Method and device for improving the prediction performance of a depth learning network, and a storage medium

Abstract

The invention relates to a method, a device and a storage medium for improving the prediction performance of a depth learning network, belonging to the field of artificial intelligence and computer technology. The method comprises the following steps of: iteratively training a first neural network model by m cycles using a preset training set; verifying the trained neural network model by the preset verification set; determining the corresponding performance indexes based on the scenarios of the neural network mode; selecting multiple neural network models whose performance indexes reach the fusion standard from the neural network models trained by m iterative training; acquiring network parameters of a plurality of selected neural network models, and fusing the network parameters of the plurality of neural network models; assigning the fused network parameters to the second neural network model to obtain a neural network model with the fused network parameters; not only does not increase the training time of the model, but also can improve the network prediction efficiency, and can meet the different needs of the application scenario.

Description

technical field [0001] The present application relates to a method, device and storage medium for improving the prediction performance of a deep learning network, and belongs to the field of artificial intelligence and computer technology. Background technique [0002] Deep learning refers to a collection of algorithms that use various machine learning (Machine Learning) algorithms to solve various problems such as images and texts on a multi-layer neural network. The core of deep learning is feature learning, which aims to obtain hierarchical feature information through layered networks. In order to improve the prediction accuracy of the neural network model, the neural network models in various situations can be fused. [0003] Model fusion includes single model fusion and multi-model fusion. Single model fusion includes multi-layer feature fusion and network snapshot fusion. Multi-layer feature fusion is to fuse features with complementary information in different layer...

AI Technical Summary

Problems solved by technology

[0005] The present application provides a method, device and storage medium for improving the prediction performance of a deep learning network, which can solve the problems of long training time and long prediction time when improving the prediction performance of a deep learning network through single model fusion or multi-model fusion question

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