Recently the OpenAI team made news again by releasing a 335-million parameter pre-trained natural language model. This model, using Python and TensorFlow , can generate text based on preceding text with such impressive capabilities that is can be used to translate and answer questions. This team actually has models several times larger, but have not yet released them due to risks of abuse.
Today I have released my small contribution to this awesome project - A deployable TensorFlow model and Java-based reference implementation which uses only the core (i.
I have been looking at TensorFlow again, after a couple of years, and I was very pleased to see there is now an official Java release of the API, interop, and native binaries. These are all published to maven and easily loaded into the process using the standard Java API. Hurray!
However, I quickly ran into limitations. The API gave a bridge to the native runtime, so you effectively have the functionality of the C++ API within Java, but the C++ API doesn’t do everything.
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Recently, Google open-sourced a toolkit called TensorFlow which provides a platform for neural networks. It provides a native core written in C, and many examples written in Python. Although the architecture is extensible and will hopefully will be usable from Java/Scala application code in the future, I took some time recently to evaluate it using Python to perform deconvolutions (a.k.a. deblurring), the same task I recently wrote about using my own NN library.
