mapping_networks.callbacks.EarlyStopping

class mapping_networks.callbacks.EarlyStopping(monitor: str = 'val_loss', patience: int = 10, min_delta: float = 0.0, mode: str = 'min')

Stop training when a monitored metric stops improving.

Parameters:

• monitor – Name of the metric to monitor (e.g. "val_loss").

• patience – Number of epochs with no improvement before stopping.

• min_delta – Minimum change to qualify as an improvement.

• mode – "min" to minimize the metric, "max" to maximize it.

Example:

trainer = MappingTrainer(
    ...,
    callbacks=[EarlyStopping(monitor="val_loss", patience=5)],
)

__init__(monitor: str = 'val_loss', patience: int = 10, min_delta: float = 0.0, mode: str = 'min') → None

Methods

__init__([monitor, patience, min_delta, mode])
on_batch_end(trainer, batch_idx, loss_output)	Called after processing each batch with the loss output.
on_batch_start(trainer, batch_idx)	Called before processing each batch.
on_epoch_end(trainer, epoch, metrics)	Called at the end of each epoch with aggregated metrics.
on_epoch_start(trainer, epoch)	Called at the beginning of each epoch.
on_fit_end(trainer)	Called once at the end of fit().
on_fit_start(trainer)	Called once at the beginning of fit().
on_validation_end(trainer, metrics)	Called after the validation loop with validation metrics.
on_validation_start(trainer)	Called before the validation loop.