Electronic device, multi-model sample training method and computer readable storage medium

An electronic device and sample training technology, which is applied in computing, computing models, machine learning, etc., can solve the problems of model work progress, spending too much time on repeated training, etc.

Inactive Publication Date: 2018-05-11
PING AN TECH (SHENZHEN) CO LTD
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  • Abstract
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Problems solved by technology

[0003] The present invention provides an electronic device, a multi-model sample training method and a computer-readable storage m

Method used

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  • Electronic device, multi-model sample training method and computer readable storage medium
  • Electronic device, multi-model sample training method and computer readable storage medium
  • Electronic device, multi-model sample training method and computer readable storage medium

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Embodiment Construction

[0045] The principles and features of the present invention are described below in conjunction with the accompanying drawings, and the examples given are only used to explain the present invention, and are not intended to limit the scope of the present invention.

[0046] Such as figure 1 as shown, figure 1 It is a schematic flowchart of an embodiment of the multi-model sample training method of the present invention.

[0047] In this embodiment, the multi-model sample training method includes:

[0048] Step S10, receiving the sample data uploaded by the user, and determining the data attributes of the sample data, the data attributes including type and quantity;

[0049] After the user uploads the sample data, the system receives the sample data and analyzes its data attributes to determine the type and quantity of the sample data. Among them, the type of sample data mainly includes image data and data that predicts continuous values ​​(such as stock market quotations). In...

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Abstract

The invention discloses an electronic device, a multi-model sample training method and a computer readable storage medium. The method comprises the following steps of receiving sample data uploaded bya user, and determining the data attributes of the sample data, wherein the data attributes comprise types and numbers; according to the mapping relation between the preset data attributes of a machine learning model and the sample data, determining a machine learning model corresponding to the data attributes of the sample data; training the sample data respectively by the determined machine learning model; analyzing the training result of each machine learning model after being well trained; and displaying the training result conforming to a preset condition on a display interface. According to the technical scheme, the situation that the training result is poor due to the inadequate experience of a user and the wrong selection of a model can be avoided. The problem that the working progress is influenced due to the fact that the user spent too much time to repeat training the model is solved.

Description

technical field [0001] The invention relates to the field of machine learning model training, in particular to an electronic device, a multi-model sample training method and a computer-readable storage medium. Background technique [0002] At present, when using machine learning to train samples in the industry, it is necessary to manually select some machine learning models, and then use the selected machine learning models to train the sample data to obtain a classifier. However, this method of selecting a model by itself is relatively difficult for beginners or users with weak foundations. It is easy to happen that the result of the obtained classifier is poor due to wrong model selection, which does not meet the requirements and needs to be reselected. The model is trained, and repeated training takes too much time, seriously affecting the user's work progress. Contents of the invention [0003] The present invention provides an electronic device, a multi-model sample...

Claims

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Application Information

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IPC IPC(8): G06N99/00
CPCG06N20/00
Inventor 陈林
Owner PING AN TECH (SHENZHEN) CO LTD
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