ยป Keras API reference / KerasTuner / Tuners / Hyperband Tuner

Hyperband Tuner

[source]

Hyperband class

keras_tuner.Hyperband(
    hypermodel=None,
    objective=None,
    max_epochs=100,
    factor=3,
    hyperband_iterations=1,
    seed=None,
    hyperparameters=None,
    tune_new_entries=True,
    allow_new_entries=True,
    **kwargs
)

Variation of HyperBand algorithm.

Reference

Li, Lisha, and Kevin Jamieson. "Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization." Journal of Machine Learning Research 18 (2018): 1-52.

Arguments

  • 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.
  • 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_epochs: Integer, the maximum number of epochs to train one model. It is recommended to set this to a value slightly higher than the expected epochs to convergence for your largest Model, and to use early stopping during training (for example, via tf.keras.callbacks.EarlyStopping). Defaults to 100.
  • factor: Integer, the reduction factor for the number of epochs and number of models for each bracket. Defaults to 3.
  • hyperband_iterations: Integer, at least 1, the number of times to iterate over the full Hyperband algorithm. One iteration will run approximately max_epochs * (math.log(max_epochs, factor) ** 2) cumulative epochs across all trials. It is recommended to set this to as high a value as is within your resource budget. Defaults to 1.
  • 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.
  • **kwargs: Keyword arguments relevant to all Tuner subclasses. Please see the docstring for Tuner.