Machine learning model generation method and machine learning model generation device

Abstract

The invention discloses a machine learning model generation method and a machine learning model generation device. According to a specific embodiment, the method comprises the following steps: generating model parameter combinations, and generating machine learning models respectively corresponding to the model parameter combinations; dividing preset machine learning data into training data and verification data; training the machine learning models in parallel based on the training data; verifying the learning accuracy of the trained machine learning models based on the verification data to get verification scores, and determining an optimal model parameter combination corresponding to a machine learning model to be generated based on the verification scores, and generating a machine learning model corresponding to the optimal model parameter combination. Through the method and the device, machine learning models corresponding to all model parameter combinations can be trained and verified in parallel, the whole process of parameter optimization is improved, and an ideal machine learning model can be generated quickly.

Description

technical field [0001] The present application relates to the field of computers, in particular to the field of machine learning, and in particular to a method and device for generating a machine learning model. Background technique [0002] Machine learning is a widely used artificial intelligence technique. When generating a machine learning model, because different parameter combinations are configured, the learning effect of the machine learning model is different. Therefore, it is necessary to optimize the model parameters. At present, it is usually searched within a certain range with a certain step size to find out all the model parameter combinations within the range, and to train and verify the machine learning models corresponding to the model parameter combinations in sequence, that is, to train and verify in a serial manner, The optimal model parameter combination is determined according to the verification results. [0003] However, when the method is used to ...

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Problems solved by technology

[0003] However, when the method is used to determine the optimal model parameter combination of the machine learning model, the machine learning model corresponding to each parameter combination is trained and verified in a serial manner. Since the training and verification data are usually massive, each time The overhead of the training and verification process is high, which affects the execution progress of the training and verification process of the subsequent model parameter combinations, which in turn leads to the slow speed of the entire model parameter optimization process, and the ideal machine learning model cannot be obtained in a short period of time.

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