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Recommended method, device and computer storage medium for model optimization

A technology for recommending methods and models, applied in computing, instrumentation, electrical and digital data processing, etc., can solve the problems of verifying and optimizing algorithms that consume large computing power and time costs, are difficult to implement, and depend on the effect, and improve the optimization process. The effect of efficiency

Active Publication Date: 2022-05-27
BEIJING CENTURY TAL EDUCATION TECH CO LTD
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Problems solved by technology

[0004] Among them, the model optimization recommendation for the optimization algorithm needs to construct a complex optimization model, and the degree of improvement of the algorithm effect depends entirely on the complexity of the model and its application scenarios. In addition, it is difficult to implement, and it takes a lot of time to verify the optimization algorithm. Issues such as computing power and time cost
[0005] Furthermore, content-based intelligent recommendation needs to collect a large amount of user historical operation information or user feedback records to train the model, so that the model can complete personalized recommendations according to user needs. However, the effect of this method depends on the amount of data If the amount of data is not enough, it will easily lead to the problem that the accuracy of the recommended model optimization recommendation is not high

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  • Recommended method, device and computer storage medium for model optimization
  • Recommended method, device and computer storage medium for model optimization
  • Recommended method, device and computer storage medium for model optimization

Examples

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no. 1 example

[0028] figure 1 A schematic flowchart of the model optimization recommendation method according to the first embodiment of the present invention is shown. like figure 1 As shown, the model optimization recommendation method in this embodiment mainly includes the following steps:

[0029] Step S11, acquiring multiple data samples of the model to be optimized and information of the model to be optimized.

[0030] Optionally, the model to be optimized is a machine learning model with data analysis capabilities, such as an algorithm model, a prediction model, a recommendation model, an analysis model, and the like.

[0031] In this embodiment, the model to be optimized may be an algorithm model with image recognition capability or an algorithm model with speech recognition capability, but it is not limited to this, and the model to be optimized may also be other types of machine learning models. For example, a natural language processing model (NLP model) with semantic analysis...

no. 2 example

[0065] figure 2 A schematic flowchart of the model optimization recommendation method according to the second embodiment of the present invention is shown. In this embodiment, the model to be optimized is an algorithm model with image recognition capability, and the model optimization recommendation method in this embodiment mainly includes the following steps:

[0066] Step S21, acquiring multiple data samples of the model to be optimized and information of the model to be optimized.

[0067] In this embodiment, the data samples of the model to be optimized include at least one of a training sample, a test sample, and a verification sample. The definitions of the training sample, the test sample, the verification sample, and the information of the model to be optimized are the same as those of the first embodiment above. are the same, so they are not repeated here.

[0068] In step S22, the image features of the data samples are identified by the model to be optimized, and...

no. 3 example

[0082] image 3 A schematic flowchart of the model optimization recommendation method according to the third embodiment of the present invention is shown. In this embodiment, the model to be optimized is an algorithm model with speech recognition capability, and the model optimization recommendation method in this embodiment mainly includes the following steps:

[0083] Step S31, acquiring multiple data samples of the model to be optimized and information of the model to be optimized.

[0084] In this embodiment, the data samples of the model to be optimized include at least one of a training sample, a test sample, and a verification sample. The definitions of the training sample, the test sample, the verification sample, and the information of the model to be optimized are the same as those of the first embodiment above. are the same, so they are not repeated here.

[0085] Step S32, using the model to be optimized to identify the speech feature of the data sample, and obta...

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Abstract

A model optimization recommendation method, device, and computer storage medium, mainly including obtaining data samples of models to be optimized and model information to be optimized; determining data sample evaluation indicators based on data samples; using data sample evaluation indicators and model information to be optimized and preset The optimization rules are matched, and the initial model optimization recommendation corresponding to the preset optimization rules that match successfully is output. Thereby, the present invention can provide a model optimization recommendation with higher accuracy for the model to be optimized.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of model evaluation, and in particular, to a model optimization recommendation method, apparatus, and computer storage medium. Background technique [0002] Model optimization is an indispensable part of the model development process. Model optimization recommendation can effectively recommend the detailed direction of the model to be optimized, so as to improve the processing efficiency of model optimization. [0003] At present, model optimization recommendation mainly includes model optimization recommendation based on optimization algorithm and intelligent recommendation based on content. [0004] Among them, the model optimization recommendation for the optimization algorithm needs to construct a complex optimization model, and the degree of improvement of the algorithm effect depends entirely on the complexity of the model and its application scenarios. In addition, there are stil...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/2455G06F16/2457G06F16/2458
CPCG06F16/24564G06F16/2457G06F16/2465
Inventor 赵明关连正
Owner BEIJING CENTURY TAL EDUCATION TECH CO LTD
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