olm.train.callbacks.early_stopping_cb¶
Early stopping callback to prevent overfitting.
Classes¶
EarlyStoppingCallback([patience, min_delta]) |
Callback to stop training early if validation loss doesn't improve. |
|---|---|
class olm.train.callbacks.early_stopping_cb.EarlyStoppingCallback(patience: int = 5, min_delta: float = 0.0)¶
Bases: TrainerCallback
Callback to stop training early if validation loss doesn’t improve.
- Parameters:
- patience – Number of validation checks to wait for improvement.
- min_delta – Minimum change in validation loss to qualify as improvement.
on_step_end(trainer, step: int, loss: float) → None¶
Check for early stopping after each step.
class olm.train.callbacks.early_stopping_cb.TrainerCallback¶
Bases: object
Base class for trainer callbacks.
on_batch_begin(trainer: Trainer, batch_idx: int) → None¶
Called at the beginning of each batch.
on_batch_end(trainer: Trainer, batch_idx: int, loss: float) → None¶
Called at the end of each batch.
on_epoch_begin(trainer: Trainer, epoch: int) → None¶
Called at the beginning of each epoch.
on_epoch_end(trainer: Trainer, epoch: int) → None¶
Called at the end of each epoch.
on_step_begin(trainer: Trainer, step: int) → None¶
Called at the beginning of each optimization step (after gradient accumulation).
on_step_end(trainer: Trainer, step: int, loss: float) → None¶
Called at the end of each optimization step.
on_train_begin(trainer: Trainer) → None¶
Called at the beginning of training.
on_train_end(trainer: Trainer) → None¶
Called at the end of training.