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| <h1 id="callback-function">Callback Function</h1> |
| |
| <p>This tutorial provides guidelines for using and writing callback functions, |
| which can very useful in model training.</p> |
| |
| <h2 id="model-training-example">Model Training Example</h2> |
| |
| <p>Let's begin with a small example. We can build and train a model with the following code:</p> |
| <div class="highlight"><pre><code class="language-r" data-lang="r"><span class="w"> </span><span class="n">library</span><span class="p">(</span><span class="n">mxnet</span><span class="p">)</span><span class="w"> |
| </span><span class="n">data</span><span class="p">(</span><span class="n">BostonHousing</span><span class="p">,</span><span class="w"> </span><span class="n">package</span><span class="o">=</span><span class="s2">"mlbench"</span><span class="p">)</span><span class="w"> |
| </span><span class="n">train.ind</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">seq</span><span class="p">(</span><span class="m">1</span><span class="p">,</span><span class="w"> </span><span class="m">506</span><span class="p">,</span><span class="w"> </span><span class="m">3</span><span class="p">)</span><span class="w"> |
| </span><span class="n">train.x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">data.matrix</span><span class="p">(</span><span class="n">BostonHousing</span><span class="p">[</span><span class="n">train.ind</span><span class="p">,</span><span class="w"> </span><span class="m">-14</span><span class="p">])</span><span class="w"> |
| </span><span class="n">train.y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">BostonHousing</span><span class="p">[</span><span class="n">train.ind</span><span class="p">,</span><span class="w"> </span><span class="m">14</span><span class="p">]</span><span class="w"> |
| </span><span class="n">test.x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">data.matrix</span><span class="p">(</span><span class="n">BostonHousing</span><span class="p">[</span><span class="o">-</span><span class="n">train.ind</span><span class="p">,</span><span class="w"> </span><span class="m">-14</span><span class="p">])</span><span class="w"> |
| </span><span class="n">test.y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">BostonHousing</span><span class="p">[</span><span class="o">-</span><span class="n">train.ind</span><span class="p">,</span><span class="w"> </span><span class="m">14</span><span class="p">]</span><span class="w"> |
| </span><span class="n">data</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">mx.symbol.Variable</span><span class="p">(</span><span class="s2">"data"</span><span class="p">)</span><span class="w"> |
| </span><span class="n">fc1</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">mx.symbol.FullyConnected</span><span class="p">(</span><span class="n">data</span><span class="p">,</span><span class="w"> </span><span class="n">num_hidden</span><span class="o">=</span><span class="m">1</span><span class="p">)</span><span class="w"> |
| </span><span class="n">lro</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">mx.symbol.LinearRegressionOutput</span><span class="p">(</span><span class="n">fc1</span><span class="p">)</span><span class="w"> |
| </span><span class="n">mx.set.seed</span><span class="p">(</span><span class="m">0</span><span class="p">)</span><span class="w"> |
| </span><span class="n">model</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">mx.model.FeedForward.create</span><span class="p">(</span><span class="w"> |
| </span><span class="n">lro</span><span class="p">,</span><span class="w"> </span><span class="n">X</span><span class="o">=</span><span class="n">train.x</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="o">=</span><span class="n">train.y</span><span class="p">,</span><span class="w"> |
| </span><span class="n">eval.data</span><span class="o">=</span><span class="nf">list</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">test.x</span><span class="p">,</span><span class="w"> </span><span class="n">label</span><span class="o">=</span><span class="n">test.y</span><span class="p">),</span><span class="w"> |
| </span><span class="n">ctx</span><span class="o">=</span><span class="n">mx.cpu</span><span class="p">(),</span><span class="w"> </span><span class="n">num.round</span><span class="o">=</span><span class="m">10</span><span class="p">,</span><span class="w"> </span><span class="n">array.batch.size</span><span class="o">=</span><span class="m">20</span><span class="p">,</span><span class="w"> |
| </span><span class="n">learning.rate</span><span class="o">=</span><span class="m">2e-6</span><span class="p">,</span><span class="w"> </span><span class="n">momentum</span><span class="o">=</span><span class="m">0.9</span><span class="p">,</span><span class="w"> </span><span class="n">eval.metric</span><span class="o">=</span><span class="n">mx.metric.rmse</span><span class="p">)</span><span class="w"> |
