基础调优器类

[source]

Tuner class

keras_tuner.Tuner(
    oracle,
    hypermodel=None,
    max_model_size=None,
    optimizer=None,
    loss=None,
    metrics=None,
    distribution_strategy=None,
    directory=None,
    project_name=None,
    logger=None,
    tuner_id=None,
    overwrite=False,
    executions_per_trial=1,
    **kwargs
)

Tuner class for Keras models.

This is the base Tuner class for all tuners for Keras models. It manages the building, training, evaluation and saving of the Keras models. New tuners can be created by subclassing the class.

All Keras related logics are in Tuner.run_trial() and its subroutines. When subclassing Tuner, if not calling super().run_trial(), it can tune anything.

Arguments

• oracle: Instance of Oracle class.
• hypermodel: Instance of HyperModel class (or callable that takes hyperparameters and returns a Model instance). It is optional when Tuner.run_trial() is overriden and does not use self.hypermodel.
• max_model_size: Integer, maximum number of scalars in the parameters of a model. Models larger than this are rejected.
• optimizer: Optional optimizer. It is used to override the optimizer argument in the compile step for the models. If the hypermodel does not compile the models it generates, then this argument must be specified.
• loss: Optional loss. May be used to override the loss argument in the compile step for the models. If the hypermodel does not compile the models it generates, then this argument must be specified.
• metrics: Optional metrics. May be used to override the metrics argument in the compile step for the models. If the hypermodel does not compile the models it generates, then this argument must be specified.
• distribution_strategy: Optional instance of tf.distribute.Strategy. If specified, each trial will run under this scope. For example, tf.distribute.MirroredStrategy(['/gpu:0', '/gpu:1']) will run each trial on two GPUs. Currently only single-worker strategies are supported.
• directory: A string, the relative path to the working directory.
• project_name: A string, the name to use as prefix for files saved by this Tuner.
• tuner_id: Optional string, used as the ID of this Tuner.
• overwrite: Boolean, defaults to False. If False, reloads an existing project of the same name if one is found. Otherwise, overwrites the project.
• executions_per_trial: Integer, the number of executions (training a model from scratch, starting from a new initialization) to run per trial (model configuration). Model metrics may vary greatly depending on random initialization, hence it is often a good idea to run several executions per trial in order to evaluate the performance of a given set of hyperparameter values.
• **kwargs: Arguments for BaseTuner.

Attributes

• remaining_trials: Number of trials remaining, None if max_trials is not set. This is useful when resuming a previously stopped search.

[source]

get_best_hyperparameters method

Tuner.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: Optional number of HyperParameters objects to return.

Returns

List of HyperParameter objects sorted from the best to the worst.

[source]

get_best_models method

Tuner.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 for querying the models trained during the search. For best performance, it is recommended to retrain your Model on the full dataset using the best hyperparameters found during search, which can be obtained using tuner.get_best_hyperparameters().

Arguments

• num_models: Optional number of best models to return. Defaults to 1.

Returns

List of trained model instances sorted from the best to the worst.

[source]

get_state method

Tuner.get_state()

Returns the current state of this object.

This method is called during save.

Returns

A dictionary of serializable objects as the state.

[source]

load_model method

Tuner.load_model(trial)

Loads a Model from a given trial.

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

Arguments

• trial: A Trial instance, the Trial corresponding to the model to load.

[source]

on_epoch_begin method

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

Called at the beginning of an epoch.

Arguments

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

[source]

on_batch_begin method

Tuner.on_batch_begin(trial, model, batch, logs)

Called at the beginning of a batch.

Arguments

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

[source]

on_batch_end method

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

Called at the end of a batch.

Arguments

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

[source]

on_epoch_end method

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

Called at the end of an 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.

[source]

run_trial method

Tuner.run_trial(trial, )

Evaluates a set of hyperparameter values.

This method is called multiple times during search to build and evaluate the models with different hyperparameters and return the objective value.

Example

You can use it with self.hypermodel to build and fit the model.

def run_trial(self, trial, *args, **kwargs):
    hp = trial.hyperparameters
    model = self.hypermodel.build(hp)
    return self.hypermodel.fit(hp, model, *args, **kwargs)

You can also use it as a black-box optimizer for anything.

def run_trial(self, trial, *args, **kwargs):
    hp = trial.hyperparameters
    x = hp.Float("x", -2.0, 2.0)
    y = x * x + 2 * x + 1
    return y

Arguments

• trial: A Trial instance that contains the information needed to run this trial. Hyperparameters can be accessed via trial.hyperparameters.
• *args: Positional arguments passed by search.
• **kwargs: Keyword arguments passed by search.

Returns

A History object, which is the return value of model.fit(), a dictionary, a float, or a list of one of these types.

If return a dictionary, it should be a dictionary of the metrics to track. The keys are the metric names, which contains the objective name. The values should be the metric values.

If return a float, it should be the objective value.

If evaluating the model for multiple times, you may return a list of results of any of the types above. The final objective value is the average of the results in the list.

[source]

results_summary method

Tuner.results_summary(num_trials=10)

Display tuning results summary.

The method prints a summary of the search results including the hyperparameter values and evaluation results for each trial.

Arguments

• num_trials: Optional number of trials to display. Defaults to 10.

[source]

save_model method

Tuner.save_model(trial_id, model, step=0)

Saves a Model for a given trial.

Arguments

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

[source]

search method

Tuner.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.

[source]

search_space_summary method

Tuner.search_space_summary(extended=False)

Print search space summary.

The methods prints a summary of the hyperparameters in the search space, which can be called before calling the search method.

Arguments

• extended: Optional boolean, whether to display an extended summary. Defaults to False.

[source]

set_state method

Tuner.set_state(state)

Sets the current state of this object.

This method is called during reload.

Arguments

• state: A dictionary of serialized objects as the state to restore.