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| <h1 id="customized-loss-function">Customized loss function</h1> |
| |
| <p>This tutorial provides guidelines for using customized loss function in network construction.</p> |
| |
| <h2 id="model-training-example">Model Training Example</h2> |
| |
| <p>Let's begin with a small regression example. We can build and train a regression model with the following code:</p> |
| <div class="highlight"><pre><code class="language-r" data-lang="r"><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="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"mlbench"</span><span class="p">)</span><span class="w"> |
| </span><span class="n">BostonHousing</span><span class="p">[,</span><span class="w"> </span><span class="n">sapply</span><span class="p">(</span><span class="n">BostonHousing</span><span class="p">,</span><span class="w"> </span><span class="n">is.factor</span><span class="p">)]</span><span class="w"> </span><span class="o"><-</span><span class="w"> |
| </span><span class="nf">as.numeric</span><span class="p">(</span><span class="nf">as.character</span><span class="p">(</span><span class="n">BostonHousing</span><span class="p">[,</span><span class="w"> </span><span class="n">sapply</span><span class="p">(</span><span class="n">BostonHousing</span><span class="p">,</span><span class="w"> </span><span class="n">is.factor</span><span class="p">)]))</span><span class="w"> |
| </span><span class="n">BostonHousing</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">data.frame</span><span class="p">(</span><span class="n">scale</span><span class="p">(</span><span class="n">BostonHousing</span><span class="p">))</span><span class="w"> |
| |
| </span><span class="n">test.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">5</span><span class="p">)</span><span class="w"> </span><span class="c1"># 1 pt in 5 used for testing</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="o">-</span><span class="n">test.ind</span><span class="p">,</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="o">-</span><span class="n">test.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">test.ind</span><span class="p">,</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">test.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">require</span><span class="p">(</span><span class="n">mxnet</span><span class="p">)</span><span class="w"> |
| </span></code></pre></div><div class="highlight"><pre><code class="language-" data-lang="">## Loading required package: mxnet |
| </code></pre></div><div class="highlight"><pre><code class="language-r" data-lang="r"><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">label</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">"label"</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="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">14</span><span class="p">,</span><span class="w"> </span><span class="n">name</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"fc1"</span><span class="p">)</span><span class="w"> |
| </span><span class="n">tanh1</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">mx.symbol.Activation</span><span class="p">(</span><span class="n">fc1</span><span class="p">,</span><span class="w"> </span><span class="n">act_type</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"tanh"</span><span class="p">,</span><span class="w"> </span><span class="n">name</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"tanh1"</span><span class="p">)</span><span class="w"> |
| </span><span class="n">fc2</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">tanh1</span><span class="p">,</span><span class="w"> </span><span class="n">num_hidden</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">1</span><span class="p">,</span><span class="w"> </span><span class="n">name</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"fc2"</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">fc2</span><span class="p">,</span><span class="w"> </span><span class="n">name</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"lro"</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="n">lro</span><span class="p">,</span><span class="w"> </span><span class="n">X</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">train.x</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">train.y</span><span class="p">,</span><span class="w"> |
| </span><span class="n">ctx</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mx.cpu</span><span class="p">(),</span><span class="w"> |
| </span><span class="n">num.round</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">5</span><span class="p">,</span><span class="w"> |
| </span><span class="n">array.batch.size</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">60</span><span class="p">,</span><span class="w"> |
| </span><span class="n">optimizer</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"rmsprop"</span><span class="p">,</span><span class="w"> |
| </span><span class="n">verbose</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="kc">TRUE</span><span class="p">,</span><span class="w"> |
| </span><span class="n">array.layout</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"rowmajor"</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="kc">NULL</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="kc">NULL</span><span class="p">)</span><span class="w"> |
| </span></code></pre></div><div class="highlight"><pre><code class="language-" data-lang="">## Start training with 1 devices |
| </code></pre></div><div class="highlight"><pre><code class="language-r" data-lang="r"><span class="n">pred</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">predict</span><span class="p">(</span><span class="n">model</span><span class="p">,</span><span class="w"> </span><span class="n">test.x</span><span class="p">)</span><span class="w"> |
