Description: A method for Deep Neural Network (DNN) hyperparameter search using evolutionary optimization is proposed for nonlinear high-dimensional multivariate regression problems. Deep networks often lead to extensive hyperparameter searches which can become an ambiguous process due to network complexity. Therefore, we propose a user-friendly method that integrates Dakota optimization library, TensorFlow, and Galaxy HPC workflow management tool to deploy massively parallel function evaluations in a Genetic Algorithm (GA).
Presenter(s): Dr. Wes Brewer, PETTT SAIC
Location: Webcast
Date & Time: January 23, 2019, 12:00p - 1:00p ET
Distribution Statement D. Distribution authorized to the Department of Defense and U.S. DoD contractors only, Administrative or Operational Use, 29 January 2019. Other requests for this document shall be referred to the High Performance Computing Modernization Office, 3909 Halls Ferry Road, Vicksburg, MS 39180.