BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters
Abstract
Hyperparameter optimization and neural architecture search can become prohibitively expensive for regular black-box Bayesian optimization because the training and evaluation of a single model can easily take several hours. To overcome this, we introduce a comprehensive tool suite for effective multi-fidelity Bayesian optimization and the analysis of its runs. The suite, written in Python, provides a simple way to specify complex design spaces, a robust and efficient combination of Bayesian optimization and HyperBand, and a comprehensive analysis of the optimization process and its outcomes.
Details
- Externe Organisation(en)
-
Albert-Ludwigs-Universität Freiburg
Robert Bosch GmbH
- Typ
- Preprint
- Publikationsdatum
- 16.08.2019
- Publikationsstatus
- Elektronisch veröffentlicht (E-Pub)
- Elektronische Version(en)
-
https://arxiv.org/pdf/1908.06756 (Zugang:
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)
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