Their API looks.... overly complicated, right now. https://www.tensorflow.org/api_docs/
They do not even document the network API - instead, they document wrappers for that API for a few programming languages. So you'd have to take apart one of the wrappers (or add another layer of crud over top one of those wrappers - python, probably, since that's the only one they think is stable). My guess is that latency is high enough that they need this level of indirection to deflect people inclined to complain about such things. -- Raul On Thu, May 25, 2017 at 11:37 AM, Skip Cave <[email protected]> wrote: > Google has released the latest version of TensorFlow, their open-source > machine learning package at their recent Google I/O event. TensorFlow runs > on Android, iOS, Raspberry Pi and both Google and AWS cloud services, using > the same API. TensorFlow supports a wide range of CPUs, GPUs., and now TPUs > (Tensor Processing Units). Tensor is Google's name for vectors and > matrices. Google provides TensorFlow API support in four languages: Python, > C++, Java, & Go. Other groups have ported the TensorFlow API to Haskell, > Julia, C#, and R. TensorFlow is designed to hide the concurrent nature of > the underlying processes, so large numbers of parallel processes can be > treated as a single process at the top-level TensorFlow API. > > Check out the TensorFlow overview video given at Google I/O last week (36 > minutes) on YouTube at: > https://www.youtube.com/watch?v=OzAdKMPgUt4&list=TLGGqXCgIcW-mFUyNDA1MjAxNw > > Check out the basic TensorFlow machine learning formula at 18:36 in the > video. This works as a line of J code (getting rid of the square brackets, > and adding parenthesis to override J's right-to-left execution). > > Imagine! You can debug a machine-learning application on one's PC or > smartphone using a test dataset. Then you run the compute-intensive > big-data training in the cloud, running on dozens (or hundreds) of GPUs, or > now TPUs. Finally, one can run the trained models back on a local machine, > or keep the model execution in the cloud, if the model still needs lots of > processing. > > This seems like a perfect fit for a J TensorFlow library. Unfortunately, > implementation of such a library is way above my J skill level. > > Skip Cave > Cave Consulting LLC > ---------------------------------------------------------------------- > For information about J forums see http://www.jsoftware.com/forums.htm ---------------------------------------------------------------------- For information about J forums see http://www.jsoftware.com/forums.htm
