SymmetricLCWATrainingLoop

class SymmetricLCWATrainingLoop(model, triples_factory, optimizer=None, optimizer_kwargs=None, lr_scheduler=None, lr_scheduler_kwargs=None, automatic_memory_optimization=True, mode=None, result_tracker=None, result_tracker_kwargs=None)[source]

Bases: TrainingLoop[Tuple[LongTensor], Tuple[LongTensor]]

A “symmetric” LCWA scoring heads and tails at once.

This objective was introduced by [lacroix2018] as

\[l_{i,j,k}(X) = - X_{i,j,k} + \log \left( \sum_{k'} \exp(X_{i,j,k′}) \right) - X_{k,j+P,i} + \log \left( \sum_{i'} \exp (X_{k, j+P, i'}) \right)\]

which can be seen as a “symmetric LCWA”, where for one batch of triples, we score both, heads and tails, given the remainder of the triple.

Note

at the same time, there is a also a difference to the LCWATrainingLoop: we do not group by e.g., head+relation pairs. Thus, the name might be suboptimal and change in the future.

Initialize the training loop.

Parameters:

  • model (Model) – The model to train

  • triples_factory (CoreTriplesFactory) – The training triples factory

  • optimizer (Union[str, Optimizer, Type[Optimizer], None]) – The optimizer to use while training the model

  • optimizer_kwargs (Optional[Mapping[str, Any]]) – additional keyword-based parameters to instantiate the optimizer (if necessary). params will be added automatically based on the model.

  • lr_scheduler (Union[str, LRScheduler, Type[LRScheduler], None]) – The learning rate scheduler you want to use while training the model

  • lr_scheduler_kwargs (Optional[Mapping[str, Any]]) – additional keyword-based parameters to instantiate the LR scheduler (if necessary). optimizer will be added automatically.

  • automatic_memory_optimization (bool) – bool Whether to automatically optimize the sub-batch size during training and batch size during evaluation with regards to the hardware at hand.

  • mode (Optional[Literal[‘training’, ‘validation’, ‘testing’]]) – The inductive training mode. None if transductive.

  • result_tracker (Union[str, ResultTracker, Type[ResultTracker], None]) – the result tracker

  • result_tracker_kwargs (Optional[Mapping[str, Any]]) – additional keyword-based parameters to instantiate the result tracker