mlsanityData to 7 Oct 2026

TensorLayer: A Versatile Library for Efficient Deep Learning Development

arXiv 1707.08551 ↗First posted 26 Jul 2017cs.LG cs.DC stat.MLcited by 6

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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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Jul 2017.

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Citing papers per year: 2021: 4; 2022: 0; 2023: 1; 2024: 1.

6 papers.