A simple C++ implementation of Neural Nets, inspired by the functionality of Tensorflow and pyTorch.

Docs

The documentation with this readme is available at Documentation

Repository

Features

like the functional API of tensorflow
using eigen3 library
more to come

Build and Install Features

Modern CMake configuration and project, which, to the best of my knowledge, uses the best practices,
An example of a Clang-Format config, inspired from the base Google model, with minor tweaks. This is aimed only as a starting point, as coding style is a subjective matter, everyone is free to either delete it (for the LLVM default) or supply their own alternative,
Static analyzers integration, with Clang-Tidy and Cppcheck, the former being the default option,
Doxygen support, through the ENABLE_DOXYGEN option, which you can enable if you wish to use it,
Unit testing support, through GoogleTest (with an option to enable GoogleMock) or Catch2,
Code coverage, enabled by using the ENABLE_CODE_COVERAGE option, through Codecov CI integration,
Package manager support, with Conan and Vcpkg, through their respective options
CI workflows for Windows, Linux and MacOS using GitHub Actions, making use of the caching features, to ensure minimum run time,
**.md templates** for: README, Contributing Guideliness, Issues and Pull Requests,
Permissive license to allow you to integrate it as easily as possible. The template is licensed under the Unlicense,
Options to build as a header-only library or executable, not just a static or shared library.
Ccache integration, for speeding up rebuild times

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

This project is meant as a template and a good starting point for learning how create larger c++ projects, compile and run them. can be change to better suit the needs of the developer(s). If you wish to use the template as-is, meaning using the versions recommended here, then you will need:

CMake v3.15+ - found at https://cmake.org/
C++ Compiler - needs to support at least the C++17 standard, i.e. MSVC, GCC, Clang

Note: You also need to be able to provide CMake a supported generator.

Different data - MNIST AND https://home.bawue.de/~horsch/teaching/inf205/src/image-benchmark.zip

If using VSCode

There are few select extensions which are recommended

Third-party libraries used

eigen3 : https://eigen.tuxfamily.org/index.php?title=Main_Page
OpenMP : https://github.com/OpenMP
OpenBLAS : https://github.com/xianyi/OpenBLAS
csv-parser : https://github.com/vincentlaucsb/csv-parser

Installing

It is fairly easy to install the project, all you need to do is clone if from GitHub.

If you wish to clone the repository, you simply need to run:

git clone https://github.com/jkorsvik/NMBUFlowTorch/

or

git clone git@github.com:jkorsvik/NMBUFlowTorch.git

Install requirements (cmake, conan, etc..)

source install_reqs.sh

Build and install project as an executable

source automatic_rebuild_and_install.sh

If you just want to rebuild fast and you are happy with the install location, run:

source cached_build_and_install.sh

The executables of the project will then be in the

To install an already built project, you need to run the install target with CMake. For example:

cmake --build build --target install --config Release
 
# a more general syntax for that command is:
cmake --build <build_directory> --target install --config <desired_config>

If you have not built the project yet, the automatic_rebuild_and_install.sh will do fine, otherwise follow the next section:

Note: If you want to supress a lot of warnings when building, see the CMakeLists.txt at line 145 and 146, and uncomment the preferred.

Building the project

To build the project, all you need to do, after correctly installing the project, is run a similar CMake routine to the the one below:

mkdir build/ && cd build/
cmake .. -DCMAKE_INSTALL_PREFIX=/absolute/path/to/custom/install/directory
cmake --build . --target install

Note: The custom CMAKE_INSTALL_PREFIX can be omitted if you wish to install in the default install location.

More options that you can set for the project can be found in the cmake/StandardSettings.cmake file. For certain options additional configuration may be needed in their respective *.cmake files (i.e. Conan needs the CONAN_REQUIRES and might need the CONAN_OPTIONS to be setup for it work correctly; the two are set in the cmake/Conan.cmake file).

Generating the documentation

In order to generate documentation for the project, you need to configure the build to use Doxygen. This is easily done, by modifying the workflow shown above as follows:

source build_auto_docs.sh

Note: This will generate a docs/ directory in the project's root directory.

Running the tests

This project uses Google Test for unit testing. Unit testing can be disabled in the options, by setting the ENABLE_UNIT_TESTING (from cmake/StandardSettings.cmake) to be false. To run the tests, simply use CTest, from the build directory, passing the desire configuration for which to run tests for. An example of this procedure is:

cd build          # if not in the build directory already
ctest -C Release  # or `ctest -C Debug` or any other configuration you wish to test
 
# you can also run tests with the `-VV` flag for a more verbose output (i.e.
#GoogleTest output as well)

Running the main program

after installing run the following in the project directory:

nmbluflowtorch [parallel | not ] [xor | autoencoder] [--epochs int]

Authors

Jon-Mikkel Ryen Korsvik - @jkorsvik
Jørgen Navjord - @navjordj

Inspiration Repos

https://github.com/filipdutescu/modern-cpp-template Used for bootstrapping a c++ project and for managing building, installing, structure, as well as testing.
https://github.com/krocki/MLP-CXX
https://github.com/TheFirstGuy/ann
https://github.com/leondavi/cppSANN
https://github.com/Black-Phoenix/CUDA-MLP

License

This project is licensed under the Unlicense - see the [LICENSE](LICENSE) file for details