BayesianOptimization
classkeras_tuner.BayesianOptimization(
hypermodel=None,
objective=None,
max_trials=10,
num_initial_points=None,
alpha=0.0001,
beta=2.6,
seed=None,
hyperparameters=None,
tune_new_entries=True,
allow_new_entries=True,
max_retries_per_trial=0,
max_consecutive_failed_trials=3,
**kwargs
)
BayesianOptimization tuning with Gaussian process.
Arguments
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
.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_trial
models have been tested if the search space has
been exhausted. Defaults to 10.HyperParameters
instance. Can be used to
override (or register in advance) hyperparameters in the search
space.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.hyperparameters
.
Defaults to True.Trial
if the trial crashed or the results are
invalid.Trial
s. When this number is reached,
the search will be stopped. A Trial
is marked as failed when none
of the retries succeeded.Tuner
subclasses. Please
see the docstring for Tuner
.