tfc.CloudTuner

KerasTuner interface implementation backed by Vizier Service.

tfc.CloudTuner(
    hypermodel: Union[hypermodel_module.HyperModel, Callable[[hp_module.HyperParameters],
        tf.keras.Model]],
    project_id: Text,
    region: Text,
    objective: Union[Text, oracle_module.Objective] = None,
    hyperparameters: hp_module.HyperParameters = None,
    study_config: Optional[Dict[Text, Any]] = None,
    max_trials: int = None,
    study_id: Optional[Text] = None,
    **kwargs
)

CloudTuner is a implementation of KerasTuner that uses Google Cloud Vizier Service as its Oracle. To learn more about KerasTuner and Oracles please refer to:

Example:

tuner = CloudTuner(
      build_model,
      project_id="MY_PROJECT_ID",
      region='us-central1',
      objective='accuracy',
      hyperparameters=HPS,
      max_trials=5,
      directory='tmp/MY_JOB')

hypermodel Instance of HyperModel class (or callable that takes hyperparameters and returns a Model instance).
project_id A GCP project id.
region A GCP region. e.g. 'us-central1'.
objective Name of model metric to minimize or maximize, e.g. "val_accuracy".
hyperparameters 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. Note that the oracle may interrupt the search before max_trials models have been tested if the search space has been exhausted.
study_id An identifier of the study. The full study name will be projects/{project_id}/locations/{region}/studies/{study_id}.
**kwargs Keyword arguments relevant to all Tuner subclasses. Please see the docstring for Tuner.

project_dir

remaining_trials Returns the number of trials remaining.

Will return None if max_trials is not set.

Methods

get_best_hyperparameters

get_best_hyperparameters(
    num_trials=1
)

Returns the best hyperparameters, as determined by the objective.

This method can be used to reinstantiate the (untrained) best model found during the search process.

Example:

best_hp = tuner.get_best_hyperparameters()[0]
model = tuner.hypermodel.build(best_hp)

Arguments
num_trials (int, optional). Number of HyperParameters objects to return. HyperParameters will be returned in sorted order based on trial performance.

Returns
List of HyperParameter objects.

get_best_models

get_best_models(
    num_models=1
)

Returns the best model(s), as determined by the tuner's objective.

The models are loaded with the weights corresponding to their best checkpoint (at the end of the best epoch of best trial).

This method is only a convenience shortcut. For best performance, It is recommended to retrain your Model on the full dataset using the best hyperparameters found during search.

Args
num_models (int, optional): Number of best models to return. Models will be returned in sorted order. Defaults to 1.

Returns
List of trained model instances.

get_state

get_state()

Returns the current state of this object.

This method is called during save.

get_trial_dir

get_trial_dir(
    trial_id
)

load_model

load_model(
    trial
)

Loads a Model from a given trial.

Arguments
trial A Trial instance. For models that report intermediate results to the Oracle, generally load_model should load the best reported step by relying of trial.best_step

on_batch_begin

on_batch_begin(
    trial, model, batch, logs
)

A hook called at the start of every batch.

Arguments
trial A Trial instance.
model A Keras Model.
batch The current batch number within the curent epoch.
logs Additional metrics.

on_batch_end

on_batch_end(
    trial, model, batch, logs=None
)

A hook called at the end of every batch.

Arguments
trial A Trial instance.
model A Keras Model.
batch The current batch number within the curent epoch.
logs Additional metrics.

on_epoch_begin

on_epoch_begin(
    trial, model, epoch, logs=None
)

A hook called at the start of every epoch.

Arguments
trial A Trial instance.
model A Keras Model.
epoch The current epoch number.
logs Additional metrics.

on_epoch_end

on_epoch_end(
    trial, model, epoch, logs=None
)

A hook called at the end of every epoch.

Arguments
trial A Trial instance.
model A Keras Model.
epoch The current epoch number.
logs Dict. Metrics for this epoch. This should include the value of the objective for this epoch.

on_search_begin

on_search_begin()

A hook called at the beginning of search.

on_search_end

on_search_end()

A hook called at the end of search.

on_trial_begin

on_trial_begin(
    trial
)

A hook called before starting each trial.

Arguments
trial A Trial instance.

on_trial_end

on_trial_end(
    trial
)

A hook called after each trial is run.

Arguments
trial A Trial instance.

reload

reload()

Reloads this object from its project directory.

results_summary

results_summary(
    num_trials=10
)

Display tuning results summary.

Args
num_trials (int, optional): Number of trials to display. Defaults to 10.

run_trial

run_trial(
    trial, *fit_args, **fit_kwargs
)

Evaluates a set of hyperparameter values.

This method is called during search to evaluate a set of hyperparameters.

Arguments
trial A Trial instance that contains the information needed to run this trial. Hyperparameters can be accessed via trial.hyperparameters.
*fit_args Positional arguments passed by search.
*fit_kwargs Keyword arguments passed by search.

save

save()

Saves this object to its project directory.

save_model

save_model(
    trial_id, model, step=0
)

Saves a Model for a given trial.

Arguments
trial_id The ID of the Trial that corresponds to this Model.
model The trained model.
step For models that report intermediate results to the Oracle, the step that this saved file should correspond to. For example, for Keras models this is the number of epochs trained. 

search(
    *fit_args, **fit_kwargs
)

Performs a search for best hyperparameter configuations.

Arguments
*fit_args Positional arguments that should be passed to run_trial, for example the training and validation data.
*fit_kwargs Keyword arguments that should be passed to run_trial, for example the training and validation data.

search_space_summary

search_space_summary(
    extended=False
)

Print search space summary.

Args
extended Bool, optional. Display extended summary. Defaults to False.

set_state

set_state(
    state
)

Sets the current state of this object.

This method is called during reload.

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