EarlyStopping#

class ignite.handlers.early_stopping.EarlyStopping(patience, score_function, trainer, min_delta=0.0, cumulative_delta=False)[source]#

EarlyStopping handler can be used to stop the training if no improvement after a given number of events.

Parameters

patience (int) – Number of events to wait if no improvement and then stop the training.
score_function (Callable) – It should be a function taking a single argument, an Engine object, and return a score float. An improvement is considered if the score is higher.
trainer (Engine) – Trainer engine to stop the run if no improvement.
min_delta (float) – A minimum increase in the score to qualify as an improvement, i.e. an increase of less than or equal to min_delta, will count as no improvement.
cumulative_delta (bool) – It True, min_delta defines an increase since the last patience reset, otherwise, it defines an increase after the last event. Default value is False.

Examples:

from ignite.engine import Engine, Events
from ignite.handlers import EarlyStopping

def score_function(engine):
    val_loss = engine.state.metrics['nll']
    return -val_loss

handler = EarlyStopping(patience=10, score_function=score_function, trainer=trainer)
# Note: the handler is attached to an *Evaluator* (runs one epoch on validation dataset).
evaluator.add_event_handler(Events.COMPLETED, handler)

Methods

`load_state_dict`	Method replace internal state of the class with provided state dict data.
`state_dict`	Method returns state dict with `counter` and `best_score`.

load_state_dict(state_dict)[source]#

Method replace internal state of the class with provided state dict data.

Parameters: state_dict (Mapping) – a dict with “counter” and “best_score” keys/values.
Return type: None

state_dict()[source]#

Method returns state dict with counter and best_score. Can be used to save internal state of the class.

Return type: OrderedDict[str, float]