.png){kind=icon}
返回
CNN Convolutional layer optimisation based on quantum evolutionary algorithm
.png){kind=icon}
OA
AI
DOI：10.1080/09540091.2020.1841111.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, a quantum convolutional neural network (CNN) architecture is proposed to find the optimal number of convolutional layers. Since quantum bits use probability to represent binary information, the quantum CNN does not represent the actual network, but the probability of existence of each convolutional layer, thus achieving the aim of training weights and optimising the number of convolutional layers at the same time. In the simulation part, CIFAR-10 (including 50k training images and 10k test images in 10 classes) is used to train VGG-19 and 20-layer, 32-layer, 44-layer and 56-layer CNN networks, and compare the difference between the optimal and non-optimal convolutional layer networks. The simulation results show that without optimisation, the accuracy of the test data drops from approximately 90% to about 80% as the number of network layers increases to 56 layers. However, the CNN with optimisation made it possible to maintain the test accuracy at more than 90%, and the number of network parameters could be reduced by nearly half or more. This shows that the proposed method can not only improve the network performance degradation caused by too many hidden convolutional layers, but also greatly reduce the use of the network's computing resources.
Keyword:
Convolutional neural networks (CNNs)
convolutional layer optimisation
quantum evolutionary algorithm (QEA)
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.4
论文数:
850
被引数:
1.5K
机构
引用论文
Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms.png)
SENSORSOAAI
SENSORSIF3.5
OAAI分享收藏Optimisation of engineering system using a novel search algorithm: the Spacing Multi-Objective Genetic AlgorithmCONNECTION SCIENCE
CONNECTION SCIENCEIF3.4
PREAI
分享收藏Estimation of Bearing Remaining Useful Life Based on Multiscale Convolutional Neural Network基于多尺度卷积神经网络的轴承剩余寿命估算
PREAI
分享收藏分享收藏
查看更多内容