Model parameter training method, device and system

A technology of model parameters and parameter distribution, applied in the field of communication, can solve the problems of slow training process and non-convergence
CN104346629AInactive Publication Date: 2015-02-11开源物联网(广州)有限公司
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
开源物联网(广州)有限公司
Publication Date
2015-02-11
Estimated Expiration
Not applicable · inactive patent

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Abstract

The embodiment of the invention discloses a model parameter training method, a device and a system, and the method, the device and the system are used for rapidly carrying out image retrieval or parameter training of image classification. The method comprises the steps of using model parameters to carry out iterative computation on an objective function, wherein the objective function is a cost function used for image training; if the result of the iterative computation does not meet the termination condition, determining the first gradient of the objective function on the model parameters, and updating the learning rate according to the parameter distribution characteristics of the model parameters in the objective function; updating the model parameters according to the learning rate and the first gradient; repeating the previous steps until the result of the iterative computation meets the termination condition; obtaining the model parameter corresponding to the result of the iterative computation meeting the termination condition.
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Description

technical field

[0001] The present invention relates to communication technology, in particular to a model parameter training method, device and system. Background technique

[0002] There is a semantic gap problem in the traditional method of searching images based on keywords, which often leads to users not being able to retrieve the images they want to search. The content-based image retrieval (CBIR, Content Based Image Retrieval) method is a retrieval method that is more similar to human thinking. The current CBIR system mainly relies on some shallow machine learning algorithms, and its performance is greatly restricted. Deep learning is the most eye-catching direction in the field of machine learning in recent years. Its motivation is to establish and simulate the neural network of human brain for analysis and learning, which imitates the mechanism of human brain to interpret data, such as images, sounds and texts. The concept of deep learning originates from the res...

Claims

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