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

A learning model and deep learning technology, applied in the field of artificial intelligence, can solve problems such as modification of deep learning models on demand, poor degrees of freedom, etc.

Pending Publication Date: 2020-04-10
CHINA ELECTRONICS PROD RELIABILITY & ENVIRONMENTAL TESTING RES INST THE FIFTH ELECTRONICS RES INST OF MIITCEPREI LAB
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AI Technical Summary

Problems solved by technology

[0004] However, in the cloud platform in the existing technology, users cannot modify the deep lear

Method used

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

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Embodiment Construction

[0049] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.

[0050] The deep learning model construction method provided by this application can be applied to such as figure 1 shown in the application environment. figure 1 A computer device is provided, the computer device may be a server, and its internal structure diagram may be as follows figure 1shown. The computer device includes a processor, a memory, a network interface, a database, a display screen and an input device connected through a system bus. Wherein, the processor of the computer device is used to provide calculation and control capabilities. The memory of the co...

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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...

Claims

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Application Information

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IPC IPC(8): G06N20/00
CPCG06N20/00
Inventor 林家全谢浪雄杨东裕
Owner CHINA ELECTRONICS PROD RELIABILITY & ENVIRONMENTAL TESTING RES INST THE FIFTH ELECTRONICS RES INST OF MIITCEPREI LAB
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