BP神经网络进行网络训练

BP神经网络进行网络训练

close all
clear
echo on 
clc


I0=imread('D:\迅雷下载\bp1\0.jpg');
I0=imresize(I0,[24 9]);
I1=imread('D:\迅雷下载\bp1\1.JPG');
I1=imresize(I1,[24 9]);
I2=imread('D:\迅雷下载\bp1\2.jpg');
I2=imresize(I2,[24 9]);
I3=imread('D:\迅雷下载\bp1\3.jpg');
I3=imresize(I3,[24 9]);
I5=imread('D:\迅雷下载\bp1\5.JPG');
I5=imresize(I5,[24 9]);
I6=imread('D:\迅雷下载\bp1\6.jpg');
I6=imresize(I6,[24 9]);
I7=imread('D:\迅雷下载\bp1\7.jpg');
I7=imresize(I7,[24 9]);
I8=imread('D:\迅雷下载\bp1\8.jpg');
I8=imresize(I8,[24 9]);
I9=imread('D:\迅雷下载\bp1\9.jpg');
I9=imresize(I9,[24 9]);


p0=zeros(216,9);
I=[I0,I1,I2,I3,I5,I6,I7,I8,I9];




for k=0:8
    for m=0:8
        p0(m*24+1:(m+1)*24,k+1)=I(1:24,m+1+9*k);
    end
end


t0=[0 1 2 3 5 6 7 8 9];


% P 为输入矢量 
% T 为目标矢量


P=p0;
T=t0;


net_1=newff(minmax(P),[30 1],{'logsig' 'purelin'},'traingdm','learngdm');


%  当前网络层权值和阈值 
%layerWeights=net_1.LW{2,1} ;
%layerbias=net_1.b{2} ;


%  设置训练参数
net_1.trainParam.show=50;
net_1.trainParam.lr=0.01;
%net_1.trainParam.mc=0.9;
net_1.trainParam.epochs=500;
net_1.trainParam.goal=0.05;


%训练
[net_1,tr]=train(net_1,P,T);




for m=0:8
    cc0(m*24+1:(m+1)*24,1)=I1(1:24,m+1);
end


A=sim(net_1,cc0)