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| <h1 class="post-title">MNIST Example</h1> |
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| <h1 id="handwritten-digit-recognition">Handwritten Digit Recognition</h1> |
| |
| <p>This Scala tutorial guides you through a classic computer vision application: identifying hand written digits.</p> |
| |
| <p>Let's train a 3-layer network (i.e multilayer perceptron network) on the MNIST dataset to classify handwritten digits.</p> |
| |
| <h2 id="prerequisites">Prerequisites</h2> |
| |
| <p>To complete this tutorial, we need:</p> |
| |
| <ul> |
| <li>to compile the latest MXNet version. See the MXNet installation instructions for your operating system in <a href="/versions/1.7/get_started">Setup and Installation</a>.</li> |
| <li>to compile the Scala API. See Scala API build instructions in <a href="https://github.com/dmlc/mxnet/tree/master/scala-package">Build</a>.</li> |
| </ul> |
| |
| <h2 id="define-the-network">Define the Network</h2> |
| |
| <p>First, define the neural network's architecture using the Symbol API:</p> |
| <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.mxnet._</span> |
| <span class="k">import</span> <span class="nn">org.apache.mxnet.optimizer.SGD</span> |
| |
| <span class="c1">// model definition |
| </span><span class="k">val</span> <span class="nv">data</span> <span class="k">=</span> <span class="nv">Symbol</span><span class="o">.</span><span class="py">Variable</span><span class="o">(</span><span class="s">"data"</span><span class="o">)</span> |
| <span class="k">val</span> <span class="nv">fc1</span> <span class="k">=</span> <span class="nv">Symbol</span><span class="o">.</span><span class="py">api</span><span class="o">.</span><span class="py">FullyConnected</span><span class="o">(</span><span class="nc">Some</span><span class="o">(</span><span class="n">data</span><span class="o">),</span> <span class="n">num_hidden</span> <span class="k">=</span> <span class="mi">128</span><span class="o">,</span> <span class="n">name</span> <span class="k">=</span> <span class="s">"fc1"</span><span class="o">)</span> |
| <span class="k">val</span> <span class="nv">act1</span> <span class="k">=</span> <span class="nv">Symbol</span><span class="o">.</span><span class="py">api</span><span class="o">.</span><span class="py">Activation</span><span class="o">(</span><span class="nc">Some</span><span class="o">(</span><span class="n">fc1</span><span class="o">),</span> <span class="s">"relu"</span><span class="o">,</span> <span class="s">"relu1"</span><span class="o">)</span> |
| <span class="k">val</span> <span class="nv">fc2</span> <span class="k">=</span> <span class="nv">Symbol</span><span class="o">.</span><span class="py">api</span><span class="o">.</span><span class="py">FullyConnected</span><span class="o">(</span><span class="nc">Some</span><span class="o">(</span><span class="n">act1</span><span class="o">),</span> <span class="n">num_hidden</span> <span class="k">=</span> <span class="mi">64</span><span class="o">,</span> <span class="n">name</span> <span class="k">=</span> <span class="s">"fc2"</span><span class="o">)</span> |
| <span class="k">val</span> <span class="nv">act2</span> <span class="k">=</span> <span class="nv">Symbol</span><span class="o">.</span><span class="py">api</span><span class="o">.</span><span class="py">Activation</span><span class="o">(</span><span class="nc">Some</span><span class="o">(</span><span class="n">fc2</span><span class="o">),</span> <span class="s">"relu"</span><span class="o">,</span> <span class="s">"relu2"</span><span class="o">)</span> |
| <span class="k">val</span> <span class="nv">fc3</span> <span class="k">=</span> <span class="nv">Symbol</span><span class="o">.</span><span class="py">api</span><span class="o">.</span><span class="py">FullyConnected</span><span class="o">(</span><span class="nc">Some</span><span class="o">(</span><span class="n">act2</span><span class="o">),</span> <span class="n">num_hidden</span> <span class="k">=</span> <span class="mi">10</span><span class="o">,</span> <span class="n">name</span> <span class="k">=</span> <span class="s">"fc3"</span><span class="o">)</span> |
| <span class="k">val</span> <span class="nv">mlp</span> <span class="k">=</span> <span class="nv">Symbol</span><span class="o">.</span><span class="py">api</span><span class="o">.</span><span class="py">SoftmaxOutput</span><span class="o">(</span><span class="nc">Some</span><span class="o">(</span><span class="n">fc3</span><span class="o">),</span> <span class="n">name</span> <span class="k">=</span> <span class="s">"sm"</span><span class="o">)</span> |
| </code></pre></div> |
| <h2 id="load-the-data">Load the Data</h2> |
| |
| <p>Then, load the training and validation data using DataIterators.</p> |
| |
