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Added option "always save model" #1621

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Added option "always save model" #1621

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9 changes: 9 additions & 0 deletions deeppavlov/core/trainers/nn_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -76,6 +76,8 @@ class NNTrainer(FitTrainer):
log_on_k_batches: count of random train batches to calculate metrics in log (default is ``1``)
max_test_batches: maximum batches count for pipeline testing and evaluation, overrides ``log_on_k_batches``,
ignored if negative (default is ``-1``)
always_save_model: if True, we always save the obtained weights of our model, regardless of the metric.
(default if ``False``)
**kwargs: additional parameters whose names will be logged but otherwise ignored


Expand Down Expand Up @@ -107,6 +109,7 @@ def __init__(self, chainer_config: dict, *,
validate_first: bool = True,
validation_patience: int = 5, val_every_n_epochs: int = -1, val_every_n_batches: int = -1,
log_every_n_batches: int = -1, log_every_n_epochs: int = -1, log_on_k_batches: int = 1,
always_save_model: bool = False,
**kwargs) -> None:
super().__init__(chainer_config, batch_size=batch_size, metrics=metrics, evaluation_targets=evaluation_targets,
show_examples=show_examples, max_test_batches=max_test_batches, **kwargs)
Expand Down Expand Up @@ -141,6 +144,7 @@ def _improved(op):
self.max_epochs = epochs
self.epoch = start_epoch_num
self.max_batches = max_batches
self.always_save_model = always_save_model

self.train_batches_seen = 0
self.examples = 0
Expand Down Expand Up @@ -207,6 +211,11 @@ def _validate(self, iterator: DataLearningIterator,
self.score_best = score
log.info('Saving model')
self.save()
elif self.always_save_model:
log.info(f'Changed {m_name} from {self.score_best} to {score}')
self.score_best = score
log.info('But due to always_save_model, saving the model')
self.save()
else:
log.info('Did not improve on the {} of {}'.format(m_name, self.score_best))

Expand Down