| </span></code></pre></div><div class="highlight"><pre><code class="language-" data-lang=""> ## Auto detect layout of input matrix, use row major.. |
| ## Start training with 1 devices |
| ## [1] Train-rmse=16.063282524034 |
| ## [1] Validation-rmse=10.1766446093622 |
| ## [2] Train-rmse=12.2792375712573 |
| ## [2] Validation-rmse=12.4331776190813 |
| ## [3] Train-rmse=11.1984634005885 |
| ## [3] Validation-rmse=10.3303041888193 |
| ## [4] Train-rmse=10.2645236892904 |
| ## [4] Validation-rmse=8.42760407903415 |
| ## [5] Train-rmse=9.49711005504284 |
| ## [5] Validation-rmse=8.44557808483234 |
| ## [6] Train-rmse=9.07733734175182 |
| ## [6] Validation-rmse=8.33225500266177 |
| ## [7] Train-rmse=9.07884450847991 |
| ## [7] Validation-rmse=8.38827833418459 |
| ## [8] Train-rmse=9.10463850277417 |
| ## [8] Validation-rmse=8.37394452365264 |
| ## [9] Train-rmse=9.03977049028532 |
| ## [9] Validation-rmse=8.25927979725672 |
| ## [10] Train-rmse=8.96870685004475 |
| ## [10] Validation-rmse=8.19509291481822 |
| </code></pre></div> |
| <p>We also provide two optional parameters, <code>batch.end.callback</code> and <code>epoch.end.callback</code>, which can provide great flexibility in model training.</p> |
| |
| <h2 id="how-to-use-callback-functions">How to Use Callback Functions</h2> |
| |
| <p>This package provides two callback functions:</p> |
| |
| <ul> |
| <li><code>mx.callback.save.checkpoint</code> saves a checkpoint to files during each period iteration.</li> |
| </ul> |
| <div class="highlight"><pre><code class="language-r" data-lang="r"><span class="w"> </span><span class="n">model</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">mx.model.FeedForward.create</span><span class="p">(</span><span class="w"> |
| </span><span class="n">lro</span><span class="p">,</span><span class="w"> </span><span class="n">X</span><span class="o">=</span><span class="n">train.x</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="o">=</span><span class="n">train.y</span><span class="p">,</span><span class="w"> |
| </span><span class="n">eval.data</span><span class="o">=</span><span class="nf">list</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">test.x</span><span class="p">,</span><span class="w"> </span><span class="n">label</span><span class="o">=</span><span class="n">test.y</span><span class="p">),</span><span class="w"> |
| </span><span class="n">ctx</span><span class="o">=</span><span class="n">mx.cpu</span><span class="p">(),</span><span class="w"> </span><span class="n">num.round</span><span class="o">=</span><span class="m">10</span><span class="p">,</span><span class="w"> </span><span class="n">array.batch.size</span><span class="o">=</span><span class="m">20</span><span class="p">,</span><span class="w"> |
| </span><span class="n">learning.rate</span><span class="o">=</span><span class="m">2e-6</span><span class="p">,</span><span class="w"> </span><span class="n">momentum</span><span class="o">=</span><span class="m">0.9</span><span class="p">,</span><span class="w"> </span><span class="n">eval.metric</span><span class="o">=</span><span class="n">mx.metric.rmse</span><span class="p">,</span><span class="w"> |
| </span><span class="n">epoch.end.callback</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mx.callback.save.checkpoint</span><span class="p">(</span><span class="s2">"boston"</span><span class="p">))</span><span class="w"> |
| </span></code></pre></div><div class="highlight"><pre><code class="language-" data-lang=""> ## Auto detect layout of input matrix, use row major.. |
| ## Start training with 1 devices |
| ## [1] Train-rmse=19.1621424021617 |
| ## [1] Validation-rmse=20.721515592165 |
| ## Model checkpoint saved to boston-0001.params |
| ## [2] Train-rmse=13.5127391952367 |
| ## [2] Validation-rmse=14.1822123675007 |
| ## Model checkpoint saved to boston-0002.params |
| </code></pre></div> |
| <ul> |
| <li><code>mx.callback.log.train.metric</code> logs a training metric each period. You can use it either as a <code>batch.end.callback</code> or an |
| <code>epoch.end.callback</code>.</li> |
| </ul> |
| <div class="highlight"><pre><code class="language-r" data-lang="r"><span class="w"> </span><span class="n">model</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">mx.model.FeedForward.create</span><span class="p">(</span><span class="w"> |
| </span><span class="n">lro</span><span class="p">,</span><span class="w"> </span><span class="n">X</span><span class="o">=</span><span class="n">train.x</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="o">=</span><span class="n">train.y</span><span class="p">,</span><span class="w"> |
