Image regression: How to visualize the feature importance of an image in convolutional neural networks
10 vues (au cours des 30 derniers jours)
Afficher commentaires plus anciens
To investigate trained networks, we can use visualization techniques such as Grad-CAM, occlusion sensitivity, LIME, and deep dream.
But the input of these functions require to be string, char, categorical, cell.
For example, map = occlusionSensitivity(net,img,Y), where Y was the predicted value for img, however, this function shows error: Expected input number 3 to be one of these types: string, char, categorical, cell.
Could anyone tell me how to use these functions for regression analysis?
0 commentaires
Réponses (1)
Aditya Patil
le 31 Mar 2021
the third parameter for occlusionSensitivity is label which was predicted for the model. For example,
label = classify(net,X);
scoreMap = occlusionSensitivity(net,X,label);
0 commentaires
Voir également
Produits
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!