mure_interaction¶
- mure_interaction(h, b_h, r_vec, r_mat, t, b_t, p=2, power_norm=False)[source]¶
Evaluate the MuRE interaction function from [balazevic2019b].
\[-\|Rh + r - t\| + b_h + b_t\]- Parameters
h (
FloatTensor) – shape: (batch_size, num_heads, 1, 1, dim) The head representations.b_h (
FloatTensor) – shape: (batch_size, num_heads, 1, 1) The head entity bias.r_vec (
FloatTensor) – shape: (batch_size, 1, num_relations, 1, dim) The relation vector.r_mat (
FloatTensor) – shape: (batch_size, 1, num_relations, 1, dim,) The diagonal relation matrix.t (
FloatTensor) – shape: (batch_size, 1, 1, num_tails, dim) The tail representations.b_t (
FloatTensor) – shape: (batch_size, 1, 1, num_tails) The tail entity bias.p (
Union[int,float,str]) – The parameter p for selecting the norm, cf. torch.norm.power_norm (
bool) – Whether to return the powered norm instead.
- Return type
FloatTensor- Returns
shape: (batch_size, num_heads, num_relations, num_tails) The scores.