TensorFlow Hub

TensorFlow Hub
DeveloperGoogle
Initial releaseMarch 6, 2018 (2018-03-06)
Stable release
0.16.1[1] / January 30, 2024
Written inPython
Operating systemcross-platform
PlatformTensorFlow
TypeMachine learning, Artificial intelligence
LicenseApache-2.0
Websitetensorflow.org/hub Edit this on Wikidata
Repository

TensorFlow Hub (also styled TF Hub) is an open-source machine learning library and online repository that provides TensorFlow model components, called modules.[2]

It is maintained by Google as part of the TensorFlow ecosystem and allows developers to discover, publish, and reuse pretrained models for tasks such as computer vision, natural language processing, and transfer learning.[3]

Overview

TensorFlow Hub provides a central platform where developers and researchers can access pre-trained models and integrate them directly into[weasel words] TensorFlow workflows.[4] Each module encapsulates a computation graph and its trained weights, with standardized input and output signatures. Modules can be loaded using the hub.load() function or through Keras integration via hub.KerasLayer, enabling users to perform transfer learning or feature extraction.[4]

History

TensorFlow Hub was announced by Google in March 2018, with the first public version released shortly after. Its introduction coincided with the growing adoption[vague] of transfer learning techniques and the need for standardized model packaging.[according to whom?] Over time, the hub expanded to include models such as the BERT family, MobileNet, EfficientNet, and the Universal Sentence Encoder.[5]

In 2020, research on “Regret selection in TensorFlow Hub” explored the problem of identifying optimal models for downstream tasks given a large repository of alternatives.[citation needed]

Applications

TensorFlow Hub hosts a variety of models across machine learning domains:[citation needed]

Modules are widely used[by whom?] in education, academic research, and industry for prototyping and production deployment.[6]

References

  1. ^ "Release 0.16.1". January 30, 2024. Retrieved February 20, 2024.
  2. ^ Goh HA, et al. (2022). “Front-end deep learning web apps development and …” Publications / PMC (US National Library of Medicine). Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9709375/
  3. ^ "Introducing TensorFlow Hub: a library for reusable machine learning modules". TensorFlow Blog. Google. March 6, 2018. Retrieved October 13, 2025.
  4. ^ a b "TensorFlow Hub: overview", TensorFlow, retrieved from https://www.tensorflow.org/hub/overview
  5. ^ Daniel Cer (2018). "Universal Sentence Encoder". arXiv:1803.11175 [cs.CL].
  6. ^ Xiu, Minke; Eghan, Ellis E.; Ming; Jiang, Zhen Ming (Jack); Adams, Bram (2020). "Empirical Study on the Software Engineering Practices in Open Source ML Package Repositories". arXiv:2012.01403 [cs.AI].

Content Disclaimer

Informasi ini disarikan dari Wikipedia dan disajikan kembali untuk tujuan edukasi. Konten tersedia di bawah lisensi CC BY-SA 3.0. Kami tidak bertanggung jawab atas ketidakakuratan data yang bersumber dari kontribusi publik tersebut.

  1. The information displayed on this website is sourced in part or in whole from Wikipedia and has been adapted for the purpose of restating it. We strive to provide accurate and relevant information, however:
  2. There is no guarantee of absolute accuracy. Wikipedia is an open, collaborative project that can be edited by anyone, so information is subject to change.
  3. It is not intended to constitute professional advice. The content displayed is for informational and educational purposes only. For important decisions (e.g., medical, legal, or financial), please consult a professional.
  4. Content copyright. Wikipedia is licensed under the Creative Commons Attribution-ShareAlike License (CC BY-SA). This means that content may be reused with appropriate attribution and shared under a similar license.
  5. Responsible use. Any risk arising from the use of information from this website is entirely the responsibility of the user.