Release v1.2.0

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.

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.

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.

Full Changelog: 1.1.3...1.2.0