research-article

HyperNOMAD: Hyperparameter Optimization of Deep Neural Networks Using Mesh Adaptive Direct Search

Authors:

Dounia Lakhmiri,

Sébastien Le Digabel,

Christophe TribesAuthors Info & Claims

ACM Transactions on Mathematical Software (TOMS), Volume 47, Issue 3

Article No.: 27, Pages 1 - 27

https://doi.org/10.1145/3450975

Published: 26 June 2021 Publication History

Abstract

The performance of deep neural networks is highly sensitive to the choice of the hyperparameters that define the structure of the network and the learning process. When facing a new application, tuning a deep neural network is a tedious and time-consuming process that is often described as a “dark art.” This explains the necessity of automating the calibration of these hyperparameters. Derivative-free optimization is a field that develops methods designed to optimize time-consuming functions without relying on derivatives. This work introduces the HyperNOMAD package, an extension of the NOMAD software that applies the MADS algorithm [7] to simultaneously tune the hyperparameters responsible for both the architecture and the learning process of a deep neural network (DNN). This generic approach allows for an important flexibility in the exploration of the search space by taking advantage of categorical variables. HyperNOMAD is tested on the MNIST, Fashion-MNIST, and CIFAR-10 datasets and achieves results comparable to the current state of the art.

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Index Terms

HyperNOMAD: Hyperparameter Optimization of Deep Neural Networks Using Mesh Adaptive Direct Search
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
      1. Mixed discrete-continuous optimization
  2. Mathematical software
    1. Solvers

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cover image ACM Transactions on Mathematical Software

ACM Transactions on Mathematical Software Volume 47, Issue 3

September 2021

251 pages

ISSN:0098-3500

EISSN:1557-7295

DOI:10.1145/3472960

Editors:
Zhaojun Bai
University of California at Davis, USA
,
Wolfgang Bangerth
Colorado State University, USA

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Publication History

Published: 26 June 2021

Accepted: 01 February 2021

Revised: 01 February 2021

Received: 01 June 2019

Published in TOMS Volume 47, Issue 3

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