Model compression method and device

Abstract

The embodiment of the invention discloses a model compression method and device. The method comprises the steps that a terminal obtains a global parameter of a kth training period sent by a server; the terminal updates a local model of the terminal according to the global parameters of the kth training period; the terminal uses the training data set of the terminal to train the updated local modelto obtain a second local parameter of the (k + 1) th training period of the trained local model; for a convolution kernel of at least one convolution layer of a local model of the terminal, the terminal determines a contribution degree of the convolution kernel in the convolution layer according to a second local parameter of a (k + 1) th training period of the convolution kernel; and the terminal reports the second local parameter of the convolution kernel with the contribution degree meeting the set condition to the server as the first local parameter of the (k + 1) th training period of the terminal.

Description

technical field [0001] The invention relates to the field of data processing, in particular to a model compression method and device. Background technique [0002] In the field of deep learning technology, users can obtain deep learning network models with better prediction effects through training models. However, deep learning network models with better prediction effects usually have relatively complex network structures, thus occupying a large storage space. Correspondingly, when using the deep learning network model to predict the data to be predicted, due to the complex structure of the deep learning network model, it may cause waste of computing resources, making the prediction efficiency poor. [0003] Usually, there are tens of millions or even hundreds of millions of model parameters for complex network models, and the size is usually several hundred. In the current mobile network environment, the requirements for model transmission are relatively high. How to eff...

AI Technical Summary

Problems solved by technology

[0002] In the field of deep learning technology, users can obtain deep learning network models with better predictive effects through training models. However, deep learning network models with better predictive effects usually have a more complex network structure, thus

Images