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<div class="section" id="handwritten-digits-classification-competition">
<span id="handwritten-digits-classification-competition"></span><h1>Handwritten Digits Classification Competition<a class="headerlink" href="#handwritten-digits-classification-competition" title="Permalink to this headline"></a></h1>
<p><a class="reference external" href="http://yann.lecun.com/exdb/mnist/">MNIST</a> is a handwritten digits image data set created by Yann LeCun. Every digit is represented by a 28 x 28 pixel image. It’s become a standard data set for testing classifiers on simple image input. A neural network is a strong model for image classification tasks. There’s a <a class="reference external" href="https://www.kaggle.com/c/digit-recognizer">long-term hosted competition</a> on Kaggle using this data set.
This tutorial shows how to use <a class="reference external" href="https://github.com/dmlc/mxnet/tree/master/R-package">MXNet</a> to compete in this challenge.</p>
<div class="section" id="loading-the-data">
<span id="loading-the-data"></span><h2>Loading the Data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline"></a></h2>
<p>First, let’s download the data from <a class="reference external" href="https://www.kaggle.com/c/digit-recognizer/data">Kaggle</a> and put it in the <code class="docutils literal"><span class="pre">data/</span></code> folder in your working directory.</p>
<p>Now we can read it in R and convert it to matrices:</p>
<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="nf">require</span><span class="p">(</span><span class="n">mxnet</span><span class="p">)</span>
</pre></div>
</div>
<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## Loading required package: mxnet</span>
<span class="c1">## Loading required package: methods</span>
</pre></div>
</div>
<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="n">train</span> <span class="o"><-</span> <span class="nf">read.csv</span><span class="p">(</span><span class="s">'data/train.csv'</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">TRUE</span><span class="p">)</span>
<span class="n">test</span> <span class="o"><-</span> <span class="nf">read.csv</span><span class="p">(</span><span class="s">'data/test.csv'</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">TRUE</span><span class="p">)</span>
<span class="n">train</span> <span class="o"><-</span> <span class="nf">data.matrix</span><span class="p">(</span><span class="n">train</span><span class="p">)</span>
<span class="n">test</span> <span class="o"><-</span> <span class="nf">data.matrix</span><span class="p">(</span><span class="n">test</span><span class="p">)</span>
<span class="n">train.x</span> <span class="o"><-</span> <span class="n">train[</span><span class="p">,</span><span class="m">-1</span><span class="n">]</span>
<span class="n">train.y</span> <span class="o"><-</span> <span class="n">train[</span><span class="p">,</span><span class="m">1</span><span class="n">]</span>
</pre></div>
</div>
<p>Every image is represented as a single row in train/test. The greyscale of each image falls in the range [0, 255]. Linearly transform it into [0,1] by using the following command:</p>
<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="n">train.x</span> <span class="o"><-</span> <span class="nf">t</span><span class="p">(</span><span class="n">train.x</span><span class="o">/</span><span class="m">255</span><span class="p">)</span>
<span class="n">test</span> <span class="o"><-</span> <span class="nf">t</span><span class="p">(</span><span class="n">test</span><span class="o">/</span><span class="m">255</span><span class="p">)</span>
</pre></div>
</div>
<p>Transpose the input matrix to npixel x nexamples, which is the major format for columns accepted by MXNet (and the convention of R).</p>
<p>In the label section, the number of each digit is fairly evenly distributed:</p>
<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="nf">table</span><span class="p">(</span><span class="n">train.y</span><span class="p">)</span>
</pre></div>
</div>
<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## train.y</span>
<span class="c1">## 0 1 2 3 4 5 6 7 8 9</span>
<span class="c1">## 4132 4684 4177 4351 4072 3795 4137 4401 4063 4188</span>
</pre></div>
</div>
</div>
<div class="section" id="configuring-the-network">
<span id="configuring-the-network"></span><h2>Configuring the Network<a class="headerlink" href="#configuring-the-network" title="Permalink to this headline"></a></h2>
<p>Now that we have the data, let’s configure the structure of our network:</p>
