Provided is an AI model automatic generation method based on computational graph evolution. The method mainly comprises the following steps: pre-setting data; utilizing a genetic algorithm operator to generate a first-generation computational graph model, and computing the performance of the model according to a computational graph structure thereof; removing an invalid model and a repeated model, and taking the remaining models as candidate models and reserving same as seeds for the next generation; selecting a number of optimal models; the candidate models generating a new computational graph model by using the genetic algorithm operator; determining whether the new computational graph model generated in the last step has been generated; storing the new model as a computational graph model for a new generation, and determining whether same satisfies the pre-set data and an evolution ending condition; and summarizing evolution computational results, and selecting an optimal model. In the present invention, machine learning and deep learning can be carried out simultaneously; the repeated computation of the same model is prevented, and the model design efficiency is improved; the local optimum is jumped out of; the decline in the ability to search for a network is prevented; and evaluation can be directly carried out without training by means of actual data.
AI MODEL AUTOMATIC GENERATION METHOD BASED ON COMPUTATIONAL GRAPH EVOLUTION