Release v1.1.2

What's Changed

• add --profile=black to isort by @LukeWood in #672
• In model checkpointing callback, check logs before get objective value by @haifeng-jin in #674

New Contributors

• @LukeWood made their first contribution in #672

Full Changelog: 1.1.1...1.1.2

Release v1.1.2RC0

What's Changed

• add --profile=black to isort by @LukeWood in #672
• In model checkpointing callback, check logs before get objective value by @haifeng-jin in #674

New Contributors

• @LukeWood made their first contribution in #672

Full Changelog: 1.1.1...1.1.2rc0

Release v1.1.1

Highlights

• Support passing a list of objectives as the objective argument.
• Raise better error message when the return value of run_trial() or HyperModel.fit() are of wrong type.
• Various bug fixes for BayesianOptimization tuner.
• The trial IDs are changed from hex strings to integers counting from 0.

What's Changed

• Make hyperparameters names visible in Display output by @C-Pro in #634
• Replace import kerastuner with import keras_tuner by @ageron in #640
• Support multi-objective by @haifeng-jin in #641
• reorganize the tests to follow keras best practices by @haifeng-jin in #643
• keep Objective in oracle for backward compatibility by @haifeng-jin in #644
• better error check for returned eval results by @haifeng-jin in #646
• Mitigate the issue of hanging workers after chief already quits when running keras-tuner in distributed tuning mode. by @mtian29 in #645
• Ensure hallucination checks if the Gaussian regressor has been fit be… by @brydon in #650
• Resolves #609: Support for sklearn functions without sample_weight by @brydon in #651
• Resolves #652 and #605: Make human readable trial_id and sync trial numbers between worker Displays by @brydon in #653
• Update tuner.py by @haifeng-jin in #657
• fix(bayesian): scalar optimization result (#655) by @haifeng-jin in #662
• Generalize hallucination checks to avoid racing conditions by @alisterl in #664
• remove scipy from required dependency by @haifeng-jin in #665
• Import scipy.optimize by @haifeng-jin in #667

New Contributors

• @C-Pro made their first contribution in #634
• @ageron made their first contribution in #640
• @mtian29 made their first contribution in #645
• @brydon made their first contribution in #650
• @alisterl made their first contribution in #664

Full Changelog: 1.1.1rc0...1.1.1

Release v1.1.1RC0

Highlights

• Support passing a list of objectives as the objective argument.
• Raise better error message when the return value of run_trial() or HyperModel.fit() are of wrong type.

What's Changed

• Make hyperparameters names visible in Display output by @C-Pro in #634
• Replace import kerastuner with import keras_tuner by @ageron in #640
• Support multi-objective by @haifeng-jin in #641
• reorganize the tests to follow keras best practices by @haifeng-jin in #643
• keep Objective in oracle for backward compatibility by @haifeng-jin in #644
• better error check for returned eval results by @haifeng-jin in #646
• Mitigate the issue of hanging workers after chief already quits when running keras-tuner in distributed tuning mode. by @mtian29 in #645
• Ensure hallucination checks if the Gaussian regressor has been fit be… by @brydon in #650
• Resolves #609: Support for sklearn functions without sample_weight by @brydon in #651
• Resolves #652 and #605: Make human readable trial_id and sync trial numbers between worker Displays by @brydon in #653
• Update tuner.py by @haifeng-jin in #657

New Contributors

• @C-Pro made their first contribution in #634
• @ageron made their first contribution in #640
• @mtian29 made their first contribution in #645
• @brydon made their first contribution in #650

Full Changelog: 1.1.0...1.1.1rc0

1.1.0

What's Changed

• Support HyperModel.fit() to tune the fit process.
• Support Tuner.run_trial() to return a single float as the objective value to minimize.
• Support Tuner.run_trial() to return a dictionary of {metric_name: value} or Keras history.
• Allow not providing hypermodel to Tuner if override Tuner.run_trial().
• Allow not providing objective to Tuner if HyperModel.fit() or Tuner.run_trial() return a single float.
• Bug fixes

Breaking Changes

• Change internal class MultiExecutionTuner to Tuner to replace all overridden methods.
• Removed KerasHyperModel an internal class to wrap the user provided HyperModel.

New Contributors

• @liqiongyu made their first contribution in #594
• @vardhanaleti made their first contribution in #595
• @howl-anderson made their first contribution in #607

Full Changelog: 1.0.4...1.1.0rc0

1.1.0rc0

What's Changed

• Support HyperModel.fit() to tune the fit process.
• Support Tuner.run_trial() to return a single float as the objective value to minimize.
• Support Tuner.run_trial() to return a dictionary of {metric_name: value} or Keras history.
• Allow not providing hypermodel to Tuner if override Tuner.run_trial().
• Allow not providing objective to Tuner if HyperModel.fit() or Tuner.run_trial() return a single float.
• Bug fixes

Breaking Changes

• Change internal class MultiExecutionTuner to Tuner to replace all overridden methods.
• Removed KerasHyperModel an internal class to wrap the user provided HyperModel.

New Contributors

• @liqiongyu made their first contribution in #594
• @vardhanaleti made their first contribution in #595
• @howl-anderson made their first contribution in #607

Full Changelog: 1.0.4...1.1.0rc0

Release v1.0.4

• Support DataFrame in SklearnTuner.
• Support Tuner.search_space_summary() to print all the hyperparameters based on conditional_scopes.
• Support TensorFlow 2.0 for backward compatibility.
• Bug fixes and documentation improvements.
• Raise a warning when using with TF 1.
• Save TPUStrategy models with the TF format.

Release v1.0.4rc1

• Support DataFrame in SklearnTuner.
• Support Tuner.search_space_summary() to print all the hyperparameters based on conditional_scopes.
• Support TensorFlow 2.0 for backward compatibility.
• Bug fixes and documentation improvements.
• Raise a warning when using with TF 1.

Release v1.0.4rc0

• Support DataFrame in SklearnTuner.
• Support Tuner.search_space_summary() to print all the hyperparameters based on conditional_scopes.
• Support TensorFlow 2.0 for backward compatibility.
• Bug fixes and documentation improvements.

Release v1.0.3

• Renamed import name of kerastuner to keras_tuner.
• Renamed the Oracles to add the Oracle as suffix, e.g., RandomSearch oracle is renamed to RandomSearchOracle. (The RandomSearch tuner is still named RandomSearch.)
• Renamed Tuner._populate_space to Tuner.populate_space.
• Renamed Tuner._score_trail to Tuner.score_trial.
• Renamed kt.tuners.Sklearn tuner to kt.SklearnTuner and put it at the root level of import.
• Removed the CloudLogger feature, but the Logger class still works.
• Tuning sklearn.pipeline.Pipeline.
• Improved the docstrings.