Comparison of Tensorflow and PyTorch in Convolutional Neural Network - based Applications

Oproescu, Mihai; Novac, Ovidiu Constantin; Novac, Cornelia Mihaela; Bizon, Nicu; Gordan, Cornelia Emilia

doi:10.1109/ecai52376.2021.9515098

Cited by 17 publications

(3 citation statements)

References 2 publications

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“…RQ5: Our analysis showed that among the six tools used to build RNN models in the selected studies (as detailed in Table 15 and Fig 13), Pytorch and TensorFlow were the most common, being used in 55% of the studies. This prevalence can be attributed to their established recognition, open-source nature [76,77,79], and widespread adoption for neural networks [109]. Researchers have a promising opportunity to enhance code clone detection by considering alternative frameworks with high rankings for implementing DL models, such as MXNet [110] and Theano [111].…”

Section: Discussionmentioning

confidence: 99%

A systematic literature review on the applications of recurrent neural networks in code clone research

Quradaa,

Shahzad,

Almoqbily

2024

PLoS ONE

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Code clones, referring to code fragments that are either similar or identical and are copied and pasted within software systems, have negative effects on both software quality and maintenance. The objective of this work is to systematically review and analyze recurrent neural network techniques used to detect code clones to shed light on the current techniques and offer valuable knowledge to the research community. Upon applying the review protocol, we have successfully identified 20 primary studies within this field from a total of 2099 studies. A deep investigation of these studies reveals that nine recurrent neural network techniques have been utilized for code clone detection, with a notable preference for LSTM techniques. These techniques have demonstrated their efficacy in detecting both syntactic and semantic clones, often utilizing abstract syntax trees for source code representation. Moreover, we observed that most studies applied evaluation metrics like F-score, precision, and recall. Additionally, these studies frequently utilized datasets extracted from open-source systems coded in Java and C programming languages. Notably, the Graph-LSTM technique exhibited superior performance. PyTorch and TensorFlow emerged as popular tools for implementing RNN models. To advance code clone detection research, further exploration of techniques like parallel LSTM, sentence-level LSTM, and Tree-Structured GRU is imperative. In addition, more research is needed to investigate the capabilities of the recurrent neural network techniques for identifying semantic clones across different programming languages and binary codes. The development of standardized benchmarks for languages like Python, Scratch, and C#, along with cross-language comparisons, is essential. Therefore, the utilization of recurrent neural network techniques for clone identification is a promising area that demands further research.

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Section: Discussionmentioning

confidence: 99%

A systematic literature review on the applications of recurrent neural networks in code clone research

Quradaa,

Shahzad,

Almoqbily

2024

PLoS ONE

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“…The Keras library is implemented on top of TF and simplifies the learning process, albeit with slightly higher test environment accuracy but longer training times [79]. However, TF's installation process is more complex than PT's due to the lack of native GPU support [80]. PT, developed by Meta, has built-in GPU support for Apple Silicon, making it easier to install [79], and beats TF in the operating time cycles.…”

Section: Initial Setupmentioning

confidence: 99%

“…PT, developed by Meta, has built-in GPU support for Apple Silicon, making it easier to install [79], and beats TF in the operating time cycles. Despite these differences, there is no objective superiority between the libraries [80]. In the end, TF was chosen to develop a custom CNN for defect identification, with portability for use in a variety of field devices, such as a Raspberry Pi or a standard laptop.…”

Section: Initial Setupmentioning

confidence: 99%

Real-Time Optical Detection of Artificial Coating Defects in PBF-LB/P Using a Low-Cost Camera Solution and Convolutional Neural Networks

Klamert,

Achsel,

Toker

et al. 2023

Applied Sciences

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Additive manufacturing plays a decisive role in the field of industrial manufacturing in a wide range of application areas today. However, process monitoring, and especially the real-time detection of defects, is still an area where there is a lot of potential for improvement. High defect rates should be avoided in order to save costs and shorten product development times. Most of the time, effective process controls fail because of the given process parameters, such as high process temperatures in a laser-based powder bed fusion, or simply because of the very cost-intensive measuring equipment. This paper proposes a novel approach for the real-time and high-efficiency detection of coating defects on the powder bed surface during the powder bed fusion of polyamide (PBF-LB/P/PA12) by using a low-cost RGB camera system and image recognition via convolutional neural networks (CNN). The use of a CNN enables the automated detection and segmentation of objects by learning the spatial hierarchies of features from low to high-level patterns. Artificial coating defects were successfully induced in a reproducible and sustainable way via an experimental mechanical setup mounted on the coating blade, allowing the in-process simulation of particle drag, part shifting, and powder contamination. The intensity of the defect could be continuously varied using stepper motors. A low-cost camera was used to record several build processes with different part geometries. Installing the camera inside the machine allows the entire powder bed to be captured without distortion at the best possible angle for evaluation using CNN. After several training and tuning iterations of the custom CNN architecture, the accuracy, precision, and recall consistently reached >99%. Even defects that resembled the geometry of components were correctly classified. Subsequent gradient-weighted class activation mapping (Grad-CAM) analysis confirmed the classification results.

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Automated Road Surveillance System Using Machine Learning

Jacob

et al. 2023

Lecture Notes in Electrical Engineering

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Comparison of Tensorflow and PyTorch in Convolutional Neural Network - based Applications

Cited by 17 publications

References 2 publications

A systematic literature review on the applications of recurrent neural networks in code clone research

A systematic literature review on the applications of recurrent neural networks in code clone research

Real-Time Optical Detection of Artificial Coating Defects in PBF-LB/P Using a Low-Cost Camera Solution and Convolutional Neural Networks

Automated Road Surveillance System Using Machine Learning

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