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Convergence Analysis of PSO for Hyper-Parameter Selection in Deep Neural Networks

  • Jakub Nalepa
  • Pablo Ribalta Lorenzo
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 13)

Abstract

Deep Neural Networks (DNNs) have gained enormous research attention since they consistently outperform other state-of-the-art methods in a plethora of machine learning tasks. However, their performance strongly depends on the DNN hyper-parameters which are commonly tuned by experienced practitioners. Recently, we introduced Particle Swarm Optimization (PSO) and parallel PSO techniques to automate this process. In this work, we theoretically and experimentally investigate the convergence capabilities of these algorithms. The experiments were performed for several DNN architectures (both gradually augmented and hand-crafted by a human) using two challenging multi-class benchmark datasets—MNIST and CIFAR-10.

Keywords

Convergence analysis PSO Hyper-parameter selection DNNs 

Notes

Acknowledgements

This work has been supported by the Polish National Centre for Research and Development under the Innomed grant POIR.01.02.00-00-0030/15, and the Silesian University of Technology grant for young researchers (BKM-507/RAU2/2016).

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Future Processing and Silesian University of TechnologyGliwicePoland
  2. 2.Future ProcessingGliwicePoland

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