How to use CNN to build ROC curves
For binary classification problem, naturally, classes are called positives and negatives. Positive is usually a class of detection something that we are interested in, for example, spam or disease detection. According to documentation for sklearn.metrics.roc_curve, it takes as argument probabilities for positive class. For most binary classifiers, output probability usually refers to the probability of positive class (which is marked as 1 in the data). So, use probabilities of the class marked as 1 in the data.