How can i calculate precision and recall in a Siamese network

i need help,

my reseach is about building a siamese network like this example Train a Siamese Network to Compare Images

in the attached example the accurcy of the model is calculated by this code, i ask how i can calculate the precision and recall of that model

accuracy = zeros(1,5);
accuracyBatchSize = 150;

for i = 1:5
    % Extract mini-batch of image pairs and pair labels
    [X1,X2,pairLabelsAcc] = getSiameseBatch(imdsTest,accuracyBatchSize);

    % Convert mini-batch of data to dlarray. Specify the dimension labels
    % "SSCB" (spatial, spatial, channel, batch) for image data.
    X1 = dlarray(X1,"SSCB");
    X2 = dlarray(X2,"SSCB");

    % If using a GPU, then convert data to gpuArray.
    if (executionEnvironment == "auto" && canUseGPU) || executionEnvironment == "gpu"
        X1 = gpuArray(X1);
        X2 = gpuArray(X2);
    end

    % Evaluate predictions using trained network
    Y = predictSiamese(net,fcParams,X1,X2);

    % Convert predictions to binary 0 or 1
    Y = gather(extractdata(Y));
    Y = round(Y);

    % Compute average accuracy for the minibatch
    accuracy(i) = sum(Y == pairLabelsAcc)/accuracyBatchSize;
end

Answer 1

[yanqi liu]

yes，sir，may be use model to test data output，and use

[tpr,fpr,thresholds] = roc(targets,outputs)

to get roc