A comparison of deep learning packages for R

February 13, 2017

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

Oksana Kutina and Stefan Feuerriegel fom University of Freiburg recently published an in-depth comparison of four R packages for deep learning. The packages reviewed were:

  • MXNet: The R interface to the MXNet deep learning library. (The blog post refers to an older name for the package, MXNetR.)
  • darch: An R package for deep architectures and restricted Boltzmann machines.
  • deepnet: An R package implementing feed-forward neural networks, restricted Boltzmann machines, deep belief networks, and stacked autoencoders.
  • h2o: The R interface to the H2O deep-learning framework.

The blog post goes into detail about the capabilities of the packages, and compares them in terms of flexibility, ease-of-use, parallelization frameworks supported (GPUs, clusters) and performance — follow the link below for details. I include the conclusion from the paper here:

The current version of deepnet might represent the most differentiated package in terms of available architectures. However, due to its implementation, it might not be the fastest nor the most user-friendly option. Furthermore, it might not offer as many tuning parameters as some of the other packages. 

H2O and MXNetR, on the contrary, offer a highly user-friendly experience. Both also provide output of additional information, perform training quickly and achieve decent results. H2O might be more suited for cluster environments, where data scientists can use it for data mining and exploration within a straightforward pipeline. When flexibility and prototyping is more of a concern, then MXNetR might be the most suitable choice. It provides an intuitive symbolic tool that is used to build custom network architectures from scratch. Additionally, it is well optimized to run on a personal computer by exploiting multi CPU/GPU capabilities.

darch offers a limited but targeted functionality focusing on deep belief networks.

Information Systems Research R Blog: Deep Learning in R

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