Neural_Networks.zip_earthtgr_feature set MATLAB_人脸识别_机器视觉_神经网络

clc; clear; %% 参数设置 posdir='./dataset/yes/'; negdir='./dataset/no/'; possubdir = dir(posdir); negsubdir = dir(negdir); rows = 64; cols = 64; featurenum = 8*8*9; randomseed = 1234; %% 初始化 posnum = length(possubdir)-2; negnum = length(negsubdir)-2; n=1; j=0; label = zeros([posnum + negnum,1]); feature = zeros([posnum + negnum,featurenum],'double'); index = 0; %% 正样本特征提取 for i = 1:length(possubdir) if ~strcmp(possubdir(i).name ,'.') && ~strcmp(possubdir(i).name,'..') index = index + 1; img=imread([posdir,possubdir(i).name]); img=rgb2gray(img); img_feature = reshape(HOGFunc(img,8,8,9),1,featurenum); feature(index,:) = img_feature(1,:); label(index,1) = 1; else j=j+1; end end %% 负样本特征提取 for i = 1:length(negsubdir) if ~strcmp(negsubdir(i).name ,'.') && ~strcmp(negsubdir(i).name,'..') index = index + 1; img=imread([negdir,negsubdir(i).name]); img=rgb2gray(img); img_feature = reshape(HOGFunc(img,8,8,9),1,featurenum); feature(index,:) = img_feature(1,:); label(index,1) = 0; else j=j+1; end end %% 训练 rand=randperm(posnum + negnum); label = label(rand,:); feature = feature(rand,:); rangemat = ones(featurenum, 2); rangemat(:,1)=0; % 构造神经网络(包含100个隐藏层的节点) net=newff(rangemat, [100,100,1],{'tansig','tansig','logsig'},'traingd'); net.trainParam.epochs=15000; net.trainParam.goal=0.1; LP.lr=0.1; % 训练网络 net = train(net,feature',label'); save('net.mat','net'); % 显示构造的网络 view(net); % 用这个网络来预测训练集的分类 y_test = net(feature'); %输出的值四舍五入，认为大于0.5的属于类‘1’，其他的属于类‘0’ predict = round(abs(y_test)); predict = predict'; truePre = predict == label; a = find(truePre == true); accuracy = length(a)/length(truePre); fprintf('训练集准确率为%f',accuracy);