paper.md
In YTLogos/kerasR: R Interface to the Keras Deep Learning Library

title: 'kerasR: R Interface to the Keras Deep Learning Library' tags: - neural networks - convolutional neural networks - recurrent neural networks - computer vision - natural language processing authors: - name: Taylor B Arnold orcid: 0000-0003-0576-0669 affiliation: 1 affiliations: - name: University of Richmond, Department of Mathematics and Computer Science index: 1 date: 01 June 2017 bibliography: paper.bib

Summary

Keras is a high-level neural networks API, originall written in Python, and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. This package provides an interface to Keras from within R. All of the returned objects from functions in this package are either native R objects or raw pointers to python objects, making it possible for users to access the entire keras API. The main benefits of the package are (1) correct, manual parsing of R inputs to python, (2) R-sided documentation, and (3) examples written using the API. It allows, amongst other things, users to load and run popular pre-trained models such as VGG-19 [@he2015delving], ResNet50 [@he2016deep], and Inception [@szegedy2015going].

Most functions have associated examples showing a working example of how a layer or object may be used. These are mostly toy examples, made with small datasets with little regard to whether these are the correct models for a particular task. See the package vignettes for a more thorough explaination and several larger, more practical examples.