Matlab code for plotting roc curve for object detection and classification using a deep learning resnet50 model

I need to plot an roc curve from the below code.Any input on how to go about it will be greatly appreciated.

imds = imageDatastore(imageFolder, 'LabelSource', 'foldernames', 'IncludeSubfolders',true); % Determine the smallest amount of images in a category minSetCount = min(tbl{:,2});

% Limit the number of images to reduce the time it takes % run this example. maxNumImages = 100; minSetCount = min(maxNumImages,minSetCount);

% Use splitEachLabel method to trim the set. imds = splitEachLabel(imds, minSetCount, 'randomize');

% Notice that each set now has exactly the same number of images. countEachLabel(imds) % Load pretrained network net = resnet50() [trainingSet, testSet] = splitEachLabel(imds, 0.3, 'randomize'); % Create augmentedImageDatastore from training and test sets to resize % images in imds to the size required by the network. imageSize = net.Layers(1).InputSize; augmentedTrainingSet = augmentedImageDatastore(imageSize, trainingSet, 'ColorPreprocessing', 'gray2rgb'); augmentedTestSet = augmentedImageDatastore(imageSize, testSet, 'ColorPreprocessing', 'gray2rgb'); % Get the network weights for the second convolutional layer w1 = net.Layers(2).Weights;

% Scale and resize the weights for visualization w1 = mat2gray(w1); w1 = imresize(w1,5);

% Display a montage of network weights. There are 96 individual sets of % weights in the first layer. figure montage(w1) title('First convolutional layer weights') featureLayer = 'fc1000'; trainingFeatures = activations(net, augmentedTrainingSet, featureLayer, ... 'MiniBatchSize', 32, 'OutputAs', 'columns'); % Get training labels from the trainingSet trainingLabels = trainingSet.Labels;

% Train multiclass SVM classifier using a fast linear solver, and set % 'ObservationsIn' to 'columns' to match the arrangement used for training % features. classifier = fitcecoc(trainingFeatures, trainingLabels, ... 'Learners', 'Linear', 'Coding', 'onevsall', 'ObservationsIn', 'columns'); % Extract test features using the CNN testFeatures = activations(net, augmentedTestSet, featureLayer, ... 'MiniBatchSize', 32, 'OutputAs', 'columns');

% Pass CNN image features to trained classifier predictedLabels = predict(classifier, testFeatures, 'ObservationsIn', 'columns');

% Get the known labels testLabels = testSet.Labels;

% Tabulate the results using a confusion matrix. confMat = confusionmat(testLabels, predictedLabels);

% Convert confusion matrix into percentage form confMat = bsxfun(@rdivide,confMat,sum(confMat,2)) % Display the mean accuracy mean(diag(confMat))

testImage = readimage(testSet,1); testLabel = testSet.Labels(1) % Create augmentedImageDatastore to automatically resize the image when % image features are extracted using activations. ds = augmentedImageDatastore(imageSize, testImage, 'ColorPreprocessing', 'gray2rgb');

% Extract image features using the CNN imageFeatures = activations(net, ds, featureLayer, 'OutputAs', 'columns'); % Make a prediction using the classifier predictedLabel = predict(classifier, imageFeatures, 'ObservationsIn', 'columns')

Answer 1

[sinan salim]

wow zero answer ,,i have same problem