This repository contains NN models for EPED1-NN, TGLF-NN and NEOjbs-NN
Refer to the readme.md files in the subfolders for more info.
Two libraries have been used to train the NN models.
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the FANN library is a NN library that allows little flexibility in the definition onf the models, but has the advantage of being very portable, and have bindings to many languages
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the TENSORFLOW library allows great flexibility in building NN models, and gives access to the most modern machine learning techniques and algorythmes, but has the disadvantage of being more difficult to deploy the trained models outside of Python. Yet running TENSORFLOW models from C, MATLAB, FORTRAN, should be possible. For this purpose a client-server library that is capable of servicing these models across the web over TCP/IP was developed. The client side of the library is written in pure C (with available FORTRAN and Python interfaces) such that no external library dependency is required. This approach lifts the cumbersome installation requirements for doing inference with Tensorflow models from C.
We refer to as BRAINFUSE to the set of tools in this repository that are used to run these models.
Install the FANN c library:
git clone [email protected]:libfann/fann.git
cd fann
cmake .
make
Install the Python bindings to the FANN library
pip install fann2
Set in your .login file:
setenv FANN_ROOT loation_where_fann_was_cloned
Run ./compile.sh script
To start one can use the public brainfusetf server gadb-harvest.duckdns.org
to serve trained models.
A python can be run with: tf_client_server_test.py
A C example can be run with:
make brainfusetf_run.exe
brainfusetf_run.exe