https://github.com/giovannisorice/mlproject

Project for the Machine Learning course
https://github.com/giovannisorice/mlproject

armadillo backpropagation conan feedforward-neural-network machine-learning

Last synced: 2 months ago
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Project for the Machine Learning course

• Host: GitHub
• URL: https://github.com/giovannisorice/mlproject
• Owner: GiovanniSorice
• Created: 2019-11-26T14:13:20.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2020-04-14T15:14:38.000Z (over 4 years ago)
• Last Synced: 2024-10-09T19:23:06.215Z (2 months ago)
• Topics: armadillo, backpropagation, conan, feedforward-neural-network, machine-learning
• Language: C++
• Homepage:
• Size: 4.89 MB
• Stars: 7
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        




# Neuradillo

For the Machine Learning [course](https://esami.unipi.it/esami2/programma.php?c=42276&aa=2019&docente=MICHELI&insegnamento=&sd=0), we developed a library to build and train feedforward neural networks in C++ exploiting [Armadillo](http://arma.sourceforge.net/) numerical library.

The main implemented techniques are gradient descent with backpropagation, k-fold cross-validation and grid-search (parallel and sequential). Multiple activation functions are available (sigmoid, tanh, relu and linear).

We tested our library on [Monks datasets](https://archive.ics.uci.edu/ml/datasets/MONK%27s+Problems) and on a specific dataset given by the professor. All the tests were made with different models using one to five hidden layers.

More information about the project and the algorithms we implemented can be found in the [report](https://github.com/GiovanniSorice/MLProject/blob/master/docs/report/relazione.pdf). 

## Getting started

### Prerequisites 

The project use [Cmake 3.16](https://cmake.org/) as building system and it can be downloaded [here](https://cmake.org/download/). 

The package manager used is [Conan](https://conan.io/). You can install it with the following commands:

` pip install conan` 

Running the following command to solve this [issue](https://docs.conan.io/en/latest/installation.html#install-with-pip-recommended): 

`source ~/.profile`  

### Armadillo installation 

1. Clone the following repo: https://github.com/darcamo/conan-armadillo;

2. Inside the cloned repo run: `conan create . armadillo/stable`

3. If Armadillo is installed correctly an example program is execute and you can start use it [through Conan](https://docs.conan.io/en/latest/using_packages/conanfile_txt.html#requires).

## Running the project

If CMake and Armadillo were installed correctly you have to create a directory where cmake store the configuration files needed to run the project:

`mkdir build && cd build `

Inside the build folder to generate the files that are needed by Conan type: 

 `conan install ..`

Then the for the CMake files give: 

 `cmake ..` 

In the end to build the project type: 

 `cmake --build .` 

If all the process is done correctly a */bin* folder is created with a binary file inside. This file can be executed with:

`./MLProject`

The error of the training set and validation set is print during the execution.

## Results

Here we show a learning curve plot we obtained during the training phase. 

 



## Future works

- Serialization of NN: https://www.boost.org/doc/libs/1_72_0/libs/serialization 

- Plot with matplotlibcpp: https://github.com/lava/matplotlib-cpp 

## Authors

* **Giovanni Sorice**  :computer: - [Giovanni Sorice](https://github.com/GiovanniSorice)

* **Francesco Corti** :nerd_face: :computer: - [FraCorti](https://github.com/FraCorti)