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makeObjFcn.m
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makeObjFcn.m
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function ObjFcn = makeObjFcn(XTrain,YTrain,XValidation,YValidation,Parm)
% the objective function is NOT based on Validation Error but on Other
% Measure (OthMes) such as sensitivity, specificty, auc etc.
%
% net: squeezenet, googlenet, efficientnetb0
net = Parm.net;% coder.loadDeepLearningNetwork(Parm.net);
measure=Parm.ObjFcnMeasure;
%Augment=Parm.Augment;
ObjFcn = @valErrorFun;
function [valError,cons,fileName] = valErrorFun(optVars)
imageSize = [size(XTrain,1) size(XTrain,2) size(XTrain,3)];
numClasses = numel(unique(YTrain));
%initialNumFilters = round((max(imageSize)/2)/sqrt(optVars.NetworkDepth));
numMaxPools=3;
PoolSizeAvg = floor(max(imageSize)/(2^(numMaxPools)));
%filterSize = 5;
if nargin<5
% net = squeezenet;%googlenet;efficientnetb0
measure='accuracy';
end
%net.Layers(1);
inputSize = net.Layers(1).InputSize;
if isa(net,'SeriesNetwork')
lgraph = layerGraph(net.Layers);
else
lgraph = layerGraph(net);
end
[learnableLayer,classLayer] = findLayersToReplace(lgraph);
%numClasses = numel(categories(imdsTrain.Labels));
%numClasses = numel(categories(YTrain));
if isa(learnableLayer,'nnet.cnn.layer.FullyConnectedLayer')
newLearnableLayer = fullyConnectedLayer(numClasses, ...
'Name','new_fc', ...
'WeightLearnRateFactor',10, ...
'BiasLearnRateFactor',10);
elseif isa(learnableLayer,'nnet.cnn.layer.Convolution2DLayer')
newLearnableLayer = convolution2dLayer(1,numClasses, ...
'Name','new_conv', ...
'WeightLearnRateFactor',10, ...
'BiasLearnRateFactor',10);
end
lgraph = replaceLayer(lgraph,learnableLayer.Name,newLearnableLayer);
newClassLayer = classificationLayer('Name','new_classoutput');
lgraph = replaceLayer(lgraph,classLayer.Name,newClassLayer);
%figure('Units','normalized','Position',[0.3 0.3 0.4 0.4]);
%plot(lgraph)
%ylim([0,10])
%###### Freeze Initial Layers ########
% layers = lgraph.Layers;
% connections = lgraph.Connections;
%
% layers(1:10) = freezeWeights(layers(1:10));
% lgraph = createLgraphUsingConnections(layers,connections);
%######################################
% if Augment==1
% [XTrain,YTrain] = augmentDeepInsight(XTrain,YTrain);
% [XValidation,YValidation] = augmentDeepInsight(XValidation,YValidation);
% end
% pixelRange = [-30 30];
% scaleRange = [0.9 1.1];
% imageAugmenter = imageDataAugmenter( ...
% 'RandXReflection',true, ...
% 'RandXTranslation',pixelRange, ...
% 'RandYTranslation',pixelRange, ...
% 'RandXScale',scaleRange, ...
% 'RandYScale',scaleRange);
% augimdsTrain = augmentedImageDatastore(inputSize(1:2),XValidation,YValidation, ...
% 'DataAugmentation',imageAugmenter);
augimdsTrain = augmentedImageDatastore(inputSize(1:2),XTrain,YTrain);
augimdsValidation = augmentedImageDatastore(inputSize(1:2),XValidation,YValidation);
% augimdsTrain = augmentedImageDatastore(inputSize(1:2),imdsTrain);
%augimdsValidation = augmentedImageDatastore(inputSize(1:2),imdsValidation);
% miniBatchSize = 256;%128;%256;%64; %10
% % valFrequency = floor(numel(augimdsTrain.Files)/miniBatchSize);
% valFrequency = floor(size(XTrain,4)/miniBatchSize);
% gpuDevice(1);
% options = trainingOptions('sgdm', ...
% 'InitialLearnRate',optVars.InitialLearnRate,...
% 'Momentum',optVars.Momentum,...
% 'ExecutionEnvironment','gpu',...
% 'MiniBatchSize',miniBatchSize, ...
% 'L2Regularization',optVars.L2Regularization,...
% 'MaxEpochs',10, ...
% 'Shuffle','every-epoch', ...
