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@ -29,4 +29,4 @@ end
# train for 10 epochs
using IterTools: ncycle
Flux.train!(loss, ps, ncycle(train_loader, 10), opt)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/data/dataloader.jl#L13-L54">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../onehot/">« One-Hot Encoding</a><a class="docs-footer-nextpage" href="../../training/optimisers/">Optimisers »</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="Saturday 25 April 2020 05:13">Saturday 25 April 2020</span>. Using Julia version 1.4.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
Flux.train!(loss, ps, ncycle(train_loader, 10), opt)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/data/dataloader.jl#L13-L54">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../onehot/">« One-Hot Encoding</a><a class="docs-footer-nextpage" href="../../training/optimisers/">Optimisers »</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 4 May 2020 13:31">Monday 4 May 2020</span>. Using Julia version 1.3.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>

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@ -35,11 +35,11 @@ julia&gt; Flux.onehot(:c, [:a, :b, :c])
3-element Flux.OneHotVector:
0
0
1</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/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&gt; Flux.onecold([true, false, false], [:a, :b, :c])
1</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/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&gt; Flux.onecold([true, false, false], [:a, :b, :c])
:a
julia&gt; Flux.onecold([0.3, 0.2, 0.5], [:a, :b, :c])
:c</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/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&gt; using Flux: onehotbatch
:c</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/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&gt; using Flux: onehotbatch
julia&gt; onehotbatch([:b, :a, :b], [:a, :b, :c])
3×3 Flux.OneHotMatrix:
@ -55,4 +55,4 @@ julia&gt; onecold(ans, [:a, :b, :c])
3×3 Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}:
0 1 0
1 0 1
0 0 0</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/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="Saturday 25 April 2020 05:13">Saturday 25 April 2020</span>. Using Julia version 1.4.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
0 0 0</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/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 4 May 2020 13:31">Monday 4 May 2020</span>. Using Julia version 1.3.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>

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@ -47,4 +47,4 @@ julia&gt; x |&gt; cpu
10-element Array{Float32,1}:
0.235164
⋮
0.192538</code></pre></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../training/training/">« Training</a><a class="docs-footer-nextpage" href="../saving/">Saving &amp; Loading »</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="Saturday 25 April 2020 05:13">Saturday 25 April 2020</span>. Using Julia version 1.4.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
0.192538</code></pre></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../training/training/">« Training</a><a class="docs-footer-nextpage" href="../saving/">Saving &amp; Loading »</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 4 May 2020 13:31">Monday 4 May 2020</span>. Using Julia version 1.3.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>

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@ -24,4 +24,4 @@ Params([[0.66722 0.774872 0.249809; 0.843321 0.403843 0.429232; 0.683525 0.66245
)
ps = Flux.params(m[3:end])</code></pre><p>The <code>Zygote.Params</code> object <code>ps</code> now holds a reference to only the parameters of the layers passed to it.</p><p>During training, the gradients will only be computed for (and applied to) the last <code>Dense</code> layer, therefore only that would have its parameters changed.</p><p><code>Flux.params</code> also takes multiple inputs to make it easy to collect parameters from heterogenous models with a single call. A simple demonstration would be if we wanted to omit optimising the second <code>Dense</code> layer in the previous example. It would look something like this:</p><pre><code class="language-julia">Flux.params(m[1], m[3:end])</code></pre><p>Sometimes, a more fine-tuned control is needed. We can freeze a specific parameter of a specific layer which already entered a <code>Params</code> object <code>ps</code>, by simply deleting it from <code>ps</code>:</p><pre><code class="language-julia">ps = params(m)
delete!(ps, m[2].b) </code></pre></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../layers/">« Model Reference</a><a class="docs-footer-nextpage" href="../nnlib/">NNlib »</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="Saturday 25 April 2020 05:13">Saturday 25 April 2020</span>. Using Julia version 1.4.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
delete!(ps, m[2].b) </code></pre></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../layers/">« Model Reference</a><a class="docs-footer-nextpage" href="../nnlib/">NNlib »</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 4 May 2020 13:31">Monday 4 May 2020</span>. Using Julia version 1.3.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>

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@ -111,8 +111,8 @@ model2(rand(10)) # =&gt; 2-element vector</code></pre><p>This quickly starts to
m(rand(10))</code></pre><p>Likewise, <code>Chain</code> will happily work with any Julia function.</p><pre><code class="language-julia">m = Chain(x -&gt; x^2, x -&gt; x+1)
m(5) # =&gt; 26</code></pre><h2 id="Layer-helpers-1"><a class="docs-heading-anchor" href="#Layer-helpers-1">Layer helpers</a><a class="docs-heading-anchor-permalink" href="#Layer-helpers-1" title="Permalink"></a></h2><p>Flux provides a set of helpers for custom layers, which you can enable by calling</p><pre><code class="language-julia">Flux.@functor Affine</code></pre><p>This enables a useful extra set of functionality for our <code>Affine</code> layer, such as <a href="../../training/optimisers/">collecting its parameters</a> or <a href="../../gpu/">moving it to the GPU</a>.</p><p>For some more helpful tricks, including parameter freezing, please checkout the <a href="../advanced/">advanced usage guide</a>.</p><h2 id="Utility-functions-1"><a class="docs-heading-anchor" href="#Utility-functions-1">Utility functions</a><a class="docs-heading-anchor-permalink" href="#Utility-functions-1" title="Permalink"></a></h2><p>Flux provides some utility functions to help you generate models in an automated fashion.</p><p><code>outdims</code> enables you to calculate the spatial output dimensions of layers like <code>Conv</code> when applied to input images of a given size. Currently limited to the following layers:</p><ul><li><code>Chain</code></li><li><code>Dense</code></li><li><code>Conv</code></li><li><code>Diagonal</code></li><li><code>Maxout</code></li><li><code>ConvTranspose</code></li><li><code>DepthwiseConv</code></li><li><code>CrossCor</code></li><li><code>MaxPool</code></li><li><code>MeanPool</code></li></ul><article class="docstring"><header><a class="docstring-binding" id="Flux.outdims" href="#Flux.outdims"><code>Flux.outdims</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">outdims(c::Chain, isize)</code></pre><p>Calculate the output dimensions given the input dimensions, <code>isize</code>.</p><pre><code class="language-julia">m = Chain(Conv((3, 3), 3 =&gt; 16), Conv((3, 3), 16 =&gt; 32))
outdims(m, (10, 10)) == (6, 6)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/layers/basic.jl#L50-L59">source</a></section><section><div><pre><code class="language-none">outdims(l::Dense, isize)</code></pre><p>Calculate the output dimensions given the input dimensions, <code>isize</code>.</p><pre><code class="language-julia">m = Dense(10, 5)
outdims(m, (10, 10)) == (6, 6)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/layers/basic.jl#L50-L59">source</a></section><section><div><pre><code class="language-none">outdims(l::Dense, isize)</code></pre><p>Calculate the output dimensions given the input dimensions, <code>isize</code>.</p><pre><code class="language-julia">m = Dense(10, 5)
outdims(m, (5, 2)) == (5,)
