Confusion matrix outputs NaN values after classification using Neural Network

I am trying to classify data containing 10 classes using neural network. My data is in the form of wavfiles. I computed my feature vector using n-point FFT. The problem is that after classification, I am getting NaN as an output from the confusion matrix, although my input to the Neural Network doesnt contain any NaN values. When I classify the data for 3 classes, there is no such problem. But if I increase the number of my classes from 5 and more, I get NaN values.

Answer 1

[Greg Heath]

Isn't it obvious?

   1. Rows 9 and 10 contain nothing but zeros.
   2. Ratios and percentages obtained by dividing by zero will yield NaNs

Hope this helps.

Thank you for formally accepting my answer

Greg