clear
close all

load('KSA.mat');

%% Split randomly 80% for training and 20% for testing
n=length(trainimages);
k=ceil(0.2*length(trainimages));
ind=randperm(n,k);
testimage=trainimages(:,:,1,ind) ;
testlabel=trainlabels(ind);
indx=1:n;
indx(ind)=[];
trainimage=trainimages(:,:,:,indx);
trainlabels(ind)=[];
trainlabel=removecats(trainlabels);

%% Define the Network Layers

layers = [
    imageInputLayer([28 28 1],'Normalization','none','Name','input')
    convolution2dLayer(5,64,'Name','conv1')
    batchNormalizationLayer('Name','BN1')
    reluLayer('Name','relu1')
% 24x24
    convolution2dLayer(5,128,'Name','conv2')
    batchNormalizationLayer('Name','BN2')
    reluLayer('Name','relu2')
% 20x20
    maxPooling2dLayer(2,'Stride',2,'Name','max1')
% 10x10 
    convolution2dLayer(5,176,'Name','conv3')
    batchNormalizationLayer('Name','BN3')
    reluLayer('Name','relu3')
% 6x6 
    convolution2dLayer(5,208,'Name','conv4')
    batchNormalizationLayer('Name','BN4')
    reluLayer('Name','relu4')
% 2x2   
    maxPooling2dLayer(2,'Stride',2,'Name','max2')
  
    fullyConnectedLayer(27,'Name','fc')
    softmaxLayer('Name','softmax')
    classificationLayer('Name','classOutput')];

%% Specify the Training Options

options = trainingOptions('sgdm','LearnRateSchedule','piecewise',...
      'MiniBatchSize',120,'LearnRateDropFactor',0.7,... 
      'LearnRateDropPeriod',2,... 
      'MaxEpochs',10, ...
	'InitialLearnRate',1/40,...
    'Shuffle','every-epoch');  

   convnet = trainNetwork(trainimage,trainlabel,layers,options);

YTest = classify(convnet,testimage);
accuracy = sum(YTest == testlabel)/numel(testlabel);
Error=(1-accuracy)*100