Evidence map›Paper›PMID 36433470›Full record

ArticleSensors (Basel, Switzerland)2022

Analysis of the Application Efficiency of TensorFlow and PyTorch in Convolutional Neural Network.

Ovidiu-Constantin Novac, Mihai Cristian Chirodea, Cornelia Mihaela Novac, Nicu Bizon, Mihai Oproescu, Ovidiu Petru Stan, Cornelia Emilia Gordan

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Open-Source Machine Learning in Computational Chemistry.Journal of chemical information and modeling · 2023
    Review
  10. Transverse Deflection for Extreme Ultraviolet Pellicles.Materials (Basel, Switzerland) · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Ovidiu-Constantin NovacDepartment of Computers and Information Technology, Electrical Engineering and Information Technology Faculty, University of Oradea, 410087 Oradea, Romania.ORCID 0000-0002-5472-6611
Mihai Cristian ChirodeaDepartment of Computers and Information Technology, Electrical Engineering and Information Technology Faculty, University of Oradea, 410087 Oradea, Romania.
Cornelia Mihaela NovacDepartment of Electrical Engineering, Electrical Engineering and Information Technology Faculty, University of Oradea, 410087 Oradea, Romania.
Nicu BizonDepartment of Electronics, Computers and Electrical Engineering, Faculty of Electronics, Telecommunication, and Computer Science, University of Pitesti, 110040 Pitesti, Romania.ORCID 0000-0001-9311-7598
Mihai OproescuDepartment of Electronics, Computers and Electrical Engineering, Faculty of Electronics, Telecommunication, and Computer Science, University of Pitesti, 110040 Pitesti, Romania.ORCID 0000-0003-0324-0846
Ovidiu Petru StanDepartment of Automation, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.ORCID 0000-0002-2006-9633
Cornelia Emilia GordanDepartment of Electronics and Telecommunications, Electrical Engineering and Information Technology Faculty, University of Oradea, 410087 Oradea, Romania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Neural Networks, Computerconvolutional neural networknetwork designnetwork trainingPyTorchTensorFlow

Identifiers

PMID36433470
PMCPMC9699128

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.