Method, device and apparatus for determining structure of neural network,and readable medium

Abstract

Embodiments of the present disclosure disclose a method, device and apparatus for determining the structure of a neural network, and readable medium. The method comprises the following steps: samplingthe current network structure through a sampler; calculating an objective function value of the current network structure; adjusting a parameter of the sampler according to the objective function value; returning to the operation of performing sampling out of the current network structure by the sampler until the objective function value reaches the preset function value and / or the number of adjustments reaches the number of times threshold. The disclosed embodiment can realize automatic searching of the network structure, and is not confined to a fixed network structure, but continuously obtains a new and higher quality network structure through the value of the objective function, and can be applied to data processing of almost all kinds of scenes.

Description

technical field [0001] Embodiments of the present disclosure relate to computer vision technology, and in particular to a method, device, device, and readable medium for determining the structure of a neural network. Background technique [0002] With the development of computer vision, data such as images and sounds can be processed through neural networks, such as object detection, object tracking, segmentation, and classification of objects in images. [0003] With the improvement of user needs and the development of terminal technology, higher requirements are put forward for the accuracy and speed of data processing, which requires a neural network with better processing effect. In the prior art, most of the relatively mature neural networks such as RCNN, TOLO, and SSD are used for image processing. However, the inventors found during the research process that these mature neural networks are not suitable for all data processing. For example, when processing some These...

AI Technical Summary

Problems solved by technology

In the prior art, most of the relatively mature neural networks such as RCNN, TOLO, and SSD are used for image processing. However, the inventors found during the research process that these mature neural networks are not suitable for all data processing. For example, when processing some These neural networks are less effective when processing images

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