the cascade network added 104 nodes and its output is almost exactly the same as when there were only 4 nodes. it looks like 4 red points were missed and 0 blue points were missed. network error was 0.0852 (i had set it up to stop training at 0.05). the problem, i believe, is that training is ending prematurely for both the hidden and output nodes. i observed some behavior in which the “change in error” first drops to a very low number, then starts to rise again. if it drops so low that the minimum error change level is passed, training will of the weights will stop.
