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<div class="section" id="mxnet-scala-model-api">
<span id="mxnet-scala-model-api"></span><h1>MXNet Scala Model API<a class="headerlink" href="#mxnet-scala-model-api" title="Permalink to this headline"></a></h1>
<p>The model API provides a simplified way to train neural networks using common best practices.
It’s a thin wrapper built on top of the <a class="reference internal" href="ndarray.html"><em>ndarray</em></a> and <a class="reference internal" href="symbol.html"><em>symbolic</em></a>
modules that make neural network training easy.</p>
<p>Topics:</p>
<ul class="simple">
<li><a class="reference external" href="#train-a-model">Train a Model</a></li>
<li><a class="reference external" href="#save-the-model">Save the Model</a></li>
<li><a class="reference external" href="#periodic-checkpointing">Periodic Checkpoint</a></li>
<li><a class="reference external" href="#use-multiple-devices">Multiple Devices</a></li>
<li><a class="reference external" href="#https://mxnet.incubator.apache.org/api/scala/docs/index.html#ml.dmlc.mxnet.Model">Model API Reference</a></li>
</ul>
<div class="section" id="train-the-model">
<span id="train-the-model"></span><h2>Train the Model<a class="headerlink" href="#train-the-model" title="Permalink to this headline"></a></h2>
<p>To train a model, perform two steps: configure the model using the symbol parameter,
then call <code class="docutils literal"><span class="pre">model.Feedforward.create</span></code> to create the model.
The following example creates a two-layer neural network.</p>
<div class="highlight-scala"><div class="highlight"><pre><span></span> <span class="c1">// configure a two layer neuralnetwork</span>
<span class="k">val</span> <span class="n">data</span> <span class="k">=</span> <span class="nc">Symbol</span><span class="o">.</span><span class="nc">Variable</span><span class="o">(</span><span class="s">"data"</span><span class="o">)</span>
<span class="k">val</span> <span class="n">fc1</span> <span class="k">=</span> <span class="nc">Symbol</span><span class="o">.</span><span class="nc">FullyConnected</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="nc">Map</span><span class="o">(</span><span class="s">"data"</span> <span class="o">-></span> <span class="n">data</span><span class="o">,</span> <span class="s">"num_hidden"</span> <span class="o">-></span> <span class="mi">128</span><span class="o">))</span>
<span class="k">val</span> <span class="n">act1</span> <span class="k">=</span> <span class="nc">Symbol</span><span class="o">.</span><span class="nc">Activation</span><span class="o">(</span><span class="n">name</span> <span class="k">=</span> <span class="s">"relu1"</span><span class="o">)()(</span><span class="nc">Map</span><span class="o">(</span><span class="s">"data"</span> <span class="o">-></span> <span class="n">fc1</span><span class="o">,</span> <span class="s">"act_type"</span> <span class="o">-></span> <span class="s">"relu"</span><span class="o">))</span>
<span class="k">val</span> <span class="n">fc2</span> <span class="k">=</span> <span class="nc">Symbol</span><span class="o">.</span><span class="nc">FullyConnected</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="nc">Map</span><span class="o">(</span><span class="s">"data"</span> <span class="o">-></span> <span class="n">act1</span><span class="o">,</span> <span class="s">"num_hidden"</span> <span class="o">-></span> <span class="mi">64</span><span class="o">))</span>
<span class="k">val</span> <span class="n">softmax</span> <span class="k">=</span> <span class="nc">Symbol</span><span class="o">.</span><span class="nc">SoftmaxOutput</span><span class="o">(</span><span class="n">name</span> <span class="k">=</span> <span class="s">"sm"</span><span class="o">)()(</span><span class="nc">Map</span><span class="o">(</span><span class="s">"data"</span> <span class="o">-></span> <span class="n">fc2</span><span class="o">))</span>
<span class="c1">// Construct the FeedForward model and fit on the input training data</span>
<span class="k">val</span> <span class="n">model</span> <span class="k">=</span> <span class="nc">FeedForward</span><span class="o">.</span><span class="n">newBuilder</span><span class="o">(</span><span class="n">softmax</span><span class="o">)</span>
<span class="o">.</span><span class="n">setContext</span><span class="o">(</span><span class="nc">Context</span><span class="o">.</span><span class="n">cpu</span><span class="o">())</span>
<span class="o">.</span><span class="n">setNumEpoch</span><span class="o">(</span><span class="n">num_epoch</span><span class="o">)</span>
<span class="o">.</span><span class="n">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">0.01f</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="n">setTrainData</span><span class="o">(</span><span class="n">trainDataIter</span><span class="o">)</span>
<span class="o">.</span><span class="n">setEvalData</span><span class="o">(</span><span class="n">valDataIter</span><span class="o">)</span>
<span class="o">.</span><span class="n">build</span><span class="o">()</span>
</pre></div>
</div>
<p>You can also use the <code class="docutils literal"><span class="pre">scikit-learn-style</span></code> construct and <code class="docutils literal"><span class="pre">fit</span></code> function to create a model.</p>
<div class="highlight-scala"><div class="highlight"><pre><span></span> <span class="c1">// create a model using sklearn-style two-step way</span>
<span class="k">val</span> <span class="n">model</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">FeedForward</span><span class="o">(</span><span class="n">softmax</span><span class="o">,</span>
<span class="n">numEpoch</span> <span class="k">=</span> <span class="n">numEpochs</span><span class="o">,</span>
<span class="n">argParams</span> <span class="k">=</span> <span class="n">argParams</span><span class="o">,</span>
<span class="n">auxParams</span> <span class="k">=</span> <span class="n">auxParams</span><span class="o">,</span>
<span class="n">beginEpoch</span> <span class="k">=</span> <span class="n">beginEpoch</span><span class="o">,</span>
<span class="n">epochSize</span> <span class="k">=</span> <span class="n">epochSize</span><span class="o">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">trainData</span> <span class="k">=</span> <span class="n">train</span><span class="o">)</span>
</pre></div>
</div>
<p>For more information, see <a class="reference external" href="https://mxnet.incubator.apache.org/api/scala/docs/index.html">API Reference</a>.</p>
</div>
<div class="section" id="save-the-model">
<span id="save-the-model"></span><h2>Save the Model<a class="headerlink" href="#save-the-model" title="Permalink to this headline"></a></h2>
<p>After the job is done, save your work.