| </span></code></pre></div><div class="highlight"><pre><code class="language-" data-lang="">## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor.. |
| </code></pre></div><div class="highlight"><pre><code class="language-r" data-lang="r"><span class="nf">sum</span><span class="p">((</span><span class="n">test.y</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">pred</span><span class="p">[</span><span class="m">1</span><span class="p">,])</span><span class="o">^</span><span class="m">2</span><span class="p">)</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="nf">length</span><span class="p">(</span><span class="n">test.y</span><span class="p">)</span><span class="w"> |
| </span></code></pre></div><div class="highlight"><pre><code class="language-" data-lang="">## [1] 0.2485236 |
| </code></pre></div> |
| <p>Besides the <code>LinearRegressionOutput</code>, we also provide <code>LogisticRegressionOutput</code> and <code>MAERegressionOutput</code>. However, this might not be enough for real-world models. You can provide your own loss function by using <code>mx.symbol.MakeLoss</code> when constructing the network.</p> |
| |
| <h2 id="how-to-use-your-own-loss-function">How to Use Your Own Loss Function</h2> |
| |
| <p>We still use our previous example, but this time we use <code>mx.symbol.MakeLoss</code> to minimize the <code>(pred-label)^2</code></p> |
| <div class="highlight"><pre><code class="language-r" data-lang="r"><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">label</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">"label"</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="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">14</span><span class="p">,</span><span class="w"> </span><span class="n">name</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"fc1"</span><span class="p">)</span><span class="w"> |
| </span><span class="n">tanh1</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">mx.symbol.Activation</span><span class="p">(</span><span class="n">fc1</span><span class="p">,</span><span class="w"> </span><span class="n">act_type</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"tanh"</span><span class="p">,</span><span class="w"> </span><span class="n">name</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"tanh1"</span><span class="p">)</span><span class="w"> |
| </span><span class="n">fc2</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">tanh1</span><span class="p">,</span><span class="w"> </span><span class="n">num_hidden</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">1</span><span class="p">,</span><span class="w"> </span><span class="n">name</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"fc2"</span><span class="p">)</span><span class="w"> |
| </span><span class="n">lro2</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">mx.symbol.MakeLoss</span><span class="p">(</span><span class="n">mx.symbol.square</span><span class="p">(</span><span class="n">mx.symbol.Reshape</span><span class="p">(</span><span class="n">fc2</span><span class="p">,</span><span class="w"> </span><span class="n">shape</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="o">-</span><span class="w"> </span><span class="n">label</span><span class="p">),</span><span class="w"> </span><span class="n">name</span><span class="o">=</span><span class="s2">"lro2"</span><span class="p">)</span><span class="w"> |
| </span></code></pre></div> |
| <p>Then we can train the network just as usual.</p> |
| <div class="highlight"><pre><code class="language-r" data-lang="r"><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">model2</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="n">lro2</span><span class="p">,</span><span class="w"> </span><span class="n">X</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">train.x</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">train.y</span><span class="p">,</span><span class="w"> |
| </span><span class="n">ctx</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mx.cpu</span><span class="p">(),</span><span class="w"> |
| </span><span class="n">num.round</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">5</span><span class="p">,</span><span class="w"> |
| </span><span class="n">array.batch.size</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">60</span><span class="p">,</span><span class="w"> |
| </span><span class="n">optimizer</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"rmsprop"</span><span class="p">,</span><span class="w"> |
| </span><span class="n">verbose</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="kc">TRUE</span><span class="p">,</span><span class="w"> |
| </span><span class="n">array.layout</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"rowmajor"</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="kc">NULL</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="kc">NULL</span><span class="p">)</span><span class="w"> |
| </span></code></pre></div><div class="highlight"><pre><code class="language-" data-lang="">## Start training with 1 devices |
| </code></pre></div> |
| <p>We should get very similar results because we are actually minimizing the same loss function. However, the result is quite different.</p> |
| <div class="highlight"><pre><code class="language-r" data-lang="r"><span class="n">pred2</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">predict</span><span class="p">(</span><span class="n">model2</span><span class="p">,</span><span class="w"> </span><span class="n">test.x</span><span class="p">)</span><span class="w"> |
| </span></code></pre></div><div class="highlight"><pre><code class="language-" data-lang="">## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor.. |
