How to increase the validation accuracy to more than 90%?
Used Layers and options are following:
layers = [
imageInputLayer([227 227 3],"Name","data")
convolution2dLayer([11 11],94,"Name","conv1","BiasLearnRateFactor",2,"Stride",[4 4])
reluLayer("Name","relu1")
crossChannelNormalizationLayer(5,"Name","norm1","K",1)
maxPooling2dLayer([3 3],"Name","pool1","Stride",[2 2])
groupedConvolution2dLayer([5 5],94,2,"Name","conv2","BiasLearnRateFactor",2,"Padding",[2 2 2 2])
reluLayer("Name","relu2")
crossChannelNormalizationLayer(5,"Name","norm2","K",1)
maxPooling2dLayer([3 3],"Name","pool2","Stride",[2 2])
convolution2dLayer([3 3],94,"Name","conv3","BiasLearnRateFactor",2,"Padding",[1 1 1 1])
reluLayer("Name","relu3")
groupedConvolution2dLayer([2 2],64,2,"Name","conv4","BiasLearnRateFactor",2,"Padding",[1 1 1 1])
reluLayer("Name","relu4")
groupedConvolution2dLayer([3 3],128,2,"Name","conv5","BiasLearnRateFactor",2,"Padding",[1 1 1 1])
reluLayer("Name","relu5")
maxPooling2dLayer([3 3],"Name","pool5","Stride",[2 2])
fullyConnectedLayer(500,"Name","fc6","BiasLearnRateFactor",2)
reluLayer("Name","relu6")
dropoutLayer(0.5,"Name","drop6")
fullyConnectedLayer(500,"Name","fc7","BiasLearnRateFactor",2)
reluLayer("Name","relu7")
dropoutLayer(0.5,"Name","drop7")
fullyConnectedLayer(100,"Name","fc8","BiasLearnRateFactor",2)
fullyConnectedLayer(4,"Name","new fc","BiasLearnRateFactor",10,"WeightLearnRateFactor",10)
softmaxLayer("Name","prob")
classificationLayer("Name","classoutput")];
miniBatchSize =25; % 128
valFrequency = floor(numel(augimdsTrain.Files)/miniBatchSize);
options = trainingOptions('sgdm', ...
'MiniBatchSize',36, ... %32
'MaxEpochs',10, ...
'InitialLearnRate',0.001, ... %0.01
'LearnRateDropFactor',0.1, ...
'Shuffle','every-epoch', ...
'ValidationData',augimdsValidation, ...
'ValidationFrequency',valFrequency, ...
'ValidationPatience',4,'Verbose',false, ...
'Plots','training-progress');
