listing bab 5

% Training Patterns

X = train_pattern;

nu = 0.04;

% Displaying the 15 training patterns

figure(1)

for i = 1:15,

subplot(5,3,i)

display_image(X(:,i),6,6,1);

end

% Testing Patterns

Y = test_pattern;

nu = 0.04;

% Displaying the 15 testing patterns

figure(2)

for i = 1:15,

subplot(5,3,i)

display_image(Y(:,i),6,6,1);

end

% Initializations

index = zeros(2,6);

counter1 = 0;

counter2 = 0;

% Assign random weights initially at the start of training

w_hidden = (rand(6,36)-0.5)*2

w_output = (rand(2,6)-0.5)*2

%load w_hidden.mat

%load w_output.mat

% Function to calculate the parameters (z,y at the hidden and output

layers given the weights at the two layers)

[z_hidden, w_hidden, y_hidden, z_output, w_output, y_output, counter] =calculation(w_hidden, w_output, X);

disp('Before Any Changes')

w_output

z_output

y_output

save z_output z_output;

save z_hidden z_hidden;

save y_hidden y_hidden;

save y_output y_output;

counter

%i = 1;

%min_z_output = min(abs(z_output));

disp('At counter minimum')

if (counter~= 0),

[w_output_min,z_index] = min_case(z_output,w_output,counter,y_hidden,nu);

[z_hidden_min, w_hidden_min, y_hidden_min, z_output_min,w_output_min, y_output_min, counter1] = calculation(w_hidden, w_output_min, X);

counter1

end

w_output_min;

z_output_min;

y_output_min;

if (counter > counter1),

%load w_output.mat;

%load z_output.mat;

%load y_output.mat;

counter = counter1;

w_output = w_output_min;

z_output = z_output_min;

y_output = y_output_min;

index(2,z_index) = 1;

end

[w_output_max,z_ind] = max_case(z_output,w_output,counter,y_hidden,nu);

[z_hidden_max, w_hidden_max, y_hidden_max, z_output_max, w_output_max,y_output_max, counter2] = calculation(w_hidden, w_output_max, X);

disp('At Counter minimum')

counter2

w_output_max;

z_output_max;

y_output_max;

if (counter2<counter),

counter = counter2;

w_output = w_output_max;

z_output = z_output_max;

y_output = y_output_max;

index(2,z_ind) = 1;

end

% Adjusting the weights of the hidden layer

hidden_ind = zeros(1,6);

z_hid_asc = sort(abs(z_hidden));

for i = 1:6,

for k = 1:6,

if z_hid_asc(i) == abs(z_hidden(k)),

    hidden_ind(i) = k;

end

end

end

r1 = hidden_ind(1);

r2 = hidden_ind(2);

r3 = hidden_ind(3);

r4 = hidden_ind(4);

r5 = hidden_ind(5);

r6 = hidden_ind(6);

disp('At the beginning of the hidden layer Weight Changes - Neuron 1')

%load w_hidden.mat;

if ((counter~=0)&(counter>6)),

[w_hidden_min] = min_hidden_case(z_hidden,w_hidden,counter,X,nu,hidden_ind(1));

[z_hidden_min, w_hidden_min, y_hidden_min, z_output_min, w_output,y_output_min, counter3] = calculation(w_hidden_min, w_output, X);

counter3

end

w_hidden;

if (counter3<counter),

counter=counter3;

w_hidden = w_hidden_min;

y_hidden = y_hidden_min;

z_hidden = z_hidden_min;

z_output = z_output_min;

y_output = y_output_min;

index(1,r1) = 1;

end

disp('Hidden Layer - Neuron 2')

%load w_hidden.mat;

%counter=counter2;

if ((counter~=0)&(counter>6)),

[w_hidden_min] =min_hidden_case(z_hidden,w_hidden,counter,X,nu,hidden_ind(2));

[z_hidden_min, w_hidden_min, y_hidden_min, z_output_min, w_output,y_output_min, counter3] = calculation(w_hidden_min, w_output, X);

counter3

end

w_hidden;

w_hidden_min;

if (counter3<counter),

counter = counter3;

w_hidden = w_hidden_min;

y_hidden = y_hidden_min;

z_hidden = z_hidden_min;

z_output = z_output_min;

y_output = y_output_min;

index(1,r2)=1;

end

disp(`Hidden Layer - Neuron 3')

%load w_hidden.mat;

%counter=counter2;

if ((counter~=0)&(counter>6)),

[w_hidden_min] =

min_hidden_case(z_hidden,w_hidden,counter,X,nu,hidden_ind(3));

