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NeuralNetwork_2.2_A.m
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NeuralNetwork_2.2_A.m
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%% Intelligent Systems %%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%% NeuralNetwork 2.2 - A %%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%% Utsav Shah %%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clc;
clear all;
close all;
%% Loading Data
data.image.all = dlmread('MNISTnumImages5000.txt');
data.label.all = dlmread('MNISTnumLabels5000.txt');
%% Defining Train and Test Data
data.image.train = data.image.all(1:4000,:);
data.image.test = data.image.all(4001:end,:);
data.label.train = data.label.all(1:4000,:);
data.label.test = data.label.all(4001:end,:);
%% Taking Number of Layers as Input from User
j=1;
while j<=0 && ~isinteger(j)
j = input('How many hidden layers you need \n');
end
%% Initializing Matrices
tic;
% w_jk = 0.2 .* rand(200,785) - 0.1; % hidden layer - weight from input k to hidden neuron j
load hw3_problem2.mat
w_ij = 0.2 .* rand(10,201) - 0.1; % output layer - weight from hidden neuron j to output i
eta = 0.2; % learning rate
alpha = 0.4; % momentum
error=zeros(10,3500); % error_test = zeros(10,1000);
J=zeros(1,4000);
hit_rate_train=zeros(50,1);
confusion_train=zeros(10,10);
flag=0; % J2 error flag, 0 => error is not below threshold
for epoch = 1:500
shuffle_indices=randperm(4000);
image_data=data.image.train(shuffle_indices,:);
label_data=data.label.train(shuffle_indices,:);
y=zeros(10,3500);
hits_train=0;
% Presenting a subset of training set
for q = 1:3500
% Feed forward
x = image_data(q,:).'; % n = all columns = 784+1(later)
y(label_data(q,:) + 1,q) = 1; % actual output
% Calculating s for hidden layer
s_j = w_jk * [1;x];
h = perceptron(s_j); % hidden layer output which will be given to output layer input
s_i = w_ij * [1;h]; % output layer
y_hat = perceptron(s_i); % calculated output l=10
error(:,q) = y(:,q) - y_hat; % error(10x1)
J(q) = 0.5 * (sum(error(:,q)).^2); % loss function
% if J(q)<=1e-20 && J(q-1)<=1e-20
% flag=1;
% break
% end
% Calculating hit_rate performance
if length(find(y_hat==max(y_hat)))==1 ...
&& find(y_hat==max(y_hat))-1==label_data(q,:)
hits_train=hits_train+1;
end
% Back Propogation
% output layer delta
delta_i = error(:,q) .* diffy(s_i);
% Applying Momentum
if q==1
dw_ij = eta * delta_i*[1;h].';
else
dw_ij = dw_ij + alpha * dw_ij;
end
% % hidden layer delta
% delta_j=diffy(s_j) .* (w_ij(:,2:end).'*delta_i);
% if q==1
% dw_jk=eta*delta_j*[1;x].';
% else
% dw_jk = dw_jk + alpha*dw_jk;
% end
% Weight updation
w_ij=w_ij+dw_ij;
% w_jk=w_jk+dw_jk;
% Confusion Matrix for Train Data
confusion_train(label_data(q,:) + 1,find(y_hat==max(y_hat)))=...
confusion_train(label_data(q,:) + 1,find(y_hat==max(y_hat)))+1;
end
% hit rate for training data
hit_rate_train(epoch)=hits_train*100/q;
% if flag==1
% break
% end
end
%% Testing using the last 1000 datapoints, Feed forward only
error_test=zeros(10,1000);
y_test=zeros(10,1000);
hits_test=0;
confusion_test=zeros(10,10);
for q = 1:1000
x_test = data.image.test(q,:).'; % n = all columns = 784
y_test(data.label.test(q,:)+1,q) = 1; % generating y for label data
s_j_test = w_jk * [1;x_test]; % hidden layer s
h_test = perceptron(s_j_test); % hidden layer output which will be given to output layer input
s_i_test = w_ij * [1;h_test]; % output layer s
y_hat_test = perceptron(s_i_test); % calculated output
error_test(:,q)= y_test(:,q) - y_hat_test; % error
% Calculating hit_rate performance
if length(find(y_hat_test==max(y_hat_test)))==1 ...
&& find(y_hat_test==max(y_hat_test))-1==data.label.test(q,:)
hits_test=hits_test+1;
end
% Confusion Matrix for Test Data
confusion_test(data.label.test(q,:) + 1,find(y_hat_test==max(y_hat_test)))=...
confusion_test(data.label.test(q,:) + 1,find(y_hat_test==max(y_hat_test)))+1;
end
%% Store data for testing epochs for plotting
% hit rate for testing data
hit_rate_test=hits_test*100/1000;
t = toc;
%% Tring Tring
[yyy,Fs] = audioread('Tring.mp3');
sound(yyy,Fs);
%% Plots
% Training set hit rate, test set hit rate as a line
figure(1)
plot(1:length(hit_rate_train),1-(hit_rate_train)/100,...
1:length(hit_rate_train),ones(500)*1-hit_rate_test/100,'r')
xlabel('Epoch');
ylabel('(1 - Hit Rate)');
title('\bf{Hit Rate Performance}');
legend('Training Hit Rate Performance vs Epoch','Test Hit Rate Performance (Single Value)')
% Training Confusion Matrix
figure(2)
b1=bar3(confusion_train);
xlabel('Target')
ylabel('Actual')
zlabel('Number classified as')
digits={'0','1','2','3','4','5','6','7','8','9'};
set(gca,'XTickLabel',digits,'YTickLabel',digits)
title('\bf{Training Confusion Matrix}')
colorbar
for k = 1:length(b1)
zdata = b1(k).ZData;
b1(k).CData = zdata;
b1(k).FaceColor = 'interp';
end
view([90 0])
% Test Confusion Matrix
figure(3)
b2=bar3(confusion_test);
xlabel('Target')
ylabel('Actual')
zlabel('Number classified as')
digits={'0','1','2','3','4','5','6','7','8','9'};
set(gca,'XTickLabel',digits,'YTickLabel',digits)
title('\bf{Test Confusion Matrix}')
for k = 1:length(b2)
zdata = b2(k).ZData;
b2(k).CData = zdata;
b2(k).FaceColor = 'interp';
end
view([90 0])
colorbar
colormap jet