> I wondered how I would change the quick documentation examples to make the outputs repeatable ( via rng(0) ) and more understandable ( using
0 <= NMSE <= 1 AND 0 <= PCTERR <= 100 ...
> % CLASSIFICATION AND PATTERN RECOGNITION > tic, x = input; t = target; tind = vec2ind( t ); rng( 0 ) > net = patternnet; [ net tr y e ] = train( net, x, t ); view( net ) > yind = vec2ind( y ); PCTERR = 100 * mean ( tind ~= yind ) > NMSE = mse( e ) / mean( var( t', 1 ) ), time = toc > > % REMARKS: > 1. No plots of inputs, targets and outputs were obtained. > 2. NMSE was included for classification because training tries to minimize NMSE, not PCTERR.
PATTERNET TRIES TO MINIMIZE THE CROSSENTROPY BETWEEN OUTPUT AND TARGET !!!
1. SEARCH FOR CROSSENTROPY IN WIKIPEDIA
2. SEARCH FOR THE MATLAB CROSSENTROPY DOCUMENTATION: