ncalab.training.traininghistory

Classes

TrainingStatus	Encodes last status of the training.
TrainingHistory	Stores data about the training progress. Populated during training

Module Contents

class ncalab.training.traininghistory.TrainingStatus(*args, **kwds)

Bases: enum.Enum

Encodes last status of the training.

STATUS_NONE = 0

STATUS_RUNNING = 1

STATUS_DONE = 2

class ncalab.training.traininghistory.TrainingHistory(path: pathlib.Path | pathlib.PosixPath | None, metrics: Dict[str, float], current_epoch: int, current_model: ncalab.models.AbstractNCAModel, best_accuracy: float = 0, best_epoch: int = 0, best_model: ncalab.models.AbstractNCAModel | None = None, verbose: bool = True)

Stores data about the training progress. Populated during training with ncalab.training.BasicNCATrainer.

Parameters:

• path (Optional[Path | PosixPath]) – Save and load path.

• metrics (Dict[str, float]) – Dict of validation metrics

• current_epoch (int) – Current training epoch.

• current_model (AbstractNCAModel) – Currently trained model.

• best_accuracy (float, optional) – Best validation accuracy, defaults to 0

• best_epoch (int, optional) – Epoch of best validation accuracy, defaults to 0

• best_model (Optional[AbstractNCAModel], optional) – Model with best validation accuracy, defaults to None

• verbose (bool, optional) – Whether to print updates of validation accuracy, defaults to True

path

metrics

current_epoch

current_model

best_accuracy = 0

best_epoch = 0

best_model = None

verbose = True

created_timestamp

modified_timestamp

loss: List[float] = []

update(epoch: int, model: ncalab.models.AbstractNCAModel, accuracy: float, overwrite: bool = False)

Populates history with current iteration’s values.

Automatically recognizes changes in accuracy.

Parameters:

• epoch (int) – Current epoch

• model (AbstractNCAModel) – Current model

• accuracy (float) – Current accuracy, based on model’s validation metric

• overwrite (bool, optional) – Whether to overwrite best accuracy even with no improvement, defaults to False

save()

Saves history and model checkpoint.

to_dict() → Dict

Return dict of recorded values

Returns:

Dict of recorded values

Return type:

Dict