# TensorLayer: A Versatile Library for Efficient Deep Learning Development

arXiv: https://arxiv.org/abs/1707.08551 · HTML: https://mlsanity.com/paper/1707.08551/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2017.

## Built on by (most cited first, 6 of 6)

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- [Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-art](https://mlsanity.com/paper/2308.16316/) (2023)
- [Fast and Flexible Human Pose Estimation with HyperPose](https://mlsanity.com/paper/2108.11826/) (2021)
- [A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction](https://mlsanity.com/paper/2101.10599/) (2021)
- [Power-Efficient Image Storage: Leveraging Super Resolution Generative Adversarial Network for Sustainable Compression and Reduced Carbon Footprint](https://mlsanity.com/paper/2404.04642/) (2024)

## Abstract (arXiv)

Deep learning has enabled major advances in the fields of computer vision, natural language processing, and multimedia among many others. Developing a deep learning system is arduous and complex, as it involves constructing neural network architectures, managing training/trained models, tuning optimization process, preprocessing and organizing data, etc. TensorLayer is a versatile Python library that aims at helping researchers and engineers efficiently develop deep learning systems. It offers rich abstractions for neural networks, model and data management, and parallel workflow mechanism. While boosting efficiency, TensorLayer maintains both performance and scalability. TensorLayer was released in September 2016 on GitHub, and has helped people from academia and industry develop real-world applications of deep learning.