| </code></pre></div><div class="highlight"><pre><code class="language-r" data-lang="r"><span class="nf">sum</span><span class="p">((</span><span class="n">test.y</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">pred2</span><span class="p">)</span><span class="o">^</span><span class="m">2</span><span class="p">)</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="nf">length</span><span class="p">(</span><span class="n">test.y</span><span class="p">)</span><span class="w"> |
| </span></code></pre></div><div class="highlight"><pre><code class="language-" data-lang="">## [1] 1.234584 |
| </code></pre></div> |
| <p>This is because output of <code>mx.symbol.MakeLoss</code> is the gradient of loss with respect to the input data. We can get the real prediction as below.</p> |
| <div class="highlight"><pre><code class="language-r" data-lang="r"><span class="n">internals</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">internals</span><span class="p">(</span><span class="n">model2</span><span class="o">$</span><span class="n">symbol</span><span class="p">)</span><span class="w"> |
| </span><span class="n">fc_symbol</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">internals</span><span class="p">[[</span><span class="n">match</span><span class="p">(</span><span class="s2">"fc2_output"</span><span class="p">,</span><span class="w"> </span><span class="n">outputs</span><span class="p">(</span><span class="n">internals</span><span class="p">))]]</span><span class="w"> |
| |
| </span><span class="n">model3</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="nf">list</span><span class="p">(</span><span class="n">symbol</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">fc_symbol</span><span class="p">,</span><span class="w"> |
| </span><span class="n">arg.params</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">model2</span><span class="o">$</span><span class="n">arg.params</span><span class="p">,</span><span class="w"> |
| </span><span class="n">aux.params</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">model2</span><span class="o">$</span><span class="n">aux.params</span><span class="p">)</span><span class="w"> |
| |
| </span><span class="nf">class</span><span class="p">(</span><span class="n">model3</span><span class="p">)</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="s2">"MXFeedForwardModel"</span><span class="w"> |
| |
| </span><span class="n">pred3</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">predict</span><span class="p">(</span><span class="n">model3</span><span class="p">,</span><span class="w"> </span><span class="n">test.x</span><span class="p">)</span><span class="w"> |
| </span></code></pre></div><div class="highlight"><pre><code class="language-" data-lang="">## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor.. |
| </code></pre></div><div class="highlight"><pre><code class="language-r" data-lang="r"><span class="nf">sum</span><span class="p">((</span><span class="n">test.y</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">pred3</span><span class="p">[</span><span class="m">1</span><span class="p">,])</span><span class="o">^</span><span class="m">2</span><span class="p">)</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="nf">length</span><span class="p">(</span><span class="n">test.y</span><span class="p">)</span><span class="w"> |
| </span></code></pre></div><div class="highlight"><pre><code class="language-" data-lang="">## [1] 0.248294 |
| </code></pre></div> |
| <p>We have provided many operations on the symbols. An example of <code>|pred-label|</code> can be found below.</p> |
| <div class="highlight"><pre><code class="language-r" data-lang="r"><span class="n">lro_abs</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">mx.symbol.MakeLoss</span><span class="p">(</span><span class="n">mx.symbol.abs</span><span class="p">(</span><span class="n">mx.symbol.Reshape</span><span class="p">(</span><span class="n">fc2</span><span class="p">,</span><span class="w"> </span><span class="n">shape</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="o">-</span><span class="w"> </span><span class="n">label</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">model4</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="n">lro_abs</span><span class="p">,</span><span class="w"> </span><span class="n">X</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">train.x</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">train.y</span><span class="p">,</span><span class="w"> |
| </span><span class="n">ctx</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mx.cpu</span><span class="p">(),</span><span class="w"> |
| </span><span class="n">num.round</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">20</span><span class="p">,</span><span class="w"> |
| </span><span class="n">array.batch.size</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">60</span><span class="p">,</span><span class="w"> |
| </span><span class="n">optimizer</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"sgd"</span><span class="p">,</span><span class="w"> |
| </span><span class="n">learning.rate</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">0.001</span><span class="p">,</span><span class="w"> |
| </span><span class="n">verbose</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="kc">TRUE</span><span class="p">,</span><span class="w"> |
| </span><span class="n">array.layout</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"rowmajor"</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="kc">NULL</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="kc">NULL</span><span class="p">)</span><span class="w"> |
| </span></code></pre></div><div class="highlight"><pre><code class="language-" data-lang="">## Start training with 1 devices |
