Demystifying Deep Learning Frameworks- A Comparative Analysis


Divyanshu Sinha,JP Pandey,Bhavesh Chauhan,



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


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