matlabroot = 'C:\Users\user a\Desktop\Hind\Image C\data101';
DatasetPath = fullfile(matlabroot);
Data = imageDatastore(DatasetPath,'IncludeSubfolders',true,'LabelSource','foldernames');
CountLabel = Data.countEachLabel;
auds = augmentedImageDatastore([224 224],Data);
trainData1 = auds;
[trainData] = auds;
netWidth = 16;
layers = [
imageInputLayer([224 224 3],'Name','input')
convolution2dLayer(3,netWidth,'Padding','same','Name','convInp')
batchNormalizationLayer('Name','BNInp')
reluLayer('Name','reluInp')
convolutionalUnit(netWidth,1,'S1U1')
additionLayer(2,'Name','add11')
reluLayer('Name','relu11')
convolutionalUnit(netWidth,1,'S1U2')
additionLayer(2,'Name','add12')
reluLayer('Name','relu12')
convolutionalUnit(2*netWidth,2,'S2U1')
additionLayer(2,'Name','add21')
reluLayer('Name','relu21')
convolutionalUnit(2*netWidth,1,'S2U2')
additionLayer(2,'Name','add22')
reluLayer('Name','relu22')
convolutionalUnit(4*netWidth,2,'S3U1')
additionLayer(2,'Name','add31')
reluLayer('Name','relu31')
convolutionalUnit(4*netWidth,1,'S3U2')
additionLayer(2,'Name','add32')
reluLayer('Name','relu32')
averagePooling2dLayer(8,'Name','globalPool')
fullyConnectedLayer(2,'Name','fcFinal')
softmaxLayer('Name','softmax')
classificationLayer('Name','classoutput')
];
lgraph = layerGraph(layers);
lgraph = connectLayers(lgraph,'reluInp','add11/in2');
lgraph = connectLayers(lgraph,'relu11','add12/in2');
skip1 = [
convolution2dLayer(1,2*netWidth,'Stride',2,'Name','skipConv1')
batchNormalizationLayer('Name','skipBN1')];
lgraph = addLayers(lgraph,skip1);
lgraph = connectLayers(lgraph,'relu12','skipConv1');
lgraph = connectLayers(lgraph,'skipBN1','add21/in2');
lgraph = connectLayers(lgraph,'relu21','add22/in2');
skip2 = [
convolution2dLayer(1,4*netWidth,'Stride',2,'Name','skipConv2')
batchNormalizationLayer('Name','skipBN2')];
lgraph = addLayers(lgraph,skip2);
lgraph = connectLayers(lgraph,'relu22','skipConv2');
lgraph = connectLayers(lgraph,'skipBN2','add31/in2');
lgraph = connectLayers(lgraph,'relu31','add32/in2');
options = trainingOptions('sgdm', 'MiniBatchSize',128,'MaxEpochs',10,'InitialLearnRate',1e-4,'ExecutionEnvironment','parallel');
[trainedNet1,traininfo] = trainNetwork(trainData,lgraph,options);
