Model parameter optimization method, related system, and storage medium
AssigneeHuawei Technologies Co., Ltd.
InventorsZiyu Wang, Jiaxun Lu, Bowen Ding, Daqi Xie, Yunfeng Shao, Qibin Wu
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Abstract
Embodiments of this application provide a model parameter optimization method, a related system, and a storage medium, to avoid introducing a complex hyperparameter for balancing an amount of prior data and an amount of posterior data during parameter optimization, and improve model accuracy.
Claims18
A model parameter optimization method, comprising:
- obtaining a plurality of reference values of a parameter of a first model based on a prior data pair of the first model and a posterior data pair of the first model;
- traversing the plurality of reference values based on preset model evaluation information, the first model, and the prior data pair, to obtain at least one target value of the parameter, wherein a loss value that is of a model on the prior data pair and that corresponds to the target value is not greater than an upper bound of a loss value of a model on the prior data pair in the preset model evaluation information, and/or a test accuracy value that is of the model on the prior data pair and that corresponds to the target value is not less than a lower bound of a test accuracy value of the model on the prior data pair in the preset model evaluation information;
- determining an optimized value of the parameter based on the at least one target value, the prior data pair, and the posterior data pair;
- and
- updating the parameter of the first model based on the optimized value.
A model parameter optimization apparatus, comprising a processor and a memory, wherein the memory is configured to store program code, and the program code, when executed by the processor, causes the processor to:
- obtain a plurality of reference values of a parameter of a first model based on a prior data pair of the first model and a posterior data pair of the first model;
- traverse the plurality of reference values based on preset model evaluation information, the first model, and the prior data pair, to obtain at least one target value of the parameter, wherein a loss value that is of a model on the prior data pair and that corresponds to the target value is not greater than an upper bound of a loss value of a model on the prior data pair in the preset model evaluation information, and/or a test accuracy value that is of the model on the prior data pair and that corresponds to the target value is not less than a lower bound of a test accuracy value of the model on the prior data pair in the preset model evaluation information;
- determine an optimized value of the parameter based on the at least one target value, the prior data pair, and the posterior data pair;
- and
- update the parameter of the first model based on the optimized value.
A computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program, when executed by a processor, causes the processor to:
- obtain a plurality of reference values of a parameter of a first model based on a prior data pair of the first model and a posterior data pair of the first model;
- traverse the plurality of reference values based on preset model evaluation information, the first model, and the prior data pair, to obtain at least one target value of the parameter, wherein a loss value that is of a model on the prior data pair and that corresponds to the target value is not greater than an upper bound of a loss value of a model on the prior data pair in the preset model evaluation information, and/or a test accuracy value that is of the model on the prior data pair and that corresponds to the target value is not less than a lower bound of a test accuracy value of the model on the prior data pair in the preset model evaluation information;
- determine an optimized value of the parameter based on the at least one target value, the prior data pair, and the posterior data pair;
- and
- update the parameter of the first model based on the optimized value.
Dependencies are inferred from the claim text.