We also provide <code class="docutils literal"><span class="pre">save</span></code> and <code class="docutils literal"><span class="pre">load</span></code> functions. You can use the <code class="docutils literal"><span class="pre">load</span></code> function to load a model checkpoint from a file.</p>
<div class="highlight-scala"><div class="highlight"><pre><span></span> <span class="c1">// checkpoint the model data into file,</span>
<span class="c1">// save a model to modelPrefix-symbol.json and modelPrefix-0100.params</span>
<span class="k">val</span> <span class="n">modelPrefix</span><span class="k">:</span> <span class="kt">String</span> <span class="o">=</span> <span class="s">"checkpt"</span>
<span class="k">val</span> <span class="n">num_epoch</span> <span class="k">=</span> <span class="mi">100</span>
<span class="nc">Model</span><span class="o">.</span><span class="n">saveCheckpoint</span><span class="o">(</span><span class="n">modelPrefix</span><span class="o">,</span> <span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="o">,</span> <span class="n">symbol</span><span class="o">,</span> <span class="n">argParams</span><span class="o">,</span> <span class="n">auxStates</span><span class="o">)</span>
<span class="c1">// load model back</span>
<span class="k">val</span> <span class="n">model_loaded</span> <span class="k">=</span> <span class="nc">FeedForward</span><span class="o">.</span><span class="n">load</span><span class="o">(</span><span class="n">modelPrefix</span><span class="o">,</span> <span class="n">num_epoch</span><span class="o">)</span>
</pre></div>
</div>
<p>The advantage of these two <code class="docutils literal"><span class="pre">save</span></code> and <code class="docutils literal"><span class="pre">load</span></code> functions is that they are language agnostic.
You should be able to save and load directly into cloud storage, such as Amazon S3 and HDFS.</p>
</div>
<div class="section" id="periodic-checkpointing">
<span id="periodic-checkpointing"></span><h2>Periodic Checkpointing<a class="headerlink" href="#periodic-checkpointing" title="Permalink to this headline"></a></h2>
<p>We recommend checkpointing your model after each iteration.