outdims(m, (10,)) == (5,)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/layers/basic.jl#L139-L149">source</a></section><section><div><pre><code class="language-none">outdims(l::Conv, isize::Tuple)</code></pre><p>Calculate the output dimensions given the input dimensions <code>isize</code>. Batch size and channel size are ignored as per <a href="https://github.com/FluxML/NNlib.jl">NNlib.jl</a>.</p><pre><code class="language-julia">m = Conv((3, 3), 3 =&gt; 16)
outdims(m, (10,)) == (5,)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/layers/basic.jl#L139-L149">source</a></section><section><div><pre><code class="language-none">outdims(l::Conv, isize::Tuple)</code></pre><p>Calculate the output dimensions given the input dimensions <code>isize</code>. Batch size and channel size are ignored as per <a href="https://github.com/FluxML/NNlib.jl">NNlib.jl</a>.</p><pre><code class="language-julia">m = Conv((3, 3), 3 =&gt; 16)
outdims(m, (10, 10)) == (8, 8)
outdims(m, (10, 10, 1, 3)) == (8, 8)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/layers/conv.jl#L101-L112">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../../">« Home</a><a class="docs-footer-nextpage" href="../recurrence/">Recurrence »</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="Saturday 25 April 2020 05:13">Saturday 25 April 2020</span>. Using Julia version 1.4.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
outdims(m, (10, 10, 1, 3)) == (8, 8)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/layers/conv.jl#L153-L164">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../../">« Home</a><a class="docs-footer-nextpage" href="../recurrence/">Recurrence »</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 4 May 2020 13:31">Monday 4 May 2020</span>. Using Julia version 1.3.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>

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@ -28,4 +28,4 @@ a = randomly sampled from uniform distribution U(l, u)</code></pre><p>Randomized
batched_adjoint(A)</code></pre><p>Equivalent to applying <code>transpose</code> or <code>adjoint</code> to each matrix <code>A[:,:,k]</code>.</p><p>These exist to control how <code>batched_mul</code> behaves, as it operated on such matrix slices of an array with <code>ndims(A)==3</code>.</p><pre><code class="language-none">BatchedTranspose{T, N, S} &lt;: AbstractBatchedMatrix{T, N}
BatchedAdjoint{T, N, S}</code></pre><p>Lazy wrappers analogous to <code>Transpose</code> and <code>Adjoint</code>, returned by <code>batched_transpose</code></p></div></section></article><article class="docstring"><header><a class="docstring-binding" id="NNlib.batched_transpose" href="#NNlib.batched_transpose"><code>NNlib.batched_transpose</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">batched_transpose(A::AbstractArray{T,3})
batched_adjoint(A)</code></pre><p>Equivalent to applying <code>transpose</code> or <code>adjoint</code> to each matrix <code>A[:,:,k]</code>.</p><p>These exist to control how <code>batched_mul</code> behaves, as it operated on such matrix slices of an array with <code>ndims(A)==3</code>.</p><pre><code class="language-none">BatchedTranspose{T, N, S} &lt;: AbstractBatchedMatrix{T, N}
BatchedAdjoint{T, N, S}</code></pre><p>Lazy wrappers analogous to <code>Transpose</code> and <code>Adjoint</code>, returned by <code>batched_transpose</code></p></div></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../advanced/">« Advanced Model Building</a><a class="docs-footer-nextpage" href="../../data/onehot/">One-Hot Encoding »</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="Saturday 25 April 2020 05:13">Saturday 25 April 2020</span>. Using Julia version 1.4.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
BatchedAdjoint{T, N, S}</code></pre><p>Lazy wrappers analogous to <code>Transpose</code> and <code>Adjoint</code>, returned by <code>batched_transpose</code></p></div></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../advanced/">« Advanced Model Building</a><a class="docs-footer-nextpage" href="../../data/onehot/">One-Hot Encoding »</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 4 May 2020 13:31">Monday 4 May 2020</span>. Using Julia version 1.3.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>

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@ -39,4 +39,4 @@ m = Flux.Recur(rnn, h)
y = m(x)</code></pre><p>The <code>Recur</code> wrapper stores the state between runs in the <code>m.state</code> field.</p><p>If you use the <code>RNN(10, 5)</code> constructor – as opposed to <code>RNNCell</code> – you&#39;ll see that it&#39;s simply a wrapped cell.</p><pre><code class="language-julia">julia&gt; RNN(10, 5)
Recur(RNNCell(10, 5, tanh))</code></pre><h2 id="Sequences-1"><a class="docs-heading-anchor" href="#Sequences-1">Sequences</a><a class="docs-heading-anchor-permalink" href="#Sequences-1" title="Permalink"></a></h2><p>Often we want to work with sequences of inputs, rather than individual <code>x</code>s.</p><pre><code class="language-julia">seq = [rand(10) for i = 1:10]</code></pre><p>With <code>Recur</code>, applying our model to each element of a sequence is trivial:</p><pre><code class="language-julia">m.(seq) # returns a list of 5-element vectors</code></pre><p>This works even when we&#39;ve chain recurrent layers into a larger model.</p><pre><code class="language-julia">m = Chain(LSTM(10, 15), Dense(15, 5))
m.(seq)</code></pre><p>Finally, we can reset the hidden state of the cell back to its initial value using <code>reset!(m)</code>.</p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../basics/">« Basics</a><a class="docs-footer-nextpage" href="../regularisation/">Regularisation »</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="Saturday 25 April 2020 05:13">Saturday 25 April 2020</span>. Using Julia version 1.4.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
m.(seq)</code></pre><p>Finally, we can reset the hidden state of the cell back to its initial value using <code>reset!(m)</code>.</p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../basics/">« Basics</a><a class="docs-footer-nextpage" href="../regularisation/">Regularisation »</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 4 May 2020 13:31">Monday 4 May 2020</span>. Using Julia version 1.3.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>

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@ -36,4 +36,4 @@ julia&gt; activations(c, rand(10))
Float32[0.5192045, 0.48079553]
julia&gt; sum(norm, ans)
2.1166067f0</code></pre><article class="docstring"><header><a class="docstring-binding" id="Flux.activations" href="#Flux.activations"><code>Flux.activations</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">activations(c::Chain, input)</code></pre><p>Calculate the forward results of each layers in Chain <code>c</code> with <code>input</code> as model input.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/layers/basic.jl#L67-L71">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../recurrence/">« Recurrence</a><a class="docs-footer-nextpage" href="../layers/">Model Reference »</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="Saturday 25 April 2020 05:13">Saturday 25 April 2020</span>. Using Julia version 1.4.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
2.1166067f0</code></pre><article class="docstring"><header><a class="docstring-binding" id="Flux.activations" href="#Flux.activations"><code>Flux.activations</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">activations(c::Chain, input)</code></pre><p>Calculate the forward results of each layers in Chain <code>c</code> with <code>input</code> as model input.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/layers/basic.jl#L67-L71">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../recurrence/">« Recurrence</a><a class="docs-footer-nextpage" href="../layers/">Model Reference »</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 4 May 2020 13:31">Monday 4 May 2020</span>. Using Julia version 1.3.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>

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@ -17,4 +17,4 @@ y_batch = reduce(hcat, ys)
function loss_total(x_batch::Matrix, y_batch::Matrix)
y_preds = model(x_batch)
sum(loss.(y_preds, y_batch))
