A method for classifying and recognizing capsule network image based of improved reconstruct network

Abstract

The invention discloses a method for classifying and recognizing capsule network images of an improved reconstructed network. The method includes: S1, constructing a capsule network; 2, inputting an image training set to that capsule network, wherein the capsule network complete image classification recognition calibration after training and learning; 3, inputting an image to be classified to thatcapsule network, wherein the maximum value of the output vector vj of the work network is the obtain recognition result; S4, the capsule network outputting the recognition result of the image to be classified, wherein the reconstructed network structure of the capsule network is deconvolution operation. The beneficial effects are: a new reconstructed network structure is provided, in which the vector is restored to the image by deconvolution operation, the network parameters are adjusted by comparing the error between the restored image and the original image, the computational parameters arereduced, and more running memory is freed for the hardware equipment.

Description

technical field [0001] The invention relates to the application of a capsule network in image classification, in particular to a capsule network image classification and recognition method for improving the reconstructed network. Background technique [0002] In recent years, convolutional neural networks have achieved rapid development in image recognition, target detection, and semantic segmentation. Convolutional neural networks are usually composed of convolutional layers, activation layers, pooling layers, and fully connected layers. The pooling layer is An important part of the convolutional neural network, typically the maximum pooling and average pooling operations, the pooling layer can reduce the size of the input feature map and reduce the amount of calculation of the model, but the pooling layer also has the loss of position information question. [0003] Aiming at the problem of loss of location information in the pooling layer of the convolutional neural netwo...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problem of a large amount of parameters in the capsule network, the present invention proposes a new reconstruction network structure for the existing capsule network, which restores the vector to an image through a deconvolution operation, and compares the error betwe

Images