| </span><span class="n">eval.data</span><span class="o">=</span><span class="nf">list</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">test.x</span><span class="p">,</span><span class="w"> </span><span class="n">label</span><span class="o">=</span><span class="n">test.y</span><span class="p">),</span><span class="w"> |
| </span><span class="n">ctx</span><span class="o">=</span><span class="n">mx.cpu</span><span class="p">(),</span><span class="w"> </span><span class="n">num.round</span><span class="o">=</span><span class="m">10</span><span class="p">,</span><span class="w"> </span><span class="n">array.batch.size</span><span class="o">=</span><span class="m">20</span><span class="p">,</span><span class="w"> |
| </span><span class="n">learning.rate</span><span class="o">=</span><span class="m">2e-6</span><span class="p">,</span><span class="w"> </span><span class="n">momentum</span><span class="o">=</span><span class="m">0.9</span><span class="p">,</span><span class="w"> </span><span class="n">eval.metric</span><span class="o">=</span><span class="n">mx.metric.rmse</span><span class="p">,</span><span class="w"> |
| </span><span class="n">batch.end.callback</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mx.callback.log.train.metric</span><span class="p">(</span><span class="m">5</span><span class="p">))</span><span class="w"> |
| </span></code></pre></div><div class="highlight"><pre><code class="language-" data-lang=""> ## Auto detect layout of input matrix, use row major.. |
| ## Start training with 1 devices |
| ## Batch [5] Train-rmse=17.6514558545416 |
| ## [1] Train-rmse=15.2879610219001 |
| ## [1] Validation-rmse=12.3332062820921 |
| ## Batch [5] Train-rmse=11.939392828565 |
| ## [2] Train-rmse=11.4382242547217 |
| ## [2] Validation-rmse=9.91176550103181 |
| ............ |
| </code></pre></div> |
| <p>You also can save the training and evaluation errors for later use by passing a reference class:</p> |
| <div class="highlight"><pre><code class="language-r" data-lang="r"><span class="w"> </span><span class="n">logger</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">mx.metric.logger</span><span class="o">$</span><span class="n">new</span><span class="p">()</span><span class="w"> |
| </span><span class="n">model</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">mx.model.FeedForward.create</span><span class="p">(</span><span class="w"> |
| </span><span class="n">lro</span><span class="p">,</span><span class="w"> </span><span class="n">X</span><span class="o">=</span><span class="n">train.x</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="o">=</span><span class="n">train.y</span><span class="p">,</span><span class="w"> |
| </span><span class="n">eval.data</span><span class="o">=</span><span class="nf">list</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">test.x</span><span class="p">,</span><span class="w"> </span><span class="n">label</span><span class="o">=</span><span class="n">test.y</span><span class="p">),</span><span class="w"> |
| </span><span class="n">ctx</span><span class="o">=</span><span class="n">mx.cpu</span><span class="p">(),</span><span class="w"> </span><span class="n">num.round</span><span class="o">=</span><span class="m">10</span><span class="p">,</span><span class="w"> </span><span class="n">array.batch.size</span><span class="o">=</span><span class="m">20</span><span class="p">,</span><span class="w"> |
| </span><span class="n">learning.rate</span><span class="o">=</span><span class="m">2e-6</span><span class="p">,</span><span class="w"> </span><span class="n">momentum</span><span class="o">=</span><span class="m">0.9</span><span class="p">,</span><span class="w"> </span><span class="n">eval.metric</span><span class="o">=</span><span class="n">mx.metric.rmse</span><span class="p">,</span><span class="w"> |
| </span><span class="n">epoch.end.callback</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mx.callback.log.train.metric</span><span class="p">(</span><span class="m">5</span><span class="p">,</span><span class="w"> </span><span class="n">logger</span><span class="p">))</span><span class="w"> |
| </span></code></pre></div><div class="highlight"><pre><code class="language-" data-lang=""> ## Auto detect layout of input matrix, use row major.. |
| ## Start training with 1 devices |
| ## [1] Train-rmse=19.1083228733256 |
| ## [1] Validation-rmse=12.7150687428974 |
| ## [2] Train-rmse=15.7684378116157 |
| ## [2] Validation-rmse=14.8105319420491 |
| ............ |
| </code></pre></div><div class="highlight"><pre><code class="language-r" data-lang="r"><span class="w"> </span><span class="n">head</span><span class="p">(</span><span class="n">logger</span><span class="o">$</span><span class="n">train</span><span class="p">)</span><span class="w"> |