end</code></pre><p>When doing this kind of concatenation use <code>reduce(hcat, xs)</code> rather than <code>hcat(xs...)</code>. This will avoid the splatting penalty, and will hit the optimised <code>reduce</code> method.</p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../utilities/">« Utility Functions</a><a class="docs-footer-nextpage" href="../datasets/">Datasets »</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="Saturday 25 April 2020 05:13">Saturday 25 April 2020</span>. Using Julia version 1.4.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
end</code></pre><p>When doing this kind of concatenation use <code>reduce(hcat, xs)</code> rather than <code>hcat(xs...)</code>. This will avoid the splatting penalty, and will hit the optimised <code>reduce</code> method.</p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../utilities/">« Utility Functions</a><a class="docs-footer-nextpage" href="../datasets/">Datasets »</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 4 May 2020 13:31">Monday 4 May 2020</span>. Using Julia version 1.3.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>

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@ -47,4 +47,4 @@ evalcb = throttle(30) do
# Show loss
@save &quot;model-checkpoint.bson&quot; model
end</code></pre><p>This will update the <code>&quot;model-checkpoint.bson&quot;</code> file every thirty seconds.</p><p>You can get more advanced by saving a series of models throughout training, for example</p><pre><code class="language-julia">@save &quot;model-$(now()).bson&quot; model</code></pre><p>will produce a series of models like <code>&quot;model-2018-03-06T02:57:10.41.bson&quot;</code>. You could also store the current test set loss, so that it&#39;s easy to (for example) revert to an older copy of the model if it starts to overfit.</p><pre><code class="language-julia">@save &quot;model-$(now()).bson&quot; model loss = testloss()</code></pre><p>You can even store optimiser state alongside the model, to resume training exactly where you left off.</p><pre><code class="language-julia">opt = ADAM()
@save &quot;model-$(now()).bson&quot; model opt</code></pre></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../gpu/">« GPU Support</a><a class="docs-footer-nextpage" href="../ecosystem/">The Julia Ecosystem »</a></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> on <span class="colophon-date" title="Saturday 25 April 2020 05:13">Saturday 25 April 2020</span>. Using Julia version 1.4.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
@save &quot;model-$(now()).bson&quot; model opt</code></pre></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../gpu/">« GPU Support</a><a class="docs-footer-nextpage" href="../ecosystem/">The Julia Ecosystem »</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 4 May 2020 13:31">Monday 4 May 2020</span>. Using Julia version 1.3.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>

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@ -6,4 +6,4 @@ m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
ga('create', 'UA-36890222-9', 'auto');
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@ -27,8 +27,8 @@ end</code></pre><p>Running this will alter the parameters <code>W</code> and <co
for p in (W, b)
update!(opt, p, grads[p])
end</code></pre><p>An optimiser <code>update!</code> accepts a parameter and a gradient, and updates the parameter according to the chosen rule. We can also pass <code>opt</code> to our <a href="../training/">training loop</a>, which will update all parameters of the model in a loop. However, we can now easily replace <code>Descent</code> with a more advanced optimiser such as <code>ADAM</code>.</p><h2 id="Optimiser-Reference-1"><a class="docs-heading-anchor" href="#Optimiser-Reference-1">Optimiser Reference</a><a class="docs-heading-anchor-permalink" href="#Optimiser-Reference-1" title="Permalink"></a></h2><p>All optimisers return an object that, when passed to <code>train!</code>, will update the parameters passed to it.</p><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.update!" href="#Flux.Optimise.update!"><code>Flux.Optimise.update!</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">update!(x, x̄)</code></pre><p>Update the array <code>x</code> according to <code>x .-= x̄</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/optimise/train.jl#L6-L10">source</a></section><section><div><pre><code class="language-none">update!(opt, p, g)
update!(opt, ps::Params, gs)</code></pre><p>Perform an update step of the parameters <code>ps</code> (or the single parameter <code>p</code>) according to optimizer <code>opt</code> and the gradients <code>gs</code> (the gradient <code>g</code>).</p><p>As a result, the parameters are mutated and the optimizer&#39;s internal state may change.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/optimise/train.jl#L15-L23">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.Descent" href="#Flux.Optimise.Descent"><code>Flux.Optimise.Descent</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia">Descent(η = 0.1)</code></pre><p>Classic gradient descent optimiser with learning rate <code>η</code>. For each parameter <code>p</code> and its gradient <code>δp</code>, this runs <code>p -= η*δp</code></p><p><strong>Parameters</strong></p><ul><li>Learning rate (<code>η</code>): Amount by which gradients are discounted before updating the weights.</li></ul><p><strong>Examples</strong></p><pre><code class="language-julia">opt = Descent()
end</code></pre><p>An optimiser <code>update!</code> accepts a parameter and a gradient, and updates the parameter according to the chosen rule. We can also pass <code>opt</code> to our <a href="../training/">training loop</a>, which will update all parameters of the model in a loop. However, we can now easily replace <code>Descent</code> with a more advanced optimiser such as <code>ADAM</code>.</p><h2 id="Optimiser-Reference-1"><a class="docs-heading-anchor" href="#Optimiser-Reference-1">Optimiser Reference</a><a class="docs-heading-anchor-permalink" href="#Optimiser-Reference-1" title="Permalink"></a></h2><p>All optimisers return an object that, when passed to <code>train!</code>, will update the parameters passed to it.</p><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.update!" href="#Flux.Optimise.update!"><code>Flux.Optimise.update!</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">update!(x, x̄)</code></pre><p>Update the array <code>x</code> according to <code>x .-= x̄</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/optimise/train.jl#L6-L10">source</a></section><section><div><pre><code class="language-none">update!(opt, p, g)
update!(opt, ps::Params, gs)</code></pre><p>Perform an update step of the parameters <code>ps</code> (or the single parameter <code>p</code>) according to optimizer <code>opt</code> and the gradients <code>gs</code> (the gradient <code>g</code>).</p><p>As a result, the parameters are mutated and the optimizer&#39;s internal state may change.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/optimise/train.jl#L15-L23">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.Descent" href="#Flux.Optimise.Descent"><code>Flux.Optimise.Descent</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia">Descent(η = 0.1)</code></pre><p>Classic gradient descent optimiser with learning rate <code>η</code>. For each parameter <code>p</code> and its gradient <code>δp</code>, this runs <code>p -= η*δp</code></p><p><strong>Parameters</strong></p><ul><li>Learning rate (<code>η</code>): Amount by which gradients are discounted before updating the weights.</li></ul><p><strong>Examples</strong></p><pre><code class="language-julia">opt = Descent()
opt = Descent(0.3)
@ -38,35 +38,35 @@ gs = gradient(ps) do
loss(x, y)
end
Flux.Optimise.update!(opt, ps, gs)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/optimise/optimisers.jl#L8-L32">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.Momentum" href="#Flux.Optimise.Momentum"><code>Flux.Optimise.Momentum</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia">Momentum(η = 0.01, ρ = 0.9)</code></pre><p>Gradient descent optimizer with learning rate <code>η</code> and momentum <code>ρ</code>.</p><p><strong>Parameters</strong></p><ul><li>Learning rate (<code>η</code>): Amount by which gradients are discounted before updating the weights.</li><li>Momentum (<code>ρ</code>): Controls the acceleration of gradient descent in the prominent direction, in effect dampening oscillations.</li></ul><p><strong>Examples</strong></p><pre><code class="language-julia">opt = Momentum()
