if there is some potential to see a tidy-esque approach to a GPU modeling framework involving recipes and some version of caret that offloads some of the lower-level programming required for unique modeling techniques that may not be predefined within keras or tensorflow already
That hadn't crossed my mind. There is/will be connections between
tensorflow packages. The last release of
caret contains two neural net models build on
keras and I'm playing with adding autoencoders to
One issue is that "not be predefined within
tensorflow already" means a lot of close-to-the-metal work in
tensorflow and right now I would avoid that since 1) I don't know the intricacies of that system and 2) the api might change a lot.
Also, it's my belief that the gpu is optimized and fast for gpu-things (like matrix calculations) and doesn't help that much otherwise. For example, I don't know what that would offer for something like trees etc.
I think that a more likely integration would be to have some recipes steps off-loaded to
tensorflow. The autoencoder is a good prototype for that. I'd like to have complete backends for recipes (as in
dplyr) so that you can use remote data in another system and use recipes to tell the system, what to do.