| </span></code></pre></div><div class="highlight"><pre><code class="language-" data-lang=""> ## [1] 19.108323 15.768438 13.531470 11.386050 9.555477 9.351324 |
| </code></pre></div><div class="highlight"><pre><code class="language-r" data-lang="r"><span class="w"> </span><span class="n">head</span><span class="p">(</span><span class="n">logger</span><span class="o">$</span><span class="n">eval</span><span class="p">)</span><span class="w"> |
| </span></code></pre></div><div class="highlight"><pre><code class="language-" data-lang=""> ## [1] 12.715069 14.810532 15.840361 10.898733 9.349706 9.363087 |
| </code></pre></div> |
| <h2 id="how-to-write-your-own-callback-functions">How to Write Your Own Callback Functions</h2> |
| |
| <p>You can find the source code for the two callback functions on <a href="https://github.com/dmlc/mxnet/blob/master/R-package/R/callback.R">GitHub</a> and use it as a template:</p> |
| |
| <p>Basically, all callback functions follow the following structure:</p> |
| <div class="highlight"><pre><code class="language-r" data-lang="r"><span class="w"> </span><span class="n">mx.callback.fun</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="k">function</span><span class="p">()</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="k">function</span><span class="p">(</span><span class="n">iteration</span><span class="p">,</span><span class="w"> </span><span class="n">nbatch</span><span class="p">,</span><span class="w"> </span><span class="n">env</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span></code></pre></div> |
| <p>The following <code>mx.callback.save.checkpoint</code> function is stateless. It gets the model from the environment and saves it:.</p> |
| <div class="highlight"><pre><code class="language-r" data-lang="r"><span class="w"> </span><span class="n">mx.callback.save.checkpoint</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="k">function</span><span class="p">(</span><span class="n">prefix</span><span class="p">,</span><span class="w"> </span><span class="n">period</span><span class="o">=</span><span class="m">1</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="k">function</span><span class="p">(</span><span class="n">iteration</span><span class="p">,</span><span class="w"> </span><span class="n">nbatch</span><span class="p">,</span><span class="w"> </span><span class="n">env</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">iteration</span><span class="w"> </span><span class="o">%%</span><span class="w"> </span><span class="n">period</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="m">0</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="n">mx.model.save</span><span class="p">(</span><span class="n">env</span><span class="o">$</span><span class="n">model</span><span class="p">,</span><span class="w"> </span><span class="n">prefix</span><span class="p">,</span><span class="w"> </span><span class="n">iteration</span><span class="p">)</span><span class="w"> |
| </span><span class="n">cat</span><span class="p">(</span><span class="n">sprintf</span><span class="p">(</span><span class="s2">"Model checkpoint saved to %s-%04d.params\n"</span><span class="p">,</span><span class="w"> </span><span class="n">prefix</span><span class="p">,</span><span class="w"> </span><span class="n">iteration</span><span class="p">))</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span><span class="nf">return</span><span class="p">(</span><span class="kc">TRUE</span><span class="p">)</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span></code></pre></div> |
| <p>The <code>mx.callback.log.train.metric</code> is a little more complex. It holds a reference class and updates it during the training |
| process:</p> |
| <div class="highlight"><pre><code class="language-r" data-lang="r"><span class="w"> </span><span class="n">mx.callback.log.train.metric</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="k">function</span><span class="p">(</span><span class="n">period</span><span class="p">,</span><span class="w"> </span><span class="n">logger</span><span class="o">=</span><span class="kc">NULL</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="k">function</span><span class="p">(</span><span class="n">iteration</span><span class="p">,</span><span class="w"> </span><span class="n">nbatch</span><span class="p">,</span><span class="w"> </span><span class="n">env</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">nbatch</span><span class="w"> </span><span class="o">%%</span><span class="w"> </span><span class="n">period</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="o">&&</span><span class="w"> </span><span class="o">!</span><span class="nf">is.null</span><span class="p">(</span><span class="n">env</span><span class="o">$</span><span class="n">metric</span><span class="p">))</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="n">result</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">env</span><span class="o">$</span><span class="n">metric</span><span class="o">$</span><span class="n">get</span><span class="p">(</span><span class="n">env</span><span class="o">$</span><span class="n">train.metric</span><span class="p">)</span><span class="w"> |