Flux.Optimise.update!(opt, ps, gs)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/optimise/optimisers.jl#L8-L32">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.Momentum" href="#Flux.Optimise.Momentum"><code>Flux.Optimise.Momentum</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia">Momentum(η = 0.01, ρ = 0.9)</code></pre><p>Gradient descent optimizer with learning rate <code>η</code> and momentum <code>ρ</code>.</p><p><strong>Parameters</strong></p><ul><li>Learning rate (<code>η</code>): Amount by which gradients are discounted before updating the weights.</li><li>Momentum (<code>ρ</code>): Controls the acceleration of gradient descent in the prominent direction, in effect dampening oscillations.</li></ul><p><strong>Examples</strong></p><pre><code class="language-julia">opt = Momentum()
opt = Momentum(0.01, 0.99)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/optimise/optimisers.jl#L43-L60">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.Nesterov" href="#Flux.Optimise.Nesterov"><code>Flux.Optimise.Nesterov</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia">Nesterov(η = 0.001, ρ = 0.9)</code></pre><p>Gradient descent optimizer with learning rate <code>η</code> and Nesterov momentum <code>ρ</code>.</p><p><strong>Parameters</strong></p><ul><li>Learning rate (<code>η</code>): Amount by which gradients are discounted before updating the weights.</li><li>Nesterov momentum (<code>ρ</code>): Controls the acceleration of gradient descent in the prominent direction, in effect dampening oscillations.</li></ul><p><strong>Examples</strong></p><pre><code class="language-julia">opt = Nesterov()
opt = Momentum(0.01, 0.99)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/optimise/optimisers.jl#L43-L60">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.Nesterov" href="#Flux.Optimise.Nesterov"><code>Flux.Optimise.Nesterov</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia">Nesterov(η = 0.001, ρ = 0.9)</code></pre><p>Gradient descent optimizer with learning rate <code>η</code> and Nesterov momentum <code>ρ</code>.</p><p><strong>Parameters</strong></p><ul><li>Learning rate (<code>η</code>): Amount by which gradients are discounted before updating the weights.</li><li>Nesterov momentum (<code>ρ</code>): Controls the acceleration of gradient descent in the prominent direction, in effect dampening oscillations.</li></ul><p><strong>Examples</strong></p><pre><code class="language-julia">opt = Nesterov()
opt = Nesterov(0.003, 0.95)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/optimise/optimisers.jl#L76-L93">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.RMSProp" href="#Flux.Optimise.RMSProp"><code>Flux.Optimise.RMSProp</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia">RMSProp(η = 0.001, ρ = 0.9)</code></pre><p>Optimizer using the <a href="https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf">RMSProp</a> algorithm. Often a good choice for recurrent networks. Parameters other than learning rate generally don&#39;t need tuning.</p><p><strong>Parameters</strong></p><ul><li>Learning rate (<code>η</code>): Amount by which gradients are discounted before updating the weights.</li><li>Momentum (<code>ρ</code>): Controls the acceleration of gradient descent in the prominent direction, in effect dampening oscillations.</li></ul><p><strong>Examples</strong></p><pre><code class="language-julia">opt = RMSProp()
opt = Nesterov(0.003, 0.95)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/optimise/optimisers.jl#L76-L93">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.RMSProp" href="#Flux.Optimise.RMSProp"><code>Flux.Optimise.RMSProp</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia">RMSProp(η = 0.001, ρ = 0.9)</code></pre><p>Optimizer using the <a href="https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf">RMSProp</a> algorithm. Often a good choice for recurrent networks. Parameters other than learning rate generally don&#39;t need tuning.</p><p><strong>Parameters</strong></p><ul><li>Learning rate (<code>η</code>): Amount by which gradients are discounted before updating the weights.</li><li>Momentum (<code>ρ</code>): Controls the acceleration of gradient descent in the prominent direction, in effect dampening oscillations.</li></ul><p><strong>Examples</strong></p><pre><code class="language-julia">opt = RMSProp()
opt = RMSProp(0.002, 0.95)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/optimise/optimisers.jl#L110-L130">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.ADAM" href="#Flux.Optimise.ADAM"><code>Flux.Optimise.ADAM</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia">ADAM(η = 0.001, β::Tuple = (0.9, 0.999))</code></pre><p><a href="https://arxiv.org/abs/1412.6980v8">ADAM</a> optimiser.</p><p><strong>Parameters</strong></p><ul><li>Learning rate (<code>η</code>): Amount by which gradients are discounted before updating the weights.</li><li>Decay of momentums (<code>β::Tuple</code>): Exponential decay for the first (β1) and the second (β2) momentum estimate.</li></ul><p><strong>Examples</strong></p><pre><code class="language-julia">opt = ADAM()
opt = RMSProp(0.002, 0.95)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/optimise/optimisers.jl#L110-L130">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.ADAM" href="#Flux.Optimise.ADAM"><code>Flux.Optimise.ADAM</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia">ADAM(η = 0.001, β::Tuple = (0.9, 0.999))</code></pre><p><a href="https://arxiv.org/abs/1412.6980v8">ADAM</a> optimiser.</p><p><strong>Parameters</strong></p><ul><li>Learning rate (<code>η</code>): Amount by which gradients are discounted before updating the weights.</li><li>Decay of momentums (<code>β::Tuple</code>): Exponential decay for the first (β1) and the second (β2) momentum estimate.</li></ul><p><strong>Examples</strong></p><pre><code class="language-julia">opt = ADAM()
opt = ADAM(0.001, (0.9, 0.8))</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/optimise/optimisers.jl#L146-L163">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.RADAM" href="#Flux.Optimise.RADAM"><code>Flux.Optimise.RADAM</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia">RADAM(η = 0.001, β::Tuple = (0.9, 0.999))</code></pre><p><a href="https://arxiv.org/pdf/1908.03265v1.pdf">Rectified ADAM</a> optimizer.</p><p><strong>Parameters</strong></p><ul><li>Learning rate (<code>η</code>): Amount by which gradients are discounted before updating the weights.</li><li>Decay of momentums (<code>β::Tuple</code>): Exponential decay for the first (β1) and the second (β2) momentum estimate.</li></ul><p><strong>Examples</strong></p><pre><code class="language-julia">opt = RADAM()
opt = ADAM(0.001, (0.9, 0.8))</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/optimise/optimisers.jl#L146-L163">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.RADAM" href="#Flux.Optimise.RADAM"><code>Flux.Optimise.RADAM</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia">RADAM(η = 0.001, β::Tuple = (0.9, 0.999))</code></pre><p><a href="https://arxiv.org/pdf/1908.03265v1.pdf">Rectified ADAM</a> optimizer.</p><p><strong>Parameters</strong></p><ul><li>Learning rate (<code>η</code>): Amount by which gradients are discounted before updating the weights.</li><li>Decay of momentums (<code>β::Tuple</code>): Exponential decay for the first (β1) and the second (β2) momentum estimate.</li></ul><p><strong>Examples</strong></p><pre><code class="language-julia">opt = RADAM()
opt = RADAM(0.001, (0.9, 0.8))</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/optimise/optimisers.jl#L182-L199">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.AdaMax" href="#Flux.Optimise.AdaMax"><code>Flux.Optimise.AdaMax</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia">AdaMax(η = 0.001, β::Tuple = (0.9, 0.999))</code></pre><p><a href="https://arxiv.org/abs/1412.6980v9">AdaMax</a> is a variant of ADAM based on the ∞-norm.</p><p><strong>Parameters</strong></p><ul><li>Learning rate (<code>η</code>): Amount by which gradients are discounted before updating the weights.</li><li>Decay of momentums (<code>β::Tuple</code>): Exponential decay for the first (β1) and the second (β2) momentum estimate.</li></ul><p><strong>Examples</strong></p><pre><code class="language-julia">opt = AdaMax()
