cppflow VS tensorflow_cpp

It’s been awhile since I’ve looked at it, so not sure how hard it would be to get to work. I only commented since you mentioned that you would support other operating systems. For others interested in cross platform support there is also cppflow.

what about start with keras and convert model to c++ ? https://github.com/pplonski/keras2cpp https://github.com/serizba/cppflow

My solution so far: I am using a compiled Tensorflow C DLL in combination with cppflow (https://github.com/serizba/cppflow). However, I get problems when I take models which use operations from the tensorflow_text python module since I don’t know how to get their C++ API.

I use cppflow to run compiled tensorflow models natively in C++. It works like a charm :)

I toyed around with this idea a while back but I never got around to finishing the implementation. If all you need is inference with no training and you are relatively familiar with c++ you could look into creating a module for Godot that interfaces with the Tensorflow C API. Something like cppflow would provide an even easier API to work with. Looking into that project could also explain how they interface with the Tensorflow C API if you'd rather cut out the middle man. A module like this would let you train your model in Python and then load it and perform inference in Godot natively.

If your re using keras with TensorFlow you can save it as a saved model format and then you can easily use cppflow to perform inference with it.

The whole game is written in C++ using SFML for the graphics, entt as Entity-Component-System and tensorflow for the Neural Network. Tensorflow itself is written in C, so I use cppflow to integrate it into my C++ framework.

Found this which seems more recent (uses TF 2, updated 4 days ago): https://github.com/serizba/cppflow

Looks like tfcompile still exists, but I have never used it myself. Doesn't look to be much better documented than the C++ API itself. The full C++ API of course gives you way more flexibility and doesn't involve this special process of compiling a specific model. We have been pretty happy with our additional wrapper library tensorflow_cpp, allowing us to easily load arbitrary frozen graphs and saved models for inference.