<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="n">data</span> <span class="o"><-</span> <span class="nf">mx.symbol.Variable</span><span class="p">(</span><span class="s">"data"</span><span class="p">)</span>
<span class="n">fc1</span> <span class="o"><-</span> <span class="nf">mx.symbol.FullyConnected</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s">"fc1"</span><span class="p">,</span> <span class="n">num_hidden</span><span class="o">=</span><span class="m">128</span><span class="p">)</span>
<span class="n">act1</span> <span class="o"><-</span> <span class="nf">mx.symbol.Activation</span><span class="p">(</span><span class="n">fc1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s">"relu1"</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="s">"relu"</span><span class="p">)</span>
<span class="n">fc2</span> <span class="o"><-</span> <span class="nf">mx.symbol.FullyConnected</span><span class="p">(</span><span class="n">act1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s">"fc2"</span><span class="p">,</span> <span class="n">num_hidden</span><span class="o">=</span><span class="m">64</span><span class="p">)</span>
<span class="n">act2</span> <span class="o"><-</span> <span class="nf">mx.symbol.Activation</span><span class="p">(</span><span class="n">fc2</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s">"relu2"</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="s">"relu"</span><span class="p">)</span>
<span class="n">fc3</span> <span class="o"><-</span> <span class="nf">mx.symbol.FullyConnected</span><span class="p">(</span><span class="n">act2</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s">"fc3"</span><span class="p">,</span> <span class="n">num_hidden</span><span class="o">=</span><span class="m">10</span><span class="p">)</span>
<span class="n">softmax</span> <span class="o"><-</span> <span class="nf">mx.symbol.SoftmaxOutput</span><span class="p">(</span><span class="n">fc3</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s">"sm"</span><span class="p">)</span>
</pre></div>
</div>
<ol class="simple">
<li>In <code class="docutils literal"><span class="pre">mxnet</span></code>, we use the data type <code class="docutils literal"><span class="pre">symbol</span></code> to configure the network. <code class="docutils literal"><span class="pre">data</span> <span class="pre"><-</span> <span class="pre">mx.symbol.Variable("data")</span></code> uses <code class="docutils literal"><span class="pre">data</span></code> to represent the input data, i.e., the input layer.</li>
<li>We set the first hidden layer with <code class="docutils literal"><span class="pre">fc1</span> <span class="pre"><-</span> <span class="pre">mx.symbol.FullyConnected(data,</span> <span class="pre">name="fc1",</span> <span class="pre">num_hidden=128)</span></code>. This layer has <code class="docutils literal"><span class="pre">data</span></code> as the input, its name, and the number of hidden neurons.</li>
<li>Activation is set with <code class="docutils literal"><span class="pre">act1</span> <span class="pre"><-</span> <span class="pre">mx.symbol.Activation(fc1,</span> <span class="pre">name="relu1",</span> <span class="pre">act_type="relu")</span></code>. The activation function takes the output from the first hidden layer, <code class="docutils literal"><span class="pre">fc1</span></code>.</li>
<li>The second hidden layer takes the result from <code class="docutils literal"><span class="pre">act1</span></code> as input, with its name as “fc2” and the number of hidden neurons as 64.</li>
<li>The second activation is almost the same as <code class="docutils literal"><span class="pre">act1</span></code>, except we have a different input source and name.</li>
<li>This generates the output layer. Because there are only 10 digits, we set the number of neurons to 10.</li>
<li>Finally, we set the activation to softmax to get a probabilistic prediction.</li>
</ol>
</div>
<div class="section" id="training">
<span id="training"></span><h2>Training<a class="headerlink" href="#training" title="Permalink to this headline"></a></h2>
<p>We are almost ready for the training process. Before we start the computation, let’s decide which device to use:</p>
<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="n">devices</span> <span class="o"><-</span> <span class="nf">mx.cpu</span><span class="p">()</span>
</pre></div>
</div>
<p>We assign CPU to <code class="docutils literal"><span class="pre">mxnet</span></code>. Now, you can run the following command to train the neural network! Note that <code class="docutils literal"><span class="pre">mx.set.seed</span></code> is the function that controls the random process in <code class="docutils literal"><span class="pre">mxnet</span></code>:</p>