opt = RADAM(0.001, (0.9, 0.8))</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/optimise/optimisers.jl#L182-L199">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.AdaMax" href="#Flux.Optimise.AdaMax"><code>Flux.Optimise.AdaMax</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia">AdaMax(η = 0.001, β::Tuple = (0.9, 0.999))</code></pre><p><a href="https://arxiv.org/abs/1412.6980v9">AdaMax</a> is a variant of ADAM based on the ∞-norm.</p><p><strong>Parameters</strong></p><ul><li>Learning rate (<code>η</code>): Amount by which gradients are discounted before updating the weights.</li><li>Decay of momentums (<code>β::Tuple</code>): Exponential decay for the first (β1) and the second (β2) momentum estimate.</li></ul><p><strong>Examples</strong></p><pre><code class="language-julia">opt = AdaMax()
opt = AdaMax(0.001, (0.9, 0.995))</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/optimise/optimisers.jl#L225-L242">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.ADAGrad" href="#Flux.Optimise.ADAGrad"><code>Flux.Optimise.ADAGrad</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia">ADAGrad(η = 0.1)</code></pre><p><a href="http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf">ADAGrad</a> optimizer. It has parameter specific learning rates based on how frequently it is updated. Parameters don&#39;t need tuning.</p><p><strong>Parameters</strong></p><ul><li>Learning rate (<code>η</code>): Amount by which gradients are discounted before updating the weights.</li></ul><p><strong>Examples</strong></p><pre><code class="language-julia">opt = ADAGrad()
opt = AdaMax(0.001, (0.9, 0.995))</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/optimise/optimisers.jl#L225-L242">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.ADAGrad" href="#Flux.Optimise.ADAGrad"><code>Flux.Optimise.ADAGrad</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia">ADAGrad(η = 0.1)</code></pre><p><a href="http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf">ADAGrad</a> optimizer. It has parameter specific learning rates based on how frequently it is updated. Parameters don&#39;t need tuning.</p><p><strong>Parameters</strong></p><ul><li>Learning rate (<code>η</code>): Amount by which gradients are discounted before updating the weights.</li></ul><p><strong>Examples</strong></p><pre><code class="language-julia">opt = ADAGrad()
opt = ADAGrad(0.001)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/optimise/optimisers.jl#L261-L278">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.ADADelta" href="#Flux.Optimise.ADADelta"><code>Flux.Optimise.ADADelta</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia">ADADelta(ρ = 0.9)</code></pre><p><a href="https://arxiv.org/abs/1212.5701">ADADelta</a> is a version of ADAGrad adapting its learning rate based on a window of past gradient updates. Parameters don&#39;t need tuning.</p><p><strong>Parameters</strong></p><ul><li>Rho (<code>ρ</code>): Factor by which the gradient is decayed at each time step.</li></ul><p><strong>Examples</strong></p><pre><code class="language-julia">opt = ADADelta()
opt = ADAGrad(0.001)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/optimise/optimisers.jl#L261-L278">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.ADADelta" href="#Flux.Optimise.ADADelta"><code>Flux.Optimise.ADADelta</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia">ADADelta(ρ = 0.9)</code></pre><p><a href="https://arxiv.org/abs/1212.5701">ADADelta</a> is a version of ADAGrad adapting its learning rate based on a window of past gradient updates. Parameters don&#39;t need tuning.</p><p><strong>Parameters</strong></p><ul><li>Rho (<code>ρ</code>): Factor by which the gradient is decayed at each time step.</li></ul><p><strong>Examples</strong></p><pre><code class="language-julia">opt = ADADelta()
opt = ADADelta(0.89)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/optimise/optimisers.jl#L293-L309">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.AMSGrad" href="#Flux.Optimise.AMSGrad"><code>Flux.Optimise.AMSGrad</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia">AMSGrad(η = 0.001, β::Tuple = (0.9, 0.999))</code></pre><p>The <a href="https://openreview.net/forum?id=ryQu7f-RZ">AMSGrad</a> version of the ADAM optimiser. Parameters don&#39;t need tuning.</p><p><strong>Parameters</strong></p><ul><li>Learning rate (<code>η</code>): Amount by which gradients are discounted before updating the weights.</li><li>Decay of momentums (<code>β::Tuple</code>): Exponential decay for the first (β1) and the second (β2) momentum estimate.</li></ul><p><strong>Examples</strong></p><pre><code class="language-julia">opt = AMSGrad()
opt = ADADelta(0.89)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/optimise/optimisers.jl#L293-L309">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.AMSGrad" href="#Flux.Optimise.AMSGrad"><code>Flux.Optimise.AMSGrad</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia">AMSGrad(η = 0.001, β::Tuple = (0.9, 0.999))</code></pre><p>The <a href="https://openreview.net/forum?id=ryQu7f-RZ">AMSGrad</a> version of the ADAM optimiser. Parameters don&#39;t need tuning.</p><p><strong>Parameters</strong></p><ul><li>Learning rate (<code>η</code>): Amount by which gradients are discounted before updating the weights.</li><li>Decay of momentums (<code>β::Tuple</code>): Exponential decay for the first (β1) and the second (β2) momentum estimate.</li></ul><p><strong>Examples</strong></p><pre><code class="language-julia">opt = AMSGrad()
opt = AMSGrad(0.001, (0.89, 0.995))</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/optimise/optimisers.jl#L326-L344">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.NADAM" href="#Flux.Optimise.NADAM"><code>Flux.Optimise.NADAM</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia">NADAM(η = 0.001, β::Tuple = (0.9, 0.999))</code></pre><p><a href="http://cs229.stanford.edu/proj2015/054_report.pdf">NADAM</a> is a Nesterov variant of ADAM. Parameters don&#39;t need tuning.</p><p><strong>Parameters</strong></p><ul><li>Learning rate (<code>η</code>): Amount by which gradients are discounted before updating the weights.</li><li>Decay of momentums (<code>β::Tuple</code>): Exponential decay for the first (β1) and the second (β2) momentum estimate.</li></ul><p><strong>Examples</strong></p><pre><code class="language-julia">opt = NADAM()
opt = AMSGrad(0.001, (0.89, 0.995))</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/optimise/optimisers.jl#L326-L344">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.NADAM" href="#Flux.Optimise.NADAM"><code>Flux.Optimise.NADAM</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia">NADAM(η = 0.001, β::Tuple = (0.9, 0.999))</code></pre><p><a href="http://cs229.stanford.edu/proj2015/054_report.pdf">NADAM</a> is a Nesterov variant of ADAM. Parameters don&#39;t need tuning.</p><p><strong>Parameters</strong></p><ul><li>Learning rate (<code>η</code>): Amount by which gradients are discounted before updating the weights.</li><li>Decay of momentums (<code>β::Tuple</code>): Exponential decay for the first (β1) and the second (β2) momentum estimate.</li></ul><p><strong>Examples</strong></p><pre><code class="language-julia">opt = NADAM()
opt = NADAM(0.002, (0.89, 0.995))</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/optimise/optimisers.jl#L362-L380">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.ADAMW" href="#Flux.Optimise.ADAMW"><code>Flux.Optimise.ADAMW</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">ADAMW(η = 0.001, β::Tuple = (0.9, 0.999), decay = 0)</code></pre><p><a href="https://arxiv.org/abs/1711.05101">ADAMW</a> is a variant of ADAM fixing (as in repairing) its weight decay regularization.</p><p><strong>Parameters</strong></p><ul><li>Learning rate (<code>η</code>): Amount by which gradients are discounted before updating the weights.</li><li>Decay of momentums (<code>β::Tuple</code>): Exponential decay for the first (β1) and the second (β2) momentum estimate.</li><li><code>decay</code>: Decay applied to weights during optimisation.</li></ul><p><strong>Examples</strong></p><pre><code class="language-julia">opt = ADAMW()
