nerve_evalue.m

基于nvidia CUDA架构的BP神经网络程序

fid=fopen('d:\0_W1Ex.dat','rb')
W1Ex=fread(fid,[3,11],'float32')
fid=fopen('d:\0_W2Ex.dat','rb')
W2Ex=fread(fid,[11,3],'float32')
fclose(fid)
W1Ex=W1Ex'
W2Ex=W2Ex'



InDim = 2;
OutDim = 3;
HiddenUnitNum=10;
W1=W1Ex(:,1:InDim);
B1=W1Ex(:,InDim+1);
W2=W2Ex(:,1:HiddenUnitNum);
B2=W2Ex(:,1+HiddenUnitNum);
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