A subproject of Machine Intelligence Core framework.
The repository contains solutions and applications related to (deep) reinforcement learning. In particular, it contains several classical problems (N-armed bandits, several variations of Gridworld), POMDP environments (Gridworld, Maze of Digits, MNIST digit) and algorithms (from simple Value Iteartion and Q-learning to DQN with Experience Replay).
- 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 testing 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
- 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
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.
- OpenGL/GLUT - a cross-language, cross-platform application programming interface for rendering 2D and 3D vector graphics.
- OpenBlas (optional) - An optimized library implementing BLAS routines. If present - used for fastening operation on matrices.
- Doxygen (optional) - Tool for generation of documentation.
- GTest (optional) - Framework for unit testing.
sudo apt-get install git cmake doxygen libboost1.54-all-dev libeigen3-dev freeglut3-dev libxmu-dev libxi-dev
To install GTest on Ubuntu:
sudo apt-get install libgtest-dev
brew install git cmake doxygen boost eigen glfw3
To install GTest on Mac OS X:
brew install --HEAD https://gist.githubusercontent.com/Kronuz/96ac10fbd8472eb1e7566d740c4034f8/raw/gtest.rb
- MI-Toolchain - the core of MIC framework.
- MI-Algorithms - contains basic (core) types and algorithms.
- MI-Visualization - contains OpenGL-based visualization.
This step is required only when not downloaded/installed the listed MIC dependencies earlier.
In directory scripts one can find script that will download and install all required MIC modules.
git clone git@github.com:IBM/mi-neural-nets.git
cd mi-neural-nets
./scripts/install_mic_deps.sh ../mic
Then one can install the module by calling the following.
./scripts/build_mic_module.sh ../mic
Please note that it will create a directory 'deps' and download all sources into that directory. After compilation all dependencies will be installed in the directory '../mic'.
The following assumes that all MIC dependencies are installed in the directory '../mic'.
git clone git@github.com:IBM/mi-neural-nets.git
cd mi-neural-nets
./scripts/build_mic_module.sh ../mic
- make install - install applications to ../mic/bin, headers to ../mic/include, libraries to ../mic/lib, cmake files to ../mic/share
- make configs - install config files to ../mic/bin
- make datasets - install config files to ../mic/datasets
In order to generate a "living" documentation of the code please run Doxygen:
cd 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: