Bug fixes

• If you have a protobuf version > 3.20, it would through an error when import KerasTuner. It is now fixed.

• KerasTuner would install protobuf 3.19 with protobuf<=3.20. We want to install 3.20.3, so we changed it to protobuf<=3.20.3. It is now fixed.

Full Changelog: v1.3.2...v1.3.3

Bug fixes

• It use to install protobuf 4.22.1 if install with TensorFlow 2.12, which is not compatible with KerasTuner. We limited the protobuf version to <=3.20, which is compatible with all TensorFlow versions so far.

Full Changelog: v1.3.1...v1.3.2

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.

New Contributors

• @jkittner made their first contribution in #860

Full Changelog: v1.3.0...v1.3.1

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 Oracles instead of Tuners. Please usekeras_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.

Full Changelog: 1.2.1...v1.3.0

Bug fixes

• The resume feature (overwrite=False) would crash in 1.2.0. This is now fixed.

New Contributors

• @nkovela1 made their first contribution in #834

Full Changelog: 1.2.0...1.2.1

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.

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.

New Contributors

• @Firas-RHIMI made their first contribution in #711
• @HanxiaoLyu made their first contribution in #746
• @leleogere made their first contribution in #794
• @LuNoX made their first contribution in #815

Full Changelog: 1.1.3...1.2.0

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.

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.
• 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.

New Contributors

• @Firas-RHIMI made their first contribution in #711
• @HanxiaoLyu made their first contribution in #746
• @leleogere made their first contribution in #794
• @LuNoX made their first contribution in #815

Full Changelog: 1.1.3...1.2.0rc0

Summary

Bug fixes to better support AutoKeras.

What's Changed

• Fixed issue #677 by @Anselmoo in #678
• Adopt safe model and trial saving practices in the multi-worker setting by @jamesmullenbach in #684
• tuner_utils: use datetime to calculate elapsed time by @mebeim in #690
• Add pre_create_trial callback by @jamesmullenbach in #695
• Multi-worker file writing checks by @jamesmullenbach in #694
• Update actions.yml by @haifeng-jin in #698
• Add "declare_hyperparameters" to HyperModel by @jamesmullenbach in #696
• Record best epoch info with update_trial by @haifeng-jin in #706

New Contributors

• @Anselmoo made their first contribution in #678
• @jamesmullenbach made their first contribution in #684
• @mebeim made their first contribution in #690

Full Changelog: 1.1.2...1.1.3

What's Changed

• Fixed issue #677 by @Anselmoo in #678
• Adopt safe model and trial saving practices in the multi-worker setting by @jamesmullenbach in #684
• tuner_utils: use datetime to calculate elapsed time by @mebeim in #690
• Add pre_create_trial callback by @jamesmullenbach in #695
• Multi-worker file writing checks by @jamesmullenbach in #694
• Update actions.yml by @haifeng-jin in #698
• Add "declare_hyperparameters" to HyperModel by @jamesmullenbach in #696
• Record best epoch info with update_trial by @haifeng-jin in #706

New Contributors

• @Anselmoo made their first contribution in #678
• @jamesmullenbach made their first contribution in #684
• @mebeim made their first contribution in #690

Full Changelog: 1.1.2...1.1.3rc0