PROGRESSIVE DIFFERENTIABLE ARCHITECTURE SEARCH METHOD AND SYSTEM BASED ON BAYESIAN OPTIMIZATION

摘要

A progressive differentiable architecture search method and system based on Bayesian optimization. The method is applied to a neural architecture search of automatic machine learning. The method comprises: on the basis of a PDARTS, during the selection of operations between nodes, using Bayesian optimization to select some operations (S100); by means of the Bayesian optimization, sampling channels connected between the nodes, and carrying out an operation search (S200); and after the Bayesian optimization for the channel connected to each node is finished, introducing an attention mechanism to carry out weight value superposition, thereby realizing an architecture search (S300). The method can significantly reduce occupied storage resources and computing consumption during an architecture search, and significantly reduce the search time.