Each epoch, if there's improvement in the monitored metric we serialize the model weights to a temp file. When training is done, we reload weights from the best model.
Arguments
- monitor
A string in the format
<set>_<metric>
where<set>
can be 'train' or 'valid' and<metric>
can be the abbreviation of any metric that you are tracking during training. The metric name is case insensitive.- mode
Specifies the direction that is considered an improvement. By default 'min' is used. Can also be 'max' (higher is better) and 'zero' (closer to zero is better).
- min_delta
Minimum improvement to reset the patience counter.
See also
Other luz_callbacks:
luz_callback_auto_resume()
,
luz_callback_csv_logger()
,
luz_callback_early_stopping()
,
luz_callback_interrupt()
,
luz_callback_lr_scheduler()
,
luz_callback_metrics()
,
luz_callback_mixed_precision()
,
luz_callback_mixup()
,
luz_callback_model_checkpoint()
,
luz_callback_profile()
,
luz_callback_progress()
,
luz_callback_resume_from_checkpoint()
,
luz_callback_train_valid()
,
luz_callback()