neptunecontrib.monitoring.kerastuner
¶
Module Contents¶
Classes¶
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Logs hyperparameter optimization process to Neptune. |
Functions¶
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Logs runs results and parameters to neptune. |
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class
neptunecontrib.monitoring.kerastuner.
NeptuneLogger
(experiment=None)¶ Bases:
kerastuner.engine.logger.Logger
Logs hyperparameter optimization process to Neptune.
- For each iteration it logs run parameters (‘hyperparameters/values’ text log),
and all the metrics and losses to Neptune.
- Parameters
experiment (neptune.experiments.Experiment) – Neptune experiment. Default is None.
Examples
Initialize neptune_monitor:
import neptune import neptunecontrib.monitoring.kerastuner as npt_utils neptune.init(api_token='ANONYMOUS', project_qualified_name='shared/keras-tuner-integration') neptune.create_experiment(name='bayesian-sweep') neptune_logger = npt_utils.NeptuneLogger()
Run Keras Tuner search passing neptune_logger as logger:
... tuner = BayesianOptimization( build_model, objective='val_accuracy', max_trials=10, num_initial_points=3, executions_per_trial=3, project_name='bayesian-sweep', logger=npt_utils.NeptuneLogger())
You can explore an example experiment in Neptune: https://ui.neptune.ai/o/shared/org/keras-tuner-integration/e/KER-19/charts
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report_trial_state
(self, trial_id, trial_state)¶ Gives the logger information about trial status.
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register_tuner
(self, tuner_state)¶
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register_trial
(self, trial_id, trial_state)¶
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exit
(self)¶
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neptunecontrib.monitoring.kerastuner.
log_tuner_info
(tuner, experiment=None, log_project_dir=True)¶ Logs runs results and parameters to neptune.
Logs all hyperparameter optimization results to Neptune. Those include best score (‘best_score’ metric), best parameters (‘best_parameters’ property), score for every run (‘run_score’ metric), tuner project directory as an artifact, parameter space (‘hyperparameters/space’ text log), tuner id (‘tuner_id’ property), best trial id (‘best_trial_id’ property), name of the metric/loss used as objective, and it’s direction (‘objective/name’ and ‘objective/direction’ property).
- Parameters
tuner ('kerastuner.engine.tuner.Tuner') – Keras Tuner object after training is completed.
experiment (neptune.experiments.Experiment) – Neptune experiment. Default is None.
log_project_dir ('bool') – Whether Keras Tuner project directory, with all the trial information, should be logged to Neptune.
Examples
Initialize neptune experiment:
import neptune neptune.init(api_token='ANONYMOUS', project_qualified_name='shared/keras-tuner-integration') neptune.create_experiment(name='bayesian-sweep')
Run Keras Tuner search:
... tuner = BayesianOptimization(...) tuner.search(x=x, y=y, epochs=5, validation_data=(val_x, val_y))
Log information from the Tuner object to Neptune:
import neptunecontrib.monitoring.kerastuner as npt_utils npt_utils.log_tuner_info(tuner)
You can explore an example experiment in Neptune: https://ui.neptune.ai/o/shared/org/keras-tuner-integration/e/KER-19/details