Depth learning model matrix compression method and device

A technology of deep learning and compression methods, applied in the computer field, can solve the problems of reducing the amount of calculation, large amount of calculation, and no consideration

Inactive Publication Date: 2015-12-23
HANGZHOU LANGHE TECH
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  • Claims
  • Application Information

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Problems solved by technology

However, the model compression method based on SVD matrix decomposition technology treats these elements close to 0 and elements with strong feedback relationship in a similar way, and the amount of calculation is still very large
In addition, the model compression method based on SVD matrix decomposition technology does not consider how to choose K to optimize the operation results and reduce the amount of operation

Method used

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  • Depth learning model matrix compression method and device
  • Depth learning model matrix compression method and device
  • Depth learning model matrix compression method and device

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Embodiment Construction

[0028] The principle and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are given only to enable those skilled in the art to better understand and implement the present invention, rather than to limit the scope of the present invention in any way. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0029] Those skilled in the art know that the embodiments of the present invention can be implemented as a system, device, device, method or computer program product. Therefore, the present disclosure may be embodied in the form of complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0030] According to an embodiment of the present invention, a matrix compression method an...

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Abstract

The invention provides a depth learning model matrix compression method and device. The last linear layer of a depth learning model is connected with M hidden nodes and N classification nodes. The last linear layer is corresponding to a weight matrix W. The method comprises the steps that S101 according to the absolute values of the elements of the weight matrix W, a K value is calculated; and Step S102 the last linear layer is divided into a first linear layer and a second linear layer, wherein the weight matrix of the first linear layer is a matrix P of M*K, and the weight matrix of the second linear layer is a matrix Q of K*N. The output of the first linear layer is the input of the second linear layer. M*N is greater than K*(M+N), and the weight matrix W is compressed.

Description

technical field [0001] Embodiments of the present invention relate to the field of computers, and more specifically, embodiments of the present invention relate to a matrix compression method and device for deep learning models. Background technique [0002] This section is intended to provide a background or context for implementations of the invention that are recited in the claims. The descriptions herein are not admitted to be prior art by inclusion in this section. [0003] The deep learning model is more and more widely used in data analysis. It maps and calculates the data through the linear layer and nonlinear layer between the nodes of different layers, and trains the model during the processing. Correct, update, and ultimately improve classification or prediction accuracy. In deep learning models, usually the last linear layer is the processing layer that connects the hidden nodes to the output classification nodes. Assuming that the number of hidden nodes is M ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/08
Inventor 陈海波李晓燕
Owner HANGZHOU LANGHE TECH
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