TensorLayer: A Versatile Library for Efficient Deep Learning Development

Hao Dong, Akara Supratak, Luo Mai, Fangde Liu, Axel Oehmichen, Simiao Yu, Yike Guo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationProceedings of the 25th ACM International Conference on Multimedia
Place of PublicationNew York, NY, USA
PublisherACM Association for Computing Machinery
Pages1201–1204
Number of pages4
ISBN (Print)9781450349062
DOIs
Publication statusPublished - 19 Oct 2017
Event25th ACM International Conference on Multimedia - Mountain View, United States
Duration: 23 Oct 201727 Oct 2017
http://2017.acmmm.org/

Conference

Conference25th ACM International Conference on Multimedia
Abbreviated titleACMMM 2017
Country/TerritoryUnited States
CityMountain View
Period23/10/1727/10/17
Internet address

Keywords

  • deep learning
  • natural language processing
  • parallel computation
  • reinforcement learning
  • computer vision
  • data management

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