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1 October 2018 Heuristic hyperparameter optimization for multilayer perceptron with one hidden layer
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Proceedings Volume 10808, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018; 108082A (2018) https://doi.org/10.1117/12.2501569
Event: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018, 2018, Wilga, Poland
Abstract
One of the crucial steps of preparing a neural network model is the process of tuning its hyperparameters. This process can be time-consuming and hard to be done properly by hand. Tuned hyperparameters allow to obtain high accuracy of classification as well as fast training. In this paper we explore the usage of selected heuristic algorithms based on evolutionary approach: Covariance Matrix Adaptation Evolution Strategy (CMAES), Differential Evolution Strategy (DES) and jSO for the hyperparameter tuning task. Results of Multilayer Perceptron’s (MLP) hyperparameter optimization for a real-life dataset are presented. An improvement in models’ performance is observed through the usage of presented approach.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Łukasz Neumann and Robert M. Nowak "Heuristic hyperparameter optimization for multilayer perceptron with one hidden layer", Proc. SPIE 10808, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018, 108082A (1 October 2018); https://doi.org/10.1117/12.2501569
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