Comparison of deep learning libraries on the problem of handwritten digit classification

Abstract

This paper presents a comparative analysis of several popular and freely available deep learning frameworks. We compare functionality and usability of the frameworks trying to solve popular computer vision problems like hand-written digit recognition. Four libraries have been chosen for the detailed study: Caffe, Pylearn2, Torch, and Theano. We give a brief description of these libraries, consider key features and capabilities, and provide case studies. We also investigate the performance of the libraries. This study allows making a decision which deep learning framework suites us best and will be used for our future research.