| <p>You can download the MNIST data using the <a href="https://github.com/dmlc/mxnet/blob/master/scala-package/core/scripts/get_mnist_data.sh">get_mnist_data script</a>. We've already written a DataIterator for the MNIST dataset:</p> |
| <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="c1">// load MNIST dataset |
| </span><span class="k">val</span> <span class="nv">trainDataIter</span> <span class="k">=</span> <span class="nv">IO</span><span class="o">.</span><span class="py">MNISTIter</span><span class="o">(</span><span class="nc">Map</span><span class="o">(</span> |
| <span class="s">"image"</span> <span class="o">-></span> <span class="s">"data/train-images-idx3-ubyte"</span><span class="o">,</span> |
| <span class="s">"label"</span> <span class="o">-></span> <span class="s">"data/train-labels-idx1-ubyte"</span><span class="o">,</span> |
| <span class="s">"data_shape"</span> <span class="o">-></span> <span class="s">"(1, 28, 28)"</span><span class="o">,</span> |
| <span class="s">"label_name"</span> <span class="o">-></span> <span class="s">"sm_label"</span><span class="o">,</span> |
| <span class="s">"batch_size"</span> <span class="o">-></span> <span class="s">"50"</span><span class="o">,</span> |
| <span class="s">"shuffle"</span> <span class="o">-></span> <span class="s">"1"</span><span class="o">,</span> |
| <span class="s">"flat"</span> <span class="o">-></span> <span class="s">"0"</span><span class="o">,</span> |
| <span class="s">"silent"</span> <span class="o">-></span> <span class="s">"0"</span><span class="o">,</span> |
| <span class="s">"seed"</span> <span class="o">-></span> <span class="s">"10"</span><span class="o">))</span> |
| |
| <span class="k">val</span> <span class="nv">valDataIter</span> <span class="k">=</span> <span class="nv">IO</span><span class="o">.</span><span class="py">MNISTIter</span><span class="o">(</span><span class="nc">Map</span><span class="o">(</span> |
| <span class="s">"image"</span> <span class="o">-></span> <span class="s">"data/t10k-images-idx3-ubyte"</span><span class="o">,</span> |
| <span class="s">"label"</span> <span class="o">-></span> <span class="s">"data/t10k-labels-idx1-ubyte"</span><span class="o">,</span> |
| <span class="s">"data_shape"</span> <span class="o">-></span> <span class="s">"(1, 28, 28)"</span><span class="o">,</span> |
| <span class="s">"label_name"</span> <span class="o">-></span> <span class="s">"sm_label"</span><span class="o">,</span> |
| <span class="s">"batch_size"</span> <span class="o">-></span> <span class="s">"50"</span><span class="o">,</span> |
| <span class="s">"shuffle"</span> <span class="o">-></span> <span class="s">"1"</span><span class="o">,</span> |
| <span class="s">"flat"</span> <span class="o">-></span> <span class="s">"0"</span><span class="o">,</span> <span class="s">"silent"</span> <span class="o">-></span> <span class="s">"0"</span><span class="o">))</span> |
| </code></pre></div> |
| <h2 id="train-the-model">Train the model</h2> |
| |
| <p>We can use the FeedForward builder to train our network:</p> |
| <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="c1">// setup model and fit the training data |
| </span><span class="k">val</span> <span class="nv">model</span> <span class="k">=</span> <span class="nv">FeedForward</span><span class="o">.</span><span class="py">newBuilder</span><span class="o">(</span><span class="n">mlp</span><span class="o">)</span> |
| <span class="o">.</span><span class="py">setContext</span><span class="o">(</span><span class="nv">Context</span><span class="o">.</span><span class="py">cpu</span><span class="o">())</span> |
| <span class="o">.</span><span class="py">setNumEpoch</span><span class="o">(</span><span class="mi">10</span><span class="o">)</span> |
| <span class="o">.</span><span class="py">setOptimizer</span><span class="o">(</span><span class="k">new</span> <span class="nc">SGD</span><span class="o">(</span><span class="n">learningRate</span> <span class="k">=</span> <span class="mf">0.1f</span><span class="o">,</span> <span class="n">momentum</span> <span class="k">=</span> <span class="mf">0.9f</span><span class="o">,</span> <span class="n">wd</span> <span class="k">=</span> <span class="mf">0.0001f</span><span class="o">))</span> |
| <span class="o">.</span><span class="py">setTrainData</span><span class="o">(</span><span class="n">trainDataIter</span><span class="o">)</span> |
| <span class="o">.</span><span class="py">setEvalData</span><span class="o">(</span><span class="n">valDataIter</span><span class="o">)</span> |
| <span class="o">.</span><span class="py">build</span><span class="o">()</span> |
| </code></pre></div> |
| <h2 id="make-predictions">Make predictions</h2> |
| |
| <p>Finally, let's make predictions against the validation dataset and compare the predicted labels with the real labels.</p> |
