This actually makes the dataset harder to fit to. It is not the same thing here as the "training with noise" method where random noise would be added to each batch, as an alternative means of Tikhonov regularization.
wih that particular data set, it looks like it really just adds more data, and more importantly, fills in the gaps along the spirals which is where my setup was having troubles.
The noise doesn't go far enough to start confusing points between different clusters, but it adds more points.
That said, my knowledge of neural nets is fairly limited.