To do this, use <code class="docutils literal"><span class="pre">EpochEndCallback</span></code> to add a <code class="docutils literal"><span class="pre">Model.saveCheckpoint(<parameters>)</parameters></span></code> checkpoint callback to the function after each iteration .</p>
<div class="highlight-scala"><div class="highlight"><pre><span></span> <span class="c1">// modelPrefix-symbol.json will be saved for symbol.</span>
<span class="c1">// modelPrefix-epoch.params will be saved for parameters.</span>
<span class="c1">// Checkpoint the model into file. Can specify parameters.</span>
<span class="c1">// For more information, check API doc.</span>
<span class="k">val</span> <span class="n">modelPrefix</span><span class="k">:</span> <span class="kt">String</span> <span class="o">=</span> <span class="s">"checkpt"</span>
<span class="k">val</span> <span class="n">checkpoint</span><span class="k">:</span> <span class="kt">EpochEndCallback</span> <span class="o">=</span>
<span class="k">if</span> <span class="o">(</span><span class="n">modelPrefix</span> <span class="o">==</span> <span class="kc">null</span><span class="o">)</span> <span class="kc">null</span>
<span class="k">else</span> <span class="k">new</span> <span class="nc">EpochEndCallback</span> <span class="o">{</span>
<span class="k">override</span> <span class="k">def</span> <span class="n">invoke</span><span class="o">(</span><span class="n">epoch</span><span class="k">:</span> <span class="kt">Int</span><span class="o">,</span> <span class="n">symbol</span><span class="k">:</span> <span class="kt">Symbol</span><span class="o">,</span>
<span class="n">argParams</span><span class="k">:</span> <span class="kt">Map</span><span class="o">[</span><span class="kt">String</span>, <span class="kt">NDArray</span><span class="o">],</span>
<span class="n">auxStates</span><span class="k">:</span> <span class="kt">Map</span><span class="o">[</span><span class="kt">String</span>, <span class="kt">NDArray</span><span class="o">])</span><span class="k">:</span> <span class="kt">Unit</span> <span class="o">=</span> <span class="o">{</span>
<span class="nc">Model</span><span class="o">.</span><span class="n">saveCheckpoint</span><span class="o">(</span><span class="n">modelPrefix</span><span class="o">,</span> <span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="o">,</span> <span class="n">symbol</span><span class="o">,</span> <span class="n">argParams</span><span class="o">,</span> <span class="n">auxParams</span><span class="o">)</span>
<span class="o">}</span>
<span class="o">}</span>
<span class="c1">// Load model checkpoint from file. Returns symbol, argParams, auxParams.</span>
<span class="k">val</span> <span class="o">(</span><span class="k">_</span><span class="o">,</span> <span class="n">argParams</span><span class="o">,</span> <span class="k">_</span><span class="o">)</span> <span class="k">=</span> <span class="nc">Model</span><span class="o">.</span><span class="n">loadCheckpoint</span><span class="o">(</span><span class="n">modelPrefix</span><span class="o">,</span> <span class="n">num_epoch</span><span class="o">)</span>
</pre></div>
</div>
<p>You can load the model checkpoint later using <code class="docutils literal"><span class="pre">Model.loadCheckpoint(modelPrefix,</span> <span class="pre">num_epoch)</span></code>.</p>
</div>
<div class="section" id="use-multiple-devices">
<span id="use-multiple-devices"></span><h2>Use Multiple Devices<a class="headerlink" href="#use-multiple-devices" title="Permalink to this headline"></a></h2>
<p>Set <code class="docutils literal"><span class="pre">ctx</span></code> to the list of devices that you want to train on. You can create a list of devices in any way you want.</p>
<div class="highlight-scala"><div class="highlight"><pre><span></span> <span class="k">val</span> <span class="n">devices</span> <span class="k">=</span> <span class="nc">Array</span><span class="o">(</span><span class="nc">Context</span><span class="o">.</span><span class="n">gpu</span><span class="o">(</span><span class="mi">0</span><span class="o">),</span> <span class="nc">Context</span><span class="o">.</span><span class="n">gpu</span><span class="o">(</span><span class="mi">1</span><span class="o">))</span>
<span class="k">val</span> <span class="n">model</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">FeedForward</span><span class="o">(</span><span class="n">ctx</span> <span class="k">=</span> <span class="n">devices</span><span class="o">,</span>
<span class="n">symbol</span> <span class="k">=</span> <span class="n">network</span><span class="o">,</span>
<span class="n">numEpoch</span> <span class="k">=</span> <span class="n">numEpochs</span><span class="o">,</span>
<span class="n">optimizer</span> <span class="k">=</span> <span class="n">optimizer</span><span class="o">,</span>
<span class="n">epochSize</span> <span class="k">=</span> <span class="n">epochSize</span><span class="o">,</span>
<span class="o">...)</span>
</pre></div>
</div>
<p>Training occurs in parallel on the GPUs that you specify.</p>
</div>
<div class="section" id="next-steps">
<span id="next-steps"></span><h2>Next Steps<a class="headerlink" href="#next-steps" title="Permalink to this headline"></a></h2>
<ul class="simple">
<li>See <a class="reference internal" href="symbol.html"><em>Symbolic API</em></a> for operations on NDArrays that assemble neural networks from layers.</li>
<li>See <a class="reference internal" href="io.html"><em>IO Data Loading API</em></a> for parsing and loading data.</li>
<li>See <a class="reference internal" href="ndarray.html"><em>NDArray API</em></a> for vector/matrix/tensor operations.</li>
<li>See <a class="reference internal" href="kvstore.html"><em>KVStore API</em></a> for multi-GPU and multi-host distributed training.</li>
</ul>
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</div>
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<h3><a href="../../index.html">Table Of Contents</a></h3>
<ul>
<li><a class="reference internal" href="#">MXNet Scala Model API</a><ul>
<li><a class="reference internal" href="#train-the-model">Train the Model</a></li>
<li><a class="reference internal" href="#save-the-model">Save the Model</a></li>
<li><a class="reference internal" href="#periodic-checkpointing">Periodic Checkpointing</a></li>
<li><a class="reference internal" href="#use-multiple-devices">Use Multiple Devices</a></li>
<li><a class="reference internal" href="#next-steps">Next Steps</a></li>
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