| </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">nbatch</span><span class="w"> </span><span class="o">!=</span><span class="w"> </span><span class="m">0</span><span class="p">)</span><span class="w"> |
| </span><span class="n">cat</span><span class="p">(</span><span class="n">paste0</span><span class="p">(</span><span class="s2">"Batch ["</span><span class="p">,</span><span class="w"> </span><span class="n">nbatch</span><span class="p">,</span><span class="w"> </span><span class="s2">"] Train-"</span><span class="p">,</span><span class="w"> </span><span class="n">result</span><span class="o">$</span><span class="n">name</span><span class="p">,</span><span class="w"> </span><span class="s2">"="</span><span class="p">,</span><span class="w"> </span><span class="n">result</span><span class="o">$</span><span class="n">value</span><span class="p">,</span><span class="w"> </span><span class="s2">"\n"</span><span class="p">))</span><span class="w"> |
| </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="o">!</span><span class="nf">is.null</span><span class="p">(</span><span class="n">logger</span><span class="p">))</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="nf">class</span><span class="p">(</span><span class="n">logger</span><span class="p">)</span><span class="w"> </span><span class="o">!=</span><span class="w"> </span><span class="s2">"mx.metric.logger"</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="n">stop</span><span class="p">(</span><span class="s2">"Invalid mx.metric.logger."</span><span class="p">)</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span><span class="n">logger</span><span class="o">$</span><span class="n">train</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="nf">c</span><span class="p">(</span><span class="n">logger</span><span class="o">$</span><span class="n">train</span><span class="p">,</span><span class="w"> </span><span class="n">result</span><span class="o">$</span><span class="n">value</span><span class="p">)</span><span class="w"> |
| </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="o">!</span><span class="nf">is.null</span><span class="p">(</span><span class="n">env</span><span class="o">$</span><span class="n">eval.metric</span><span class="p">))</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="n">result</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">env</span><span class="o">$</span><span class="n">metric</span><span class="o">$</span><span class="n">get</span><span class="p">(</span><span class="n">env</span><span class="o">$</span><span class="n">eval.metric</span><span class="p">)</span><span class="w"> |
| </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">nbatch</span><span class="w"> </span><span class="o">!=</span><span class="w"> </span><span class="m">0</span><span class="p">)</span><span class="w"> |
| </span><span class="n">cat</span><span class="p">(</span><span class="n">paste0</span><span class="p">(</span><span class="s2">"Batch ["</span><span class="p">,</span><span class="w"> </span><span class="n">nbatch</span><span class="p">,</span><span class="w"> </span><span class="s2">"] Validation-"</span><span class="p">,</span><span class="w"> </span><span class="n">result</span><span class="o">$</span><span class="n">name</span><span class="p">,</span><span class="w"> </span><span class="s2">"="</span><span class="p">,</span><span class="w"> </span><span class="n">result</span><span class="o">$</span><span class="n">value</span><span class="p">,</span><span class="w"> </span><span class="s2">"\n"</span><span class="p">))</span><span class="w"> |
| </span><span class="n">logger</span><span class="o">$</span><span class="n">eval</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="nf">c</span><span class="p">(</span><span class="n">logger</span><span class="o">$</span><span class="n">eval</span><span class="p">,</span><span class="w"> </span><span class="n">result</span><span class="o">$</span><span class="n">value</span><span class="p">)</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span><span class="nf">return</span><span class="p">(</span><span class="kc">TRUE</span><span class="p">)</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span></code></pre></div> |
| <p>Now you might be curious why both callback functions <code>return(TRUE)</code>.</p> |
| |
| <p>Can we <code>return(FALSE)</code>?</p> |
| |
| <p>Yes! You can stop the training early with <code>return(FALSE)</code>. See the following examples.</p> |