<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="nf">mx.set.seed</span><span class="p">(</span><span class="m">0</span><span class="p">)</span>
<span class="n">model</span> <span class="o"><-</span> <span class="nf">mx.model.FeedForward.create</span><span class="p">(</span><span class="n">softmax</span><span class="p">,</span> <span class="n">X</span><span class="o">=</span><span class="n">train.x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">train.y</span><span class="p">,</span>
<span class="n">ctx</span><span class="o">=</span><span class="n">devices</span><span class="p">,</span> <span class="n">num.round</span><span class="o">=</span><span class="m">10</span><span class="p">,</span> <span class="n">array.batch.size</span><span class="o">=</span><span class="m">100</span><span class="p">,</span>
<span class="n">learning.rate</span><span class="o">=</span><span class="m">0.07</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="m">0.9</span><span class="p">,</span> <span class="n">eval.metric</span><span class="o">=</span><span class="n">mx.metric.accuracy</span><span class="p">,</span>
<span class="n">initializer</span><span class="o">=</span><span class="nf">mx.init.uniform</span><span class="p">(</span><span class="m">0.07</span><span class="p">),</span>
<span class="n">epoch.end.callback</span><span class="o">=</span><span class="nf">mx.callback.log.train.metric</span><span class="p">(</span><span class="m">100</span><span class="p">))</span>
</pre></div>
</div>
<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## Start training with 1 devices</span>
<span class="c1">## Batch [100] Train-accuracy=0.6563</span>
<span class="c1">## Batch [200] Train-accuracy=0.777999999999999</span>
<span class="c1">## Batch [300] Train-accuracy=0.827466666666665</span>
<span class="c1">## Batch [400] Train-accuracy=0.855499999999999</span>
<span class="c1">## [1] Train-accuracy=0.859832935560859</span>
<span class="c1">## Batch [100] Train-accuracy=0.9529</span>
<span class="c1">## Batch [200] Train-accuracy=0.953049999999999</span>
<span class="c1">## Batch [300] Train-accuracy=0.955866666666666</span>
<span class="c1">## Batch [400] Train-accuracy=0.957525000000001</span>
<span class="c1">## [2] Train-accuracy=0.958309523809525</span>
<span class="c1">## Batch [100] Train-accuracy=0.968</span>
<span class="c1">## Batch [200] Train-accuracy=0.9677</span>
<span class="c1">## Batch [300] Train-accuracy=0.9696</span>
<span class="c1">## Batch [400] Train-accuracy=0.970650000000002</span>
<span class="c1">## [3] Train-accuracy=0.970809523809526</span>
<span class="c1">## Batch [100] Train-accuracy=0.973</span>
<span class="c1">## Batch [200] Train-accuracy=0.974249999999999</span>
<span class="c1">## Batch [300] Train-accuracy=0.976</span>
<span class="c1">## Batch [400] Train-accuracy=0.977100000000003</span>
<span class="c1">## [4] Train-accuracy=0.977452380952384</span>
<span class="c1">## Batch [100] Train-accuracy=0.9834</span>
<span class="c1">## Batch [200] Train-accuracy=0.981949999999999</span>
<span class="c1">## Batch [300] Train-accuracy=0.981900000000001</span>
<span class="c1">## Batch [400] Train-accuracy=0.982600000000003</span>
<span class="c1">## [5] Train-accuracy=0.983000000000003</span>
<span class="c1">## Batch [100] Train-accuracy=0.983399999999999</span>
<span class="c1">## Batch [200] Train-accuracy=0.98405</span>
<span class="c1">## Batch [300] Train-accuracy=0.985000000000001</span>
<span class="c1">## Batch [400] Train-accuracy=0.985725000000003</span>
<span class="c1">## [6] Train-accuracy=0.985952380952384</span>
<span class="c1">## Batch [100] Train-accuracy=0.988999999999999</span>
<span class="c1">## Batch [200] Train-accuracy=0.9876</span>
<span class="c1">## Batch [300] Train-accuracy=0.988100000000001</span>
<span class="c1">## Batch [400] Train-accuracy=0.988750000000003</span>
<span class="c1">## [7] Train-accuracy=0.988880952380955</span>
<span class="c1">## Batch [100] Train-accuracy=0.991999999999999</span>
<span class="c1">## Batch [200] Train-accuracy=0.9912</span>
<span class="c1">## Batch [300] Train-accuracy=0.990066666666668</span>
<span class="c1">## Batch [400] Train-accuracy=0.990275000000003</span>
<span class="c1">## [8] Train-accuracy=0.990452380952384</span>
<span class="c1">## Batch [100] Train-accuracy=0.9937</span>
<span class="c1">## Batch [200] Train-accuracy=0.99235</span>
<span class="c1">## Batch [300] Train-accuracy=0.991966666666668</span>
<span class="c1">## Batch [400] Train-accuracy=0.991425000000003</span>
<span class="c1">## [9] Train-accuracy=0.991500000000003</span>
