@proceedings {423,
	title = {TensorLayer: A Versatile Library for Efficient Deep Learning Development},
	journal = {ACM on Multimedia Conference},
	year = {2017},
	month = {11/2017},
	publisher = {ACM},
	address = {Mountain View, CA, USA},
	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 trainingrelated
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 TensorLayer is 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.},
	keywords = {Best Open-source Software Award},
	author = {Hao Dong and Akara Supratak and Luo Mai and Fangde Liu and Axel Oehmichen and Simiao Yu and Yike Guo}
}
