drop.m~

%% Initialization
%clear ; close all; clc


%% Setup the parameters you will use for this exercise
input_layer_size  = 400;  % x1 and x2
hidden_layer_size = 25;   % 5 hidden units
num_labels = 10;          % 2 labels, 0 and 1   
lambda = 1;     %for regularization
                         

%% Load Data
%  The first two columns contains X and the third column
%  contains label.
data = load('ex4data1.mat');

%X = data(:, [1, 2]); y = data(:, 3);


m = size(X, 1);



%% ================ Initializing Pameters ================


fprintf('\nInitializing Neural Network Parameters ...\n')

initial_Theta1 = randInitializeWeights(input_layer_size, hidden_layer_size);
initial_Theta2 = randInitializeWeights(hidden_layer_size, num_labels);

% Unroll parameters
initial_nn_params = [initial_Theta1(:) ; initial_Theta2(:)];

%J = new1(nn_params, input_layer_size, hidden_layer_size, ...
%                   num_labels, X, y, lambda);



%%==================== Dropout ==========

for iteration = 1:1
increment = uint16(8);  %used for deciding size of mini-batch

increment
for xyz = 1:increment:m   %this for-loop iterates over all
fprintf('\n');
xyz
if xyz+increment>m
	X_ran = X(xyz:m, :);
	y_ran = y(xyz:m);
else
	X_ran = X(xyz:xyz+increment, :);
	y_ran = y(xyz:xyz+increment);
end

%%Randomly ommiting hidden units with probability 0.5
c = 0;
percent = 0.5;
ran_mat = rand(hidden_layer_size, 1);
for i=1:hidden_layer_size
    if ran_mat(i)>=percent
	c++;
    end
end


   
ini_theta1_ran = zeros(c, input_layer_size+1);
ini_theta2_ran = zeros(num_labels, c+1);

k=0;
for i=1:hidden_layer_size
    if ran_mat(i)>=percent
	k++;
	for j=1:input_layer_size+1
		ini_theta1_ran(k,j) = initial_Theta1(i, j);
	end
	
    end
end



for i=1:num_labels
	ini_theta2_ran(i, 1) = initial_Theta2(i, 1);
end

k=1;
for i=1:hidden_layer_size
    if ran_mat(i)>=percent
	k++;
	for j=1:num_labels
		ini_theta2_ran(j, k) = initial_Theta2(j, (i+1));
	end
    end
end

initial_nn_params_ran = [ini_theta1_ran(:) ; ini_theta2_ran(:)];


c

%% ===================  Training NN ===================

%
%fprintf('\nTraining Neural Network... \n')
options = optimset('MaxIter', 60);   %setting maxiyer to desired value
lambda = 0;
costFunction = @(p) nnCostFunction(p, ...
                                   input_layer_size, ...
                                   c, ...
                                   num_labels, X_ran, y_ran, lambda);


[nn_params_ran, cost] = fmincg(costFunction, initial_nn_params_ran, options);





% Obtain Theta1 and Theta2 back from nn_params
ini_theta1_ran = reshape(nn_params_ran(1:c * (input_layer_size + 1)), ...
                 c, (input_layer_size + 1));

ini_theta2_ran = reshape(nn_params_ran((1 + (c * (input_layer_size + 1))):end), ...
                 num_labels, (c + 1));

%fprintf('Program paused. Press enter to continue.\n');
%pause;



%%%%%
%putting back these in the original theta
%%%

k=0;
for i=1:hidden_layer_size
    if ran_mat(i)>=percent
	k++;
	for j=1:input_layer_size+1
		initial_Theta1(i, j) = ini_theta1_ran(k,j);
	end
	
    end
end


k=1;
for i=1:num_labels
	 initial_Theta2(i, 1) = ini_theta2_ran(i, 1);
end

for i=1:hidden_layer_size
    if ran_mat(i)>=percent
	k++;
	for j=1:num_labels
		initial_Theta2(j, (i+1)) = ini_theta2_ran(j, k);
	end
    end
end 



end %ending loop of xyz

end %end of iterations loop


Theta1 = initial_Theta1;
Theta2 = initial_Theta2;



%% ================= Implement Predict =================
for i=1:size(Theta2, 1)
   for j=1:size(Theta2, 2)
	Theta2(i, j) = Theta2(i, j)/2;  %outgoing weights halved
    end
end

pred = predict1(Theta1, Theta2, X);

fprintf('\nTraining Set Accuracy: %f\n', mean(double(pred == y)) * 100);