Mnist

10000 MNIST training images (1000 of every digit), reformatted into a tensor: 28 x 10000 x 28.
1000 MNIST test images (100 of every digit), reformatted into a tensor: 28 x 1000 x 28

datasets

A set of tools for basic tensor operators. A tensor in the context of data analysis in a multidimensional array. The tools in this package rely on using any discrete transformation (e.g. Fast Fourier Transform (FFT)). Standard tools included are the Eigenvalue decomposition of a tensor, the QR decomposition and LU decomposition. Other functionality includes the inverse of a tensor and the transpose of a symmetric tensor. Functionality in the package is outlined in Kernfeld, E., Kilmer, M., and Aeron, S. (2015) <doi:10.1016/j.laa.2015.07.021>.

Kyle A Caudle

TensorTools

Multilinear Algebra

Kyle Caudle

Randy Hoover

Jackson Cates

Evertt Sandbo

Mnist function

The format is:
Mnist$train$images, Mnist$train$labels
Mnist$test$images, Mnist$test$labels

Format

Subset of MNIST training and testing data. — Mnist

Subset of MNIST training and testing data.

Mnist: Subset of MNIST training and testing data.

Description

Usage

Arguments

Format

References

Examples