Chainer: A Deep Learning Framework for Accelerating the Research Cycle

Autor:	Tokui, Seiya, Okuta, Ryosuke, Akiba, Takuya, Niitani, Yusuke, Ogawa, Toru, Saito, Shunta, Suzuki, Shuji, Uenishi, Kota, Vogel, Brian, Vincent, Hiroyuki Yamazaki
Rok vydání:	2019
Předmět:	Computer Science - Machine Learning Computer Science - Computer Vision and Pattern Recognition Computer Science - Distributed Parallel and Cluster Computing Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Software frameworks for neural networks play a key role in the development and application of deep learning methods. In this paper, we introduce the Chainer framework, which intends to provide a flexible, intuitive, and high performance means of implementing the full range of deep learning models needed by researchers and practitioners. Chainer provides acceleration using Graphics Processing Units with a familiar NumPy-like API through CuPy, supports general and dynamic models in Python through Define-by-Run, and also provides add-on packages for state-of-the-art computer vision models as well as distributed training. Comment: Accepted for Applied Data Science Track in KDD'19
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1908.00213 Zobrazit plný text záznamu View this record from Arxiv