Genetic Network Architecture Search

July 05, 2019 · Entered Twilight · 🏛 arXiv.org

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Repo contents: .idea, LICENSE, README.md, cnn_utils.py, common.py, config.py, configs, data.py, gif_creator.py, gnas, images, main.py, models, modules, plot_result.py, rnn_utils.py, tests

Authors Hai Victor Habi, Gil Rafalovich arXiv ID 1907.02871 Category cs.NE: Neural & Evolutionary Cross-listed cs.GT Citations 2 Venue arXiv.org Repository https://github.com/haihabi/GeneticNAS ⭐ 30 Last Checked 2 months ago

Abstract

We propose a method for learning the neural network architecture that based on Genetic Algorithm (GA). Our approach uses a genetic algorithm integrated with standard Stochastic Gradient Descent(SGD) which allows the sharing of weights across all architecture solutions. The method uses GA to design a sub-graph of Convolution cell which maximizes the accuracy on the validation-set. Through experiments, we demonstrate this methods performance on both CIFAR10 and CIFAR100 dataset with an accuracy of 96% and 80.1%. The code and result of this work available in GitHub:https://github.com/haihabi/GeneticNAS.