| </code></pre></div><div class="highlight"><pre><code class="language-r" data-lang="r"><span class="n">internals</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">internals</span><span class="p">(</span><span class="n">model4</span><span class="o">$</span><span class="n">symbol</span><span class="p">)</span><span class="w"> |
| </span><span class="n">fc_symbol</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">internals</span><span class="p">[[</span><span class="n">match</span><span class="p">(</span><span class="s2">"fc2_output"</span><span class="p">,</span><span class="w"> </span><span class="n">outputs</span><span class="p">(</span><span class="n">internals</span><span class="p">))]]</span><span class="w"> |
| |
| </span><span class="n">model5</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="nf">list</span><span class="p">(</span><span class="n">symbol</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">fc_symbol</span><span class="p">,</span><span class="w"> |
| </span><span class="n">arg.params</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">model4</span><span class="o">$</span><span class="n">arg.params</span><span class="p">,</span><span class="w"> |
| </span><span class="n">aux.params</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">model4</span><span class="o">$</span><span class="n">aux.params</span><span class="p">)</span><span class="w"> |
| |
| </span><span class="nf">class</span><span class="p">(</span><span class="n">model5</span><span class="p">)</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="s2">"MXFeedForwardModel"</span><span class="w"> |
| |
| </span><span class="n">pred5</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">predict</span><span class="p">(</span><span class="n">model5</span><span class="p">,</span><span class="w"> </span><span class="n">test.x</span><span class="p">)</span><span class="w"> |
| </span></code></pre></div><div class="highlight"><pre><code class="language-" data-lang="">## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor.. |
| </code></pre></div><div class="highlight"><pre><code class="language-r" data-lang="r"><span class="nf">sum</span><span class="p">(</span><span class="nf">abs</span><span class="p">(</span><span class="n">test.y</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">pred5</span><span class="p">[</span><span class="m">1</span><span class="p">,]))</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="nf">length</span><span class="p">(</span><span class="n">test.y</span><span class="p">)</span><span class="w"> |
| </span></code></pre></div><div class="highlight"><pre><code class="language-" data-lang="">## [1] 0.7056902 |
| </code></pre></div><div class="highlight"><pre><code class="language-r" data-lang="r"><span class="n">lro_mae</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">mx.symbol.MAERegressionOutput</span><span class="p">(</span><span class="n">fc2</span><span class="p">,</span><span class="w"> </span><span class="n">name</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"lro"</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">model6</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="n">lro_mae</span><span class="p">,</span><span class="w"> </span><span class="n">X</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">train.x</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">train.y</span><span class="p">,</span><span class="w"> |
| </span><span class="n">ctx</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mx.cpu</span><span class="p">(),</span><span class="w"> |
| </span><span class="n">num.round</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">20</span><span class="p">,</span><span class="w"> |
| </span><span class="n">array.batch.size</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">60</span><span class="p">,</span><span class="w"> |
| </span><span class="n">optimizer</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"sgd"</span><span class="p">,</span><span class="w"> |
| </span><span class="n">learning.rate</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">0.001</span><span class="p">,</span><span class="w"> |
| </span><span class="n">verbose</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="kc">TRUE</span><span class="p">,</span><span class="w"> |
| </span><span class="n">array.layout</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"rowmajor"</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="kc">NULL</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="kc">NULL</span><span class="p">)</span><span class="w"> |
| </span></code></pre></div><div class="highlight"><pre><code class="language-" data-lang="">## Start training with 1 devices |
| </code></pre></div><div class="highlight"><pre><code class="language-r" data-lang="r"><span class="n">pred6</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">predict</span><span class="p">(</span><span class="n">model6</span><span class="p">,</span><span class="w"> </span><span class="n">test.x</span><span class="p">)</span><span class="w"> |
| </span></code></pre></div><div class="highlight"><pre><code class="language-" data-lang="">## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor.. |
| </code></pre></div><div class="highlight"><pre><code class="language-r" data-lang="r"><span class="nf">sum</span><span class="p">(</span><span class="nf">abs</span><span class="p">(</span><span class="n">test.y</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">pred6</span><span class="p">[</span><span class="m">1</span><span class="p">,]))</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="nf">length</span><span class="p">(</span><span class="n">test.y</span><span class="p">)</span><span class="w"> |
| </span></code></pre></div><div class="highlight"><pre><code class="language-" data-lang="">## [1] 0.7056902 |
| </code></pre></div> |
| <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> |
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