TensorNetwork for Machine Learning
Alphabet (Google) X, Mountain View, CA 94043, USA
arXiv:1906.06329 [cs.LG], (7 Jun 2019)
@misc{efthymiou2019tensornetwork,
title={TensorNetwork for Machine Learning},
author={Stavros Efthymiou and Jack Hidary and Stefan Leichenauer},
year={2019},
eprint={1906.06329},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
We demonstrate the use of tensor networks for image classification with the TensorNetwork open source library. We explain in detail the encoding of image data into a matrix product state form, and describe how to contract the network in a way that is parallelizable and well-suited to automatic gradients for optimization. Applying the technique to the MNIST and Fashion-MNIST datasets we find out-of-the-box performance of 98% and 88% accuracy, respectively, using the same tensor network architecture. The TensorNetwork library allows us to seamlessly move from CPU to GPU hardware, and we see a factor of more than 10 improvement in computational speed using a GPU.
June 20, 2019 by hgpu
