Performance comparison of deep learning frameworks

M. Mutlu YAPICI; Nurettin TOPALOĞLU

Article Details

Performance comparison of deep learning frameworks

Authors:

M. Mutlu YAPICI | Nurettin TOPALOĞLU

Publisher:

Adem TEKEREK

Main Topics:

Special computer methods

Journal:

Computers and Informatics 2021 Vol.1 Issue 1, pp.1-11

Document Type:

Article

Publication Date:

2/2021

Document Language:

English

Document ID (Org No.):

2000911769

Frequency:

Bi-Annual

eISSN:

2757-8259

Abstract:

 

Deep learning (DL) is branch of machine learning and imitates the neural activity of brain on to artificial neural networks. Meanwhile it can be trained to define characteristics of data such as image, voice or different complex patterns. DL is capable of to find solutions for complex and NP-hard problems. In literature, there are many DL frameworks, libraries and tools to develop solutions. In this study, the most commonly used DL frameworks such as Torch, Theano, Caffe, Caffe2, MXNet, Keras, TensorFlow and Computational Network Tool Kit (CNTK) are investigated and performance comparison of the frameworks is provided. . In addition, the GPU performances have been tested for the best frameworks which have been determined according to the literature: TensorFlow, Keras (TensorFlow Backend), Theano, Keras (Theano Backend), Torch. The GPU performance comparison of these frameworks has been made by the experimental results obtained through MNIST and GPDS signature datasets. According to experimental results TensorFlow was detected best one, while other researches in the literature claimed that Pytorch is better. The contributions of in this study is to eliminate the contradiction in the literature by revealing the cause. In this way, it is aimed to assist the researchers in choosing the most appropriate DL framework for their studies.

Article Details

Performance comparison of deep learning frameworks

Authors: M. Mutlu YAPICI | Nurettin TOPALOĞLU

Publisher: Adem TEKEREK

Main Topics: Special computer methods

Journal: Computers and Informatics 2021 Vol.1 Issue 1, pp.1-11

Document Type: Article

Publication Date: 2/2021

Document Language: English

Document ID (Org No.): 2000911769

Frequency: Bi-Annual

eISSN: 2757-8259

Issues

Authors:

M. Mutlu YAPICI | Nurettin TOPALOĞLU

Publisher:

Adem TEKEREK

Main Topics:

Special computer methods

Journal:

Computers and Informatics 2021 Vol.1 Issue 1, pp.1-11

Document Type:

Article

Publication Date:

2/2021

Document Language:

English

Document ID (Org No.):

2000911769

Frequency:

Bi-Annual

eISSN:

2757-8259