<span class="c1">## Batch [100] Train-accuracy=0.9942</span>
<span class="c1">## Batch [200] Train-accuracy=0.99245</span>
<span class="c1">## Batch [300] Train-accuracy=0.992433333333334</span>
<span class="c1">## Batch [400] Train-accuracy=0.992275000000002</span>
<span class="c1">## [10] Train-accuracy=0.992380952380955</span>
</pre></div>
</div>
</div>
<div class="section" id="making-a-prediction-and-submitting-to-the-competition">
<span id="making-a-prediction-and-submitting-to-the-competition"></span><h2>Making a Prediction and Submitting to the Competition<a class="headerlink" href="#making-a-prediction-and-submitting-to-the-competition" title="Permalink to this headline"></a></h2>
<p>To make a prediction, type:</p>
<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="n">preds</span> <span class="o"><-</span> <span class="nf">predict</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">test</span><span class="p">)</span>
<span class="nf">dim</span><span class="p">(</span><span class="n">preds</span><span class="p">)</span>
</pre></div>
</div>
<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## [1] 10 28000</span>
</pre></div>
</div>
<p>It is a matrix with 28000 rows and 10 cols, containing the desired classification probabilities from the output layer. To extract the maximum label for each row, use <code class="docutils literal"><span class="pre">max.col</span></code>:</p>
<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="n">pred.label</span> <span class="o"><-</span> <span class="nf">max.col</span><span class="p">(</span><span class="nf">t</span><span class="p">(</span><span class="n">preds</span><span class="p">))</span> <span class="o">-</span> <span class="m">1</span>
<span class="nf">table</span><span class="p">(</span><span class="n">pred.label</span><span class="p">)</span>
</pre></div>
</div>
<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## pred.label</span>
<span class="c1">## 0 1 2 3 4 5 6 7 8 9</span>
<span class="c1">## 2818 3195 2744 2767 2683 2596 2798 2790 2784 2825</span>
</pre></div>
</div>
<p>With a little extra effort to modify the .csv format, our submission is ready for the competition!</p>
<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="n">submission</span> <span class="o"><-</span> <span class="nf">data.frame</span><span class="p">(</span><span class="n">ImageId</span><span class="o">=</span><span class="m">1</span><span class="o">:</span><span class="nf">ncol</span><span class="p">(</span><span class="n">test</span><span class="p">),</span> <span class="n">Label</span><span class="o">=</span><span class="n">pred.label</span><span class="p">)</span>
<span class="nf">write.csv</span><span class="p">(</span><span class="n">submission</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="s">'submission.csv'</span><span class="p">,</span> <span class="n">row.names</span><span class="o">=</span><span class="kc">FALSE</span><span class="p">,</span> <span class="n">quote</span><span class="o">=</span><span class="kc">FALSE</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="lenet">
<span id="lenet"></span><h2>LeNet<a class="headerlink" href="#lenet" title="Permalink to this headline"></a></h2>
<p>Now let’s use a new network structure: <a class="reference external" href="http://yann.lecun.com/exdb/lenet/">LeNet</a>. It has been proposed by Yann LeCun for recognizing handwritten digits. We’ll demonstrate how to construct and train a LeNet in <code class="docutils literal"><span class="pre">mxnet</span></code>.</p>
<p>First, we construct the network:</p>
<div class="highlight-r"><div class="highlight"><pre><span></span><span class="c1"># input</span>
<span class="n">data</span> <span class="o"><-</span> <span class="nf">mx.symbol.Variable</span><span class="p">(</span><span class="s">'data'</span><span class="p">)</span>
<span class="c1"># first conv</span>
<span class="n">conv1</span> <span class="o"><-</span> <span class="nf">mx.symbol.Convolution</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">kernel</span><span class="o">=</span><span class="nf">c</span><span class="p">(</span><span class="m">5</span><span class="p">,</span><span class="m">5</span><span class="p">),</span> <span class="n">num_filter</span><span class="o">=</span><span class="m">20</span><span class="p">)</span>
<span class="n">tanh1</span> <span class="o"><-</span> <span class="nf">mx.symbol.Activation</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">conv1</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="s">"tanh"</span><span class="p">)</span>
