<htmllang="en"><head><metacharset="UTF-8"/><metaname="viewport"content="width=device-width, initial-scale=1.0"/><title>Advanced Model Building · Flux</title><script>(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
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Params([[0.66722 0.774872 0.249809; 0.843321 0.403843 0.429232; 0.683525 0.662455 0.065297]])</code></pre><p>Only the fields returned by <code>trainable</code> will be collected as trainable parameters of the layer when calling <code>Flux.params</code>.</p><p>Another way of achieving this is through the <code>@functor</code> macro directly. Here, we can mark the fields we are interested in by grouping them in the second argument:</p><pre><codeclass="language-julia">Flux.@functor Affine (W,)</code></pre><p>However, doing this requires the <code>struct</code> to have a corresponding constructor that accepts those parameters.</p><h2id="Freezing-Layer-Parameters-1"><aclass="docs-heading-anchor"href="#Freezing-Layer-Parameters-1">Freezing Layer Parameters</a><aclass="docs-heading-anchor-permalink"href="#Freezing-Layer-Parameters-1"title="Permalink"></a></h2><p>When it is desired to not include all the model parameters (for e.g. transfer learning), we can simply not pass in those layers into our call to <code>params</code>.</p><p>Consider a simple multi-layer perceptron model where we want to avoid optimising the first two <code>Dense</code> layers. We can obtain this using the slicing features <code>Chain</code> provides:</p><pre><codeclass="language-julia">m = Chain(
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><codeclass="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><codeclass="language-julia">ps = params(m)
delete!(ps, m[2].b) </code></pre></article><navclass="docs-footer"><aclass="docs-footer-prevpage"href="../layers/">« Model Reference</a><aclass="docs-footer-nextpage"href="../nnlib/">NNlib »</a></nav></div><divclass="modal"id="documenter-settings"><divclass="modal-background"></div><divclass="modal-card"><headerclass="modal-card-head"><pclass="modal-card-title">Settings</p><buttonclass="delete"></button></header><sectionclass="modal-card-body"><p><labelclass="label">Theme</label><divclass="select"><selectid="documenter-themepicker"><optionvalue="documenter-light">documenter-light</option><optionvalue="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <ahref="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> on <spanclass="colophon-date"title="Tuesday 12 May 2020 15:14">Tuesday 12 May 2020</span>. Using Julia version 1.3.1.</p></section><footerclass="modal-card-foot"></footer></div></div></div></body></html>
