<span class="var-sub_title">Deep Learning Evolutionary Optimization for Regression of Rotorcraft Vibrational Spectra</span> SC18 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Machine Learning in HPC Environments

Deep Learning Evolutionary Optimization for Regression of Rotorcraft Vibrational Spectra

Authors: Daniel A. Martinez-Gonzalez (US Army Engineer Research and Development Center), Wesley Brewer (US Department of Defense HPC Modernization Program)

Abstract: 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). Deep Learning Evolutionary Optimization (DLEO) is the current GA implementation being presented. Compared with random generated and hand-tuned models, DLEO proved to be significantly faster and better searching for optimal architecture hyperparameter configurations. Implementing DLEO allowed us to find models with higher validation accuracies at lower computational costs in less than 72 hours, as compared with weeks of manual and random search. Moreover, parallel coordinate plots provided valuable insights about network architecture designs and their regression capabilities

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