Keras-tuner

Bug fixes
* The `Tuner.results_summary()` did not print error messages for failed trials
and did not display `Objective` information correctly. It is now fixed.
* The `BayesianOptimization` would break when not specifying the
`num_initial_points` and overriding `.run_trial()`. It is now fixed.
* TensorFlow 2.12 would break because the different protobuf version. It is now
fixed.

Breaking changes
* Removed `Logger` and `CloudLogger` and the related arguments in
`BaseTuner.__init__(logger=...)`.
* Removed `keras_tuner.oracles.BayesianOptimization`,
`keras_tuner.oracles.Hyperband`, `keras_tuner.oracles.RandomSearch`, which
were actually `Oracle`s instead of `Tuner`s. Please
use`keras_tuner.oracles.BayesianOptimizationOracle`,
`keras_tuner.oracles.HyperbandOracle`,
`keras_tuner.oracles.RandomSearchOracle` instead.
* Removed `keras_tuner.Sklearn`. Please use `keras_tuner.SklearnTuner` instead.

New features
* `keras_tuner.oracles.GridSearchOracle` is now available as a standalone
`Oracle` to be used with custom tuners.

Bug fixes
* The resume feature (`overwrite=False`) would crash in 1.2.0. This is now fixed.

Breaking changes
* If you implemented your own `Tuner`, the old use case of reporting results
with `Oracle.update_trial()` in `Tuner.run_trial()` is deprecated. Please
return the metrics in `Tuner.run_trial()` instead.
* If you implemented your own `Oracle` and overrided `Oracle.end_trial()`, you
need to change the signature of the function from
`Oracle.end_trial(trial.trial_id, trial.status)` to `Oracle.end_trial(trial)`.
* The default value of the `step` argument in
* `keras_tuner.HyperParameters.Int()` is
changed to `None`, which was `1` before. No change in default behavior.
* The default value of the `sampling` argument in
`keras_tuner.HyperParameters.Int()` is changed to `"linear"`, which was `None`
before. No change in default behavior.
* The default value of the `sampling` argument in
`keras_tuner.HyperParameters.Float()` is changed to `"linear"`, which was
`None` before. No change in default behavior.
* If you explicitly rely on protobuf values, the new protobuf bug fix may affect
you.
* Changed the mechanism of how a random sample is drawn for a hyperparameter.
They now all start from a random value between 0 and 1, and convert the value
to a random sample.

New features
* A new tuner is added, `keras_tuner.GridSearch`, which can exhaust all the
possible hyperparameter combinations.
* Better fault tolerance during the search. Added two new arguments to `Tuner`
and `Oracle` initializers, `max_retries_per_trial` and
`max_consecutive_failed_trials`.
* You can now mark a `Trial` as failed by
`raise keras_tuner.FailedTrialError("error message.")` in
`HyperModel.build()`, `HyperModel.fit()`, or your model build function.
* Provides better error messages for invalid configs for `Int` and `Float` type
hyperparameters.
* A decorator `keras_tuner.synchronized` is added to decorate the methods in
`Oracle` and its subclasses to synchronize the concurrent calls to ensure
thread safety in parallel tuning.

Bug fixes
* Protobuf was not converting Boolean type hyperparameter correctly. This is now
fixed.
* Hyperband was not loading the weights correctly for half-trained models. This
is now fixed.
* `KeyError` may occur if using `hp.conditional_scope()`, or the `parent`
argument for hyperparameters. This is now fixed.
* `num_initial_points` of the `BayesianOptimization` should defaults to `3 *
dimension`, but it defaults to 2. This is now fixed.
* It would through an error when using a concrete Keras optimizer object to
override the `HyperModel` compile arg. This is now fixed.
* Workers might crash due to `Oracle` reloading when running in parallel. This
is now fixed.