Variation of HyperBand algorithm.

Hyperband(
hypermodel,
optimizer = NULL,
loss = NULL,
metrics = NULL,
hyperparameters = NULL,
objective,
max_epochs,
factor = 3,
hyperband_iterations = 1,
seed = NULL,
tune_new_entries = TRUE,
allow_new_entries = TRUE,
distribution_strategy = NULL,
directory = NULL,
project_name = NULL,
...
)

## Arguments

hypermodel Define a model-building function. It takes an argument "hp" from which you can sample hyperparameters. An optimizer is one of the arguments required for compiling a Keras model A loss function (or objective function, or optimization score function) is one of the parameters required to compile a model A metric is a function that is used to judge the performance of your model HyperParameters class instance. Can be used to override (or register in advance) hyperparamters in the search space. A loss metrics function for tracking the model performance e.g. "val_precision". The name of the objective to optimize (whether to minimize or maximize is automatically inferred for built-in metrics) to train the model. Note that in conjunction with initial_epoch, epochs is to be understood as "final epoch". The model is not trained for a number of iterations given by epochs, but merely until the epoch of index epochs is reached. Int. Reduction factor for the number of epochs and number of models for each bracket. Int >= 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. Int. Random seed. 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. Whether the hypermodel is allowed to request hyperparameter entries not listed in hyperparameters. **kwargs: Keyword arguments relevant to all Tuner subclasses. Please see the docstring for Tuner. Scale up from running single-threaded locally to running on dozens or hundreds of workers in parallel. Distributed Keras Tuner uses a chief-worker model. The chief runs a service to which the workers report results and query for the hyperparameters to try next. The chief should be run on a single-threaded CPU instance (or alternatively as a separate process on one of the workers). Keras Tuner also supports data parallelism via tf.distribute. Data parallelism and distributed tuning can be combined. For example, if you have 10 workers with 4 GPUs on each worker, you can run 10 parallel trials with each trial training on 4 GPUs by using tf.distribute.MirroredStrategy. You can also run each trial on TPUs via tf.distribute.experimental.TPUStrategy. Currently tf.distribute.MultiWorkerMirroredStrategy is not supported, but support for this is on the roadmap. The dir where training logs are stored Detailed logs, checkpoints, etc, in the folder my_dir/helloworld, i.e. directory/project_name. Some additional arguments

## Value

a hyperparameter tuner object Hyperband

## Details

Reference: Li, Lisha, and Kevin Jamieson. ["Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization." Journal of Machine Learning Research 18 (2018): 1-52]( http://jmlr.org/papers/v18/16-558.html). # Arguments hypermodel: Instance of HyperModel class (or callable that takes hyperparameters and returns a Model instance). objective: String. Name of model metric to minimize or maximize, e.g. "val_accuracy". max_epochs: Int. The maximum number of epochs to train one model. It is recommended to set this to a value slightly higher than the expected time to convergence for your largest Model, and to use early stopping during training (for example, via tf.keras.callbacks.EarlyStopping). factor: Int. Reduction factor for the number of epochs and number of models for each bracket. hyperband_iterations: Int >= 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. seed: Int. Random seed. hyperparameters: HyperParameters class instance. Can be used to override (or register in advance) hyperparamters in the search space. tune_new_entries: 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. allow_new_entries: Whether the hypermodel is allowed to request hyperparameter entries not listed in hyperparameters. **kwargs: Keyword arguments relevant to all Tuner subclasses. Please see the docstring for Tuner.

## Reference

Li, Lisha, and Kevin Jamieson. ["Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization." Journal of Machine Learning Research 18 (2018): 1-52]( http://jmlr.org/papers/v18/16-558.html).