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Perform sensitivity, specificity, precision, recall, f_measure in CNN

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Hello experts,
I want to perform [sensitivity, specificity, precision, recall, f_measure] in the following script, but I dont' know how.
Please help me how to write code to evaluate them!
outputFolder = fullfile('Caltech')
rootFolder = fullfile(outputFolder, '101_ObjectCategories')
categories = {'data1', 'data2'}
imds = imageDatastore(fullfile(rootFolder,categories), 'LabelSource','foldernames')
tbl = countEachLabel(imds)
minSetCount = min(tbl{:,2})
imds = splitEachLabel(imds, minSetCount, 'randomize')
countEachLabel(imds)
net = resnet50();
lgraph = layerGraph(net);
clear net;
numClasses = 2;
%numel(lgraph.Layers(end).ClassNames);
[trainingSet, testSet] = splitEachLabel(imds, 0.7, 'randomize');
imageSize = [224 224 3];
augmentedTrainingSet = augmentedImageDatastore(imageSize,...
trainingSet, 'ColorPreprocessing', 'gray2rgb');
augmentedTestSet = augmentedImageDatastore(imageSize,...
testSet, 'ColorPreprocessing', 'gray2rgb');
% New Learnable Layer
newLearnableLayer = fullyConnectedLayer(numClasses, ...
'Name','new_fc', ...
'WeightLearnRateFactor',10,...
'BiasLearnRateFactor',10);
% Replacing the last layers with new layers
lgraph = replaceLayer(lgraph,'fc1000',newLearnableLayer);
newsoftmaxLayer = softmaxLayer('Name','new_softmax');
lgraph = replaceLayer(lgraph,'fc1000_softmax',newsoftmaxLayer);
newClassLayer = classificationLayer('Name','new_classoutput');
lgraph = replaceLayer(lgraph,'ClassificationLayer_fc1000',newClassLayer);
options = trainingOptions('adam',...
'MaxEpochs',6,'MiniBatchSize',8,...
'Shuffle','every-epoch', ...
'ValidationData', augmentedTestSet, ...
'ValidationFrequency', 30, ...
'InitialLearnRate',1e-4, ...
'Verbose',false, ...
'Plots','training-progress');
netTransfer = trainNetwork(augmentedTrainingSet,lgraph,options);

Risposta accettata

Pratyush Roy
Pratyush Roy il 3 Dic 2021
Hi,
The predict function might be helpful to predict the labels for the test images using the following command:
YPred = predict(netransfer, imds_test) %imds_test is the image dastore containing the test images.
After obtaining the predicted labels "YPred" the function perfcurve can be used the get the precision and recall values using the following command:
[Xpr,Ypr,Tpr,AUCpr] =perfcurve(targets, scores, 1, 'xCrit', 'reca', 'yCrit', 'prec');
Here Xpr and YPr represents recall and pres=cision respectively.
You can also use the confusion function to obtain the "Matrix of percentages" using the following command:
[c,cm,ind,per] = confusion(targets,outputs) %per represents the Matrix of percentages. Please refer to the doc for more details.
Hope this helps!
  1 Commento
Arya Faturrahman
Arya Faturrahman il 11 Ott 2022
I Get Error code
Error in DAGNetwork/predict (line 118)
Y = predictBatch( ...
Error in test_resnet (line 1)
YPred = predict(netTransfer, testSet.Labels);
%imds_test is the image dastore containing the
test images.
How to fix and run program i need example for this code. Can you give me example to run testing code about this because i very need this outputs.

Accedi per commentare.

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