课程大作业基于matlab+BP神经网络实现的人脸识别源码+数据+详细注释.zip

clc;clear;close all; load('ORL_32x32.mat'); % fea=uint8(fea); % imshow(fea); % %% A为便于观察的图片 % A=zeros(1280,320); % for n=0:39 % for i=0:31 % for j=1:10 % A(32*n+i+1,(j-1)*32+1:j*32)=fea(10*n+j,i*32+1:(i+1)*32); % end % end % end % A=uint8(A); % imshow(A); %% gnd1为戴眼镜标识 gnd1=zeros(400,1); data1=[11:20,31,32,33,38,39,51:60,64,65,70,121,122,125:140,161,162,165:170,181,182,183,186,190:193,195,197:200,261:280,301:310,331:340,359,360:370]; for i=data1 gnd1(i,1)=1; end %% 训练集测试集 p=0.3; [Train,Test]=crossvalind('Holdout',gnd1,p); input_train=fea(Train,:); output_train=gnd1(Train,:); input_test=fea(Test,:); output_test=gnd1(Test,:); %% 构建BP神经网络 cengshu=[5 20 5]; net=newff(input_train',output_train',cengshu,{'logsig','logsig'},'trainlm');%gd net.trainParam.epochs=100; %训练次数 net.trainParam.lr=0.2; %学习速率 net.trainParam.goal=0.000000000001; %训练目标最小误差 %% BP神经网络训练 net=train(net,input_train',output_train'); % 保存模型 save bestnet net; load('bestnet'); %% BP神经网络测试 an=sim(net,input_test'); %用训练好的模型进行仿真 ans1=round(an); figure(1) plot(output_test,'bo-') hold on plot(an,'r*-') hold on legend('期望值','预测值') xlabel('数据组数'),ylabel('值'),title('BP神经网络测试集预测值和期望值的误差对比') %% 计算混淆矩阵、测试精度，查准率，查全率和F1值 ans1=ans1'; cm=[sum(output_test==1 & ans1==1),sum(output_test==1 & ans1==0);sum(output_test==0 & ans1==1),sum(output_test==0 & ans1==0)]; ex=(cm(1,1)+cm(2,2))/sum(sum(cm)); P=cm(1,1)/(cm(1,1)+cm(2,1)); R=cm(1,1)/(cm(1,1)+cm(1,2)); F1=2*P*R/(P+R); cm=[sum(output_test==1 & ans1==1),sum(output_test==1 & ans1==0),sum(output_test==0 & ans1==1),sum(output_test==0 & ans1==0)]; cm=int2str(cm); cengshu=int2str(cengshu); data11 = readcell('data_1.xlsx'); data12 ={cengshu,cm,ex,P,R,F1}; xlswrite('data_1.xlsx',data12,'sheet1','A1') xlswrite('data_1.xlsx',data11,'sheet1','A2')