The network is defined in the "valErrorFun" function, which is inside the "makeObjFcn" function. Refer to the Objective Function for Optimization section for more information.
function ObjFcn = makeObjFcn(XTrain,YTrain,XValidation,YValidation)
ObjFcn = @valErrorFun;
function [valError,cons,fileName] = valErrorFun(optVars)
imageSize = [32 32 3];
numClasses = numel(unique(YTrain));
numF = round(16/sqrt(optVars.SectionDepth));
layers = [
imageInputLayer(imageSize)
% The spatial input and output sizes of these convolutional
% layers are 32-by-32, and the following max pooling layer
% reduces this to 16-by-16.
convBlock(3,numF,optVars.SectionDepth)
maxPooling2dLayer(3,'Stride',2,'Padding','same')
% The spatial input and output sizes of these convolutional
% layers are 16-by-16, and the following max pooling layer
% reduces this to 8-by-8.
convBlock(3,2*numF,optVars.SectionDepth)
maxPooling2dLayer(3,'Stride',2,'Padding','same')
% The spatial input and output sizes of these convolutional
% layers are 8-by-8. The global average pooling layer averages
% over the 8-by-8 inputs, giving an output of size
% 1-by-1-by-4*initialNumFilters. With a global average
% pooling layer, the final classification output is only
% sensitive to the total amount of each feature present in the
% input image, but insensitive to the spatial positions of the
% features.
convBlock(3,4*numF,optVars.SectionDepth)
averagePooling2dLayer(8)
% Add the fully connected layer and the final softmax and
% classification layers.
fullyConnectedLayer(numClasses)
softmaxLayer
classificationLayer];
miniBatchSize = 256;
validationFrequency = floor(numel(YTrain)/miniBatchSize);
options = trainingOptions('sgdm', ...
'InitialLearnRate',optVars.InitialLearnRate, ...
'Momentum',optVars.Momentum, ...
'MaxEpochs',60, ...
'LearnRateSchedule','piecewise', ...
'LearnRateDropPeriod',40, ...
'LearnRateDropFactor',0.1, ...
'MiniBatchSize',miniBatchSize, ...
'L2Regularization',optVars.L2Regularization, ...
'Shuffle','every-epoch', ...
'Verbose',false, ...
'Plots','training-progress', ...
'ValidationData',{XValidation,YValidation}, ...
'ValidationFrequency',validationFrequency);
pixelRange = [-4 4];
imageAugmenter = imageDataAugmenter( ...
'RandXReflection',true, ...
'RandXTranslation',pixelRange, ...
'RandYTranslation',pixelRange);
datasource = augmentedImageDatastore(imageSize,XTrain,YTrain,'DataAugmentation',imageAugmenter);
trainedNet = trainNetwork(datasource,layers,options);
close(findall(groot,'Tag','NNET_CNN_TRAININGPLOT_UIFIGURE'))
YPredicted = classify(trainedNet,XValidation);
valError = 1 - mean(YPredicted == YValidation);
fileName = num2str(valError) + ".mat";
save(fileName,'trainedNet','valError','options')
cons = [];
end
end
