Data category identification method and apparatus based on deep neural network
First Claim
1. A data category identification method based on a deep neural network for an apparatus comprising a memory and a processor, the method comprising:
- establishing an initial deep neural network;
generating a linear category analysis function after data category information is added to a locally saved initial linear category analysis function in the memory, the linear category analysis function being generated from an input training sample vector set;
generating an optimization function of the initial deep neural network from a locally saved unsupervised auto-encoding model optimization function in the memory and the linear category analysis function;
acquiring a parameter of the initial deep neural network from the optimization function of the initial deep neural network;
generating the deep neural network from a locally saved classification neural network in the memory, the initial deep neural network, and the parameter of the initial deep neural network, the deep neural network having a multi-layer network structure comprising at least an input layer and an output layer;
receiving to-be-identified data from a user device and inputting the to-be-identified data to the input layer of the deep neural network, the to-be-identified data comprising image information or text information;
applying the deep neural network to the image information or the text information comprising the to-be-identified data to obtain information for a category of the to-be-identified data;
determining information for the category of the to-be-identified data in the image information or the text information in response to applying the deep neural network to the image information or the text information; and
outputting the information for the category by the output layer of the deep neural network to permit categorization of the to-be-identified data, the linear category analysis function ζ
lda (W) being;
2 Assignments
0 Petitions
Accused Products
Abstract
A deep neural network to which data category information is added is established locally, to-be-identified data is input to an input layer of the deep neural network generated based on the foregoing data category information, and information of a category to which the to-be-identified data belongs is acquired, where the information of the category is output by an output layer of the deep neural network. A deep neural network is established based on data category information, such that category information of to-be-identified data is conveniently and rapidly obtained using the deep neural network, thereby implementing a category identification function of the deep neural network, and facilitating discovery of an underlying law of the to-be-identified data according to the category information of the to-be-identified data.
-
Citations
12 Claims
-
1. A data category identification method based on a deep neural network for an apparatus comprising a memory and a processor, the method comprising:
-
establishing an initial deep neural network; generating a linear category analysis function after data category information is added to a locally saved initial linear category analysis function in the memory, the linear category analysis function being generated from an input training sample vector set; generating an optimization function of the initial deep neural network from a locally saved unsupervised auto-encoding model optimization function in the memory and the linear category analysis function; acquiring a parameter of the initial deep neural network from the optimization function of the initial deep neural network; generating the deep neural network from a locally saved classification neural network in the memory, the initial deep neural network, and the parameter of the initial deep neural network, the deep neural network having a multi-layer network structure comprising at least an input layer and an output layer; receiving to-be-identified data from a user device and inputting the to-be-identified data to the input layer of the deep neural network, the to-be-identified data comprising image information or text information; applying the deep neural network to the image information or the text information comprising the to-be-identified data to obtain information for a category of the to-be-identified data; determining information for the category of the to-be-identified data in the image information or the text information in response to applying the deep neural network to the image information or the text information; and outputting the information for the category by the output layer of the deep neural network to permit categorization of the to-be-identified data, the linear category analysis function ζ
lda (W) being; - View Dependent Claims (2, 3, 4, 10)
-
-
5. A data category identification apparatus based on a deep neural network, comprising:
-
a memory comprising instructions; and a processor coupled to the memory, wherein the instructions cause the processor to be configured to; establish an initial deep neural network; generate a linear category analysis function after data category information is added to a locally saved initial linear category analysis function, the linear category analysis function being generated from an input training sample vector set; generate an optimization function of the initial deep neural network, the optimization function being acquired from a locally saved unsupervised auto-encoding model optimization function and the linear category analysis function; acquire a parameter of the initial deep neural network from the optimization function of the initial deep neural network; generate the deep neural network from a locally saved classification neural network, the initial deep neural network, and the parameter of the initial deep neural network, the deep neural network having a multi-layer network structure comprising at least an input layer and an output layer; receive to-be-identified data from a user device and inputting the to-be-identified data to the input layer of the deep neural network, the to-be-identified data comprising image information or text information; apply the deep neural network to the image information or the text information comprising the to-be-identified data to obtain information for a category of the to-be-identified data; determine information for the category of the to-be-identified data in the image information or the text information in response to applying the deep neural network to the image information or the text information; and output the information for the category by the output layer of the deep neural network to permit categorization of the to-be-identified data, the linear category analysis function ζ
lda (W) being; - View Dependent Claims (6, 7, 8, 11)
-
-
9. A non-transitory computer readable medium storing codes which, when executed by a processor of a network system causes the processor to:
-
establish an initial deep neural network; generate a linear category analysis function after data category information is added to a locally saved initial linear category analysis function, the linear category analysis function being generated from an input training sample vector set; generate an optimization function of the initial deep neural network, the optimization function being acquired from a locally saved unsupervised auto-encoding model optimization function and the linear category analysis function; acquire a parameter of the initial deep neural network from the optimization function of the initial deep neural network; generate the deep neural network from a locally saved classification neural network, the initial deep neural network, and the parameter of the initial deep neural network, the deep neural network having a multi-layer network structure comprising at least an input layer and an output layer; receive to-be-identified data from a user device and input the to-be-identified data to the input layer of the deep neural network, the to-be-identified data comprising image information or text information; apply the deep neural network to the image information or the text information comprising to-be-identified data to obtain information for a category of the to-be-identified data; determine information for the category of the to-be-identified data in the image information or the text information in response to applying the deep neural network to the image information or the text information; and output the information for the category by the output layer of the deep neural network to permit categorization of the to-be-identified data, the linear category analysis function ζ
lda (W) being; - View Dependent Claims (12)
-
Specification