Neural network model compression method and apparatus

A neural network model and neural network technology, applied in the field of neural network model compression methods and devices, can solve problems such as use requirements that cannot achieve accuracy
CN108510083AActive Publication Date: 2018-09-07GUOXIN YOUE DATA CO LTD
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Patent Information

Authority / Receiving Office
CN Β· China
Current Assignee / Owner
GUOXIN YOUE DATA CO LTD
Publication Date
2018-09-07

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Abstract

The invention provides a neural network model compression method and apparatus. The method comprises the steps of inputting training data to a to-be-compressed neural network model and a target neuralnetwork model; and based on an eigenvector and a classification result extracted for the training data by the to-be-compressed neural network model, training the target neural network model to obtaina compressed neural network model, wherein a parameter quantity of the target neural network model is smaller than that of the to-be-compressed neural network model. Based on the eigenvector and theclassification result extracted for the training data by the to-be-compressed neural network model, the target neural network model is guided to be trained, and classification results for the same training data by the finally obtained compressed neural network model and the to-be-compressed neural network model are same, so that the precision loss is not caused in the model compression process, the model size can be compressed on the premise of ensuring the precision, and double demands on the precision and the model size can be met.
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Description

technical field

[0001] The present invention relates to the technical field of machine learning, in particular to a neural network model compression method and device. Background technique

[0002] With the rapid development of neural networks in the fields of image, voice, and text, it has promoted the landing of a series of intelligent products. In order to allow the neural network to better learn the characteristics of the training data to improve the model effect, the parameters used to represent the neural network model increase rapidly, and the number of layers of the neural network continues to increase, resulting in a large number of parameters in the deep neural network model. Model training and application Insufficient amount of calculation in the process; this leads to the fact that most of the products based on neural network rely on the drive of server-side computing power, and are very dependent on a good operating environment and network environment, which lim...

Claims

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