> Of course, maybe you were pointing out that if your derivative > calculation depends in some intrinsic way on the topology of some > graph, then your best bet is to have an automatic way to recompute it > from scratch for each new graph you see. In that case, fair enough!
That is indeed what I had in mind. In neural networks, Markov random fields, Bayesian networks, graph regularization etc it is something that has to be dealt with all the time. > Right, and what I want is to do those correctness checks once, and > then save the validated derivative function somewhere and know that it > won't break the next time I upgrade some library or make some > seemingly-irrelevant change to the original code. Exactly. What I was getting at is: even if it is not feasible to get a pretty printed python output, the byte code can still be validated (somewhat) with a few numeric sanity checks. So, yes the derivatives needn't/shouldn't be re-computed in runtime all the time and an API that that returns even some opaque but computable representation of the derivative that can be validated and then "frozen" would be helpful. I think one can go further and formally prove the correctness of the derivative computing engine. I dont know if anyone has done it. Maybe Theano does it. Should be possible for a statically typed sublanguage. _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
