how to improve accuracy in matlab.?

Here code below attachment its my accuracy image i want accuracy 90% above can u help how to improve my accuracy.?

clear
clc
imds = imageDatastore('E:\MATLAB\coursework\dataset\BloodCellDataSet','IncludeSubfolders',true, ...
    'FileExtensions','.jpeg','LabelSource','foldernames');

figure;
perm = randperm(9957,20); 
for i = 1:20
subplot(4,5,i);
%imshow(imds.Files{i});
imshow(imds.Files{perm(i)});

end

label = countEachLabel(imds);
minsetcount = min(imds.countEachLabel{:,2});
trainingNumfiles = round(minsetcount);
rng(1);

[imdsTrain,imdsValidation] = splitEachLabel(imds,trainingNumfiles,'randomize');

inputSize=[240 320 3];
netLayers = [
    imageInputLayer(inputSize)
    convolution2dLayer(3,8,'Padding','same')
    batchNormalizationLayer
    reluLayer
    
    maxPooling2dLayer(2,'Stride',2)
    convolution2dLayer(3,16,'Padding','same')
    batchNormalizationLayer
    reluLayer
    
    maxPooling2dLayer(2,'Stride',2)
    convolution2dLayer(3,32,'Padding','same')
    batchNormalizationLayer
    reluLayer
    
    fullyConnectedLayer(4)
    softmaxLayer
    classificationLayer];

options = trainingOptions('sgdm', ... 
    'InitialLearnRate',0.001, ... 
    'MaxEpochs',6, ... 
    'Shuffle','every-epoch', ...
    'ValidationData',imdsValidation, ... 
    'ValidationFrequency',30, ... 
    'Verbose',false, ... 
    'Plots','training-progress');

net = trainNetwork(imdsTrain,netLayers,options);

YPred = classify(net,imdsValidation); 
YValidation = imdsValidation.Labels;

accuracy = sum(YPred == YValidation)/numel(YValidation);

[Walter Roberson]

I used to be involved with a lot of data classification work. We never got into Deep Learning, but with the other techniques we used, even with complicated automatic determination of methods, getting 90% or more was very rare. Getting above roughly 84% was uncommon

Answer 1

[vaibhav mishra]

hi, ways to improve your validation accuracy are to apply regularization on your loss, or to apply some dropout so that model generalizes well and if possible try to decrease the learning rate, or use more training data.

You can use above methods to see the increase in your accuracy.