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Clustering model training method and device, electronic equipment and computer storage medium

A technology of model training and electronic equipment, applied in the field of artificial intelligence, can solve problems such as high data exchange costs and no memory for processors

Inactive Publication Date: 2018-06-29
BEIJING SENSETIME TECH DEV CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Different from parallel computing, in a distributed system, there is often no shared memory between processors, and the data exchange between processors cannot pass through the shared memory but needs to be communicated through the network, and the cost of data exchange is relatively high.

Method used

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  • Clustering model training method and device, electronic equipment and computer storage medium
  • Clustering model training method and device, electronic equipment and computer storage medium
  • Clustering model training method and device, electronic equipment and computer storage medium

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Embodiment Construction

[0092] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangements of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0093] At the same time, it should be understood that, for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0094] The following description of at least one exemplary embodiment is merely illustrative in nature and in no way taken as limiting the invention, its application or uses.

[0095] Techniques, methods and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, such techniques, methods and devices should be considered part of the descriptio...

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Abstract

The embodiment of the invention discloses a clustering model training method and device, electronic equipment and a computer storage medium. The method comprises the steps that a global clustering model is acquired based on master nodes; according to any one of at least one slave node in a distributed system, the global clustering model is acquired from the master nodes in the distributed system,clustering estimation is performed based on the global clustering model and calculation data allocated to any corresponding slave node, and a local clustering model corresponding to any slave node isobtained; the distributed system comprises the master nodes and at least one slave node in communicating connection with the master nodes, wherein the master nodes are in communicating connection withall the slave nodes; and the global clustering model is trained based on the obtained local clustering models corresponding to all the slave nodes. Through the clustering model training method in theembodiment, the synchronization rate among the calculation nodes is lowered, and clustering efficiency is improved.

Description

technical field [0001] The invention relates to artificial intelligence technology, in particular to a clustering model training method and device, electronic equipment, and a computer storage medium. Background technique [0002] Computing performed on a distributed computing system is called distributed computing. A distributed computing system usually consists of multiple computing nodes, and each computing node is connected to each other through a network. Similar to parallel computing, distributed computing requires mobilizing all processors to complete computing tasks in parallel. Different from parallel computing, in a distributed system, there is often no shared memory between processors, and the data exchange between processors cannot pass through the shared memory but needs to be communicated through the network, and the cost of data exchange is relatively high. [0003] Cluster analysis is an important class of unsupervised learning methods. The purpose of cluste...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/23G06F18/214
Inventor 王若晖林达华汤晓鸥
Owner BEIJING SENSETIME TECH DEV CO LTD
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