How can i calculate precision and recall in a Siamese network
5 vues (au cours des 30 derniers jours)
Afficher commentaires plus anciens
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
0 commentaires
Réponses (1)
yanqi liu
le 24 Mar 2022
yes,sir,may be use model to test data output,and use
[tpr,fpr,thresholds] = roc(targets,outputs)
to get roc
0 commentaires
Voir également
Catégories
En savoir plus sur Deep Learning Toolbox dans Help Center et File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!