ROC curve for deep learning I am working in a deep learning project for image classification. I am using Vgg16 to create the model and the dataset has 100 classes. The testing accuracy is 98.9% and loss is 0.1731

And I got the following ROC and Average Precision curves: 



I do not now if the ROC and Average Precision result are good results or there is a problem in the curves because the curves are perfect. Do you know why I got ROC =1? Is there any problem in ROC and average precision?
Mathematically, it is not possible to have the area under the curve equal to 1 if you have a precision less than 1... If you have a doubt, you should check the confusion matrix and calcuate the scores yourself. You'll know directly if you did a mistake or not

A few other remarks: 


Accuracy is not precision
The loss is of no help here