One-Hot Encoding

It's common to encode categorical variables (like true, false or cat, dog) in "one-of-k" or "one-hot" form. Flux provides the onehot function to make this easy.

julia> using Flux: onehot, onecold

julia> onehot(:b, [:a, :b, :c])
3-element Flux.OneHotVector:
 0
 1
 0

julia> onehot(:c, [:a, :b, :c])
3-element Flux.OneHotVector:
 0
 0
 1

The inverse is onecold (which can take a general probability distribution, as well as just booleans).

julia> onecold(ans, [:a, :b, :c])
:c

julia> onecold([true, false, false], [:a, :b, :c])
:a

julia> onecold([0.3, 0.2, 0.5], [:a, :b, :c])
:c

Flux.onehot — Function

onehot(l, labels[, unk])

Return a OneHotVector where only first occourence of l in labels is 1 and all other elements are 0.

If l is not found in labels and unk is present, the function returns onehot(unk, labels); otherwise the function raises an error.

Examples

julia> Flux.onehot(:b, [:a, :b, :c])
3-element Flux.OneHotVector:
 0
 1
 0

julia> Flux.onehot(:c, [:a, :b, :c])
3-element Flux.OneHotVector:
 0
 0
 1

source

Flux.onecold — Function

onecold(y[, labels = 1:length(y)])

Inverse operations of onehot.

Examples

julia> Flux.onecold([true, false, false], [:a, :b, :c])
:a

julia> Flux.onecold([0.3, 0.2, 0.5], [:a, :b, :c])
:c

source

Batches

onehotbatch creates a batch (matrix) of one-hot vectors, and onecold treats matrices as batches.

julia> using Flux: onehotbatch

julia> onehotbatch([:b, :a, :b], [:a, :b, :c])
3×3 Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}:
 0  1  0
 1  0  1
 0  0  0

julia> onecold(ans, [:a, :b, :c])	
3-element Array{Symbol,1}:	
 :b	
 :a	
 :b

Note that these operations returned OneHotVector and OneHotMatrix rather than Arrays. OneHotVectors behave like normal vectors but avoid any unnecessary cost compared to using an integer index directly. For example, multiplying a matrix with a one-hot vector simply slices out the relevant row of the matrix under the hood.

Flux.onehotbatch — Function

onehotbatch(ls, labels[, unk...])

Return a OneHotMatrix where kth column of the matrix is onehot(ls[k], labels).

If one of the input labels ls is not found in labels and unk is given, return onehot(unk, labels) ; otherwise the function will raise an error.

Examples

julia> Flux.onehotbatch([:b, :a, :b], [:a, :b, :c])
3×3 Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}:
 0  1  0
 1  0  1
 0  0  0

source