This repository contains tensorflow examples written in C++. The examples are primarily geared towards usage of C++ for inference aspect. At present, whereever a model is required I use/provide a pre-trained model and/or a python script to generate the model.

Jul 14, 2016 · To run a graph in C++ the steps are loading the graph definition; creating a session; initialize the graph within the session and running our graph. I will first present the entire code then provide explanations.

Nov 14, 2015 · If you want to use the TensorFlow C++ API to load, inspect, and run saved models and frozen graphs in C++, we suggest that you also check out our helper library tensorflow_cpp.

With the input tensors defined, let's set up the computational graph. In TensorFlow 2.0, AutoGraph is automatically applied when using tf.function. AutoGraph transforms a subset of Python which...

Jan 17, 2017 · If you really want to use just the C API, use TF_GraphImportGraphDef to load a graph. Note that the C API isn't particularly convenient to use (it is intended to build bindings in other languages such as Go, Java, Rust, Haskell etc.) For example: free(data); .

Clone Tensorflow source code and compile to get the C API headers and binaries. Build the simpliest model using Python and Tensorflow and export it as a TF model that can be read by the C API. Build a simple C program, compile it with "gcc," and run it like a normal execution file.

Feb 19, 2016 · For TensorFlow v2, it's suggested to use tensorflow::LoadSavedModel which can take model (SavedModel) saved via Python API mode.save(). So you don't need to use FreezeSavedModel and GrapeDef. Suppose that your TensorFlow model files are saved in model/ directory:

Nov 20, 2015 · In the TensorFlow repo there are more involved examples, such as building a graph in C++. However, the C++ API for constructing graphs is not as complete as the Python API. Many features...

Oct 27, 2020 · TensorFlow currently provides a C++ API for registering a custom graph optimizer in Grappler. This project aims to create a modular/plugin-based TensorFlow implementation with C APIs. Plugins will be able to register custom graph optimizers.

This gist demonstrates taking a model (a TensorFlow graph) created by a Python program and running the training loop in a C program. The model is a trivial one, trying to learn the function: f(x) = W\*x + b, where W and b are model parameters.