function mednet()
%for counter=0:5
unzip('medData8.zip');
inputSize=[128 128];
imds = imageDatastore('medData8', ...
    'IncludeSubfolders',true, ...
    'LabelSource','foldernames')
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7);
imdstmp=imdsValidation;
imds = augmentedImageDatastore(inputSize,imds);
net =googlenet;
analyzeNetwork(net)
net.Layers(1)
%  display('hello');
inputSize = net.Layers(1).InputSize
if isa(net,'SeriesNetwork') 
  lgraph = layerGraph(net.Layers); 
else
  lgraph = layerGraph(net);
end
display('nihao');
[learnableLayer,classLayer] = findLayersToReplace(lgraph);
% [learnableLayer,classLayer] 
numClasses = numel(categories(imdsTrain.Labels));

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])
layers = lgraph.Layers;
connections = lgraph.Connections;

layers(1:10) = freezeWeights(layers(1:10));
lgraph = createLgraphUsingConnections(layers,connections);
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),imdsTrain, ...
    'DataAugmentation',imageAugmenter);
augimdsValidation = augmentedImageDatastore(inputSize(1:2),imdsValidation);
% augimds=augmentedImageDatastore(inputSize(1:2),imds);
miniBatchSize = 10;
valFrequency = floor(numel(augimdsTrain.Files)/miniBatchSize);
options = trainingOptions('FCGDCKS', ...
    'MiniBatchSize',miniBatchSize, ...
    'MaxEpochs',6, ...
    'InitialLearnRate',3e-4, ...
    'Shuffle','every-epoch', ...
    'ValidationData',augimdsValidation, ...
    'ValidationFrequency',valFrequency, ...
    'Verbose',true,'VerboseFrequency',19, ...
    'Plots','training-progress',...
    'GradientThresholdMethod','l2norm');
net = trainNetwork(augimdsTrain,lgraph,options);
[YPred,probs] = classify(net,augimdsValidation);
accuracy = mean(YPred == imdsValidation.Labels)
save net
%end
% [YPred,probs] = classify(net,augimds);
figure
plotconfusion(imdstmp.Labels,YPred);
figure
[xroc,yroc,troc,auc]=perfcurve(augimds.Files,probs(:,1),1);
plot(xroc,yroc);
% auc
% idx = randperm(numel(imdsValidation.Files),4);
% figure
% for i = 1:4
%     subplot(2,2,i)
%     I = readimage(imdsValidation,idx(i));
%     imshow(I)
%     label = YPred(idx(i));
%     title(string(label) + ", " + num2str(100*max(probs(idx(i),:)),3) + "%");
% end