Accelerating configuration of machine-learning models

摘要

Machine-learning models (MLM) can be configured more rapidly using some examples described herein. For example, a MLM can be configured by executing an iterative process, where each iteration includes a series of operations. The series of operations can include determining a current weight value for the current iteration, determining a current gradient direction for the current iteration based on the current weight value, and determining a current learning rate for the current iteration based on the current gradient direction. The operations can also include determining a current multistage momentum value for the current iteration. A next weight value for a next iteration can then be determined based on (i) the current weight value, (ii) the current gradient direction, (iii) the current learning rate, and (iv) the current multistage momentum value. The next weight value may also be determined based on a predefined learning rate that was preset, in some examples.