| <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">val</span> <span class="nv">probArrays</span> <span class="k">=</span> <span class="nv">model</span><span class="o">.</span><span class="py">predict</span><span class="o">(</span><span class="n">valDataIter</span><span class="o">)</span> |
| <span class="c1">// in this case, we do not have multiple outputs |
| </span><span class="nf">require</span><span class="o">(</span><span class="nv">probArrays</span><span class="o">.</span><span class="py">length</span> <span class="o">==</span> <span class="mi">1</span><span class="o">)</span> |
| <span class="k">val</span> <span class="nv">prob</span> <span class="k">=</span> <span class="nf">probArrays</span><span class="o">(</span><span class="mi">0</span><span class="o">)</span> |
| |
| <span class="c1">// get real labels |
| </span><span class="k">import</span> <span class="nn">scala.collection.mutable.ListBuffer</span> |
| <span class="nv">valDataIter</span><span class="o">.</span><span class="py">reset</span><span class="o">()</span> |
| <span class="k">val</span> <span class="nv">labels</span> <span class="k">=</span> <span class="nv">ListBuffer</span><span class="o">.</span><span class="py">empty</span><span class="o">[</span><span class="kt">NDArray</span><span class="o">]</span> |
| <span class="nf">while</span> <span class="o">(</span><span class="nv">valDataIter</span><span class="o">.</span><span class="py">hasNext</span><span class="o">)</span> <span class="o">{</span> |
| <span class="k">val</span> <span class="nv">evalData</span> <span class="k">=</span> <span class="nv">valDataIter</span><span class="o">.</span><span class="py">next</span><span class="o">()</span> |
| <span class="n">labels</span> <span class="o">+=</span> <span class="nv">evalData</span><span class="o">.</span><span class="py">label</span><span class="o">(</span><span class="mi">0</span><span class="o">).</span><span class="py">copy</span><span class="o">()</span> |
| <span class="o">}</span> |
| <span class="k">val</span> <span class="nv">y</span> <span class="k">=</span> <span class="nv">NDArray</span><span class="o">.</span><span class="py">concatenate</span><span class="o">(</span><span class="n">labels</span><span class="o">)</span> |
| |
| <span class="c1">// get predicted labels |
| </span><span class="k">val</span> <span class="nv">predictedY</span> <span class="k">=</span> <span class="nv">NDArray</span><span class="o">.</span><span class="py">argmax_channel</span><span class="o">(</span><span class="n">prob</span><span class="o">)</span> |
| <span class="nf">require</span><span class="o">(</span><span class="nv">y</span><span class="o">.</span><span class="py">shape</span> <span class="o">==</span> <span class="nv">predictedY</span><span class="o">.</span><span class="py">shape</span><span class="o">)</span> |
| |
| <span class="c1">// calculate accuracy |
| </span><span class="k">var</span> <span class="n">numCorrect</span> <span class="k">=</span> <span class="mi">0</span> |
| <span class="k">var</span> <span class="n">numTotal</span> <span class="k">=</span> <span class="mi">0</span> |
| <span class="nf">for</span> <span class="o">((</span><span class="n">labelElem</span><span class="o">,</span> <span class="n">predElem</span><span class="o">)</span> <span class="k"><-</span> <span class="nv">y</span><span class="o">.</span><span class="py">toArray</span> <span class="n">zip</span> <span class="nv">predictedY</span><span class="o">.</span><span class="py">toArray</span><span class="o">)</span> <span class="o">{</span> |
| <span class="nf">if</span> <span class="o">(</span><span class="n">labelElem</span> <span class="o">==</span> <span class="n">predElem</span><span class="o">)</span> <span class="o">{</span> |
| <span class="n">numCorrect</span> <span class="o">+=</span> <span class="mi">1</span> |
| <span class="o">}</span> |
| <span class="n">numTotal</span> <span class="o">+=</span> <span class="mi">1</span> |
| <span class="o">}</span> |
| <span class="k">val</span> <span class="nv">acc</span> <span class="k">=</span> <span class="nv">numCorrect</span><span class="o">.</span><span class="py">toFloat</span> <span class="o">/</span> <span class="n">numTotal</span> |
| <span class="nf">println</span><span class="o">(</span><span class="n">s</span><span class="s">"Final accuracy = $acc"</span><span class="o">)</span> |
| </code></pre></div> |
| <p>Check out more MXNet Scala examples below.</p> |
| |
| <h2 id="next-steps">Next Steps</h2> |
| |
| <ul> |
| <li><a href="/versions/1.7/api/scala">Scala API</a></li> |
| <li><a href="https://github.com/dmlc/mxnet/tree/master/scala-package/examples/">More Scala Examples</a></li> |
| <li><a href="/versions/1.7/api">MXNet tutorials index</a></li> |
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