Yeah, it's a really good point. I haven't played with automatic parameter selection much (but have been seeing more papers on it recently) so I hadn't really c

Yeah, it's a really good point. I haven't played with automatic parameter selection much (but have been seeing more papers on it recently) so I hadn't really considered it all that closely. While I'd like to give people a fair amount of control over model parameters if they want, it probably is very important that I make things as turnkey as I can. Shouldn't be too tough to hack something together and make it an option during training. While I'm trying to start things off relatively simply, the overall goal really is towards allowing people to create models that act as parts of much larger systems, maybe larger neural nets themselves. A sort of genetic algorithm that spawns new neural networks with random parameters and random connections to previous networks could be kind of neat, and making the base elements of those types of architectures (a single fully connected deep net, for example) easily accessible is a first step towards that goal.