This package provides access to the classic MNIST data set of
handwritten digits that has been used as a testbed for new
machine learning methods. The MNIST data set is included with
the package for convenience without any claim of copyright to
the images, which are the property of Yann LeCun and Corinna
Cortes. The images were downloaded into their original IDX
format from http://yann.lecun.com/exdb/mnist/ and are stored
in the data/ directory.
To work with the data, you will typically want to store the digits in Julian matrices. To load the i-th image or label, use:
trainfeatures(i)testfeatures(i)trainlabel(i)testlabel(i)
The features will be stored in a 784-entry Float64 vector and
the label will be returned as a Float64 scalar.
To access the entire data set at once, use:
traindata()testdata()
The traindata method will return a tuple of two items: the first
element of the tuple will be a 784x60,000 Float64 matrix
containing all of the images in the training set. The second element
of the tuple will be a 60,000 entry Float64 vector containing
the labels of all of the images in the train set. The testdata
method will return an equivalent tuple for the test set, which contains
10,000 images instead of 60,000.
using MNIST
features = trainfeatures(1)
label = trainlabel(1)
trainX, trainY = traindata()
testX, testY = testdata()