Method, device, apparatus and medium for tuning super-parameters in machine learning model

Abstract

The invention provides a method, a device, an apparatus and a medium for optimizing super parameters in a machine learning model. The method comprises: acquiring a template code for training a machinelearning model, wherein the template code identifies a respective set of values of each of one or more hyperparameters; parsing the template code to identify the respective set of values of one or more super parameters; generating at least partial combinations of values of one or more super parameters based on respective sets of values of the one or more super parameters; acquiring an evaluationresult on a model effect obtained by training a machine learning model according to each of at least partial value combinations; and determining an optimal value of one or more super-parameters of themachine learning model based on the evaluation results. Thus, the consumption of manual parameter adjustment can be reduced, and the development efficiency of machine learning (such as deep learning)can be effectively improved.

Description

technical field [0001] The present invention generally relates to the field of machine learning, and more specifically, relates to a method and device for tuning hyperparameters in a machine learning model, a computing device, and a non-transitory machine-readable storage medium. Background technique [0002] Before training a machine learning model, it is necessary to determine the hyperparameters used when training the machine learning model. Hyperparameters can be regarded as the framework parameters of the machine learning model, which are parameters that describe the machine learning model from a higher level. For example, the hyperparameter may be various parameters describing the learning rate of the machine learning model, the dropout (discarding) rate of the node, the batch size (batch size), and the like. [0003] At present, in the training process of the machine learning model, it is mainly to determine the appropriate hyperparameters through manual parameter ad...

AI Technical Summary

Problems solved by technology

[0003] At present, in the training process of the machine learning model, the appropriate hyperparameters are mainly determined through manual parameter adjustment. This parameter adjustment method consumes a lot of energy and time, and is very dependent on personal experience.

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