BasesDecomposition
- class BasesDecomposition(input_dim, num_relations, num_bases=None, output_dim=None, memory_intense=False)[source]
Bases:
pykeen.nn.message_passing.DecompositionRepresent relation-weights as a linear combination of base transformation matrices.
The basis decomposition represents the relation-specific transformation matrices as a weighted combination of base matrices, \(\{\mathbf{B}_i^l\}_{i=1}^{B}\), i.e.,
\[\mathbf{W}_r^l = \sum \limits_{b=1}^B \alpha_{rb} \mathbf{B}^l_i\]Initialize the layer.
- Parameters
input_dim (
int) – >0 The input dimension.num_relations (
int) – >0 The number of relations.output_dim (
Optional[int]) – >0 The output dimension. If None is given, defaults to input_dim.memory_intense (
bool) – Enable memory-intense forward pass which may be faster, in particular if the number of different relations is small.
- Raises
ValueError – If the
num_basesis greater thannum_relations
Methods Summary
forward(x, source, target, edge_type[, ...])Relation-specific message passing from source to target.
Reset the parameters of this layer.
Methods Documentation
- forward(x, source, target, edge_type, edge_weights=None, accumulator=None)[source]
Relation-specific message passing from source to target.
- Parameters
x (
FloatTensor) – shape: (num_nodes, input_dim) The node representations.source (
LongTensor) – shape: (num_edges,) The source indices.target (
LongTensor) – shape: (num_edges,) The target indices.edge_type (
LongTensor) – shape: (num_edges,) The edge types.edge_weights (
Optional[FloatTensor]) – shape: (num_edges,) Precomputed edge weights.accumulator (
Optional[FloatTensor]) – shape: (num_nodes, output_dim) a pre-allocated output accumulator. may be used if multiple different message passing steps are performed and accumulated by sum. If none is given, create an accumulator filled with zeroes.
- Return type
FloatTensor- Returns
shape: (num_nodes, output_dim) The enriched node embeddings.