Source code for deephyper.stopper.integration.deepxde

from deepxde.callbacks import Callback

[docs]class DeepXDEStopperCallback(Callback): def __init__(self, job, mode="min", monitor="loss_test"): super().__init__() """Callback to use in conjonction with a DeepHyper ``RunningJob`` to stop the training when the ``Stopper`` is triggered. .. code-block:: python def run(job): callback = DeepXDEStopperCallback(job, ...) ... model.train(..., callbacks=[callback]) ... Args: job (RunningJob): The running job created by DeepHyper. monitor (str, optional): The metric to monitor. It can be any metric collected in the ``History``. Defaults to "loss_test" (equivalent to validation loss). mode (str, optional): If the metric is maximized or minimized. Value in ``["max", "min"]``. Defaults to "min". """ self.job = job self.monitor = monitor assert mode in ["max", "min"] self.mode = mode self.budget = 0 self.stopped = False def on_epoch_end(self): self.budget += 1 self.observe_and_stop(self.budget) def observe_and_stop(self, budget): objective = self.get_monitor_value() if self.mode == "min": objective = -objective self.job.record(budget, objective) if self.job.stopped(): self.model.stop_training = True self.stopped = True def get_monitor_value(self): if self.monitor == "loss_train": result = sum(self.model.train_state.loss_train) elif self.monitor == "loss_test": result = sum(self.model.train_state.loss_test) else: raise ValueError("The specified monitor function is incorrect.") return result