fixed the dropout issues - easier to scale the inputs than the weights, because otherwise we'll need more fprop functions, and adding more code is bad. also, there was a lame error in the rmsprop which was preventing me from getting roughly what alec radford was getting for his results in his presentation - he definitely rounded up to 99.0% though, even his own code maxes out at 98.81. next goal will be to reproduce the 0.95% error (99.05%) accuracy obtained by dropout NN + max-norm constraint using 2 8192-sized layers - will need to take a look at the hyperparameters used for that