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Release v1.4.7

Bug fixes

New features

Release v1.4.6

Bug fixes

  • When running in parallel, the chief may exit before some client ask for another trial, which informs the client to exit. Now, it is fixed.

New features

  • Updated the dependency from keras-core to keras version 3 and above. Also support keras version 2 for backward compatibility.

Release v1.4.5

Bug fixes

  • When running in parallel, the client oracle used to wait forever when the chief oracle is not responding. Now, it is fixed.
  • When running in parallel, the client would call the chief after calling oracle.end_trial(), when the chief have already ended. Now, it is fixed.
  • When running in parallel, the chief used to start to block in tuner.__init__(). However, it makes more sense to block when calling tuner.search(). Now, it is fixed.
  • Could not do from keras_tuner.engine.hypermodel import HyperModel. It is now fixed.
  • Could not do from keras_tuner.engine.hyperparameters import HyperParameters. It is now fixed.
  • Could not do from keras_tuner.engine.metrics_tracking import infer_metric_direction. It is now fixed.
  • Could not do from keras_tuner.engine.oracle import Objective. It is now fixed.
  • Could not do from keras_tuner.engine.oracle import Oracle. It is now fixed.

Release v1.4.4

Bug fixes

  • Could not do from keras_tuner.engine.hyperparameters import serialize. It is now fixed.
  • Could not do from keras_tuner.engine.hyperparameters import deserialize. It is now fixed.
  • Could not do from keras_tuner.engine.tuner import maybe_distribute. It is now fixed.

Release v1.4.3

Bug fixes

  • Could not do from keras_tuner.engine.tuner import Tuner. It is now fixed.
  • When TensorFlow version is low, it would error out with keras models have no attributed called get_build_config. It is now fixed.

Release v1.4.2

Bug fixes

  • Could not do from keras_tuner.engine import trial. It is now fixed.

Release v1.4.1

Bug fixes

  • Could not do from keras_tuner.engine import base_tuner. It is now fixed.

Release v1.4.0

Breaking changes

  • All private APIs are hidden under keras_tuner.src.*. For example, if you use keras_tuner.some_private_api, it will now be keras_tuner.src.some_private_api.

New features

  • Support Keras Core with multi-backend.

Release v1.3.5

Breaking changes

  • Removed TensorFlow from the required dependencies of KerasTuner. The user need to install TensorFlow either separately with KerasTuner or with pip install keras_tuner[tensorflow]. This change is because some people may want to use KerasTuner with tensorflow-cpu instead of tensorflow.

Bug fixes

  • KerasTuner used to require protobuf version to be under 3.20. The limit is removed. Now, it support both protobuf 3 and 4.

Release v1.3.4

Bug fixes

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

Release v1.3.3

Bug fixes

  • 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.

Release v1.3.2

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 version to <=3.20. Now it is fixed.

Release v1.3.1

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.

Release v1.3.0

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.

Release v1.2.1

Bug fixes

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

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.