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Genetic Network Architecture Search

07/05/2019
by   Hai Victor Habi, et al.
Tel Aviv University
12

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 result of this work available in GitHub:https://github.com/haihabi/GeneticNAS.

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Code Repositories

GeneticNAS

The genetic neural architecture search (GeneticNAS) is a neural architecture search method that is based on genetic algorithm which utilized weight sharing across all candidate network.


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