digit_recognition.m

function digit_recognition


disp(' Hit any key to visualise bit maps of the digits.')
disp(' ')
pause 


% visualise bit maps of the digits,可视化这些数字的字母
[digit1,digit2,digit3,digit4,digit5,digit6,digit7,digit8,digit9,digit0] = bit_maps;

disp(' Hit any key to obtain ten 45-element input vectors denoted by "p".') 
pause 

p=[digit1(:),digit2(:),digit3(:),digit4(:),digit5(:),digit6(:),digit7(:),digit8(:),digit9(:),digit0(:)]

disp(' Hit any key to define ten 10-element target vectors denoted by "t". ')
pause

t = eye(10)%生成单位矩阵


%初始化网络的隐藏层和输出层的神经元数量
disp(' Hit any key to define the network architecture.')
pause 

s1=12; % Number of neurons in the hidden layer  
s2=10; % Number of neurons in the output layer

disp(' ')
fprintf(1,' s1=%.0f;    Number of neurons in the hidden layer\n',s1);
fprintf(1,' s2=%.0f;    Number of neurons in the output layer\n',s2);
disp(' ')


%权重等训练参数的确定
net = newff(minmax(p),[s1 s2],{'logsig' 'purelin'},'traingdx');

net.trainParam.show = 20;      % Number of epochs between showing the progress
net.trainParam.epochs = 1000;  % Maximum number of epochs
net.trainParam.goal = 0.001;   % Performance goal
net.trainParam.lr=0.1;         % Learning rate

%开始训练网络，使用matlab 神经网络工具箱

net = train(net,p,t);

disp(' Hit any key to see how the network recognises a digit, for example digit 3.')
pause 

digit3
probe=digit3(:);
a=sim(net,probe);
disp(' a=sim(net,probe)')
a=round(a)
        
disp(' Hit any key to see how "noise" distorts the bit map of a digit, for example digit 5.')
disp(' ')
pause 

probe=digit5;
figure('name','"Noisy" bit maps')
subplot(1,2,1) 
probe_plot(probe)
title('Noise level: 0%')

disp(' Hit any key to continue.')
disp(' ')
pause

probe=digit5+randn(size(probe))*0.1;
subplot(1,2,2) 
probe_plot(probe)
title('Noise level: 10%')

disp(' Hit any key to continue.')
disp(' ')
pause

probe=digit5+randn(size(probe))*0.2;
figure('name','"Noisy" bit maps')
subplot(1,2,1)
probe_plot(probe)
title('Noise level: 20%')

disp(' Hit any key to continue.')
disp(' ')
pause

probe=digit5+randn(size(probe))*0.5;
subplot(1,2,2) 
probe_plot(probe)
title('Noise level: 50%')

disp(' Hit any key to evaluate the digit recognition neural network.')
disp(' ')
pause 

% 测试部分
noise_range = 0:.05:.50;  % Range of noise
max_test = 100;           % Number of test examples for each digit to be recognised
average_error = [];       % Average recognition error for a particular noise level 

% Evaluate the digit recognition network.
for noise_level=noise_range
   error=0;

  for i=1:max_test
    probe=p+randn(size(p))*noise_level;
    a=compet(sim(net,probe));
    error=error+sum(sum(abs(a-t)))/2;
  end
  
  average_error = [average_error error/10/max_test];
  fprintf('Noise level: %.0f percent; Average error: %.0f percent\n',noise_level*100,error/10/max_test*100);
end

disp(' ')
disp(' Hit any key to plot the test results.')
disp(' ')
pause

h = figure;
plot(noise_range*100,average_error*100,'b-');
title('Performance of the digit recognition network')
xlabel('Noise level, %');
ylabel('Recognition error, %');

disp(' ')
disp(' Hit any key to train the digit recognition network with "noisy" examples.')
disp(' ')
pause

net.trainParam.epochs = 1000;   

t_noise = [t t t t];
for pass = 1:10
  fprintf('Pass = %.0f\n',pass);
  p_noise=[p p (p+randn(size(p))*0.1) (p+randn(size(p))*0.2)];
  net= train(net,p_noise,t_noise);
end

disp(' Hit any key to evaluate the digit recognition network trained with "noisy" examples.')
disp(' ')
pause 

average_error = []; 

for noise_level = noise_range
   error = 0;

  for i=1:max_test
    probe=p+randn(size(p))*noise_level;
    a=compet(sim(net,probe));
    error=error+sum(sum(abs(a-t)))/2;
  end
  
  average_error = [average_error error/10/max_test];
  fprintf('Noise level: %.0f percent; Average error: %.0f percent\n',noise_level*100,error/10/max_test*100);
end

disp(' ')
disp(' Hit any key to plot the test results.')
disp(' ')
pause

figure(h)
hold on
plot(noise_range*100,average_error*100,'r-');
legend('Network trained with "perfect" examples','Network trained with "noisy" examples',2);
hold off

disp('end of digit_recognition')

function probe_plot(probe);

[m n]=size(probe);
probe_plot=[probe probe(:,[n])]';
probe_plot=[probe_plot probe_plot(:,[m])]';
pcolor(probe_plot)
colormap(gray)
axis('ij')
axis image