nervenet.m

基于nvidia CUDA架构的BP神经网络程序

function main()
InDim = 2;
OutDim = 3;

figure
colordef(gcf,'white')
echo off
clc
axis([-2,2,-2,2])
axis on 
grid
xlabel('Input x')
ylabel('input y')
line([-1 1],[1 1])
line([1 -1],[1 0])
line([-1 -1],[0 1])
line([-1 1],[-0.5 -0.5])
line([-1 1],[-1.5 -1.5])
line([-1 -1],[-0.5 -1.5])
line([1 1],[-0.5 -1.5])
hold on

SamNum=200;
rand('state',sum(100*clock))
SamIn=(rand(2,SamNum)-0.5)*4;

SamOut=[];
for i =1:SamNum
Sam = SamIn(:,i);
x = Sam(1,1);
y = Sam(2,1);
if((x>-1)&(x<1))==1
if((y>x/2+1/2)&(y<1))==1
plot(x,y,'k+')
class=[0 1 0]';
elseif((y<-0.5)&(y>-1.5))==1
plot(x,y,'ks')
class=[0 0 1]';
else
plot(x,y,'ko')
class=[1 0 0]';
end
else
plot(x,y,'ko')
class=[1 0 0]';
end
SamOut=[SamOut class];
end

HiddenUnitNum=10;
MaxEpochs=10000;
lr=0.1;
E0=0.01;

W1=0.2*rand(HiddenUnitNum,InDim)-0.1;
B1=0.2*rand(HiddenUnitNum,1)-0.1;
W2=0.2*rand(OutDim,HiddenUnitNum)-0.1;
B2=0.2*rand(OutDim,1)-0.1;

W1Ex=[W1 B1];
W2Ex=[W2 B2];

fid=fopen('d:\W1ex.dat','wb')
fwrite(fid,W1Ex,'float32')
fclose(fid)
fid=fopen('d:\W2ex.dat','wb')
fwrite(fid,W2Ex,'float32')
fclose(fid)


SamInEx=[SamIn' ones(SamNum,1)]';
ErrHistory=[];

for i=1:1
HiddenOut=logsig(W1Ex*SamInEx);
HiddenOutEx=[HiddenOut' ones(SamNum,1)]';
NetworkOut=logsig(W2Ex*HiddenOutEx);
Error=SamOut-NetworkOut;
SSE=sumsqr(Error);

ErrHistory=[ErrHistory SSE];

if SSE<E0,break,end

Delta2=Error.*NetworkOut.*(1-NetworkOut);
Delta1=W2'*Delta2.*HiddenOut.*(1-HiddenOut);

dW2Ex=Delta2*HiddenOutEx';
dW1Ex=Delta1*SamInEx';

W1Ex=W1Ex+lr*dW1Ex;
W2Ex=W2Ex+lr*dW2Ex;

W2=W2Ex(:,1:HiddenUnitNum);
end

W1=W1Ex(:,1:InDim);
B1=W1Ex(:,InDim+1);
W2=W2Ex(:,1:HiddenUnitNum);
B2=W2Ex(:,1+HiddenUnitNum);

figure
hold on;
grid
[xx,Num]=size(ErrHistory);
plot(1:Num,ErrHistory,'k-');

TestSamNum=5000;
TestSamIn=(rand(2,TestSamNum)-0.5)*4;

TestHiddenOut=logsig(W1*TestSamIn+repmat(B1,1,TestSamNum));
TestNetworkOut=logsig(W2*TestHiddenOut + repmat(B2,1,TestSamNum));
[Val,NNClass]=max(TestNetworkOut);

TestTargetOut=[];
for i=1:TestSamNum;
Sam=TestSamIn(:,i);
x=Sam(1,1);
y=Sam(2,1);
if((x>-1)&(x<1))==1
if((y>x/2+0.5)&(y<1))==1
TestTargetOut=[TestTargetOut 2];
elseif((y<-0.5)&(y>-1.5))==1;
TestTargetOut=[TestTargetOut 3];
else
TestTargetOut=[TestTargetOut 1];
end
else
TestTargetOut=[TestTargetOut 1];
end
end


NNC1Flag=abs(NNClass-1)<0.1;
NNC2Flag=abs(NNClass-2)<0.1;
NNC3Flag=abs(NNClass-3)<0.1;

TargetC1Flag=abs(TestTargetOut-1)<0.1;
TargetC2Flag=abs(TestTargetOut-2)<0.1;
TargetC3Flag=abs(TestTargetOut-3)<0.1;

Test_C1_num=sum(NNC1Flag)
Test_C2_num=sum(NNC2Flag)
Test_C3_num=sum(NNC3Flag)

Test_C1_C1=1.0*NNC1Flag*TargetC1Flag'
Test_C1_C2=1.0*NNC1Flag*TargetC2Flag'
Test_C1_C3=1.0*NNC1Flag*TargetC3Flag'

Test_C2_c1=1.0*NNC2Flag*TargetC1Flag'
Test_C2_C2=1.0*NNC2Flag*TargetC2Flag'
Test_C2_C3=1.0*NNC2Flag*TargetC3Flag'

Test_C3_C1=1.0*NNC3Flag*TargetC1Flag'
Test_C3_C2=1.0*NNC3Flag*TargetC2Flag'
Test_C3_C3=1.0*NNC3Flag*TargetC3Flag'

Test_Correct=(Test_C1_C1+Test_C2_C2+Test_C3_C3)/TestSamNum