<span class="n">pool1</span> <span class="o"><-</span> <span class="nf">mx.symbol.Pooling</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">tanh1</span><span class="p">,</span> <span class="n">pool_type</span><span class="o">=</span><span class="s">"max"</span><span class="p">,</span>
<span class="n">kernel</span><span class="o">=</span><span class="nf">c</span><span class="p">(</span><span class="m">2</span><span class="p">,</span><span class="m">2</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="nf">c</span><span class="p">(</span><span class="m">2</span><span class="p">,</span><span class="m">2</span><span class="p">))</span>
<span class="c1"># second conv</span>
<span class="n">conv2</span> <span class="o"><-</span> <span class="nf">mx.symbol.Convolution</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">pool1</span><span class="p">,</span> <span class="n">kernel</span><span class="o">=</span><span class="nf">c</span><span class="p">(</span><span class="m">5</span><span class="p">,</span><span class="m">5</span><span class="p">),</span> <span class="n">num_filter</span><span class="o">=</span><span class="m">50</span><span class="p">)</span>
<span class="n">tanh2</span> <span class="o"><-</span> <span class="nf">mx.symbol.Activation</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">conv2</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="s">"tanh"</span><span class="p">)</span>
<span class="n">pool2</span> <span class="o"><-</span> <span class="nf">mx.symbol.Pooling</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">tanh2</span><span class="p">,</span> <span class="n">pool_type</span><span class="o">=</span><span class="s">"max"</span><span class="p">,</span>
<span class="n">kernel</span><span class="o">=</span><span class="nf">c</span><span class="p">(</span><span class="m">2</span><span class="p">,</span><span class="m">2</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="nf">c</span><span class="p">(</span><span class="m">2</span><span class="p">,</span><span class="m">2</span><span class="p">))</span>
<span class="c1"># first fullc</span>
<span class="n">flatten</span> <span class="o"><-</span> <span class="nf">mx.symbol.Flatten</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">pool2</span><span class="p">)</span>
<span class="n">fc1</span> <span class="o"><-</span> <span class="nf">mx.symbol.FullyConnected</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">flatten</span><span class="p">,</span> <span class="n">num_hidden</span><span class="o">=</span><span class="m">500</span><span class="p">)</span>
<span class="n">tanh3</span> <span class="o"><-</span> <span class="nf">mx.symbol.Activation</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">fc1</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="s">"tanh"</span><span class="p">)</span>
<span class="c1"># second fullc</span>
<span class="n">fc2</span> <span class="o"><-</span> <span class="nf">mx.symbol.FullyConnected</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">tanh3</span><span class="p">,</span> <span class="n">num_hidden</span><span class="o">=</span><span class="m">10</span><span class="p">)</span>
<span class="c1"># loss</span>
<span class="n">lenet</span> <span class="o"><-</span> <span class="nf">mx.symbol.SoftmaxOutput</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">fc2</span><span class="p">)</span>
</pre></div>
</div>
<p>Then let’s reshape the matrices into arrays:</p>
<div class="highlight-r"><div class="highlight"><pre><span></span><span class="n">train.array</span> <span class="o"><-</span> <span class="n">train.x</span>
<span class="nf">dim</span><span class="p">(</span><span class="n">train.array</span><span class="p">)</span> <span class="o"><-</span> <span class="nf">c</span><span class="p">(</span><span class="m">28</span><span class="p">,</span> <span class="m">28</span><span class="p">,</span> <span class="m">1</span><span class="p">,</span> <span class="nf">ncol</span><span class="p">(</span><span class="n">train.x</span><span class="p">))</span>
<span class="n">test.array</span> <span class="o"><-</span> <span class="n">test</span>
<span class="nf">dim</span><span class="p">(</span><span class="n">test.array</span><span class="p">)</span> <span class="o"><-</span> <span class="nf">c</span><span class="p">(</span><span class="m">28</span><span class="p">,</span> <span class="m">28</span><span class="p">,</span> <span class="m">1</span><span class="p">,</span> <span class="nf">ncol</span><span class="p">(</span><span class="n">test</span><span class="p">))</span>
</pre></div>
</div>
<p>We want to compare training speed on different devices, so define the devices:</p>
