research-article

TensorLayer: A Versatile Library for Efficient Deep Learning Development

Authors:

Akara Supratak,

Axel Oehmichen,

Yike GuoAuthors Info & Claims

MM '17: Proceedings of the 25th ACM international conference on Multimedia

Pages 1201 - 1204

https://doi.org/10.1145/3123266.3129391

Published: 19 October 2017 Publication History

Abstract

Recently we have observed emerging uses of deep learning techniques in multimedia systems. Developing a practical deep learning system is arduous and complex. It involves labor-intensive tasks for constructing sophisticated neural networks, coordinating multiple network models, and managing a large amount of training-related data. To facilitate such a development process, we propose TensorLayer which is a Python-based versatile deep learning library. TensorLayer provides high-level modules that abstract sophisticated operations towards neuron layers, network models, training data and dependent training jobs. In spite of offering simplicity, it has transparent module interfaces that allows developers to flexibly embed low-level controls within a backend engine, with the aim of supporting fine-grain tuning towards training. Real-world cluster experiment results show that TensorLayeris able to achieve competitive performance and scalability in critical deep learning tasks. TensorLayer was released in September 2016 on GitHub. Since after, it soon become one of the most popular open-sourced deep learning library used by researchers and practitioners.

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Karwowska KWierzbicki D(2024)Modified ESRGAN with Uformer for Video Satellite Imagery Super-ResolutionRemote Sensing10.3390/rs1611192616:11(1926)Online publication date: 27-May-2024
https://doi.org/10.3390/rs16111926
Jiang JYang JZhang YWang ZYou HChen J(2024)A Post-training Framework for Improving the Performance of Deep Learning Models via Model TransformationACM Transactions on Software Engineering and Methodology10.1145/363001133:3(1-41)Online publication date: 15-Mar-2024
https://dl.acm.org/doi/10.1145/3630011
Calderón-Cordova CSarango RCastillo DLakshminarayanan V(2024)A Deep Reinforcement Learning Framework for Control of Robotic Manipulators in Simulated EnvironmentsIEEE Access10.1109/ACCESS.2024.343274112(103133-103161)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3432741
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Index Terms

TensorLayer: A Versatile Library for Efficient Deep Learning Development
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language features
        Frameworks

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Published In

cover image ACM Conferences

MM '17: Proceedings of the 25th ACM international conference on Multimedia

October 2017

2028 pages

ISBN:9781450349062

DOI:10.1145/3123266

General Chairs:
Qiong Liu
FXPAL, USA
,
Rainer Lienhart
Universität Augsburg, Germany
,
Haohong Wang
TCL America, USA
,
Program Chairs:
Sheng-Wei "Kuan-Ta" Chen
Academia Sinica, Taiwan
,
Susanne Boll
University of Oldenburg, Germany
,
Phoebe Chen
La Trobe University, Australia
,
Gerald Friedland
Lawrence Livermore National Lab, USA
,
Jia Li
Google, USA
,
Shuicheng Yan
Qihoo 360, China

Copyright © 2017 ACM.

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Published: 19 October 2017

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MM '17: ACM Multimedia Conference

October 23 - 27, 2017

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MM '17 Paper Acceptance Rate 189 of 684 submissions, 28%;

Overall Acceptance Rate 995 of 4,171 submissions, 24%

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Cited By

Karwowska KWierzbicki D(2024)Modified ESRGAN with Uformer for Video Satellite Imagery Super-ResolutionRemote Sensing10.3390/rs1611192616:11(1926)Online publication date: 27-May-2024
https://doi.org/10.3390/rs16111926
Jiang JYang JZhang YWang ZYou HChen J(2024)A Post-training Framework for Improving the Performance of Deep Learning Models via Model TransformationACM Transactions on Software Engineering and Methodology10.1145/363001133:3(1-41)Online publication date: 15-Mar-2024
https://dl.acm.org/doi/10.1145/3630011
Calderón-Cordova CSarango RCastillo DLakshminarayanan V(2024)A Deep Reinforcement Learning Framework for Control of Robotic Manipulators in Simulated EnvironmentsIEEE Access10.1109/ACCESS.2024.343274112(103133-103161)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3432741
Chakraborty TReddy K S UNaik SPanja MManvitha B(2024)Ten years of generative adversarial nets (GANs): a survey of the state-of-the-artMachine Learning: Science and Technology10.1088/2632-2153/ad1f775:1(011001)Online publication date: 29-Jan-2024
https://doi.org/10.1088/2632-2153/ad1f77
Ko YHamed Mozaffari MLi Y(2024)Fire and Smoke Image RecognitionIntelligent Building Fire Safety and Smart Firefighting10.1007/978-3-031-48161-1_13(305-333)Online publication date: 26-Jan-2024
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Kang SGuo HBo ZZhang LDong QXue YWu Y(2023)SAB: Stacking Action Blocks for Efficiently Generating Diverse Multimodal Critical Driving Scenario2023 30th Asia-Pacific Software Engineering Conference (APSEC)10.1109/APSEC60848.2023.00031(211-220)Online publication date: 4-Dec-2023
https://doi.org/10.1109/APSEC60848.2023.00031
Kumar KRajendran A(2023)Deep Convolutional Neural Network for Brain Tumor SegmentationJournal of Electrical Engineering & Technology10.1007/s42835-023-01479-y18:5(3925-3932)Online publication date: 29-Mar-2023
https://doi.org/10.1007/s42835-023-01479-y
Zou CCui XChen GJiang YWang Y(2023)Toward a Lossless Conversion for Spiking Neural Networks with Negative‐Spike DynamicsAdvanced Intelligent Systems10.1002/aisy.2023003835:12Online publication date: 22-Oct-2023
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Raymann JRajalakshmi R(2022)GAR-Net: Guided Attention Residual Network for Polyp Segmentation from Colonoscopy Video FramesDiagnostics10.3390/diagnostics1301012313:1(123)Online publication date: 30-Dec-2022
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Sambath Kumar KRajendran A(2022)An automatic brain tumor segmentation using modified inception module based U-Net modelJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-21187942:3(2743-2754)Online publication date: 1-Jan-2022
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