How to get Precision, Recall,ROC,F_Mesure?

Hello anyone I want to get Precision, Recall, Sensitivity,Sprecificity,ROC. But I don't know how to implements code. i get Error at perfCurve and i don't know to fix it.

This my Train Code

imds = imageDatastore('Dataset', 'IncludeSubfolders', true, '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);

And This my Test code for get the precision and any parameters

YPred = predict(netTransfer, augmentedTestSet); %imds_test is the image dastore containing the test images.

[Xpr,Ypr,Tpr,AUCpr] =perfcurve(testSet, newClassLayer, 1, 'xCrit', 'reca', 'yCrit', 'prec');

[c,cm,ind,per] = confusion(targets,outputs); %per represents the Matrix of percentages. Please refer to the doc for more details.

Can you help me to fix and get the data?.