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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="../../assets/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" id="search-form" action="../../search/"><input id="search-query" name="q" type="text" placeholder="Search docs"/></form><ul><li><a class="toctext" href="../../">Home</a></li><li><span class="toctext">Building Models</span><ul><li><a class="toctext" href="../../models/basics/">Basics</a></li><li><a class="toctext" href="../../models/recurrence/">Recurrence</a></li><li><a class="toctext" href="../../models/regularisation/">Regularisation</a></li><li><a class="toctext" href="../../models/layers/">Model Reference</a></li></ul></li><li><span class="toctext">Training Models</span><ul><li><a class="toctext" href="../optimisers/">Optimisers</a></li><li class="current"><a class="toctext" href>Training</a><ul class="internal"><li><a class="toctext" href="#Loss-Functions-1">Loss Functions</a></li><li><a class="toctext" href="#Model-parameters-1">Model parameters</a></li><li><a class="toctext" href="#Datasets-1">Datasets</a></li><li><a class="toctext" href="#Callbacks-1">Callbacks</a></li></ul></li></ul></li><li><a class="toctext" href="../../data/onehot/">One-Hot Encoding</a></li><li><a class="toctext" href="../../gpu/">GPU Support</a></li><li><a class="toctext" href="../../saving/">Saving & Loading</a></li><li><a class="toctext" href="../../performance/">Performance Tips</a></li><li><a class="toctext" href="../../community/">Community</a></li></ul></nav><article id="docs"><header><nav><ul><li>Training Models</li><li><a href>Training</a></li></ul><a class="edit-page" href="https://github.com/FluxML/Flux.jl/blob/master/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><h1><a class="nav-anchor" id="Training-1" href="#Training-1">Training</a></h1><p>To actually train a model we need four things:</p><ul><li>A <em>objective function</em>, that evaluates how well a model is doing given some input data.</li><li>The trainable parameters of the model.</li><li>A collection of data points that will be provided to the objective function.</li><li>An <a href="../optimisers/">optimiser</a> that will update the model parameters appropriately.</li></ul><p>With these we can call <code>Flux.train!</code>:</p><pre><code class="language-julia">Flux.train!(objective, params, 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 objective function must return a number representing how far the model is from its target – the <em>loss</em> of the model. The <code>loss</code> function that we defined in <a href="../../models/basics/">basics</a> will work as an objective. We can also define an objective in terms of some model:</p><pre><code class="language-julia">m = Chain(
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Dense(784, 32, σ),
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Dense(32, 10), softmax)
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loss(x, y) = Flux.mse(m(x), y)
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ps = Flux.params(m)
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# later
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Flux.train!(loss, ps, data, opt)</code></pre><p>The objective 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>crossentropy</code> for cross entropy loss, but you can calculate it however you want.</p><p>At first glance it may seem strange that the model that we want to train is not part of the input arguments of <code>Flux.train!</code> too. However the target of the optimizer is not the model itself, but the objective function that represents the departure between modelled and observed data. In other words, the model is implicitly defined in the objective function, and there is no need to give it explicitly. Passing the objective function instead of the model and a cost function separately provides more flexibility, and the possibility of optimizing the calculations.</p><h2><a class="nav-anchor" id="Model-parameters-1" href="#Model-parameters-1">Model parameters</a></h2><p>The model to be trained must have a set of tracked parameters that are used to calculate the gradients of the objective function. In the <a href="../../models/basics/">basics</a> section it is explained how to create models with such parameters. The second argument of the function <code>Flux.train!</code> must be an object containing those parameters, which can be obtained from a model <code>m</code> as <code>params(m)</code>.</p><p>Such an object contains a reference to the model's parameters, not a copy, such that after their training, the model behaves according to their updated values.</p><h2><a class="nav-anchor" id="Datasets-1" href="#Datasets-1">Datasets</a></h2><p>The <code>data</code> argument provides a collection of data to train with (usually a set of inputs <code>x</code> and target outputs <code>y</code>). For example, here's a dummy data set with only one data point:</p><pre><code class="language-julia">x = rand(784)
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y = rand(10)
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data = [(x, y)]</code></pre><p><code>Flux.train!</code> will call <code>loss(x, y)</code>, calculate gradients, update the weights and then move on to the next data point if there is one. We can train the model on the same data three times:</p><pre><code class="language-julia">data = [(x, y), (x, y), (x, y)]
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# Or equivalently
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data = Iterators.repeated((x, y), 3)</code></pre><p>It's common to load the <code>x</code>s and <code>y</code>s separately. In this case you can use <code>zip</code>:</p><pre><code class="language-julia">xs = [rand(784), rand(784), rand(784)]
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ys = [rand( 10), rand( 10), rand( 10)]
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data = zip(xs, ys)</code></pre><p>Note that, by default, <code>train!</code> only loops over the data once (a single "epoch"). A convenient way to run multiple epochs from the REPL is provided by <code>@epochs</code>.</p><pre><code class="language-julia">julia> using Flux: @epochs
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julia> @epochs 2 println("hello")
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INFO: Epoch 1
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hello
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INFO: Epoch 2
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hello
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julia> @epochs 2 Flux.train!(...)
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# Train for two epochs</code></pre><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's used for callbacks so that you can observe the training process. For example:</p><pre><code class="language-julia">train!(objective, ps, data, opt, cb = () -> println("training"))</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 ...
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evalcb() = @show(loss(test_x, test_y))
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Flux.train!(objective, ps, data, opt,
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cb = throttle(evalcb, 5))</code></pre><p>Calling <code>Flux.stop()</code> in a callback will exit the training loop early.</p><pre><code class="language-julia">cb = function ()
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accuracy() > 0.9 && Flux.stop()
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end</code></pre><footer><hr/><a class="previous" href="../optimisers/"><span class="direction">Previous</span><span class="title">Optimisers</span></a><a class="next" href="../../data/onehot/"><span class="direction">Next</span><span class="title">One-Hot Encoding</span></a></footer></article></body></html>
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