Deep neural network model-based compression method and apparatus, terminal and storage medium

Abstract

The invention discloses a deep neural network model-based compression method and apparatus, a terminal and a storage medium. The method comprises the steps of acquiring a trained deep neural network model; by adopting preset quantization levels corresponding to all layers, carrying out iterative quantization on parameters of the layers in the deep neural network model layer by layer, until the quantized deep neural network model meets a preset compression volume demand, wherein the parameter of the ith layer is subjected to the following quantization processing: according to the preset quantization level corresponding to the ith layer, quantizing the parameter of the ith layer; testing the quantized deep neural network model by adopting a verification set sample; when a test result shows that the precision after the quantization does not meet a preset precision threshold value, restoring the parameter of the ith layer to be the parameter before the quantization; and when the preset precision threshold value is met, continuing to perform quantization processing on the parameters of other layers. By the adoption of the scheme, the precision and effectiveness of the deep neural network model can be taken into account when the deep neural network model is compressed.

Description

technical field [0001] The present invention relates to the technical field of information processing, in particular to a compression method and device, a terminal, and a storage medium of a deep neural network model. Background technique [0002] With the rapid development of deep neural network-related technology research, a large number of deep neural network-related technologies have emerged in related fields, such as convolutional neural networks in the field of vision and recurrent neural networks in the field of speech recognition or natural language processing. etc. These neural network technologies have greatly improved the processing accuracy in the corresponding fields. [0003] Deep Neural Network Compared with shallow learning, the development potential of deep neural network is huge. Through the multi-layer processing structure of the deep neural network model, the characteristic features of the sample can be extracted and analyzed, and the sample features can...

AI Technical Summary

Problems solved by technology

Although this can compress the neural network model to a certain extent, the accuracy and effectiveness of the compressed deep neural network model are low

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