deephyper.stopper.integration.tf_keras2.TFKerasStopperCallback

deephyper.stopper.integration.tf_keras2.TFKerasStopperCallback#

class deephyper.stopper.integration.tf_keras2.TFKerasStopperCallback(*args: Any, **kwargs: Any)[source]#

Bases: Callback

Tensorflow/Keras callback to be used with a DeepHyper RunningJob.

This stops the training when the Stopper is triggered.

def run(job):
    callback = TFKerasStopperCallback(job, ...)
    ...
    model.fit(..., callbacks=[callback])
    ...
Parameters:
  • 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 "val_loss".

  • mode (str, optional) – If the metric is maximized or minimized. Value in `` [“max”, “min”]``. Defaults to "max".

Methods

on_epoch_end

Called at the end of an epoch during training.

__call__(*args: Any, **kwargs: Any) Any#

Call self as a function.

on_epoch_end(epoch, logs=None)[source]#

Called at the end of an epoch during training.

Parameters:
  • epoch – Integer, index of epoch.

  • logs – Dict, metric results for this training epoch, and for the validation epoch if validation is performed. Validation result keys are prefixed with val_. For training epoch, the values of the Model’s metrics are returned. Example: {‘loss’: 0.2, ‘accuracy’: 0.7}.