<div class="highlight-r"><div class="highlight"><pre><span></span><span class="n">n.gpu</span> <span class="o"><-</span> <span class="m">1</span>
<span class="n">device.cpu</span> <span class="o"><-</span> <span class="nf">mx.cpu</span><span class="p">()</span>
<span class="n">device.gpu</span> <span class="o"><-</span> <span class="nf">lapply</span><span class="p">(</span><span class="m">0</span><span class="o">:</span><span class="p">(</span><span class="n">n.gpu</span><span class="m">-1</span><span class="p">),</span> <span class="nf">function</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="p">{</span>
<span class="nf">mx.gpu</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
<span class="p">})</span>
</pre></div>
</div>
<p>We can pass a list of devices to ask MXNet to train on multiple GPUs (you can do this for CPUs,
but because internal computation of CPUs is already multi-threaded, there is less gain than with using GPUs).</p>
<p>Start by training on the CPU first. Because this takes a bit time, we run it for just one iteration.</p>
<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="nf">mx.set.seed</span><span class="p">(</span><span class="m">0</span><span class="p">)</span>
<span class="n">tic</span> <span class="o"><-</span> <span class="nf">proc.time</span><span class="p">()</span>
<span class="n">model</span> <span class="o"><-</span> <span class="nf">mx.model.FeedForward.create</span><span class="p">(</span><span class="n">lenet</span><span class="p">,</span> <span class="n">X</span><span class="o">=</span><span class="n">train.array</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">train.y</span><span class="p">,</span>
<span class="n">ctx</span><span class="o">=</span><span class="n">device.cpu</span><span class="p">,</span> <span class="n">num.round</span><span class="o">=</span><span class="m">1</span><span class="p">,</span> <span class="n">array.batch.size</span><span class="o">=</span><span class="m">100</span><span class="p">,</span>
<span class="n">learning.rate</span><span class="o">=</span><span class="m">0.05</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="m">0.9</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="m">0.00001</span><span class="p">,</span>
<span class="n">eval.metric</span><span class="o">=</span><span class="n">mx.metric.accuracy</span><span class="p">,</span>
<span class="n">epoch.end.callback</span><span class="o">=</span><span class="nf">mx.callback.log.train.metric</span><span class="p">(</span><span class="m">100</span><span class="p">))</span>
</pre></div>
</div>
<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## Start training with 1 devices</span>
<span class="c1">## Batch [100] Train-accuracy=0.1066</span>
<span class="c1">## Batch [200] Train-accuracy=0.16495</span>
<span class="c1">## Batch [300] Train-accuracy=0.401766666666667</span>
<span class="c1">## Batch [400] Train-accuracy=0.537675</span>
<span class="c1">## [1] Train-accuracy=0.557136038186157</span>
</pre></div>
</div>
<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="nf">print</span><span class="p">(</span><span class="nf">proc.time</span><span class="p">()</span> <span class="o">-</span> <span class="n">tic</span><span class="p">)</span>
</pre></div>
</div>
<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## user system elapsed</span>
<span class="c1">## 130.030 204.976 83.821</span>
</pre></div>
</div>
<p>Train on a GPU:</p>
<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="nf">mx.set.seed</span><span class="p">(</span><span class="m">0</span><span class="p">)</span>
<span class="n">tic</span> <span class="o"><-</span> <span class="nf">proc.time</span><span class="p">()</span>
<span class="n">model</span> <span class="o"><-</span> <span class="nf">mx.model.FeedForward.create</span><span class="p">(</span><span class="n">lenet</span><span class="p">,</span> <span class="n">X</span><span class="o">=</span><span class="n">train.array</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">train.y</span><span class="p">,</span>
<span class="n">ctx</span><span class="o">=</span><span class="n">device.gpu</span><span class="p">,</span> <span class="n">num.round</span><span class="o">=</span><span class="m">5</span><span class="p">,</span> <span class="n">array.batch.size</span><span class="o">=</span><span class="m">100</span><span class="p">,</span>
<span class="n">learning.rate</span><span class="o">=</span><span class="m">0.05</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="m">0.9</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="m">0.00001</span><span class="p">,</span>
