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...
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Autor*in:	Ovidiu-Constantin Novac [verfasserIn] Mihai Cristian Chirodea [verfasserIn] Cornelia Mihaela Novac [verfasserIn] Nicu Bizon [verfasserIn] Mihai Oproescu [verfasserIn] Ovidiu Petru Stan [verfasserIn] Cornelia Emilia Gordan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	convolutional neural network TensorFlow PyTorch network training network design

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 22(2022), 22, p 8872
Übergeordnetes Werk:	volume:22 ; year:2022 ; number:22, p 8872

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s22228872

Katalog-ID:	DOAJ025761900

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spelling	10.3390/s22228872 doi (DE-627)DOAJ025761900 (DE-599)DOAJ7814f1b1bb2b4ba6b9e0b3715acf1506 DE-627 ger DE-627 rakwb eng TP1-1185 Ovidiu-Constantin Novac verfasserin aut Analysis of the Application Efficiency of TensorFlow and PyTorch in Convolutional Neural Network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. convolutional neural network TensorFlow PyTorch network training network design Chemical technology Mihai Cristian Chirodea verfasserin aut Cornelia Mihaela Novac verfasserin aut Nicu Bizon verfasserin aut Mihai Oproescu verfasserin aut Ovidiu Petru Stan verfasserin aut Cornelia Emilia Gordan verfasserin aut In Sensors MDPI AG, 2003 22(2022), 22, p 8872 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:22, p 8872 https://doi.org/10.3390/s22228872 kostenfrei https://doaj.org/article/7814f1b1bb2b4ba6b9e0b3715acf1506 kostenfrei https://www.mdpi.com/1424-8220/22/22/8872 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 22, p 8872
allfields_unstemmed	10.3390/s22228872 doi (DE-627)DOAJ025761900 (DE-599)DOAJ7814f1b1bb2b4ba6b9e0b3715acf1506 DE-627 ger DE-627 rakwb eng TP1-1185 Ovidiu-Constantin Novac verfasserin aut Analysis of the Application Efficiency of TensorFlow and PyTorch in Convolutional Neural Network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. convolutional neural network TensorFlow PyTorch network training network design Chemical technology Mihai Cristian Chirodea verfasserin aut Cornelia Mihaela Novac verfasserin aut Nicu Bizon verfasserin aut Mihai Oproescu verfasserin aut Ovidiu Petru Stan verfasserin aut Cornelia Emilia Gordan verfasserin aut In Sensors MDPI AG, 2003 22(2022), 22, p 8872 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:22, p 8872 https://doi.org/10.3390/s22228872 kostenfrei https://doaj.org/article/7814f1b1bb2b4ba6b9e0b3715acf1506 kostenfrei https://www.mdpi.com/1424-8220/22/22/8872 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 22, p 8872
allfieldsGer	10.3390/s22228872 doi (DE-627)DOAJ025761900 (DE-599)DOAJ7814f1b1bb2b4ba6b9e0b3715acf1506 DE-627 ger DE-627 rakwb eng TP1-1185 Ovidiu-Constantin Novac verfasserin aut Analysis of the Application Efficiency of TensorFlow and PyTorch in Convolutional Neural Network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. convolutional neural network TensorFlow PyTorch network training network design Chemical technology Mihai Cristian Chirodea verfasserin aut Cornelia Mihaela Novac verfasserin aut Nicu Bizon verfasserin aut Mihai Oproescu verfasserin aut Ovidiu Petru Stan verfasserin aut Cornelia Emilia Gordan verfasserin aut In Sensors MDPI AG, 2003 22(2022), 22, p 8872 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:22, p 8872 https://doi.org/10.3390/s22228872 kostenfrei https://doaj.org/article/7814f1b1bb2b4ba6b9e0b3715acf1506 kostenfrei https://www.mdpi.com/1424-8220/22/22/8872 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 22, p 8872
allfieldsSound	10.3390/s22228872 doi (DE-627)DOAJ025761900 (DE-599)DOAJ7814f1b1bb2b4ba6b9e0b3715acf1506 DE-627 ger DE-627 rakwb eng TP1-1185 Ovidiu-Constantin Novac verfasserin aut Analysis of the Application Efficiency of TensorFlow and PyTorch in Convolutional Neural Network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. convolutional neural network TensorFlow PyTorch network training network design Chemical technology Mihai Cristian Chirodea verfasserin aut Cornelia Mihaela Novac verfasserin aut Nicu Bizon verfasserin aut Mihai Oproescu verfasserin aut Ovidiu Petru Stan verfasserin aut Cornelia Emilia Gordan verfasserin aut In Sensors MDPI AG, 2003 22(2022), 22, p 8872 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:22, p 8872 https://doi.org/10.3390/s22228872 kostenfrei https://doaj.org/article/7814f1b1bb2b4ba6b9e0b3715acf1506 kostenfrei https://www.mdpi.com/1424-8220/22/22/8872 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 22, p 8872
language	English
source	In Sensors 22(2022), 22, p 8872 volume:22 year:2022 number:22, p 8872
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authorswithroles_txt_mv	Ovidiu-Constantin Novac @@aut@@ Mihai Cristian Chirodea @@aut@@ Cornelia Mihaela Novac @@aut@@ Nicu Bizon @@aut@@ Mihai Oproescu @@aut@@ Ovidiu Petru Stan @@aut@@ Cornelia Emilia Gordan @@aut@@
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callnumber-first	T - Technology
author	Ovidiu-Constantin Novac
spellingShingle	Ovidiu-Constantin Novac misc TP1-1185 misc convolutional neural network misc TensorFlow misc PyTorch misc network training misc network design misc Chemical technology Analysis of the Application Efficiency of TensorFlow and PyTorch in Convolutional Neural Network
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topic_title	TP1-1185 Analysis of the Application Efficiency of TensorFlow and PyTorch in Convolutional Neural Network convolutional neural network TensorFlow PyTorch network training network design
topic	misc TP1-1185 misc convolutional neural network misc TensorFlow misc PyTorch misc network training misc network design misc Chemical technology
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title	Analysis of the Application Efficiency of TensorFlow and PyTorch in Convolutional Neural Network
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title_auth	Analysis of the Application Efficiency of TensorFlow and PyTorch in Convolutional Neural Network
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Analysis of the Application Efficiency of TensorFlow and PyTorch in Convolutional Neural Network
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