Deep learning model building method and device, equipment and storage medium

Abstract

The invention relates to a deep learning model building method and a device, equipment and a storage medium. The method comprises the following steps: obtaining an application type selected by a userbased on a visual interface by a server; according to the corresponding relationship between the application type and the deep learning model, displaying a function component required when a target deep learning model corresponding to the application type is built, obtaining a target function component and parameters selected by the user from the function component required by the target deep learning model, and building and storing the target deep learning model according to the target function component and the parameters of the target function component. The server selects the deep learningmodels of different application types according to the user; according to the method, the component information and the parameter information required for constructing the model are displayed to theuser, so that the user can set and debug the deep learning model more intuitively and clearly in the process of constructing the personalized deep learning model, and the degree of freedom of constructing the deep learning model by the user is improved.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, in particular to a deep learning model building method, device, equipment and storage medium. Background technique [0002] Deep neural networks are currently the basis for many artificial intelligence applications. Due to the breakthrough applications of deep neural networks in speech recognition, image recognition, and natural language processing, the number of applications using deep neural networks has exploded. At present, the use of deep neural network models requires certain machine learning knowledge and programming capabilities, and the training of deep neural network models depends on some software and hardware environments. For example, in terms of software environments, it is necessary to build common open source architectures, such as tensorflow, pytorch, PaddlePaddle, etc., in terms of hardware environment, it is necessary to purchase a Graphics Processing...

AI Technical Summary

Problems solved by technology

[0004] However, in the cloud platform in the existing technology, users cannot modify the deep learning model in the algorithm according to their own business needs, and the degree of freedom is poor

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