JPMML should be able to handle PMML 3.X and newer versions of NeuralNetwork models without problem. Moreover, it should be able to handle all the normalization and denormalization transformations that may accompany such models.
I could use a clarification that why are you interested in converting PMML models to Java code in the first place. This complicates the whole matter a lot and it doesn't add any value. The JPMML library itself is rather compact and has minimal external dependencies (at the moment of writing this, it only depends on commons-math). There shouldn't be much difference performance-wise. You can reasonably expect to obtain up to 10'000 scorings/sec on a modern desktop computer.
The JPMML codebase has recently moved to GitHub: http://github.com/jpmml/jpmml
Fellow coders in Turn Inc. have forked this codebase and are implementing PMML-to-Java translation (see top-level module "pmml-translation") for selected model types: https://github.com/turn/jpmml
At the moment I recommend you to check out the Openscoring project (uses JPMML internally): http://www.openscoring.org
Then, you could try the following:
- Deploy your XML file using the HTTP PUT method.
- Get your model summary information using the HTTP GET method. If the request succeeds (as opposed to failing with an HTTP status 500 error code) then your model is well supported.
- Execute the model either in single prediction mode or batch prediction mode using the HTTP POST method. Try sending larger batches to see if it meets your performance requirements.
- Undeploy the model using the HTTP DELETE method.
You can always try contacting project owners for more insight. I'm sure they are nice people.