[z_hidden_min, w_hidden_min, y_hidden_min, z_output_min, w_output,

y_output_min, counter3] = calculation(w_hidden_min, w_output, X);

counter3

end

w_hidden;

w_hidden_min;

if (counter3<counter),

counter = counter3;

w_hidden = w_hidden_min;

y_hidden = y_hidden_min;

z_hidden = z_hidden_min;

z_output = z_output_min;

y_output = y_output_min;

index(1,r3) = 1;

end

disp('Hidden Layer - Neuron 4')

%load w_hidden.mat;

%counter=counter2;

if ((counter~=0)&(counter>6)),

[w_hidden_min] =min_hidden_case(z_hidden,w_hidden,counter,X,nu,hidden_ind(4));

[z_hidden_min, w_hidden_min, y_hidden_min, z_output_min, w_output,y_output_min, counter3] = calculation(w_hidden_min, w_output, X);

counter3

end

w_hidden;

w_hidden_min;

if (counter3<counter),

counter = counter3;

w_hidden = w_hidden_min;

y_hidden = y_hidden_min;

z_hidden = z_hidden_min;

z_output = z_output_min;

y_output = y_output_min;

index(1,r4)=1;

end

disp('Hidden Layer - Neuron 5')

%load w_hidden.mat;

%counter=counter2;

if (counter~=0),

[w_hidden_min] =min_hidden_case(z_hidden,w_hidden,counter,X,nu,hidden_ind(5));

[z_hidden_min, w_hidden_min, y_hidden_min, z_output_min, w_output,y_output_min, counter3] = calculation(w_hidden_min, w_output, X);

counter3

end

end

w_hidden;

w_hidden_min;

if (counter3<counter),

counter = counter3;

w_hidden = w_hidden_min;

y_hidden = y_hidden_min;

z_hidden = z_hidden_min;

z_output = z_output_min;

y_output = y_output_min;

index(1,r5)=1;

end

disp('Combined Output Layer Neurons weight change');

%load w_hidden.mat;

%counter = counter2;

if ((counter~=0)&(index(2,[1:2])~=1)&(counter>6)),

[w_output_two] =min_output_double(z_hidden,y_hidden,counter,X,nu,w_output);

[z_hidden_min, w_hidden_min, y_hidden_min, z_output_min, w_output,y_output_min, counter3] = calculation(w_hidden,w_output_two, X);

counter3

end

end

w_output;

%w_output_two;

if (counter3<counter),

counter = counter3;

%w_hidden = w_hidden_min;

y_hidden = y_hidden_min;

z_hidden = z_hidden_min;

z_output = z_output_min;

y_output = y_output_min;

w_output = w_output_two;

end

disp('Begin 2 neuron changes - First Pair')

%load w_hidden.mat;

%counter = counter2;

if ((counter~=0)&(index(1,r1)~=1)&(index(1,r2)~=1)&(counter>6)),

[w_hidden_two] =min_hidden_double(z_hidden,w_hidden,counter,X,nu,hidden_ind(1),hidden_ind(2));

[z_hidden_min, w_hidden_min, y_hidden_min, z_output_min, w_output,y_output_min, counter3] = calculation(w_hidden_two, w_output, X);

counter3

end

w_hidden;

w_hidden_min;

if (counter3<counter),

counter = counter3;

w_hidden = w_hidden_min;

y_hidden = y_hidden_min;

z_hidden = z_hidden_min;

z_output = z_output_min;

y_output = y_output_min;

end

disp('Begin 2 neuron changes - Second Pair')

%load w_hidden.mat;

%counter = counter2;

if ((counter~=0)&(index(1,r2)~=1)&(index(1,r3)~=1)&(counter>6)),

[w_hidden_two] =min_hidden_double(z_hidden,w_hidden,counter,X,nu,hidden_ind(2),hidden_ind(3));

[z_hidden_min, w_hidden_min, y_hidden_min, z_output_min, w_output,y_output_min, counter3] = calculation(w_hidden_two, w_output, X);

counter3

end

w_hidden;

w_hidden_min;

if (counter3<counter),

counter = counter3;

w_hidden = w_hidden_min;

y_hidden = y_hidden_min;

z_hidden = z_hidden_min;

z_output = z_output_min;

y_output = y_output_min;

end

disp('Begin 2 neuron changes - Third Pair')

%load w_hidden.mat;

%counter = counter2;

if ((counter~=0)&(index(1,r3)~=1)&(index(1,r4)~=1)&(counter>6)),

[w_hidden_two] =min_hidden_double(z_hidden,w_hidden,counter,X,nu,hidden_ind(3),hidden_ind(4));

