Efficient Neural Network Architecture Search Based on Proxy Model Evolution
Aiming at the problems of time complexity and debugging difficulties in traditional artificial neural net-work design,this paper studies an efficient neural network architecture automatic search method based on neural net-work architecture search,solves the network design scheme,and proposes an efficient neural network search method u-sing proxy model evolution method.In this method,the proxy model was integrated into the genetic algorithm,and the prediction of CNN performance was transformed into a binary classification problem,which can speed up the network evaluation process.A fitness evaluation mechanism was designed to measure the quality and diversity of candidate in-dividuals and retain those with good performance for further training.In addition,in the latest generation of parent model,the sub-model inherits the parameters of the parent model to train fewer times on the training data set and ac-celerate the generation of the optimal network.The classification accuracy of this method on Cifar-10 data set is 95.6%,and the model parameter is 3.4M.The classification accuracy of Cifar-100 data set is 77.46%,and the mod-el parameter is 4.3M.Due to the use of proxy model and parameter inheritance,the method takes only 2.5 days on the CIRAR-10 dataset and avoids 43.8%candidate individual training.
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