Multilayer convolution neural network optimization system and method

Abstract

The invention relates to a multilayer convolution neural network optimization system and a multilayer convolution neural network optimization method. The multilayer convolution neural network optimization system comprises an image positioning module, a sampling module based on CP decomposition, a micro-sampling module, a parameter tuning module based on a BP algorithm, and a convolution neural network feature output module, wherein the image positioning module sets a parameter matrix theta by means of a regression function according to dimensionality of a convolutional layer; the sampling module based on CP decomposition performs tensor decomposition on a result after convolution operation to obtain two rank-one tensors p and q; the micro-sampling module adopts a bilinear interpolation algorithm for carrying out linear interpolation on pixel points in different directions of an image, and obtains network output V; the parameter tuning module based on a BP algorithm updates a parameter theta; and the convolution neural network feature output module is used for introducing an updated parameter theta<hat> into a network, carrying out iterative operation and outputting convolution neural network features. The multilayer convolution neural network optimization system and the multilayer convolution neural network optimization method are conductive to extracting space-invariant features, and improving operational efficiency.

Description

technical field [0001] The present application relates to the technical field of neural networks, in particular to a multi-layer convolutional neural network optimization system and method. Background technique [0002] Convolutional neural network (CNN) is a feed-forward neural network. Unlike traditional algorithms, the neural units between adjacent layers of convolutional neural network are not fully connected, but partially connected, and For the convolution operation weight sharing of a convolution kernel, the number of parameters is reduced, and the purpose of feature extraction is achieved through multiple convolution and pooling processes. Convolution can be used to achieve image blurring, edge detection is beneficial to feature extraction, and pooling operations can be used to reduce the dimensionality of images more easily, thereby reducing parameters and calculations. [0003] In the traditional convolutional neural network, the network is designed as a convoluti...

AI Technical Summary

Problems solved by technology

[0004] This application provides a multi-layer convolutional neural network optimization system and method to solve the technical problem in the prior art that the network structure needs to be set to multiple layers to extract spatially invariant features

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