opt = NADAM(0.002, (0.89, 0.995))</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/optimise/optimisers.jl#L362-L380">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.ADAMW" href="#Flux.Optimise.ADAMW"><code>Flux.Optimise.ADAMW</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">ADAMW(η = 0.001, β::Tuple = (0.9, 0.999), decay = 0)</code></pre><p><a href="https://arxiv.org/abs/1711.05101">ADAMW</a> is a variant of ADAM fixing (as in repairing) its weight decay regularization.</p><p><strong>Parameters</strong></p><ul><li>Learning rate (<code>η</code>): Amount by which gradients are discounted before updating the weights.</li><li>Decay of momentums (<code>β::Tuple</code>): Exponential decay for the first (β1) and the second (β2) momentum estimate.</li><li><code>decay</code>: Decay applied to weights during optimisation.</li></ul><p><strong>Examples</strong></p><pre><code class="language-julia">opt = ADAMW()
opt = ADAMW(0.001, (0.89, 0.995), 0.1)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/optimise/optimisers.jl#L399-L418">source</a></section></article><h2 id="Optimiser-Interface-1"><a class="docs-heading-anchor" href="#Optimiser-Interface-1">Optimiser Interface</a><a class="docs-heading-anchor-permalink" href="#Optimiser-Interface-1" title="Permalink"></a></h2><p>Flux&#39;s optimisers are built around a <code>struct</code> that holds all the optimiser parameters along with a definition of how to apply the update rule associated with it. We do this via the <code>apply!</code> function which takes the optimiser as the first argument followed by the parameter and its corresponding gradient.</p><p>In this manner Flux also allows one to create custom optimisers to be used seamlessly. Let&#39;s work this with a simple example.</p><pre><code class="language-julia">mutable struct Momentum
opt = ADAMW(0.001, (0.89, 0.995), 0.1)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/optimise/optimisers.jl#L399-L418">source</a></section></article><h2 id="Optimiser-Interface-1"><a class="docs-heading-anchor" href="#Optimiser-Interface-1">Optimiser Interface</a><a class="docs-heading-anchor-permalink" href="#Optimiser-Interface-1" title="Permalink"></a></h2><p>Flux&#39;s optimisers are built around a <code>struct</code> that holds all the optimiser parameters along with a definition of how to apply the update rule associated with it. We do this via the <code>apply!</code> function which takes the optimiser as the first argument followed by the parameter and its corresponding gradient.</p><p>In this manner Flux also allows one to create custom optimisers to be used seamlessly. Let&#39;s work this with a simple example.</p><pre><code class="language-julia">mutable struct Momentum
eta
rho
velocity
end
Momentum(eta::Real, rho::Real) = Momentum(eta, rho, IdDict())</code></pre><p>The <code>Momentum</code> type will act as our optimiser in this case. Notice that we have added all the parameters as fields, along with the velocity which we will use as our state dictionary. Each parameter in our models will get an entry in there. We can now define the rule applied when this optimiser is invoked.</p><pre><code class="language-julia">function apply!(o::Momentum, x, Δ)
Momentum(eta::Real, rho::Real) = Momentum(eta, rho, IdDict())</code></pre><p>The <code>Momentum</code> type will act as our optimiser in this case. Notice that we have added all the parameters as fields, along with the velocity which we will use as our state dictionary. Each parameter in our models will get an entry in there. We can now define the rule applied when this optimiser is invoked.</p><pre><code class="language-julia">function Flux.Optimise.apply!(o::Momentum, x, Δ)
η, ρ = o.eta, o.rho
v = get!(o.velocity, x, zero(x))::typeof(x)
@. v = ρ * v - η * Δ
@ -88,4 +88,4 @@ end
loss(rand(10)) # around 0.9</code></pre><p>In this manner it is possible to compose optimisers for some added flexibility.</p><h2 id="Decays-1"><a class="docs-heading-anchor" href="#Decays-1">Decays</a><a class="docs-heading-anchor-permalink" href="#Decays-1" title="Permalink"></a></h2><p>Similar to optimisers, Flux also defines some simple decays that can be used in conjunction with other optimisers, or standalone.</p><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.ExpDecay" href="#Flux.Optimise.ExpDecay"><code>Flux.Optimise.ExpDecay</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia">ExpDecay(η = 0.001, decay = 0.1, decay_step = 1000, clip = 1e-4)</code></pre><p>Discount the learning rate <code>η</code> by the factor <code>decay</code> every <code>decay_step</code> steps till a minimum of <code>clip</code>.</p><p><strong>Parameters</strong></p><ul><li>Learning rate (<code>η</code>): Amount by which gradients are discounted before updating the weights.</li><li><code>decay</code>: Factor by which the learning rate is discounted.</li><li><code>decay_step</code>: Schedule decay operations by setting the number of steps between two decay operations.</li><li><code>clip</code>: Minimum value of learning rate.</li></ul><p><strong>Examples</strong></p><p>To apply exponential decay to an optimiser:</p><pre><code class="language-julia">Optimiser(ExpDecay(..), Opt(..))
opt = Optimiser(ExpDecay(), ADAM())</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/optimise/optimisers.jl#L476-L497">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.InvDecay" href="#Flux.Optimise.InvDecay"><code>Flux.Optimise.InvDecay</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia">InvDecay(γ = 0.001)</code></pre><p>Apply inverse time decay to an optimiser, so that the effective step size at iteration <code>n</code> is <code>eta / (1 + γ * n)</code> where <code>eta</code> is the initial step size. The wrapped optimiser&#39;s step size is not modified.</p><p><strong>Examples</strong></p><pre><code class="language-julia">Optimiser(InvDecay(..), Opt(..))</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/optimise/optimisers.jl#L449-L460">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.WeightDecay" href="#Flux.Optimise.WeightDecay"><code>Flux.Optimise.WeightDecay</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia">WeightDecay(wd = 0)</code></pre><p>Decay weights by <code>wd</code>.</p><p><strong>Parameters</strong></p><ul><li>Weight decay (<code>wd</code>)</li></ul></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/optimise/optimisers.jl#L518-L525">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../../data/dataloader/">« DataLoader</a><a class="docs-footer-nextpage" href="../training/">Training »</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="Saturday 25 April 2020 05:13">Saturday 25 April 2020</span>. Using Julia version 1.4.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
opt = Optimiser(ExpDecay(), ADAM())</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/optimise/optimisers.jl#L476-L497">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.InvDecay" href="#Flux.Optimise.InvDecay"><code>Flux.Optimise.InvDecay</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia">InvDecay(γ = 0.001)</code></pre><p>Apply inverse time decay to an optimiser, so that the effective step size at iteration <code>n</code> is <code>eta / (1 + γ * n)</code> where <code>eta</code> is the initial step size. The wrapped optimiser&#39;s step size is not modified.</p><p><strong>Examples</strong></p><pre><code class="language-julia">Optimiser(InvDecay(..), Opt(..))</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/optimise/optimisers.jl#L449-L460">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.WeightDecay" href="#Flux.Optimise.WeightDecay"><code>Flux.Optimise.WeightDecay</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia">WeightDecay(wd = 0)</code></pre><p>Decay weights by <code>wd</code>.</p><p><strong>Parameters</strong></p><ul><li>Weight decay (<code>wd</code>)</li></ul></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/optimise/optimisers.jl#L518-L525">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../../data/dataloader/">« DataLoader</a><a class="docs-footer-nextpage" href="../training/">Training »</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 4 May 2020 13:31">Monday 4 May 2020</span>. Using Julia version 1.3.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>

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@ -24,7 +24,7 @@ julia&gt; Flux.unsqueeze([1 2; 3 4], 2)
[:, :, 2] =
2