| <div class="highlight"><pre><code class="language-r" data-lang="r"><span class="w"> </span><span class="n">mx.callback.early.stop</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="k">function</span><span class="p">(</span><span class="n">eval.metric</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="k">function</span><span class="p">(</span><span class="n">iteration</span><span class="p">,</span><span class="w"> </span><span class="n">nbatch</span><span class="p">,</span><span class="w"> </span><span class="n">env</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="o">!</span><span class="nf">is.null</span><span class="p">(</span><span class="n">env</span><span class="o">$</span><span class="n">metric</span><span class="p">))</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="o">!</span><span class="nf">is.null</span><span class="p">(</span><span class="n">eval.metric</span><span class="p">))</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="n">result</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">env</span><span class="o">$</span><span class="n">metric</span><span class="o">$</span><span class="n">get</span><span class="p">(</span><span class="n">env</span><span class="o">$</span><span class="n">eval.metric</span><span class="p">)</span><span class="w"> |
| </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">result</span><span class="o">$</span><span class="n">value</span><span class="w"> </span><span class="o"><</span><span class="w"> </span><span class="n">eval.metric</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="nf">return</span><span class="p">(</span><span class="kc">FALSE</span><span class="p">)</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span><span class="nf">return</span><span class="p">(</span><span class="kc">TRUE</span><span class="p">)</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span><span class="n">model</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">mx.model.FeedForward.create</span><span class="p">(</span><span class="w"> |
| </span><span class="n">lro</span><span class="p">,</span><span class="w"> </span><span class="n">X</span><span class="o">=</span><span class="n">train.x</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="o">=</span><span class="n">train.y</span><span class="p">,</span><span class="w"> |
| </span><span class="n">eval.data</span><span class="o">=</span><span class="nf">list</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">test.x</span><span class="p">,</span><span class="w"> </span><span class="n">label</span><span class="o">=</span><span class="n">test.y</span><span class="p">),</span><span class="w"> |
| </span><span class="n">ctx</span><span class="o">=</span><span class="n">mx.cpu</span><span class="p">(),</span><span class="w"> </span><span class="n">num.round</span><span class="o">=</span><span class="m">10</span><span class="p">,</span><span class="w"> </span><span class="n">array.batch.size</span><span class="o">=</span><span class="m">20</span><span class="p">,</span><span class="w"> |
| </span><span class="n">learning.rate</span><span class="o">=</span><span class="m">2e-6</span><span class="p">,</span><span class="w"> </span><span class="n">momentum</span><span class="o">=</span><span class="m">0.9</span><span class="p">,</span><span class="w"> </span><span class="n">eval.metric</span><span class="o">=</span><span class="n">mx.metric.rmse</span><span class="p">,</span><span class="w"> |
| </span><span class="n">epoch.end.callback</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mx.callback.early.stop</span><span class="p">(</span><span class="m">10</span><span class="p">))</span><span class="w"> |
| </span></code></pre></div><div class="highlight"><pre><code class="language-" data-lang=""> ## Auto detect layout of input matrix, use row major.. |
| ## Start training with 1 devices |
| ## [1] Train-rmse=18.5897984387033 |
| ## [1] Validation-rmse=13.5555213820571 |
| ## [2] Train-rmse=12.5867564040256 |
| ## [2] Validation-rmse=9.76304967080928 |
| </code></pre></div> |
| <p>When the validation metric dips below the threshold we set, the training process stops.</p> |
| |
| <h2 id="next-steps">Next Steps</h2> |
| |
| <ul> |
| <li><a href="/api/r/docs/tutorials/five_minutes_neural_network">Neural Networks with MXNet in Five Minutes</a></li> |
| <li><a href="/api/r/docs/tutorials/classify_real_image_with_pretrained_model">Classify Real-World Images with a Pretrained Model</a></li> |
| <li><a href="/api/r/docs/tutorials/mnist_competition">Handwritten Digits Classification Competition</a></li> |
| <li><a href="/api/r/docs/tutorials/char_rnn_model">Character Language Model Using RNN</a></li> |
| </ul> |
| </div> |
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