mnist_mp2c2 {TensorTest2D}

Read the pre-processed MNIST dataset

Description

A pre-processed MNIST dataset with each image of 10*10 = 100 pixels.

Usage

data(mnist_mp2c2)

Format

A list of two sublists, mnist_mp2c2$train and mnist_mp2c2$test. In each sublist, the data is stored as a list of length two, image and label. The image is a 3-dimensional array of size (10, 10, n), where n represents the data size. For i = 1, ..., n the i-th slice of image is an integer matrix with elements in [0, 255] representing the image of 10*10 = 100 pixels in grey scale. The label is a vector of length n. The i-th value is the digit of the i-th slice of image.

Details

The original MNIST handwritten digit is the image of 28*28 = 784 pixels. The pre-processing procedure is as follows. First, the original 28 by 28 image is separated into 14 by 14 clusters, each one is a 2 by 2 block. For each cluster, the maximal value in its cells is taken, and therefore, the original MNIST image is reduced into 14 by 14 image. Because the surrounding cells of the reduced image are usually zero value, we only take the center 10 by 10 sub-image by cutting the edge cells. Thus, the pre-processed MNIST dataset has images of 10*10 = 100 pixels.

Source

https://CRAN.R-project.org/package=dslabs

References

LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. (URL)

Rafael A. Irizarry and Amy Gill (2019). dslabs: Data Science Labs. R package version 0.7.3. (URL)

LeCun, Y. http://yann.lecun.com/exdb/mnist/

Examples

data(mnist_mp2c2)
dim(mnist_mp2c2$train$image)
# 10    10 60000
image(mnist_mp2c2$train$image[,,1])
mnist_mp2c2$train$label[1]