Automatic classification method of convolutional neural network constructed based on incremental branch growth

Abstract

The invention discloses an automatic classification method of a convolutional neural network constructed based on incremental branch growth. The method comprises the steps of S1, constructing and initializing a convolutional neural network model Gm; s2, training the convolutional neural network model Gm to obtain a trained model G'm; s3, performing next-generation network growth on the convolutional neural network model Gm to obtain a next-generation convolutional neural network model Gm + 1; s4, training the convolutional neural network model Gm + 1 to obtain a trained model G'm + 1; s5, if the difference between the classification test precision of the model G'm and the classification test precision of the model G'm + 1 is smaller than a preset threshold value, using the model G'm + 1 tocomplete a classification task; and if the difference between the classification test precision of the model G'm and the classification test precision of the model G'm + 1 is not smaller than a preset threshold value, m <-m + 1, and returning to the step S3. According to the method, a tedious parameter adjustment process in a neural network construction process can be avoided, and a deep convolutional neural network model suitable for a specific classification task can be more efficiently and automatically constructed.

Description

technical field [0001] The present invention relates to the field of neural networks, and more specifically, to an automatic classification method for convolutional neural networks constructed based on incremental branch growth. Background technique [0002] With the development of artificial intelligence and computer technology, the application of convolutional neural network in reality is more and more common. The task of using convolutional neural networks for automatic classification is one of the applications of convolutional neural networks. In practical applications, convolutional neural networks need to be learned. By manually adjusting the parameters of the neural network, the target features in the same classification can be recognized. , but such a process requires a cumbersome parameter tuning process, which greatly reduces the efficiency of the task. Contents of the invention [0003] The present invention provides an automatic classification method of a conv...

AI Technical Summary

Problems solved by technology

The task of using convolutional neural networks for automatic classification is one of the applications of convolutional neural networks. In practical applications, convolutional neural networks need to be learned. By manually adjusting the parameters of the neural network, the target features in the same classification can be recognized. , but such a process requires a cumbersome parameter tuning process, which greatly reduces the efficiency of the task

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