70cf20f6cf5e21c3b6165067483b925db61c161c,deepmedic/neuralnet/layers.py,SoftmaxLayer,getRpRnTpTnForTrain0OrVal1,#SoftmaxLayer#Any#Any#,518

Before Change

        for class_i in xrange(0, self._numberOfOutputClasses) :
            //Number of Real Positive, Real Negatives, True Predicted Positives and True Predicted Negatives are reported PER CLASS (first for WHOLE).
            tensorOneAtRealPos = T.eq(y, class_i)
            tensorOneAtRealNeg = T.neq(y, class_i)

            tensorOneAtPredictedPos = T.eq(yPredToUse, class_i)
            tensorOneAtPredictedNeg = T.neq(yPredToUse, class_i)
            tensorOneAtTruePos = T.and_(tensorOneAtRealPos,tensorOneAtPredictedPos)
            tensorOneAtTrueNeg = T.and_(tensorOneAtRealNeg,tensorOneAtPredictedNeg)
                    
            returnedListWithNumberOfRpRnTpTnForEachClass.append( T.sum(tensorOneAtRealPos) )
            returnedListWithNumberOfRpRnTpTnForEachClass.append( T.sum(tensorOneAtRealNeg) )
            returnedListWithNumberOfRpRnTpTnForEachClass.append( T.sum(tensorOneAtTruePos) )
            returnedListWithNumberOfRpRnTpTnForEachClass.append( T.sum(tensorOneAtTrueNeg) )
            
        return returnedListWithNumberOfRpRnTpTnForEachClass

After Change

        for class_i in range(0, self._numberOfOutputClasses) :
            //Number of Real Positive, Real Negatives, True Predicted Positives and True Predicted Negatives are reported PER CLASS (first for WHOLE).
            tensorOneAtRealPos = tf.equal(y, class_i)
            tensorOneAtRealNeg = tf.logical_not(tensorOneAtRealPos)

            tensorOneAtPredictedPos = tf.equal(yPredToUse, class_i)
            tensorOneAtPredictedNeg = tf.logical_not(tensorOneAtPredictedPos)
            tensorOneAtTruePos = tf.logical_and(tensorOneAtRealPos,tensorOneAtPredictedPos)

Instances