TensorLy: Tensor Learning in Python

About

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

Jean Kossaifi, Yannis Panagakis, Anima Anandkumar, Maja Pantic · 2016

Related benchmarks

Task	Dataset	Result
Kronecker regression	Random n^2 x d^2 design matrix	Loss0.0002	118
Kronecker regression	Synthetic n^2 x d^2 design matrix (test)	Running Time (s)0.0106	118
Low-rank tensor decomposition	Cardiac MRI tensor	Avg Iteration Time (s)1.187	64
Tucker decomposition	Cardiac MRI tensor	Relative Reconstruction Error0.35	63
Tensor Decomposition	COIL-100	Average Iteration Time (s)2.455	48
Tensor Decomposition	Hyperspectral image tensor	Avg Iteration Time (s)1.873	48
Tensor Reconstruction	COIL-100 1.0 (test)	Relative Reconstruction Error0.349	48
Hyperspectral Tensor Reconstruction	Hyperspectral radiance image tensor (full)	Relative Reconstruction Error0.133	47
CP Decomposition	UBER	Avg Time per ALS Round (s)64.2	4
CP Decomposition	NELL-2	Average time per ALS round (seconds)759.6	4