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<html lang="en"><head><meta charset="UTF-8"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><title>One-Hot Encoding · Flux</title><script>(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
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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</code></pre><p>The inverse is <code>onecold</code> (which can take a general probability distribution, as well as just booleans).</p><pre><code class="language-julia">julia> onecold(ans, [:a, :b, :c])
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:c
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julia> onecold([true, false, false], [:a, :b, :c])
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:a
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julia> onecold([0.3, 0.2, 0.5], [:a, :b, :c])
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:c</code></pre><article class="docstring"><header><a class="docstring-binding" id="Flux.onehot" href="#Flux.onehot"><code>Flux.onehot</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">onehot(l, labels[, unk])</code></pre><p>Create a <code>OneHotVector</code> with its <code>l</code>-th element <code>true</code> based on the possible set of <code>labels</code>. If <code>unk</code> is given, return <code>onehot(unk, labels)</code> if the input label <code>l</code> is not found in <code>labels</code>; otherwise it will error.</p><p><strong>Examples</strong></p><pre><code class="language-julia-repl">julia> Flux.onehot(:b, [:a, :b, :c])
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3-element Flux.OneHotVector:
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0
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1
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0
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julia> Flux.onehot(:c, [:a, :b, :c])
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3-element Flux.OneHotVector:
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0
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0
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1</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/7a32a703f0f2842dda73d4454aff5990ade365d5/src/onehot.jl#L45-L67">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.onecold" href="#Flux.onecold"><code>Flux.onecold</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">onecold(y[, labels = 1:length(y)])</code></pre><p>Inverse operations of <a href="#Flux.onehot"><code>onehot</code></a>.</p><p><strong>Examples</strong></p><pre><code class="language-julia-repl">julia> Flux.onecold([true, false, false], [:a, :b, :c])
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:a
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julia> Flux.onecold([0.3, 0.2, 0.5], [:a, :b, :c])
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:c</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/7a32a703f0f2842dda73d4454aff5990ade365d5/src/onehot.jl#L102-L115">source</a></section></article><h2 id="Batches-1"><a class="docs-heading-anchor" href="#Batches-1">Batches</a><a class="docs-heading-anchor-permalink" href="#Batches-1" title="Permalink"></a></h2><p><code>onehotbatch</code> creates a batch (matrix) of one-hot vectors, and <code>onecold</code> treats matrices as batches.</p><pre><code class="language-julia">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</code></pre><p>Note that these operations returned <code>OneHotVector</code> and <code>OneHotMatrix</code> rather than <code>Array</code>s. <code>OneHotVector</code>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.</p><article class="docstring"><header><a class="docstring-binding" id="Flux.onehotbatch" href="#Flux.onehotbatch"><code>Flux.onehotbatch</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">onehotbatch(ls, labels[, unk...])</code></pre><p>Create a <code>OneHotMatrix</code> with a batch of labels based on the possible set of <code>labels</code>. If <code>unk</code> is given, return <a href="#Flux.onehot"><code>onehot(unk, labels)</code></a> if one of the input labels <code>ls</code> is not found in <code>labels</code>; otherwise it will error.</p><p><strong>Examples</strong></p><pre><code class="language-julia-repl">julia> Flux.onehotbatch([:b, :a, :b], [:a, :b, :c])
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3×3 Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}:
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0 1 0
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1 0 1
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0 0 0</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/7a32a703f0f2842dda73d4454aff5990ade365d5/src/onehot.jl#L80-L96">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../../models/nnlib/">« NNlib</a><a class="docs-footer-nextpage" href="../dataloader/">DataLoader »</a></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> on <span class="colophon-date" title="Monday 6 April 2020 18:17">Monday 6 April 2020</span>. Using Julia version 1.4.0.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
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