`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.Objective`

s 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`

.