Convolutional Neural Network Graph-based Embedding for Neural Architecture Search

Abstract

Recently, network architecture search is gaining popularity. The neural network representation as a directed acyclic graph is considered for subsequent architecture optimization. Currently, most of the existing encoders rely only on the model layer properties and do not take into account the attributes of layers. This work proposes an algorithm for mapping a CNN network to a vector space considering the layer attributes, such as different dimensions of a particular layer. The proposed algorithm was compared with D-VAE and DVAE-EMB and showed less information loss caused by the mapping of a network to a vector space. As the results show, the performance of the model was shown after direct conversion to embedding and reverse conversion to architecture. The method allows more accurate neural network architecture mapping into a vector form, which will improve the search for the best architecture. The method implementation is publicly available at https://github.com/Turukmokto/GraphEmbedding-dev.