traintest_newfinal.m

用人工神经网络进行人脸识别

clear

from=5;
length=8;
%rez=100;

% gray-level shift
u=0; % add noise
lowin=0.1; highin=0.9;
lowout=0.1; highout=0.9;

% target creation
T1=[0 1 1 0;0 1 1 0;0 1 1 0;0 1 1 0];
T2=[1 1 0 0;1 1 0 0;1 1 0 0;1 1 0 0];
T3=[1 1 1 0;1 1 1 0;1 1 1 0;1 1 1 0];
T4=[0 1 1 1;0 1 1 1;0 1 1 1;0 1 1 1];
T5=[1 0 0 0;1 0 0 0;1 0 0 0;1 0 0 0];
T6=[0 0 0 1;0 0 0 1;0 0 0 1;0 0 0 1];
T7=[1 0 1 0;1 0 1 0;1 0 1 0;1 0 1 0];
T8=[0 1 0 1;0 1 0 1;0 1 0 1;0 1 0 1];
T9=[1 0 0 1;1 0 0 1;1 0 0 1;1 0 0 1];
T10=[0 0 1 1;0 0 1 1;0 0 1 1;0 0 1 1];


% read images
for i = from:length
    data1{i-4} = imread(['C:\images\f1\' num2str(i) '.tif']);
    data1{i-4} = imnoise(data1{i-4},'salt & pepper',u);
    data1{i-4} = imadjust(data1{i-4},[lowin highin],[lowout highout]);
    %temp = imresize(temp, [rez rez]);
    %data1(i-4,:) = temp(:); 
end;

for i = from:length
    data2{i-4} = imread(['C:\images\f2\' num2str(i) '.tif']);
    data2{i-4} = imnoise(data2{i-4},'salt & pepper',u);
    data2{i-4} = imadjust(data2{i-4},[lowin highin],[lowout highout]);
    %temp = imresize(temp, [rez rez]);
    %data2(i-4,:) = temp(:); 
end;

for i = from:length
    data3{i-4} = imread(['C:\images\f3\' num2str(i) '.tif']);
    data3{i-4} = imnoise(data3{i-4},'salt & pepper',u);
    data3{i-4} = imadjust(data3{i-4},[lowin highin],[lowout highout]);
    %temp = imresize(temp, [rez rez]);
    %data3(i-4,:) = temp(:); 
end;

for i = from:length
    data4{i-4} = imread(['C:\images\f4\' num2str(i) '.tif']);
    data4{i-4} = imnoise(data4{i-4},'salt & pepper',u);
    data4{i-4} = imadjust(data4{i-4},[lowin highin],[lowout highout]);
    %temp = imresize(temp, [rez rez]);
    %data4(i-4,:) = temp(:); 
end;

for i = from:length
    data5{i-4} = imread(['C:\images\f5\' num2str(i) '.tif']);
    data5{i-4} = imnoise(data5{i-4},'salt & pepper',u);
    data5{i-4} = imadjust(data5{i-4},[lowin highin],[lowout highout]);
    %temp = imresize(temp, [rez rez]);
    %data5(i-4,:) = temp(:); 
end;

for i = from:length
    data6{i-4} = imread(['C:\images\f6\' num2str(i) '.tif']);
    data6{i-4} = imnoise(data6{i-4},'salt & pepper',u);
    data6{i-4} = imadjust(data6{i-4},[lowin highin],[lowout highout]);
    %temp = imresize(temp, [rez rez]);
    %data6(i-4,:) = temp(:); 
end;

for i = from:length
    data7{i-4} = imread(['C:\images\f7\' num2str(i) '.tif']);
    data7{i-4} = imnoise(data7{i-4},'salt & pepper',u);
    data7{i-4} = imadjust(data7{i-4},[lowin highin],[lowout highout]);
    %temp = imresize(temp, [rez rez]);
    %data7(i-4,:) = temp(:); 
end;

for i = from:length
    data8{i-4} = imread(['C:\images\f8\' num2str(i) '.tif']);
    data8{i-4} = imnoise(data8{i-4},'salt & pepper',u);
    data8{i-4} = imadjust(data8{i-4},[lowin highin],[lowout highout]);
    %temp = imresize(temp, [rez rez]);
    %data8(i-4,:) = temp(:); 
end;

for i = from:length
    data9{i-4} = imread(['C:\images\f9\' num2str(i) '.tif']);
    data9{i-4} = imnoise(data9{i-4},'salt & pepper',u);
    data9{i-4} = imadjust(data9{i-4},[lowin highin],[lowout highout]);
    %temp = imresize(temp, [rez rez]);
    %data9(i-4,:) = temp(:); 
end;

for i = from:length
    data10{i-4} = imread(['C:\images\f10\' num2str(i) '.tif']);
    data10{i-4} = imnoise(data10{i-4},'salt & pepper',u);
    data10{i-4} = imadjust(data10{i-4},[lowin highin],[lowout highout]);
    %temp = imresize(temp, [rez rez]);
    %data10(i-4,:) = temp(:); 
end;


%-------------------\\\Train test set///----------------
hd=10;
lr=.25;
ep=20;




load wintface;load zintface;
WX=winit;ZX=zinit;

save wq WX;
save zq ZX;
%--------------------------

%WX=rand(900+1,10);
%ZX=rand(10+1,4);
%winit=WX;
%zinit=ZX;
%save wint winit;
%save zint zinit;
%save wq WX;
%save zq ZX;
%------------------------
%SS = who('-file','zq');
%TT = who('-file','wq');
%if SS{1}=='Z'
 %   load zq;
  %  ZX=Z;
   % save zq ZX;
   %end;
    
%if TT{1}=='W'
 %   load wq;
  %  WX=W;
   % save wq WX;
   %end;
    

tic
[WX,ZX,O1,E1]=msnn2(T1,data1,hd,lr,ep);
[WX,ZX,O2,E2]=msnn2(T2,data2,hd,lr,ep);
[WX,ZX,O3,E3]=msnn2(T3,data3,hd,lr,ep);
[WX,ZX,O4,E4]=msnn2(T4,data4,hd,lr,ep);
[WX,ZX,O5,E5]=msnn2(T5,data5,hd,lr,ep);
[WX,ZX,O6,E6]=msnn2(T6,data6,hd,lr,ep);
[WX,ZX,O7,E7]=msnn2(T7,data7,hd,lr,ep);
[WX,ZX,O8,E8]=msnn2(T8,data8,hd,lr,ep);
[WX,ZX,O9,E9]=msnn2(T9,data9,hd,lr,ep);
[WX,ZX,O10,E10]=msnn2(T10,data10,hd,lr,ep);
toc
timerTestSet=toc;


out_test1 = O1(1,:);
out_test2 = O2(1,:);
out_test3 = O3(1,:);
out_test4 = O4(1,:);
out_test5 = O5(1,:);
out_test6 = O6(1,:);
out_test7 = O7(1,:);
out_test8 = O8(1,:);
out_test9 = O9(1,:);
out_test10 = O10(1,:);



outTest = [out_test1;out_test2;out_test3;out_test4;...
           out_test5;out_test6;out_test7;out_test8;...
           out_test9;out_test10];

save N;

% Plot Error Descent
%EgraphTrain = [E1,E2,E3,E4];
%plot(EgraphTrain);
%xlabel('Epochs');
%ylabel('Training Error of Training Set');