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Analysis of the Application Efficiency of TensorFlow and PyTorch in Convolutional Neural Network
In this paper, we present an analysis of important aspects that arise during the development of neural network applications. Our aim is to determine if the choice of library can impact the system’s overall performance, either during training or design, and to extract a set of criteria that could be...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9699128/ https://www.ncbi.nlm.nih.gov/pubmed/36433470 http://dx.doi.org/10.3390/s22228872 |
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author | Novac, Ovidiu-Constantin Chirodea, Mihai Cristian Novac, Cornelia Mihaela Bizon, Nicu Oproescu, Mihai Stan, Ovidiu Petru Gordan, Cornelia Emilia |
author_facet | Novac, Ovidiu-Constantin Chirodea, Mihai Cristian Novac, Cornelia Mihaela Bizon, Nicu Oproescu, Mihai Stan, Ovidiu Petru Gordan, Cornelia Emilia |
author_sort | Novac, Ovidiu-Constantin |
collection | PubMed |
description | In this paper, we present an analysis of important aspects that arise during the development of neural network applications. Our aim is to determine if the choice of library can impact the system’s overall performance, either during training or design, and to extract a set of criteria that could be used to highlight the advantages and disadvantages of each library under consideration. To do so, we first extracted the previously mentioned aspects by comparing two of the most popular neural network libraries—PyTorch and TensorFlow—and then we performed an analysis on the obtained results, with the intent of determining if our initial hypothesis was correct. In the end, the results of the analysis are gathered, and an overall picture of what tasks are better suited for what library is presented. |
format | Online Article Text |
id | pubmed-9699128 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96991282022-11-26 Analysis of the Application Efficiency of TensorFlow and PyTorch in Convolutional Neural Network Novac, Ovidiu-Constantin Chirodea, Mihai Cristian Novac, Cornelia Mihaela Bizon, Nicu Oproescu, Mihai Stan, Ovidiu Petru Gordan, Cornelia Emilia Sensors (Basel) Article In this paper, we present an analysis of important aspects that arise during the development of neural network applications. Our aim is to determine if the choice of library can impact the system’s overall performance, either during training or design, and to extract a set of criteria that could be used to highlight the advantages and disadvantages of each library under consideration. To do so, we first extracted the previously mentioned aspects by comparing two of the most popular neural network libraries—PyTorch and TensorFlow—and then we performed an analysis on the obtained results, with the intent of determining if our initial hypothesis was correct. In the end, the results of the analysis are gathered, and an overall picture of what tasks are better suited for what library is presented. MDPI 2022-11-16 /pmc/articles/PMC9699128/ /pubmed/36433470 http://dx.doi.org/10.3390/s22228872 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Novac, Ovidiu-Constantin Chirodea, Mihai Cristian Novac, Cornelia Mihaela Bizon, Nicu Oproescu, Mihai Stan, Ovidiu Petru Gordan, Cornelia Emilia Analysis of the Application Efficiency of TensorFlow and PyTorch in Convolutional Neural Network |
title | Analysis of the Application Efficiency of TensorFlow and PyTorch in Convolutional Neural Network |
title_full | Analysis of the Application Efficiency of TensorFlow and PyTorch in Convolutional Neural Network |
title_fullStr | Analysis of the Application Efficiency of TensorFlow and PyTorch in Convolutional Neural Network |
title_full_unstemmed | Analysis of the Application Efficiency of TensorFlow and PyTorch in Convolutional Neural Network |
title_short | Analysis of the Application Efficiency of TensorFlow and PyTorch in Convolutional Neural Network |
title_sort | analysis of the application efficiency of tensorflow and pytorch in convolutional neural network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9699128/ https://www.ncbi.nlm.nih.gov/pubmed/36433470 http://dx.doi.org/10.3390/s22228872 |
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