor[source]

返回给定三元组的损失值。

Parameters:
Return type:

Tensor