Hi Mark, I want to correct one thing. My company is behind dl4j. We are also the second largest contributor to keras. We have near perfect model import (with keras contributors maintaining it). Dl4j also has TF import and a native graph runner: https://github.com/deeplearning4j/dl4j-examples/tree/master/...

Java itself tends to be fairly verbose I know, but not only are there wrappers but for inference if you want to use python for basics and then us for inference. We enable several ways to do that. Folks tend to oversensationlize these "us vs them" framework "fights".

Rather than do that, why not just use what's good for each use case? Python for training, java for deployment?

Beyond that, you could also consider building your own wrapper maybe? In clojure, there's jutsu.ai for example: https://github.com/hswick/jutsu.ai