calculate the classification accuracy after training a "pretrained model"
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Rayan Matlob
on 28 Jun 2022
Commented: Dehia
on 2 Oct 2023
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
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Accepted Answer
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
2 Comments
Dehia
on 2 Oct 2023
Could you assist me in calculating the F-score, recall, sensitivity, and ROC curve, please?
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