This folder has been autogenerated by TensorFlow, and contains source, header, and project files needed to build a single TensorFlow Lite Micro target using the Mbed command line interface.
To load the dependencies this code requires, run:
mbed config root .
mbed deploy
TensorFlow requires C++ 11, so you'll need to update your profiles to reflect this. Here's a short Python command that does that:
python -c 'import fileinput, glob;
for filename in glob.glob("mbed-os/tools/profiles/*.json"):
for line in fileinput.input(filename, inplace=True):
print line.replace("\"-std=gnu++98\"","\"-std=c++11\", \"-fpermissive\"")'
With that setting updated, you should now be able to compile:
mbed compile -m auto -t GCC_ARM
If this works, it will give you a .bin file that you can flash onto the device you're targeting. For example, using a Discovery STM3246G board, you can deploy it by copying the bin to the volume mounted as a USB drive, just by dragging over the file.
See tensorflow/lite/experimental/micro for details on how projects like this can be generated from the main source tree.
TensorFlow's code is covered by the Apache2 License included in the repository, and third party dependencies are covered by their respective licenses, in the third_party folder of this package.