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Deep learning network structure algorithm

A network structure and deep learning technology, applied in the field of adaptive sparse connection deep learning network structure algorithm, can solve the problems of low efficiency, huge amount of calculation and memory consumption, etc., achieve high precision, reduce the amount of calculation, and improve parallelism The effect of scalability

Inactive Publication Date: 2017-04-26
DAWNING INFORMATION IND BEIJING
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the biggest drawback of this connection method is: the amount of calculation and memory consumption are huge, and the efficiency is low
Because for many specific problems, although the full connection relationship can be used directly, it is not the best one

Method used

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  • Deep learning network structure algorithm
  • Deep learning network structure algorithm
  • Deep learning network structure algorithm

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Embodiment Construction

[0015] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of the embodiments of the present invention, rather than all embodiments; based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work, all belong to the protection scope of the present invention .

[0016] figure 2 It is a schematic flow chart of a deep learning network structure algorithm provided by Embodiment 1 of the present invention, such as figure 2 As shown, a deep learning network structure algorithm, when determining the connection relationship between each network layer, includes the following steps:

[0017] S101. Colle...

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Abstract

The invention discloses a deep learning network structure algorithm. The deep learning network structure algorithm includes the following steps when determining a connection relation among network layers: S101. collecting a subset of original data from data to be trained; S102. utilizing sample data to perform full-connection training; S103. setting a threshold value, and obtaining a sparse connection table; and S104. utilizing a new sparse connection table to calculate the original data. The deep learning network structure algorithm is suitable for the field of 3D printers, and is suitable for the field of deep learning algorithms. The deep learning network structure algorithm which can improve calculation efficiency and can also ensure calculation precision can be provided.

Description

technical field [0001] The invention relates to the technical field of deep learning algorithms, in particular to an adaptive sparse connection deep learning network structure algorithm. Background technique [0002] Deep learning is a new field in machine learning research. Its motivation is to establish and simulate the neural network of human brain for analysis and learning. It imitates the mechanism of human brain to explain data, such as images, sounds and texts. Its concept was proposed by Hinton et al. in 2006. Based on the deep belief network (DBN), a non-supervised greedy layer-by-layer training algorithm is proposed, which brings hope to solve the optimization problems related to the deep structure, and then a multi-layer autoencoder deep structure is proposed. In addition, the convolutional neural network proposed by Lecun et al. is the first real multi-layer structure learning algorithm, which uses the spatial relative relationship to reduce the number of parame...

Claims

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Application Information

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IPC IPC(8): G06N3/08
CPCG06N3/084
Inventor 窦晓光刘立许建卫
Owner DAWNING INFORMATION IND BEIJING
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