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

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

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

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

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

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

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

Batches

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

julia> using Flux: onehotbatch

julia> onehotbatch([:b, :a, :b], [:a, :b, :c])
3×3 Flux.OneHotMatrix:
 false   true  false
  true  false   true
 false  false  false

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.