4</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/utils.jl#L46-L74">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.stack" href="#Flux.stack"><code>Flux.stack</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">stack(xs, dim)</code></pre><p>Concatenate the given <code>Array</code> of <code>Array</code>s <code>xs</code> into a single <code>Array</code> along the given dimension <code>dim</code>.</p><p><strong>Examples</strong></p><pre><code class="language-julia-repl">julia&gt; xs = [[1, 2], [3, 4], [5, 6]]
4</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/utils.jl#L46-L74">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.stack" href="#Flux.stack"><code>Flux.stack</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">stack(xs, dim)</code></pre><p>Concatenate the given <code>Array</code> of <code>Array</code>s <code>xs</code> into a single <code>Array</code> along the given dimension <code>dim</code>.</p><p><strong>Examples</strong></p><pre><code class="language-julia-repl">julia&gt; xs = [[1, 2], [3, 4], [5, 6]]
3-element Array{Array{Int64,1},1}:
[1, 2]
[3, 4]
@ -40,12 +40,12 @@ julia&gt; cat(xs, dims=1)
3-element Array{Array{Int64,1},1}:
[1, 2]
[3, 4]
[5, 6]</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/utils.jl#L77-L103">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.unstack" href="#Flux.unstack"><code>Flux.unstack</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">unstack(xs, dim)</code></pre><p>Unroll the given <code>xs</code> into an <code>Array</code> of <code>Array</code>s along the given dimension <code>dim</code>.</p><p><strong>Examples</strong></p><pre><code class="language-julia-repl">julia&gt; Flux.unstack([1 3 5 7; 2 4 6 8], 2)
[5, 6]</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/utils.jl#L77-L103">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.unstack" href="#Flux.unstack"><code>Flux.unstack</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">unstack(xs, dim)</code></pre><p>Unroll the given <code>xs</code> into an <code>Array</code> of <code>Array</code>s along the given dimension <code>dim</code>.</p><p><strong>Examples</strong></p><pre><code class="language-julia-repl">julia&gt; Flux.unstack([1 3 5 7; 2 4 6 8], 2)
4-element Array{Array{Int64,1},1}:
[1, 2]
[3, 4]
[5, 6]
[7, 8]</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/utils.jl#L106-L120">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.chunk" href="#Flux.chunk"><code>Flux.chunk</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">chunk(xs, n)</code></pre><p>Split <code>xs</code> into <code>n</code> parts.</p><p><strong>Examples</strong></p><pre><code class="language-julia-repl">julia&gt; Flux.chunk(1:10, 3)
[7, 8]</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/utils.jl#L106-L120">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.chunk" href="#Flux.chunk"><code>Flux.chunk</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">chunk(xs, n)</code></pre><p>Split <code>xs</code> into <code>n</code> parts.</p><p><strong>Examples</strong></p><pre><code class="language-julia-repl">julia&gt; Flux.chunk(1:10, 3)
3-element Array{UnitRange{Int64},1}:
1:4
5:8
@ -55,18 +55,18 @@ julia&gt; Flux.chunk(collect(1:10), 3)
3-element Array{SubArray{Int64,1,Array{Int64,1},Tuple{UnitRange{Int64}},true},1}:
[1, 2, 3, 4]
[5, 6, 7, 8]
[9, 10]</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/utils.jl#L123-L142">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.frequencies" href="#Flux.frequencies"><code>Flux.frequencies</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">frequencies(xs)</code></pre><p>Count the number of times that each element of <code>xs</code> appears.</p><p><strong>Examples</strong></p><pre><code class="language-julia-repl">julia&gt; Flux.frequencies([&#39;a&#39;,&#39;b&#39;,&#39;b&#39;])
[9, 10]</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/utils.jl#L123-L142">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.frequencies" href="#Flux.frequencies"><code>Flux.frequencies</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">frequencies(xs)</code></pre><p>Count the number of times that each element of <code>xs</code> appears.</p><p><strong>Examples</strong></p><pre><code class="language-julia-repl">julia&gt; Flux.frequencies([&#39;a&#39;,&#39;b&#39;,&#39;b&#39;])
Dict{Char,Int64} with 2 entries:
&#39;a&#39; =&gt; 1
&#39;b&#39; =&gt; 2</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/utils.jl#L147-L159">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.batch" href="#Flux.batch"><code>Flux.batch</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">batch(xs)</code></pre><p>Batch the arrays in <code>xs</code> into a single array.</p><p><strong>Examples</strong></p><pre><code class="language-julia-repl">julia&gt; Flux.batch([[1,2,3],[4,5,6]])
&#39;b&#39; =&gt; 2</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/utils.jl#L147-L159">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.batch" href="#Flux.batch"><code>Flux.batch</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">batch(xs)</code></pre><p>Batch the arrays in <code>xs</code> into a single array.</p><p><strong>Examples</strong></p><pre><code class="language-julia-repl">julia&gt; Flux.batch([[1,2,3],[4,5,6]])
3×2 Array{Int64,2}:
1 4
2 5
3 6</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/utils.jl#L172-L185">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.batchseq" href="#Flux.batchseq"><code>Flux.batchseq</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">batchseq(seqs, pad)</code></pre><p>Take a list of <code>N</code> sequences, and turn them into a single sequence where each item is a batch of <code>N</code>. Short sequences will be padded by <code>pad</code>.</p><p><strong>Examples</strong></p><pre><code class="language-julia-repl">julia&gt; Flux.batchseq([[1, 2, 3], [4, 5]], 0)
3 6</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/utils.jl#L172-L185">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.batchseq" href="#Flux.batchseq"><code>Flux.batchseq</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">batchseq(seqs, pad)</code></pre><p>Take a list of <code>N</code> sequences, and turn them into a single sequence where each item is a batch of <code>N</code>. Short sequences will be padded by <code>pad</code>.</p><p><strong>Examples</strong></p><pre><code class="language-julia-repl">julia&gt; Flux.batchseq([[1, 2, 3], [4, 5]], 0)
3-element Array{Array{Int64,1},1}:
[1, 4]
[2, 5]
[3, 0]</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/utils.jl#L217-L231">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Base.rpad-Tuple{AbstractArray{T,1} where T,Integer,Any}" href="#Base.rpad-Tuple{AbstractArray{T,1} where T,Integer,Any}"><code>Base.rpad</code></a> — <span class="docstring-category">Method</span></header><section><div><p>Return the given sequence padded with <code>p</code> up to a maximum length of <code>n</code>.</p><p><strong>Examples</strong></p><pre><code class="language-julia-repl">julia&gt; rpad([1, 2], 4, 0)
[3, 0]</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/utils.jl#L217-L231">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Base.rpad-Tuple{AbstractArray{T,1} where T,Integer,Any}" href="#Base.rpad-Tuple{AbstractArray{T,1} where T,Integer,Any}"><code>Base.rpad</code></a> — <span class="docstring-category">Method</span></header><section><div><p>Return the given sequence padded with <code>p</code> up to a maximum length of <code>n</code>.</p><p><strong>Examples</strong></p><pre><code class="language-julia-repl">julia&gt; rpad([1, 2], 4, 0)
4-element Array{Int64,1}:
1
2
@ -77,15 +77,15 @@ julia&gt; rpad([1, 2, 3], 2, 0)
3-element Array{Int64,1}:
1
2
3</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/utils.jl#L196-L214">source</a></section></article><h2 id="Layer-Initialization-1"><a class="docs-heading-anchor" href="#Layer-Initialization-1">Layer Initialization</a><a class="docs-heading-anchor-permalink" href="#Layer-Initialization-1" title="Permalink"></a></h2><p>These are primarily useful if you are planning to write your own layers. Flux initializes convolutional layers and recurrent cells with <code>glorot_uniform</code> by default. To change the default on an applicable layer, pass the desired function with the <code>init</code> keyword. For example:</p><pre><code class="language-julia-repl">julia&gt; conv = Conv((3, 3), 1 =&gt; 8, relu; init=Flux.glorot_normal)
