This is a C library which can be used in deep learning applications. It allows multiple layers to be trained and also includes the dropouts technique to avoid overfitting the data.

A Python API for libdeep can be found at https://github.com/bashrc/libdeep-python

Installation

On Debian based systems:

sudo apt-get install build-essential gnuplot doxygen

On Arch based systems:

sudo pacman -S gcc gnuplot doxygen

On Fedora based systems:

sudo yum groupinstall "Development Tools"
sudo yum install gnuplot doxygen

To build from source:

make
sudo make install

This creates the library and installs it into /usr/local

Unit Tests

You can run the unit tests to check that the system is working as expected:

cd unittests
make
./tests

Or to check for any memory leaks:

valgrind --leak-check=full ./tests

Source Documentation

To generate source code documentation make sure that you have doxygen installed and then run the generatedocs.sh script. A subdirectory called docs will be created within which html and latex formated documentation can be found. For general usage information you can also see the manpage.

man libdeep

Examples

There are also some example programs within the examples directory. Reading the examples is the best way to learn how to use this library within your own code. Examples are:

• Simple face recognition
• Determining whether a cancer is malignant or benign
• Assessing wine quality from ingredients
• Predicting concrete quality from ingredients

Using trained neural nets in your system

You can export trained neural nets either as a C program or a Python program. These programs are completely independent and can be used either as commands or integrated into a larger software application. This makes it easy to use the resulting neural net without needing to link to libdeep. See the source code in the examples directory for how to use the export function.

Portability

Although this software was primarily written to run on Linux-based systems it's pretty much just vanilla C code and so it should be easily portable to other platforms, such as Microsoft Windows and Mac systems. The independent random number generator should mean that results are consistent across different compilers and platforms.

Packaging

To build packages for Debian (deb) see https://github.com/bashrc/libdeep-debian