ConvEInteraction

class ConvEInteraction(input_channels=None, output_channels=32, embedding_height=None, embedding_width=None, kernel_height=3, kernel_width=3, input_dropout=0.2, output_dropout=0.3, feature_map_dropout=0.2, embedding_dim=200, apply_batch_normalization=True)[source]

Bases: pykeen.nn.modules.FunctionalInteraction[torch.FloatTensor, torch.FloatTensor, Tuple[torch.FloatTensor, torch.FloatTensor]]

A stateful module for the ConvE interaction function.

See also

pykeen.nn.functional.conve_interaction()

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Attributes Summary

tail_entity_shape

The symbolic shapes for entity representations for tail entities, if different.

Methods Summary

func(r, t, t_bias, input_channels, …)

Evaluate the ConvE interaction function.

Attributes Documentation

tail_entity_shape: Optional[Sequence[str]] = ('d', 'k')

The symbolic shapes for entity representations for tail entities, if different. This is ony relevant for ConvE.

Methods Documentation

func(r, t, t_bias, input_channels, embedding_height, embedding_width, hr2d, hr1d)

Evaluate the ConvE interaction function.

Parameters

  • h (FloatTensor) – shape: (batch_size, num_heads, 1, 1, dim) The head representations.

  • r (FloatTensor) – shape: (batch_size, 1, num_relations, 1, dim) The relation representations.

  • t (FloatTensor) – shape: (batch_size, 1, 1, num_tails, dim) The tail representations.

  • t_bias (FloatTensor) – shape: (batch_size, 1, 1, num_tails, 1) The tail entity bias.

  • input_channels (int) – The number of input channels.

  • embedding_height (int) – The height of the reshaped embedding.

  • embedding_width (int) – The width of the reshaped embedding.

  • hr2d (Module) – The first module, transforming the 2D stacked head-relation “image”.

  • hr1d (Module) – The second module, transforming the 1D flattened output of the 2D module.

Return type

FloatTensor

Returns

shape: (batch_size, num_heads, num_relations, num_tails) The scores.