calculate the classification accuracy after training a "pretrained model"

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how to calcualte the MSE, MAE RMSE or any other classification accuracy of a pretrained model?
next is my code:
imds = imageDatastore('C:\Users\Rayan\Desktop\Work\9_5_work_on_4_groups\9_1\R_9_1_GSM', ...
'IncludeSubfolders',true, ...
'LabelSource','foldernames');
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomized');
numTrainImages = numel(imdsTrain.Labels);
idx = randperm(numTrainImages,16);
net = resnet50;
deepNetworkDesigner(net)
analyzeNetwork(net)
inputSize = net.Layers(1).InputSize;
lgraph = layerGraph(net);
edit(fullfile(matlabroot,'examples','nnet','main','findLayersToReplace.m'))
[learnableLayer,classLayer] = findLayersToReplace(lgraph);
[learnableLayer,classLayer] %#ok<NOPTS>
numClasses = numel(categories(imdsTrain.Labels));
%numClasses = 3
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);
layers = lgraph.Layers;
connections = lgraph.Connections;
layers(1:20) = freezeWeights(layers(1:20));
lgraph = createLgraphUsingConnections(layers,connections);
augimdsTrain = augmentedImageDatastore(inputSize(1:2),imdsTrain)
augimdsValidation = augmentedImageDatastore(inputSize(1:2),imdsValidation);
miniBatchSize=10;
valFrequency = floor(numel(augimdsTrain.Files)/miniBatchSize);
options = trainingOptions('sgdm', ...
'MiniBatchSize',10, ...
'MaxEpochs',6, ...
'InitialLearnRate',0.0007, ...
'Shuffle','every-epoch', ...
'ValidationFrequency',valFrequency, ...
'ValidationData',augimdsValidation, ...
'Verbose',false, ...
'Plots','training-progress');
net = trainNetwork(augimdsTrain,lgraph,options);
[YPred,probs] = classify(net,augimdsValidation);
accuracy = mean(YPred == imdsValidation.Labels);
idx = randperm(numel(imdsValidation.Files),100);
R=1;
for j =1:24
figure(j)
for i = 1:4
subplot(2,2,i)
I = readimage(imdsValidation,idx(R));
imshow(I)
label = YPred(idx(R));
title(string(label) + ", " + num2str(100*max(probs(idx(R),:)),3) + "%");
R=R+1;
end
end

Accepted Answer

Andreas Apostolatos
Andreas Apostolatos on 28 Jun 2022
Hi Rayan,
From the code snippet you share it appears that you are training a neural network for classification while you are then performing inference for some validation data,
net = trainNetwork(augimdsTrain,lgraph,options);
[YPred,probs] = classify(net,augimdsValidation);
accuracy = mean(YPred == imdsValidation.Labels);
Error measures such as the Mean Squarer Error (MSE) or the Root Mean Square Error (RMSE) are suited for regression problems where the response variables are continuous and not for classification problems.
To evaluate the performance of a classifier it is more appropriate to use a Confusion Matrix or to compute the percentage of responses that have been correctly predicted by the classifier. The corresponding workflow is underlined in the following link,
I hope that you find this information useful for needs.
Kind regards
Andreas
  1 Comment
Rayan Matlob
Rayan Matlob on 29 Jun 2022
Edited: Rayan Matlob on 29 Jun 2022
after training the previeus example , if i used (SVM) , would i be able to find MSE, MAE RMSE or any other classification accuracy
as follow:
% Call the workspace_5.mat and then run the following codes to achive the support vector machine
% Try SVM ############################################################################
[imdsTrain,imdsTest,augimdsValidation] = splitEachLabel(imds,0.7,0.2,0.1,'randomized');
numTrainImages = numel(imdsTrain.Labels);
%inputSize = net.Layers(1).InputSize;
augimdsTrain = augmentedImageDatastore(inputSize(1:2),imdsTrain);
augimdsTest = augmentedImageDatastore(inputSize(1:2),imdsTest);
augimdsValidation = augmentedImageDatastore(inputSize(1:2),imdsValidation);
layer = 'avg_pool';
featuresTrain = activations(net,augimdsTrain,layer,'OutputAs','rows');
featuresTest = activations(net,augimdsTest,layer,'OutputAs','rows');
whos featuresTrain
YTrain = imdsTrain.Labels;
YTest = imdsTest.Labels;
YVal = imdsValidation.Labels;
classifier = fitcecoc(featuresTrain,YTrain);
YPred = predict(classifier,featuresTest);
accuracy = mean(YPred == YTest)
layer = 'activation_46_relu';
featuresTrain = activations(net,augimdsTrain,layer);
featuresTest = activations(net,augimdsTest,layer);
whos featuresTrain
featuresTrain = squeeze(mean(featuresTrain,[1 2]))';
featuresTest = squeeze(mean(featuresTest,[1 2]))';
whos featuresTrain
classifier = fitcecoc(featuresTrain,YTrain);
YPred = predict(classifier,featuresTest);
accuracy = mean(YPred == YTest)
% with activation_46_relu, ---> accuracy=0.9846
augimdsValidation = augmentedImageDatastore(inputSize(1:2),imdsValidation);
[YPred,probs] = classify(net,augimdsValidation);
%Test
idx = randperm(numel(imdsValidation.Files),8);
R=1;
for j =1:2
figure(j)
for i = 1:4
subplot(2,2,i)
I = readimage(imdsValidation,idx(R));
imshow(I)
label = YPred(idx(R));
title(string(label) + ", " + num2str(100*max(probs(idx(R),:)),3) + "%");
R=R+1;
end
end

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