ConvEInteraction
- class ConvEInteraction(input_channels=None, output_channels=32, embedding_height=None, embedding_width=None, kernel_width=3, kernel_height=None, input_dropout=0.2, feature_map_dropout=0.2, output_dropout=0.3, embedding_dim=200, apply_batch_normalization=True)[source]
Bases:
FunctionalInteraction
[FloatTensor
,FloatTensor
,Tuple
[FloatTensor
,FloatTensor
]]A stateful module for the ConvE interaction function.
Initialize the interaction module.
- Parameters
input_channels (
Optional
[int
]) – the number of input channels for the convolution operation. Can be inferred from other parameters, cf._calculate_missing_shape_information()
.output_channels (
int
) – the number of input channels for the convolution operationembedding_height (
Optional
[int
]) – the height of the “image” after reshaping the concatenated head and relation embedding. Can be inferred from other parameters, cf._calculate_missing_shape_information()
.embedding_width (
Optional
[int
]) – the width of the “image” after reshaping the concatenated head and relation embedding. Can be inferred from other parameters, cf._calculate_missing_shape_information()
.kernel_width (
int
) – the width of the convolution kernelkernel_height (
Optional
[int
]) – the height of the convolution kernel. Defaults to kernel_widthinput_dropout (
float
) – the dropout applied before the convolutionfeature_map_dropout (
float
) – the dropout applied after the convolutionoutput_dropout (
float
) – the dropout applied after the linear projectionembedding_dim (
int
) – the embedding dimension of entities and relationsapply_batch_normalization (
bool
) – whether to apply batch normalization
Attributes Summary
Methods Summary
func
(r, t, t_bias, input_channels, ...)Evaluate the ConvE interaction function.
Attributes Documentation
- tail_entity_shape = ('d', '')
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_dims, dim) The head representations.r (
FloatTensor
) – shape: (*batch_dims, dim) The relation representations.t (
FloatTensor
) – shape: (*batch_dims, dim) The tail representations.t_bias (
FloatTensor
) – shape: (*batch_dims) 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_dims The scores.