University of Twente Research Information· 2016· Preprint
TensorLy: Tensor Learning in Python
- 211citations
- 2016year
Short summary
TensorLy is a new Python API for tensor methods and deep tensorized neural networks, designed to integrate with existing scientific libraries and scale computations across multiple CPUs or GPUs.
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Key points
- TensorLy offers a unified Python API for tensor methods and deep tensorized neural networks.
- It integrates with major scientific libraries (NumPy, MXNet, PyTorch, TensorFlow, CuPy).
- Computations can be scaled across multiple CPU or GPU machines.
- The library facilitates the design and training of deep tensorized neural networks.
AI-generated from the title and abstract; the full text is not read.
Abstract
Tensors are higher-order extensions of matrices. While matrix methods form the cornerstone of machine learning and data analysis, tensor methods have been gaining increasing traction. However, software support for tensor operations is not on the same footing. In order to bridge this gap, we have developed \emph{TensorLy}, a high-level API for tensor methods and deep tensorized neural networks in Python. TensorLy aims to follow the same standards adopted by the main projects of the Python scientific community, and seamlessly integrates with them. Its BSD license makes it suitable for both academic and commercial applications. TensorLy's backend system allows users to perform computations with NumPy, MXNet, PyTorch, TensorFlow and CuPy. They can be scaled on multiple CPU or GPU machines. In addition, using the deep-learning frameworks as backend allows users to easily design and train deep tensorized neural networks. TensorLy is available at https://github.com/tensorly/tensorly
The authors' abstract, as published at the source. University of Twente Research Information, 2016 · DOI ↗
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Field: Computational Mathematics
Computational MathematicsMathematics