[z_hidden_min, w_hidden_min, y_hidden_min, z_output_min, w_output,y_output_min, counter3] = calculation(w_hidden_two, w_output, X);

counter3

end

w_hidden;

w_hidden_min;

if (counter3<counter),

counter = counter3;

w_hidden = w_hidden_min;

y_hidden = y_hidden_min;

z_hidden = z_hidden_min;

z_output = z_output_min;

y_output = y_output_min;

end

disp('Begin 2 neuron changes - Fourth Pair')

%load w_hidden.mat;

%counter = counter2;

if ((counter~=0)&(index(1,r4)~=1)&(index(1,r5)~=1)&(counter>6)),

[w_hidden_two] =min_hidden_double(z_hidden,w_hidden,counter,X,nu,hidden_ind(4),hidden_ind(5));

[z_hidden_min, w_hidden_min, y_hidden_min, z_output_min, w_output,y_output_min, counter3] = calculation(w_hidden_two, w_output, X);

counter3

end

w_hidden;

w_hidden_min;

disp('Final Values For Output')

w_output

z_output

y_output

disp('Final Values for Hidden')

w_hidden

z_hidden

y_hidden

disp('Final Error Number')

counter

disp('Efficiency')

eff = 100 - counter/40*100

%*****************Function to calculate the parameters (z,y at the hidden and output layers given the weights at the two layers)******************

function [z_hidden, w_hidden, y_hidden, z_output, w_output, y_output,counter] = calculation(w_hidden, w_output, X)

% Outputs:

% z_hidden - hidden layer z value

% w_hidden - hidden layer weight

% y_hidden - hidden layer output

% Respecitvely for the output layers

% Inputs:

% Weights at the hidden and output layers and the training pattern set

counter = 0;

r = 1;

while(r<=15),

r;

for i = 1:6,

z_hidden(i) = w_hidden(i,:)*X(:,r);

if (z_hidden(i)>=0),

y_hidden(i) = 1;

else

y_hidden(i) = -1;

end %%End of If loop

end %% End of for loop

z_hidden;

y_hiddent = y_hidden';

for i = 1:2

z_output(i) = w_output(i,:)*y_hiddent;

if (z_output(i)>=0),

y_output(i) = 1;

else

y_output(i) = -1;

end %% End of If loop

end%% End of for loop

y_output;

% Desired Output

if (r<=5),

d1 = [1 1]; % For 0

else if (r>10),

d1 = [-1 -1] %For F

else

d1 = [-1 1]; % For C

end

end

for i = 1:2,

error_val(i) = d1(i)-y_output(i);

if (error_val(i)~=0),

counter = counter+1;

end

end

r = r+1;

end

%******Function to find weight changes for paired hidden layer**********

function [w_hidden_two] =min_hidden_double(z_hidden,w_hidden,counter,X,nu,k,l)

w_hidden_two = w_hidden;

for j = 1:36,

w_hidden_two(k,j) = w_hidden_two(k,j) + 2*nu*X(j,15)*counter;

w_hidden_two(l,j) = w_hidden_two(l,j) + 2*nu*X(j,15)*counter;

end

%*********Function to find weight changes at hidden layer**************

function [w_hidden_min] =min_hidden_case(z_hidden,w_hidden,counter,X,nu,k)

w_hidden_min = w_hidden;

for j = 1:36,

w_hidden_min(k,j) = w_hidden_min(k,j) + 2*nu*X(j,15)*counter;

end

%w_hidden_min

%****Function to change weights for the max of 2z values at Output****

function [w_output_max,z_ind] =max_case(z_output,w_output,counter,y_hidden,nu)

%load w_output;

%load z_output;

w_output_max = w_output;

z_ind = find(abs(z_output) == max(abs(z_output)))

for j = 1:5,

w_output_max(z_ind,j) = w_output(z_ind,j)+2*nu*y_hidden(j)*counter;

% end

% z_output(z_index) = w_output(z_index,:)*y_hiddent;

end

%****************Function to compute weight change at the output for

neuron whose Z value is close to the threshold**********************

function [w_output_min,z_index] =min_case(z_output,w_output,counter,y_hidden,nu)

z_index = find(abs(z_output) == min(abs(z_output)))

w_output_min = w_output

for j = 1:5,

w_output_min(z_index,j) = w_output(z_index,j) +

2*nu*y_hidden(j)*counter;

end

w_output_min

%*******Function to find weight changes with paired output neurons******

function [w_output_two] =min_output_double(z_hidden,y_hidden,counter,X,nu,w_output)

w_output_two = w_output;

for j = 1:6,

w_output_two([1:2],j) = w_output([1:2],j)+2*nu*y_hidden(j)*counter;

end

y_hidden;

counter;

2*nu*y_hidden*counter;