1 简介
随着经济的发展和社会的高速进步,图像数据在我们日常生活中发挥着越来越重要的作用.图像数据的爆炸式增长使得需要分类的事物种类越来越多,而且被分类的对象内容也越来越复杂.传统的图像分类方法已经不能满足现实应用的需要,如何在大数据下提高图像分类的准确率意义重大.卷积神经网络(Convolutional Neural Network,CNN)是一个新型的人工神经网络方法,在处理二维图像领域表现出良好的性能,因此卷积神经网络被广泛地应用在图像分类领域.图像分类的正确率受卷积神经网络结构的影响,因此研究卷积神经网络结构优化问题具有重要的理论价值和实用价值.本文提出一种基于蜜蜂优化算法优化卷积神经网络参数从而达到改进图像分类效果的目的。
2 部分代码
%% Bees CNN Algorithm (A Fuzzy Evolutionary Deep Leaning) - Created in 20 Jan by Seyed Muhammad Hossein Mousavi
% It is possible to fit deep learning weights and bias using evolutionary
% algorithm, right after training stage. Here, CNN is used to classify 8
% face classes. After CNN train, initial fuzzy model is created to aid the
% learning process. Finally, CNN network weights (from Fully Connected Layer)
% trains using Bees algorithm
% to be fitted in a nature inspired manner (here behavior of Bees). You can
% used your data with any number of samples and classes. R
