Chainer Framework

A Chainer Framework is a Python-based deep learning framework being developed mainly by PFN.

• Context:
  • It can contain Chainer Packages, such as: ChainerMN (distributed learning), ChainerRL (reinforcement learning), ChainerCV (computer vision).
  • …
• Example(s):
  • chainer.functions.add(),
  • chainer.functions.lstm(),
  • …
• Counter-Example(s):
  • PyTorch.
  • TensorFlow.
  • MXNet.
• See: Complex Neural Networks.

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References

2017

• (JP, 2017) ⇒ https://www.preferred-networks.jp/en/tag/chainer
  • QUOTE: Chainer is a Python-based deep learning framework being developed mainly by PFN, which has unique features and powerful performance that allow for designing complex neural networks easily and intuitively, thanks to its “Define-by-Run” approach. Since it was open-sourced in June 2015, as one of the most popular frameworks, Chainer has attracted not only the academic community but also many industrial users who need a flexible framework to harness the power of deep learning in their research and real-world applications.

    Chainer incorporates the results of the latest deep learning research. With additional packages such as ChainerMN (distributed learning), ChainerRL (reinforcement learning), ChainerCV (computer vision) and through the support of Chainer development partner companies, PFN aims to promote the most advanced research and development activities of researchers and practitioners in each field.

2015

• (JP, 2015) ⇒ https://research.preferred.jp/2015/06/deep-learning-chainer/
  • QUOTE: Chainer is a framework for learning neural networks with error back propagation. It has the following features.
    • Provided as Python library (Python 2.7+ required)
    • Flexible correspondence to the structure of every neural network
    • Intuitive code by dynamic calculation graph construction
    • GPU support, learning using multiple GPUs can also be described intuitively

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