coppolachan / pydeep

PyDeep is a machine learning / deep learning library with focus on unsupervised learning. The library has a modular design, is well documented and purely written in Python/Numpy. This allows you to understand, use, modify, and debug the code easily. Furthermore, its extensive use of unittests assures a high level of reliability and correctness.

Documentation: http://pydeep.readthedocs.io/en/latest/index.html

Welcome

PyDeep is a machine learning / deep learning library with focus on unsupervised learning. The library has a modular design, is well documented and purely written in Python/Numpy. This allows you to understand, use, modify, and debug the code easily. Furthermore, its extensive use of unittests assures a high level of reliability and correctness.

News

• The documentation is updated to restructured text
• Documentation hosted
• Next the unit tests will be added
• Upcoming: Tutorials will be added
• Upcoming: Auto encoders will be added
• Upcoming: MDP integration will be added
• Upcoming: Deep Boltzmann machines will be added
• Upcoming: Feed Forward neural networks will be added

Features

    - Principal Component Analysis (PCA)

    - Zero Phase Component Analysis (ZCA)

    - Independent Component Analysis (ICA)

    - centered BinaryBinary RBM (BB-RBM)

    - centered GaussianBinary RBM (GB-RBM) with fixed variance

    - centered GaussianBinaryVariance RBM (GB-RBM) with trainable variance

    - centered BinaryBinaryLabel RBM (BBL-RBM)

    - centered GaussianBinaryLabel RBM (GBL-RBM)

    - centered BinaryRect RBM (BR-RBM)

    - centered RectBinary RBM (RB-RBM)

    - centered RectRect RBM (RR-RBM)

    - centered GaussianRect RBM (GR-RBM)

    - centered GaussianRectVariance RBM (GRV-RBM)

    - Gibbs Sampling

    - Persistent Gibbs Sampling

    - Parallel Tempering Sampling

    - Independent Parallel Tempering Sampling

    - Annealed Importance Sampling (AIS)

    - reverse Annealed Importance Sampling (AIS)

    - Contrastive Divergence (CD)

    - Persistent Contrastive Divergence (PCD)

    - Tempering Sampling Contrastive Divergence (PT)

    - Independent Tempering Sampling Contrastive Divergence (IPT)

    - Exact Gradient (GD)

Scientific use

The library contains code I have written during my PhD research allowing you to reproduce the results described in the following publications.

• Gaussian-binary restricted Boltzmann machines for modeling natural image statistics. Melchior, J., Wang, N., & Wiskott, L.. (2017). PLOS ONE, 12(2), 1–24. <http://doi.org/10.1371/journal.pone.0171015>_

• How to Center Deep Boltzmann Machines. Melchior, J., Fischer, A., & Wiskott, L.. (2016). Journal of Machine Learning Research, 17(99), 1–61. <http://jmlr.org/papers/v17/14-237.html>_

• Gaussian-binary Restricted Boltzmann Machines on Modeling Natural Image statistics Wang, N., Melchior, J., & Wiskott, L.. (2014). (Vol. 1401.5900). arXiv.org e-Print archive. <http://arxiv.org/abs/1401.5900>_

• How to Center Binary Restricted Boltzmann Machines (Vol. 1311.1354). Melchior, J., Fischer, A., Wang, N., & Wiskott, L.. (2013). arXiv.org e-Print archive. <http://arxiv.org/pdf/1311.1354.pdf>_

• An Analysis of Gaussian-Binary Restricted Boltzmann Machines for Natural Images. Wang, N., Melchior, J., & Wiskott, L.. (2012). In Proc. 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 25–27, Bruges, Belgium (pp. 287–292). <https://www.ini.rub.de/PEOPLE/wiskott/Reprints/WangMelchiorEtAl-2012a-ProcESANN-RBMImages.pdf>_

• Learning Natural Image Statistics with Gaussian-Binary Restricted Boltzmann Machines. Melchior, J, 29.05.2012. Master’s thesis, Applied Computer Science, Univ. of Bochum, Germany. <https://www.ini.rub.de/PEOPLE/wiskott/Reprints/Melchior-2012-MasterThesis-RBMs.pdf>_

IF you want to use PyDeep in your publication, you can cite it as follows.

.. code-block:: latex

@misc{melchior2017pydeep, title={PyDeep}, author={Melchior, Jan}, year={2017}, publisher={GitHub}, howpublished={\url{https://github.com/MelJan/PyDeep.git}}, }

Contact:

Jan Melchior <https://www.ini.rub.de/the_institute/people/jan-melchior/>_