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Flux.jl/latest/training/training.html

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<html lang="en"><head><meta charset="UTF-8"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><title>Training · Flux</title><script>(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
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</script><link href="https://cdnjs.cloudflare.com/ajax/libs/normalize/4.2.0/normalize.min.css" rel="stylesheet" type="text/css"/><link href="https://fonts.googleapis.com/css?family=Lato|Roboto+Mono" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/styles/default.min.css" rel="stylesheet" type="text/css"/><script>documenterBaseURL=".."</script><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.2.0/require.min.js" data-main="../assets/documenter.js"></script><script src="../siteinfo.js"></script><script src="../../versions.js"></script><link href="../assets/documenter.css" rel="stylesheet" type="text/css"/><link href="../../flux.css" rel="stylesheet" type="text/css"/></head><body><nav class="toc"><h1>Flux</h1><select id="version-selector" onChange="window.location.href=this.value" style="visibility: hidden"></select><form class="search" action="../search.html"><input id="search-query" name="q" type="text" placeholder="Search docs"/></form><ul><li><a class="toctext" href="../index.html">Home</a></li><li><span class="toctext">Building Models</span><ul><li><a class="toctext" href="../models/basics.html">Basics</a></li><li><a class="toctext" href="../models/recurrence.html">Recurrence</a></li><li><a class="toctext" href="../models/layers.html">Layer Reference</a></li></ul></li><li><span class="toctext">Training Models</span><ul><li><a class="toctext" href="optimisers.html">Optimisers</a></li><li class="current"><a class="toctext" href="training.html">Training</a><ul class="internal"><li><a class="toctext" href="#Loss-Functions-1">Loss Functions</a></li><li><a class="toctext" href="#Callbacks-1">Callbacks</a></li></ul></li></ul></li><li><a class="toctext" href="../contributing.html">Contributing &amp; Help</a></li></ul></nav><article id="docs"><header><nav><ul><li>Training Models</li><li><a href="training.html">Training</a></li></ul><a class="edit-page" href="https://github.com/FluxML/Flux.jl/tree/3f83be7bb7cdcdf56ec19514d0639ae85d6c9c56/docs/src/training/training.md"><span class="fa"></span> Edit on GitHub</a></nav><hr/><div id="topbar"><span>Training</span><a class="fa fa-bars" href="#"></a></div></header><p>To actually train a model we need three things:</p><ul><li><p>A <em>loss function</em>, that evaluates how well a model is doing given some input data.</p></li><li><p>A collection of data points that will be provided to the loss function.</p></li><li><p>An <a href="optimisers.html">optimiser</a> that will update the model parameters appropriately.</p></li></ul><p>With these we can call <code>Flux.train!</code>:</p><pre><code class="language-julia">Flux.train!(loss, data, opt)</code></pre><p>There are plenty of examples in the <a href="https://github.com/FluxML/model-zoo">model zoo</a>.</p><h2><a class="nav-anchor" id="Loss-Functions-1" href="#Loss-Functions-1">Loss Functions</a></h2><p>The <code>loss</code> that we defined in <a href="../models/basics.html">basics</a> is completely valid for training. We can also define a loss in terms of some model:</p><pre><code class="language-julia">m = Chain(
build based on c2db42d
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Dense(784, 32, σ),
Dense(32, 10), softmax)
loss(x, y) = Flux.mse(m(x), y)</code></pre><p>The loss will almost always be defined in terms of some <em>cost function</em> that measures the distance of the prediction <code>m(x)</code> from the target <code>y</code>. Flux has several of these built in, like <code>mse</code> for mean squared error or <code>logloss</code> for cross entropy loss, but you can calculate it however you want.</p><h2><a class="nav-anchor" id="Callbacks-1" href="#Callbacks-1">Callbacks</a></h2><p><code>train!</code> takes an additional argument, <code>cb</code>, that&#39;s used for callbacks so that you can observe the training process. For example:</p><pre><code class="language-julia">train!(loss, data, opt, cb = () -&gt; println(&quot;training&quot;))</code></pre><p>Callbacks are called for every batch of training data. You can slow this down using <code>Flux.throttle(f, timeout)</code> which prevents <code>f</code> from being called more than once every <code>timeout</code> seconds.</p><p>A more typical callback might look like this:</p><pre><code class="language-julia">test_x, test_y = # ... create single batch of test data ...
evalcb() = @show(loss(test_x, test_y))
Flux.train!(loss, data, opt,
cb = throttle(evalcb, 5))</code></pre><footer><hr/><a class="previous" href="optimisers.html"><span class="direction">Previous</span><span class="title">Optimisers</span></a><a class="next" href="../contributing.html"><span class="direction">Next</span><span class="title">Contributing &amp; Help</span></a></footer></article></body></html>
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