Neural network quantization method and device, image recognition method and device and computer equipment

A neural network and quantification method technology, applied in the field of computer equipment and readable storage media, can solve the problems of unreasonable quantification and reduced accuracy of neural network prediction, and achieve the goal of reducing redundancy, reasonable target operation attribute parameters, and avoiding prediction. The effect of severely reduced accuracy

Active Publication Date: 2019-11-12
MEGVII BEIJINGTECH CO LTD
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Problems solved by technology

[0004] However, although this neural network quantization technique can reduce the computational load of the neural network, it may lead

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  • Neural network quantization method and device, image recognition method and device and computer equipment
  • Neural network quantization method and device, image recognition method and device and computer equipment
  • Neural network quantization method and device, image recognition method and device and computer equipment

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[0045] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.

[0046] In one embodiment, such as figure 1 As shown, a neural network quantification method is provided, and the application of this method to computer equipment is used as an example for illustration. The computer equipment can be but not limited to various personal computers, notebook computers, smart phones, tablet computers, servers, etc., the method Can include the following steps:

[0047] S101, based on the prediction loss and calculation loss of the training samples, adjust the network parameters and operation attribute parameters of the initial neural network to ...

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Abstract

The invention relates to a neural network quantization method, an image recognition method, a neural network quantization device, an image recognition device, computer equipment and a readable storagemedium. The method comprises the following steps: based on prediction loss and operand loss of a training sample, adjusting network parameters and operation attribute parameters of an initial neuralnetwork to obtain trained target operation attribute parameters, wherein the operation attribute parameter represents the value of the operation attribute of each network layer in the neural network,and the operand loss is positively correlated with the actual operand associated with the operation attribute parameter; and quantizing the neural network by adopting the target operation attribute parameters to obtain the quantized neural network. By adopting the method, the problem that the prediction accuracy of the quantized neural network is seriously reduced can be avoided, and reasonable quantification is realized.

Description

technical field [0001] The present application relates to the technical field of neural networks, in particular to a neural network quantization method, an image recognition method, a neural network quantization device, an image recognition device, computer equipment and a readable storage medium. Background technique [0002] With the development of neural network technology, neural network quantization technology has emerged, mainly for model parameter values ​​and activation values ​​(output values ​​or input values) in each network layer (such as convolutional layer and fully connected layer) in the neural network. , input feature maps, etc. to compress, reduce the bit width of model parameter values, bit width of activation values, and the size (height and width) of input feature maps, etc., so as to realize the compression of the data volume of neural network model files and reduce the neural network model For purposes such as computing resource requirements during the...

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06N3/04G06N3/08
CPCG06N3/08G06V10/94G06V10/40G06N3/045
Inventor 刘泽春
Owner MEGVII BEIJINGTECH CO LTD
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