Data processing method and device, computer equipment and storage medium

Abstract

The invention relates to a data processing method and device, computer equipment and a storage medium and relates to the technical field of computers, according to the embodiment of the invention, thesample data can be automatically acquired from the database; the parameter adjustment step and the model training step are executed, and the trained model can be used for executing the data processing step, so that on one hand, the automation of the data processing process is realized, the labor cost is reduced, the data processing efficiency is improved, personal errors are avoided, and the dataprocessing accuracy is improved; another aspect, exploring through an initial value, an exploring direction, a step length and the like of the hyper-parameter; determining and obtaining a target candidate value in an explored target range, and taking the target candidate value as an initial value to continue exploring; the method is advantaged in that the hyper-parameter exploration range is expanded, the process of searching the optimal hyper-parameter exploration direction is accelerated, a condition that the optimal hyper-parameter cannot be found because of improper exploration boundary setting is avoided, parameter adjustment accuracy and efficiency are improved, and integral data processing accuracy and efficiency are improved.

Description

technical field [0001] The present disclosure relates to the field of computer technology, and in particular to a data processing method, device, computer equipment and storage medium. Background technique [0002] With the development of computer technology, the data processing process of more and more scenarios uses data processing models for data processing. The training process of data processing models is determined by many hyperparameters, such as network depth, learning rate, convolution kernel size, etc. , configuring better hyperparameters for the model can effectively improve the efficiency of model training and the performance of the model. [0003] At present, data processing methods are usually performed by technicians who usually configure hyperparameters for the initial data processing model, and then train the model. The technicians will manually adjust the hyperparameters according to the training results, and then perform model training, and finally get bet...

AI Technical Summary

Problems solved by technology

[0004] Manual parameter adjustment requires a lot of manpower, the efficiency of parameter adjustment is low, and the efficiency of the entire data processing method is low, and manual parameter adjustment depends on the professional experience of technicians, errors may occur, and the data of the final data processing model Processing accuracy may be lower

In the above-mentioned automatic parameter adjustment method, the exploration boundary needs to be set manually. When the exploration boundary is set too small, the optimal hyperparameter may be outside the boundary range, resulting in failure to find the optimal hyperparameter; when the exploration boundary is set too large, select the parameter The number of attempts may be large, and the time spent on parameter adjustment will be large. Therefore, the data processing efficiency is low and the accuracy is low

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