Early Stopping module

nnodely.support.earlystopping.early_stop_patience(train_losses, val_losses, params)[source]

Determines whether to stop training early based on validation loss and patience.

Parameters:

  • train_losses (dict) – A dictionary where keys are epoch numbers and values are lists of training loss values.

  • val_losses (dict) – A dictionary where keys are epoch numbers and values are lists of validation loss values.

  • params (dict) – A dictionary of parameters. Should contain ‘patience’ which is the number of epochs to wait for improvement. Optionally, it can contain ‘error’ which specifies the type of loss to be used.

Returns:

True if training should be stopped early, False otherwise.

Return type:

bool

nnodely.support.earlystopping.select_best_model(train_losses, val_losses, params)[source]

Selects the best model based on the validation or training losses.

Parameters:

  • train_losses (dict) – A dictionary where keys are epoch numbers and values are lists of training loss values.

  • val_losses (dict) – A dictionary where keys are epoch numbers and values are lists of validation loss values.

  • params (dict) – A dictionary of parameters.

Returns:

True if the current model is the best model, False otherwise.

Return type:

bool

nnodely.support.earlystopping.mean_stopping(train_losses, val_losses, params)[source]

Determines whether to stop training early based on the mean difference between training and validation losses.

Parameters:

  • train_losses (dict) – A dictionary where keys are epoch numbers and values are lists of training loss values.

  • val_losses (dict) – A dictionary where keys are epoch numbers and values are lists of validation loss values.

  • params (dict) – A dictionary of parameters. Should contain ‘tol’ which is the tolerance value for early stopping.

Returns:

True if training should be stopped early, False otherwise.

Return type:

bool

nnodely.support.earlystopping.standard_early_stopping(train_losses, val_losses, params)[source]

Determines whether to stop training early based on training and validation losses.

Parameters:

  • train_losses (dict) – A dictionary where keys are epoch numbers and values are lists of training loss values.

  • val_losses (dict) – A dictionary where keys are epoch numbers and values are lists of validation loss values.

  • params (dict) – A dictionary of parameters. Should contain ‘tol’ which is the tolerance value for early stopping.

Returns:

True if training should be stopped early, False otherwise.

Return type:

bool