55 lines
1.4 KiB
Markdown
55 lines
1.4 KiB
Markdown
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# One-Hot Encoding
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It's common to encode categorical variables (like `true`, `false` or `cat`, `dog`) in "one-of-k" or ["one-hot"](https://en.wikipedia.org/wiki/One-hot) form. Flux provides the `onehot` function to make this easy.
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```
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julia> using Flux: onehot
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julia> onehot(:b, [:a, :b, :c])
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3-element Flux.OneHotVector:
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false
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true
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false
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julia> onehot(:c, [:a, :b, :c])
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3-element Flux.OneHotVector:
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false
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false
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true
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```
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The inverse is `argmax` (which can take a general probability distribution, as well as just booleans).
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```julia
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julia> argmax(ans, [:a, :b, :c])
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:c
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julia> argmax([true, false, false], [:a, :b, :c])
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:a
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julia> argmax([0.3, 0.2, 0.5], [:a, :b, :c])
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:c
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```
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## Batches
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`onehotbatch` creates a batch (matrix) of one-hot vectors, and `argmax` treats matrices as batches.
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```julia
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julia> using Flux: onehotbatch
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julia> onehotbatch([:b, :a, :b], [:a, :b, :c])
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3×3 Flux.OneHotMatrix:
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false true false
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true false true
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false false false
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julia> onecold(ans, [:a, :b, :c])
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3-element Array{Symbol,1}:
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:b
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:a
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:b
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```
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Note that these operations returned `OneHotVector` and `OneHotMatrix` rather than `Array`s. `OneHotVector`s 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.
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