Demystifying Deep Learning Frameworks- A Comparative Analysis

Journal > Journal > Journal Vol – 14 No -1, February 2019 > Demystifying Deep Learning Frameworks- A Comparative Analysis

February 23, 2019

Journal Vol – 14 No -1, February 2019

Authors:

Divyanshu Sinha,JP Pandey,Bhavesh Chauhan,

DOI NO:

https://doi.org/10.26782/jmcms.2019.02.00010

Keywords:

Deep Learning, Feedforward MLP,Keras,Tensorflow,Theano,Caffe,Deeplearning4j,Torch,

Abstract

Deep learning is a rapidly growing field of machine learning which finds the application of its methods to provide solutions to numerous problems related to computer vision, speech recognition, natural language processing, and others. This paper gives a comparative analysis of the five deep learning tools on the grounds of training time and accuracy. Evaluation includes classifying digits from the MNIST data set making use of a fully connected neural network architecture (FCNN). Here we have selected five frameworks— Torch ,Deeplearning4j, TensorFlow, Caffe & Theano (with Keras), to evaluate their performance and accuracy. In order to enhance the comparison of the frameworks, the standard MNIST data set of handwritten digits was chosen for the classification task. When working with the data set, our goal was to identify the digits (0–9) using a fully connected neural network architecture. All computations were executed on a GPU. The key metrics addressed were training speed, classification speed, and accuracy.

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Divyanshu Sinha, JP Pandey, Bhavesh Chauhan View Download