% 'ValidationData',augimdsValidation, ...
% 'ValidationFrequency',valFrequency, ...
% 'Verbose',false, ...
% 'Plots','none');
miniBatchSize = Parm.miniBatchSize;%256;%128;%256;%64;%64;%256;%64; %10
% valFrequency = floor(numel(augimdsTrain.Files)/miniBatchSize);
valFrequency = floor(size(XTrain,4)/miniBatchSize);
%gpuDevice(1);
options = trainingOptions('sgdm', ...
'InitialLearnRate',optVars.InitialLearnRate,...
'Momentum',optVars.Momentum,...
'ExecutionEnvironment',Parm.ExecutionEnvironment,...
'MiniBatchSize',miniBatchSize, ...
'L2Regularization',optVars.L2Regularization,...
'MaxEpochs',Parm.MaxEpochs, ...
'Shuffle','every-epoch', ...
'ValidationData',augimdsValidation, ...
'ValidationFrequency',20, ...
'Verbose',false, ...
'Plots',Parm.trainingPlot);
%Shuffle,'every-epoch'
% options = trainingOptions('rmsprop', ...
% 'InitialLearnRate',4.98661e-5,...
% 'SquaredGradientDecayFactor',0.9,...
% 'ExecutionEnvironment','gpu',...
% 'MiniBatchSize',miniBatchSize, ...
% 'L2Regularization',1.25157e-2,...
% 'MaxEpochs',20, ...
% 'Shuffle','every-epoch', ...
% 'ValidationData',augimdsValidation, ...
% 'ValidationFrequency',20, ...
% 'Verbose',false, ...
% 'Plots','training-progress');
% 'ExecutionEnvironment','multi-gpu',...
% 'Plots','training-progress');
% 'Plots','none');
% 'Plots','training-progress');
rng('default');
trainedNet = trainNetwork(augimdsTrain,lgraph,options);
close(findall(groot,'Tag','NNET_CNN_TRAININGPLOT_FIGURE'))
[YPredicted,probs] = classify(trainedNet,augimdsValidation);
if strcmp(measure,'accuracy')
valError = 1 - mean(YPredicted == YValidation);
display('accuracy');
else
[a,b,c,auc] = perfcurve(YValidation,probs(:,2),'2');
valError = 1 - auc;
display('auc based');
% C=confusionmat(YValidation,YPredicted);
% TP=C(1,1);
% FN=C(1,2);
% FP=C(2,1);
% TN=C(2,2);
% Sen=TP/(TP+FN);
% Spec=TN/(TN+FP);
% valError = 1 - Spec;
end
fileName = num2str(valError) + ".mat";
save(fileName,'trainedNet','valError','options','-v7.3')
cons = [];
% options = trainingOptions('sgdm',...
% 'InitialLearnRate',optVars.InitialLearnRate,...
% 'Momentum',optVars.Momentum,...
% 'ExecutionEnvironment','multi-gpu',...
% 'MaxEpochs',10, ...
% 'LearnRateSchedule','piecewise',...
% 'LearnRateDropPeriod',35,...
% 'LearnRateDropFactor',0.1,...
% 'MiniBatchSize',miniBatchSize,...
% 'L2Regularization',optVars.L2Regularization,...
% 'Shuffle','every-epoch',...
% 'Verbose',false,...
% 'Plots','none',...
% 'ValidationData',{XValidation,YValidation},...
% 'ValidationPatience',Inf,...
% 'ValidationFrequency',validationFrequency);
%'Plots','none',...
%'MaxEpochs',100,...
% 'Plots','training-progress',...
% imageAugmenter = imageDataAugmenter( ...
% 'RandRotation',[-5,5], ...
% 'RandXTranslation',[-3 3], ...
% 'RandYTranslation',[-3 3]);
%
% datasource = augmentedImageDatastore(imageSize,XTrain,YTrain,...
% 'DataAugmentation',imageAugmenter,...
% 'OutputSizeMode','randcrop');
% trainedNet = trainNetwork(datasource,lgraph,options);
% trainedNet = trainNetwork(XTrain,YTrain,lgraph,options);
% close(findall(groot,'Tag','NNET_CNN_TRAININGPLOT_FIGURE'))
%
% YPredicted = classify(trainedNet,XValidation);
% valError = 1 - mean(YPredicted == YValidation);
%
% fileName = num2str(valError) + ".mat";
% save(fileName,'trainedNet','valError','options')
% cons = [];
end
end