Abstract—The training and optimization of neural networks, using pre-trained, super learner and ensemble approaches is explored. Neural networks, and in particular Convolutional Neural Networks (CNNs), are often optimized using default parameters. Neural Architecture Search (NAS) enables multiple architectures to be evaluated prior to selection of the optimal architecture. Our contribution is to develop, and make available to the community, a system that integrates open source tools for the neural architecture search (OpenNAS) of image classification models. OpenNAS takes any dataset of grayscale, or RGB images, and generates the optimal CNN architecture. Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and pre-trained models serve as base learners for ensembles. Meta learner algorithms are subsequently applied to these base learners and the ensemble performance on image classification problems is evaluated. Our results show that a stacked generalization ensemble of heterogeneous models is the most effective approach to image classification within OpenNAS.
Index Terms—AutoML, transfer learning, pre-trained models, ensemble, stacking, super learner, PSO, ACO, CNN.
S. Lankford is with the Adapt Centre, Dublin City University, Ireland (e-mail: seamus.lankford@adaptcentre.ie).
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Cite: Séamus Lankford, "Open-Source Neural Architecture Search with Ensemble and Pre-trained Networks," International Journal of Modeling and Optimization vol. 11, no. 2, pp. 33-41, 2021.
Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
