Datasets

features()

Get the features of the iris dataset. This is a 4x150 matrix of Float64 elements. It has a row for each feature (sepal length, sepal width, petal length, petal width) and a column for each example.

julia> features = Flux.Data.Iris.features();

julia> summary(features)
"4×150 Array{Float64,2}"

julia> features[:, 1]
4-element Array{Float64,1}:
 5.1
 3.5
 1.4
 0.2

labels()

Get the labels of the iris dataset, a 150 element array of strings listing the species of each example.

julia> labels = Flux.Data.Iris.labels();

julia> summary(labels)
"150-element Array{String,1}"

julia> labels[1]
"Iris-setosa"

images()
images(:test)

Load the MNIST images.

Each image is a 28×28 array of Gray colour values (see Colors.jl).

Return the 60,000 training images by default; pass :test to retrieve the 10,000 test images.

labels()
labels(:test)

Load the labels corresponding to each of the images returned from images(). Each label is a number from 0-9.

Return the 60,000 training labels by default; pass :test to retrieve the 10,000 test labels.

images()
images(:test)

Load the Fashion-MNIST images.

Each image is a 28×28 array of Gray colour values (see Colors.jl).

Return the 60,000 training images by default; pass :test to retrieve the 10,000 test images.

labels()
labels(:test)

Load the labels corresponding to each of the images returned from images(). Each label is a number from 0-9.

Return the 60,000 training labels by default; pass :test to retrieve the 10,000 test labels.

phones()

Return a Vector containing the phones used in the CMU Pronouncing Dictionary.

symbols()

Return a Vector containing the symbols used in the CMU Pronouncing Dictionary. A symbol is a phone with optional auxiliary symbols, indicating for example the amount of stress on the phone.

rawdict()

Return the unfiltered CMU Pronouncing Dictionary.

cmudict()

Return a filtered CMU Pronouncing Dictionary.

It is filtered so each word contains only ASCII characters and a combination of word characters (as determined by the regex engine using \w), '-' and '.'.

train()

Return the train split of the Stanford Sentiment Treebank. The data is in treebank format.

test()

Return the test split of the Stanford Sentiment Treebank. The data is in treebank format.

dev()

Return the dev split of the Stanford Sentiment Treebank. The data is in treebank format.