Branch	Linux/OSX	Windows	License	Codacy
master

Example how to run TensorFlow lib C API on Windows, Linux and macOS(Darwin).

• Hello TF
• Load graph
• Create Tensor
• Allocate Tensor
• Run session
• Interface
• Tensor Info
• Graph Info
• Image processing

git clone --depth 1 https://github.com/Neargye/hello_tf_c_api
cd hello_tf_c_api
mkdir build
cd build
cmake -G "Visual Studio 15 2017" -A x64 ..
cmake --build . --config Debug

git clone --depth 1 https://github.com/Neargye/hello_tf_c_api
cd hello_tf_c_api
mkdir build
cd build
cmake -G "Unix Makefiles" ..
cmake --build .

git clone --depth 1 https://github.com/Neargye/hello_tf_c_api
cd hello_tf_c_api
mkdir build
cd build
cmake -G "XCode" ..
cmake --build .

• After the build, you can find the TensorFlow lib in the folder hello_tf_c_api/tensorflow/lib, and header in hello_tf_c_api/tensorflow/include.
• The tensorflow in the repository is compiled in x64 mode. Make sure that project target 64-bit platforms.
• Make sure that the tensorflow lib is in Output Directory or either in the directory contained by the %PATH% environment variable.

For x64 CPU, you can download the tensorflow.so, tensorflow.dll and tensorflow.lib from https://github.com/neargye-forks/tensorflow/releases.

Or build lib which version you need from the sources, with CPU or GPU support.

link_directories(yourpath/to/tensorflow) # path to tensorflow lib
... # other
target_link_libraries(<target> <PRIVATE|PUBLIC|INTERFACE> tensorflow)

"Project"->"Properties"->Configuration Properties"->"Linker"->"Additional Dependencies" and add path to your tensorflow.lib as a next line.

Make sure that the tensorflow.dll is in Output Directory (by default, this is Debug\Release under your project's folder) or either in the directory contained by the %PATH% environment variable.

To generated the graph.pb file need takes a graph definition and a set of checkpoints and freezes them together into a single file.

• https://www.tensorflow.org/install/lang_c
• https://medium.com/@vladislavsd/undocumented-tensorflow-c-api-b527c0b4ef6
• https://medium.com/analytics-vidhya/deploying-tensorflow-2-1-as-c-c-executable-1d090845055c