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train_cnn_adv_rel_detector.m
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attack_name = "DeepFoolAttack";
%attack_name = "GradientAttack";
%attack_name = "LBFGSAttack";
data_folder = str2mat('cifar_training_data/' + attack_name +'/rel_images');
imds = imageDatastore(data_folder,'IncludeSubfolders',true,'LabelSource','foldernames');
numTrainFiles = 3000;
[imdsTrain,imdsValidation] = splitEachLabel(imds,numTrainFiles,'randomize');
layers = [
imageInputLayer([32 32 3])
convolution2dLayer(3,64,'Padding',1)
reluLayer
convolution2dLayer(3,64,'Padding',1)
reluLayer
convolution2dLayer(3,64,'Padding',1)
reluLayer
convolution2dLayer(3,64,'Padding',1)
%batchNormalizationLayer
reluLayer
maxPooling2dLayer(2,'Stride',2)
convolution2dLayer(3,128,'Padding',1)
reluLayer
convolution2dLayer(3,128,'Padding',1)
reluLayer
convolution2dLayer(3,128,'Padding',1)
reluLayer
convolution2dLayer(3,128,'Padding',1)
%batchNormalizationLayer
reluLayer
maxPooling2dLayer(2,'Stride',2)
convolution2dLayer(3,256,'Padding',1)
%batchNormalizationLayer
reluLayer
fullyConnectedLayer(2)
softmaxLayer
classificationLayer];
options = trainingOptions('sgdm', ...
'MaxEpochs',100, ...
'ValidationData',imdsValidation, ...
'ValidationFrequency',100, ...
'Verbose',false, ...
'Plots','training-progress');
net = trainNetwork(imdsTrain,layers,options);
YPred = classify(net,imdsValidation);
YValidation = imdsValidation.Labels;
accuracy = sum(YPred == YValidation)/numel(YValidation)