3</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/utils.jl#L196-L214">source</a></section></article><h2 id="Layer-Initialization-1"><a class="docs-heading-anchor" href="#Layer-Initialization-1">Layer Initialization</a><a class="docs-heading-anchor-permalink" href="#Layer-Initialization-1" title="Permalink"></a></h2><p>These are primarily useful if you are planning to write your own layers. Flux initializes convolutional layers and recurrent cells with <code>glorot_uniform</code> by default. To change the default on an applicable layer, pass the desired function with the <code>init</code> keyword. For example:</p><pre><code class="language-julia-repl">julia&gt; conv = Conv((3, 3), 1 =&gt; 8, relu; init=Flux.glorot_normal)
Conv((3, 3), 1=&gt;8, relu)</code></pre><article class="docstring"><header><a class="docstring-binding" id="Flux.glorot_uniform" href="#Flux.glorot_uniform"><code>Flux.glorot_uniform</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">glorot_uniform(dims...)</code></pre><p>Return an <code>Array</code> of size <code>dims</code> containing random variables taken from a uniform distribution in the interval <span>$[-x, x]$</span>, where <code>x = sqrt(24 / sum(dims)) / 2</code>.</p><p><strong>Examples</strong></p><pre><code class="language-julia-repl">julia&gt; Flux.glorot_uniform(2, 3)
2×3 Array{Float32,2}:
0.601094 -0.57414 -0.814925
0.900868 0.805994 0.057514</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/utils.jl#L7-L20">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.glorot_normal" href="#Flux.glorot_normal"><code>Flux.glorot_normal</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">glorot_normal(dims...)</code></pre><p>Return an <code>Array</code> of size <code>dims</code> containing random variables taken from a normal distribution with mean 0 and standard deviation <code>sqrt(2 / sum(dims))</code>.</p><p><strong>Examples</strong></p><pre><code class="language-julia-repl">julia&gt; Flux.glorot_normal(3, 2)
0.900868 0.805994 0.057514</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/utils.jl#L7-L20">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.glorot_normal" href="#Flux.glorot_normal"><code>Flux.glorot_normal</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">glorot_normal(dims...)</code></pre><p>Return an <code>Array</code> of size <code>dims</code> containing random variables taken from a normal distribution with mean 0 and standard deviation <code>sqrt(2 / sum(dims))</code>.</p><p><strong>Examples</strong></p><pre><code class="language-julia-repl">julia&gt; Flux.glorot_normal(3, 2)
3×2 Array{Float32,2}:
0.429505 -0.0852891
0.523935 0.371009
-0.223261 0.188052</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/utils.jl#L23-L37">source</a></section></article><h2 id="Model-Abstraction-1"><a class="docs-heading-anchor" href="#Model-Abstraction-1">Model Abstraction</a><a class="docs-heading-anchor-permalink" href="#Model-Abstraction-1" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-binding" id="Flux.destructure" href="#Flux.destructure"><code>Flux.destructure</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">destructure(m)</code></pre><p>Flatten a model&#39;s parameters into a single weight vector.</p><pre><code class="language-none">julia&gt; m = Chain(Dense(10, 5, σ), Dense(5, 2), softmax)
-0.223261 0.188052</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/utils.jl#L23-L37">source</a></section></article><h2 id="Model-Abstraction-1"><a class="docs-heading-anchor" href="#Model-Abstraction-1">Model Abstraction</a><a class="docs-heading-anchor-permalink" href="#Model-Abstraction-1" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-binding" id="Flux.destructure" href="#Flux.destructure"><code>Flux.destructure</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">destructure(m)</code></pre><p>Flatten a model&#39;s parameters into a single weight vector.</p><pre><code class="language-none">julia&gt; m = Chain(Dense(10, 5, σ), Dense(5, 2), softmax)
Chain(Dense(10, 5, σ), Dense(5, 2), softmax)
julia&gt; θ, re = destructure(m);
@ -94,6 +94,6 @@ julia&gt; θ
67-element Array{Float32,1}:
-0.1407104
...</code></pre><p>The second return value <code>re</code> allows you to reconstruct the original network after making modifications to the weight vector (for example, with a hypernetwork).</p><pre><code class="language-none">julia&gt; re(θ .* 2)
Chain(Dense(10, 5, σ), Dense(5, 2), softmax)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/utils.jl#L249-L269">source</a></section></article><h2 id="Callback-Helpers-1"><a class="docs-heading-anchor" href="#Callback-Helpers-1">Callback Helpers</a><a class="docs-heading-anchor-permalink" href="#Callback-Helpers-1" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-binding" id="Flux.throttle" href="#Flux.throttle"><code>Flux.throttle</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">throttle(f, timeout; leading=true, trailing=false)</code></pre><p>Return a function that when invoked, will only be triggered at most once during <code>timeout</code> seconds.</p><p>Normally, the throttled function will run as much as it can, without ever going more than once per <code>wait</code> duration; but if you&#39;d like to disable the execution on the leading edge, pass <code>leading=false</code>. To enable execution on the trailing edge, pass <code>trailing=true</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/utils.jl#L281-L291">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.stop" href="#Flux.Optimise.stop"><code>Flux.Optimise.stop</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">stop()</code></pre><p>Call <code>Flux.stop()</code> in a callback to indicate when a callback condition is met. This will trigger the train loop to stop and exit.</p><p><strong>Examples</strong></p><pre><code class="language-julia">cb = function ()
Chain(Dense(10, 5, σ), Dense(5, 2), softmax)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/utils.jl#L249-L269">source</a></section></article><h2 id="Callback-Helpers-1"><a class="docs-heading-anchor" href="#Callback-Helpers-1">Callback Helpers</a><a class="docs-heading-anchor-permalink" href="#Callback-Helpers-1" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-binding" id="Flux.throttle" href="#Flux.throttle"><code>Flux.throttle</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">throttle(f, timeout; leading=true, trailing=false)</code></pre><p>Return a function that when invoked, will only be triggered at most once during <code>timeout</code> seconds.</p><p>Normally, the throttled function will run as much as it can, without ever going more than once per <code>wait</code> duration; but if you&#39;d like to disable the execution on the leading edge, pass <code>leading=false</code>. To enable execution on the trailing edge, pass <code>trailing=true</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/utils.jl#L281-L291">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="Flux.Optimise.stop" href="#Flux.Optimise.stop"><code>Flux.Optimise.stop</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">stop()</code></pre><p>Call <code>Flux.stop()</code> in a callback to indicate when a callback condition is met. This will trigger the train loop to stop and exit.</p><p><strong>Examples</strong></p><pre><code class="language-julia">cb = function ()
accuracy() &gt; 0.9 &amp;&amp; Flux.stop()
end</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/9237cdaf5b543e03bebfcd7113ab6505cc3c7b88/src/optimise/train.jl#L42-L54">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../ecosystem/">« The Julia Ecosystem</a><a class="docs-footer-nextpage" href="../performance/">Performance Tips »</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="Saturday 25 April 2020 05:13">Saturday 25 April 2020</span>. Using Julia version 1.4.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
end</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c444226db542502a9a0311648de783f87c738a54/src/optimise/train.jl#L42-L54">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../ecosystem/">« The Julia Ecosystem</a><a class="docs-footer-nextpage" href="../performance/">Performance Tips »</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 4 May 2020 13:31">Monday 4 May 2020</span>. Using Julia version 1.3.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>