mybp.m

神经网络源码可应用于遥感图像分类

function [w1,b1,w2,b2,w3,b3]=myBP()
%b-p网络分类实现,针对绍兴数据采用2、3、5三个波段象元灰度值
targets=eye(5);
x_max=110;
x_min=10;	
k=x_max-x_min;
p=[54;25;56];
t=targets;
s1=10;		
s2=10;	
[r,q]=size(p);
[s3,q]=size(t);	
[w1,b1]=nwtan(s1,r);	%初始化网络第一层的权值和偏差
[w2,b2]=rands(s2,s1);	%初始化网络第二层的权值和偏差
[w3,b3]=rands(s3,s2);	%初始化网络第三层的权值和偏差
disp_freq=20;	
max_epoch=6000;		
err_goal=0.000001;		
momentum=0.95;	
fpa=fopen('g:\lfx\newtask\trainingdata\SXcropland.txt','rt');
fpb=fopen('g:\lfx\newtask\trainingdata\SXwater.txt','rt');
fpc=fopen('g:\lfx\newtask\trainingdata\SXforesty.txt','rt');
fpd=fopen('g:\lfx\newtask\trainingdata\SXresident.txt','rt');
fpe=fopen('g:\lfx\newtask\trainingdata\SXteasample.txt','rt');
loop=120;
for j=1:loop
	clf;
	[class_A,count1]=fscanf(fpa,'%d',[3,1]);
	[class_B,count2]=fscanf(fpb,'%d',[3,1]);
	[class_C,count3]=fscanf(fpc,'%d',[3,1]);
	[class_D,count4]=fscanf(fpd,'%d',[3,1]);
	[class_E,count5]=fscanf(fpe,'%d',[3,1]);
	class=[class_A,class_B,class_C,class_D,class_E];
	class=(class-x_min)/k;
    p=class;
	tp=[disp_freq,max_epoch,err_goal,nan,nan,nan,momentum nan];
	[w1,b1,w2,b2,w3,b3,epochs,tr]=trainbpx(w1,b1,'logsig',w2,b2,'logsig',w3,b3,'purelin',p,t,tp);
fclose('all');
end
disp('The programe end');
end