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Deep Learning Library (DLL) 1.1

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DLL is a library that aims to provide a C++ implementation of Restricted Boltzmann Machine (RBM) and Deep Belief Network (DBN) and their convolution versions as well. It also has support for some more standard neural networks.

Features

  • Restricted Boltzmann Machine
    • Various units: Stochastic binary, Gaussian, Softmax and nRLU units
    • Contrastive Divergence and Persistence Contrastive Divergence
      • CD-1 learning by default
    • Momentum
    • Weight decay
    • Sparsity target
    • Train as Denoising autoencoder
  • Convolutional Restricted Boltzmann Machine
    • Standard version
    • Version with Probabilistic Max Pooling (Honglak Lee)
    • Binary and Gaussian visible units
    • Binary and ReLU hidden units for the standard version
    • Binary hidden units for the Probabilistic Max Pooling version
    • Training with CD-k or PCD-k (only for standard version)
    • Momentum, Weight Decay, Sparsity Target
    • Train as Denoising autoencoder
  • Deep Belief Network
    • Pretraining with RBMs
    • Fine tuning with Conjugate Gradient
    • Fine tuning with Stochastic Gradient Descent
    • Classification with SVM (libsvm)
  • Convolutional Deep Belief Network
    • Pretraining with CRBMs
    • Classification with SVM (libsvm)
  • Input data
    • Input data can be either in containers or in iterators
      • Even if iterators are supported for SVM classifier, libsvm will move all the data in memory structure.

Building

Note: When you clone the library, you need to clone the sub modules as well, using the --recursive option.

The folder include must be included with the -I option, as well as the etl/include folder.

This library is completely header-only, there is no need to build it.

However, this library makes extensive use of C++11 and C++14, therefore, a recent compiler is necessary to use it. This library has been tested on clang 3.9, clang 4.0, g++ 6.3.0 and g++ 7.1. If for some reasons, it should not work on one of the supported compilers, contact me and I'll fix it.

This has never been tested on Windows. While it should compile on Mingw, I don't expect Visual Studio to be able to compile it for now, although VS 2017 sounds promising. If you have problems compiling this library, I'd be glad to help, but cannot guarantee that this will work on other compilers.

If you want to use GPU, you should use CUDA 8.0 or superior and CUDNN 5.0.1 or superior. I haven't tried other versions, but lower versions of CUDA, such as 7, should work, and higher versions as well. If you got issues with different versions of CUDA and CUDNN, please open an issue on Github.

License

This library is distributed under the terms of the MIT license, see LICENSE file for details.