Machine Intelligence Core: Neural Nets

Description

A subproject of Machine Intelligence Core framework.

The repository contains solutions and applications related to multi-layer (deep) feed-forward (for now) neural nets.

MIC dependencies

• MIToolchain - the core of MIC framework.

External dependencies

Additionally it depends on the following external libraries:

• Boost - library of free (open source) peer-reviewed portable C++ source libraries.
• Eigen - a C++ template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms.

Installation of the dependencies/required tools

On Linux (Ubuntu 14.04):

sudo apt-get install git cmake cmake-curses-gui doxygen libboost1.54-all-dev libeigen3-dev

Applications

• mnist_patch_autoencoder_reconstruction -- application realizing MNIST patch autoencoder-based reconstruction
• mnist_patch_autoencoder_softmax -- application realizing MNIST patch autoencoder-based softmax classifier, using the imported, previously trained auto-encoder
• mlnn_sample_training_test -- (test) application for testing of training of a multi-layer neural network
• mlnn_batch_training_test -- (test) application for ttesting batch training of a multi-layer neural network
• mnist_convnet -- (test) application using Convolutional Neural Network for recognition of MNIST digits
• mnist_simple_mlnn_app -- (test) application using a simple multi-Layer neural net for recognition of MNIST digits
• mnist_batch_visualization_test -- the MNIST batch visualization test application
• mnist_mlnn_features_visualization_test -- program for visualization of features of mlnn layer trained on MNIST digits

Unit tests

• loss/lossTestsRunner -- loss functions unit tests
• optimization/artificialLandscapesTestsRunner -- artificial landscapes used for optimization testing unit tests
• optimization/optimizationFunctionsTestsRunner -- unit tests of different optimization functions/methods
• mlnn/mlnnTestsRunner -- unit tests for multi-layer neural network
• mlnn/cost_function/softmaxTestsRunner -- unit tests of the softmax layer
• mlnn/fully_connected/linearTestsRunner -- unit tests for linear (fully-connected) layer

Installation

In order to download, configure, make and install new "clean" version of mi-neural-nets please execute the following:

cd ~/workspace
git clone [email protected]:tkornut/mi-neural-nets.git
cd mi-algorithms
mkdir build
cd build
cmake .. -DCMAKE_INSTALL_PREFIX=~/workspace/mic/
make -j4 install

Documentation

In order to generate a "living" documentation of the code please run Doxygen:

cd ~/workspace/mi-neural-nets
doxygen mi-neural-nets.doxyfile
firefox html/index.html

The current documentation (generated straight from the code and automatically uploaded to github pages by Travis) is available at:

https://ibm.github.io/mi-neural-nets/

Maintainer

tkornuta