<span class="n">eval.metric</span><span class="o">=</span><span class="n">mx.metric.accuracy</span><span class="p">,</span>
<span class="n">epoch.end.callback</span><span class="o">=</span><span class="nf">mx.callback.log.train.metric</span><span class="p">(</span><span class="m">100</span><span class="p">))</span>
</pre></div>
</div>
<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## Start training with 1 devices</span>
<span class="c1">## Batch [100] Train-accuracy=0.1066</span>
<span class="c1">## Batch [200] Train-accuracy=0.1596</span>
<span class="c1">## Batch [300] Train-accuracy=0.3983</span>
<span class="c1">## Batch [400] Train-accuracy=0.533975</span>
<span class="c1">## [1] Train-accuracy=0.553532219570405</span>
<span class="c1">## Batch [100] Train-accuracy=0.958</span>
<span class="c1">## Batch [200] Train-accuracy=0.96155</span>
<span class="c1">## Batch [300] Train-accuracy=0.966100000000001</span>
<span class="c1">## Batch [400] Train-accuracy=0.968550000000003</span>
<span class="c1">## [2] Train-accuracy=0.969071428571432</span>
<span class="c1">## Batch [100] Train-accuracy=0.977</span>
<span class="c1">## Batch [200] Train-accuracy=0.97715</span>
<span class="c1">## Batch [300] Train-accuracy=0.979566666666668</span>
<span class="c1">## Batch [400] Train-accuracy=0.980900000000003</span>
<span class="c1">## [3] Train-accuracy=0.981309523809527</span>
<span class="c1">## Batch [100] Train-accuracy=0.9853</span>
<span class="c1">## Batch [200] Train-accuracy=0.985899999999999</span>
<span class="c1">## Batch [300] Train-accuracy=0.986966666666668</span>
<span class="c1">## Batch [400] Train-accuracy=0.988150000000002</span>
<span class="c1">## [4] Train-accuracy=0.988452380952384</span>
<span class="c1">## Batch [100] Train-accuracy=0.990199999999999</span>
<span class="c1">## Batch [200] Train-accuracy=0.98995</span>
<span class="c1">## Batch [300] Train-accuracy=0.990600000000001</span>
<span class="c1">## Batch [400] Train-accuracy=0.991325000000002</span>
<span class="c1">## [5] Train-accuracy=0.991523809523812</span>
</pre></div>
</div>
<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="nf">print</span><span class="p">(</span><span class="nf">proc.time</span><span class="p">()</span> <span class="o">-</span> <span class="n">tic</span><span class="p">)</span>
</pre></div>
</div>
<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1">## user system elapsed</span>
<span class="c1">## 9.288 1.680 6.889</span>
</pre></div>
</div>
<p>By using a GPU processor, we significantly speed up training!
Now, we can submit the result to Kaggle to see the improvement of our ranking!</p>
<div class="highlight-r"><div class="highlight"><pre><span></span> <span class="n">preds</span> <span class="o"><-</span> <span class="nf">predict</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">test.array</span><span class="p">)</span>
<span class="n">pred.label</span> <span class="o"><-</span> <span class="nf">max.col</span><span class="p">(</span><span class="nf">t</span><span class="p">(</span><span class="n">preds</span><span class="p">))</span> <span class="o">-</span> <span class="m">1</span>
<span class="n">submission</span> <span class="o"><-</span> <span class="nf">data.frame</span><span class="p">(</span><span class="n">ImageId</span><span class="o">=</span><span class="m">1</span><span class="o">:</span><span class="nf">ncol</span><span class="p">(</span><span class="n">test</span><span class="p">),</span> <span class="n">Label</span><span class="o">=</span><span class="n">pred.label</span><span class="p">)</span>
<span class="nf">write.csv</span><span class="p">(</span><span class="n">submission</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="s">'submission.csv'</span><span class="p">,</span> <span class="n">row.names</span><span class="o">=</span><span class="kc">FALSE</span><span class="p">,</span> <span class="n">quote</span><span class="o">=</span><span class="kc">FALSE</span><span class="p">)</span>
</pre></div>
</div>
<p><img alt="" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/knitr/mnistCompetition-kaggle-submission.png"/></p>
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<li><a class="reference internal" href="#">Handwritten Digits Classification Competition</a><ul>
<li><a class="reference internal" href="#loading-the-data">Loading the Data</a></li>
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<li><a class="reference internal" href="#training">Training</a></li>
<li><a class="reference internal" href="#making-a-prediction-and-submitting-to-the-competition">Making a Prediction and Submitting to the Competition</a></li>
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