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tfc.CloudOracle

Keras Tuner Oracle that wraps the AI Platform Vizier backend.

This is an implementation of KerasTuner Oracle that uses Google Cloud's Vizier Service.

Each Oracle class implements a particular hyperparameter tuning algorithm. An Oracle is passed as an argument to a Tuner. The Oracle tells the Tuner which hyperparameters should be tried next.

To learn more about Keras Tuner Oracles please refer to: https://keras-team.github.io/keras-tuner/documentation/oracles/

AI Platform Vizier is a black-box optimization service that helps you tune hyperparameters in complex machine learning (ML) models. When ML models have many different hyperparameters, it can be difficult and time consuming to tune them manually. AI Platform Vizier optimizes your model's output by tuning the hyperparameters for you. To learn more about AI Platform Vizier service see: https://cloud.google.com/ai-platform/optimizer/docs/overview

Examples:

oracle = CloudOracle(
    project_id=project_id,
    region='us-central1',
    objective='accuracy',
    hyperparameters=hyperparameters,
    study_config=None,
    max_trials=4,
    study_id=None,
)

project_id A GCP project id.
region A GCP region. e.g. 'us-central1'.
objective If a string, the direction of the optimization (min or max) will be inferred.
hyperparameters Mandatory and must include definitions for all hyperparameters used during the search. Can be used to override (or register in advance) hyperparameters in the search space.
study_config Study configuration for Vizier service.
max_trials Total number of trials (model configurations) to test at most. If None, it continues the search until it reaches the Vizier trial limit for each study. Users may stop the search externally (e.g. by killing the job). Note that the Oracle may interrupt the search before max_trials models have been tested.
study_id An identifier of the study. If not supplied, system-determined unique ID is given. The full study name will be projects/{project_id}/locations/{region}/studies/{study_id}, and the full trial name will be {study name}/trials/{trial_id}.

Methods

create_trial

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Create a new Trial to be run by the Tuner.

Args
tuner_id An ID that identifies the Tuner requesting a Trial. Tuners that should run the same trial (for instance, when running a multi-worker model) should have the same ID. If multiple suggestTrialsRequests have the same tuner_id, the service will return the identical suggested trial if the trial is PENDING, and provide a new trial if the last suggested trial was completed.

Returns
A Trial object containing a set of hyperparameter values to run in a Tuner.

Raises
SuggestionInactiveError Indicates that a suggestion was requested from an inactive study.

end_trial

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Record the measured objective for a set of parameter values.

get_best_trials

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Returns the trials with the best objective values found so far.

Args
num_trials positive int, number of trials to return.

Returns
List of KerasTuner Trials.

get_space

Returns the HyperParameters search space.

get_state

Returns the current state of this object.

This method is called during save.

get_trial

Returns the Trial specified by trial_id.

reload

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Reloads this object using set_state.

Arguments
fname The file name to restore from.

remaining_trials

save

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Saves this object using get_state.

Arguments
fname The file name to save to.

set_state

Sets the current state of this object.

This method is called during reload.

Arguments
state Dict. The state to restore for this object.

update_space

Add new hyperparameters to the tracking space.

Already recorded parameters get ignored.

Args
hyperparameters An updated HyperParameters object.

update_trial

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Used by a worker to report the status of a trial.