GridSearch Tuner

[source]

GridSearch class

keras_tuner.GridSearch(
    hypermodel=None,
    objective=None,
    max_trials=None,
    seed=None,
    hyperparameters=None,
    tune_new_entries=True,
    allow_new_entries=True,
    max_retries_per_trial=0,
    max_consecutive_failed_trials=3,
    **kwargs
)

The grid search tuner.

This tuner iterates over all possible hyperparameter combinations.

For example, with:

optimizer = hp.Choice("model_name", values=["sgd", "adam"])
learning_rate = hp.Choice("learning_rate", values=[0.01, 0.1])

This tuner will cover the following combinations: ["sgd", 0.01], ["sgd", 0.1], ["adam", 0.01] ["adam", 0.1].

For the following hyperparameter types, GridSearch will not exhaust all possible values:

• hp.Float() when step is left unspecified.
• hp.Int() with sampling set to "log" or "reverse_log", and step is left unspecified.

For these cases, KerasTuner will pick 10 samples in the range evenly by default. To configure the granularity of sampling for hp.Float() and hp.Int(), please use the step argument in their initializers.

Arguments

• hypermodel: Instance of HyperModel class (or callable that takes hyperparameters and returns a Model instance). It is optional when Tuner.run_trial() is overridden and does not use self.hypermodel.
• objective: A string, keras_tuner.Objective instance, or a list of keras_tuner.Objectives and strings. If a string, the direction of the optimization (min or max) will be inferred. If a list of keras_tuner.Objective, we will minimize the sum of all the objectives to minimize subtracting the sum of all the objectives to maximize. The objective argument is optional when Tuner.run_trial() or HyperModel.fit() returns a single float as the objective to minimize.
• max_trials: Optional integer, the total number of trials (model configurations) to test at most. Note that the oracle may interrupt the search before max_trial models have been tested if the search space has been exhausted. If left unspecified, it runs till the search space is exhausted.
• seed: Optional integer, the random seed.
• hyperparameters: Optional HyperParameters instance. Can be used to override (or register in advance) hyperparameters in the search space.
• tune_new_entries: Boolean, whether hyperparameter entries that are requested by the hypermodel but that were not specified in hyperparameters should be added to the search space, or not. If not, then the default value for these parameters will be used. Defaults to True.
• allow_new_entries: Boolean, whether the hypermodel is allowed to request hyperparameter entries not listed in hyperparameters. Defaults to True.
• max_retries_per_trial: Integer. Defaults to 0. The maximum number of times to retry a Trial if the trial crashed or the results are invalid.
• max_consecutive_failed_trials: Integer. Defaults to 3. The maximum number of consecutive failed Trials. When this number is reached, the search will be stopped. A Trial is marked as failed when none of the retries succeeded.
• **kwargs: Keyword arguments relevant to all Tuner subclasses. Please see the docstring for Tuner.