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<div class="section" id="module-api">
<span id="module-api"></span><h1>Module API<a class="headerlink" href="#module-api" title="Permalink to this headline"></a></h1>
<p>The module API provides an intermediate and high-level interface for performing computation with neural networks in MXNet. A <em>module</em> is an instance of subclasses of the <code class="docutils literal"><span class="pre">BaseModule</span></code>. The most widely used module class is called <code class="docutils literal"><span class="pre">Module</span></code>. Module wraps a <code class="docutils literal"><span class="pre">Symbol</span></code> and one or more <code class="docutils literal"><span class="pre">Executors</span></code>. For a full list of functions, see <code class="docutils literal"><span class="pre">BaseModule</span></code>.
A subclass of modules might have extra interface functions. This topic provides some examples of common use cases. All of the module APIs are in the <code class="docutils literal"><span class="pre">Module</span></code> namespace.</p>
<div class="section" id="preparing-a-module-for-computation">
<span id="preparing-a-module-for-computation"></span><h2>Preparing a Module for Computation<a class="headerlink" href="#preparing-a-module-for-computation" title="Permalink to this headline"></a></h2>
<p>To construct a module, refer to the constructors for the module class. For example, the <code class="docutils literal"><span class="pre">Module</span></code> class accepts a <code class="docutils literal"><span class="pre">Symbol</span></code> as input:</p>
<div class="highlight-scala"><div class="highlight"><pre><span></span> <span class="k">import</span> <span class="nn">ml.dmlc.mxnet._</span>
<span class="k">import</span> <span class="nn">ml.dmlc.mxnet.module.</span><span class="o">{</span><span class="nc">FitParams</span><span class="o">,</span> <span class="nc">Module</span><span class="o">}</span>
<span class="c1">// construct a simple MLP</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="n">data</span><span class="o">)(</span><span class="nc">Map</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="n">fc1</span><span class="o">)(</span><span class="nc">Map</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="n">act1</span><span class="o">)(</span><span class="nc">Map</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">act2</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">"relu2"</span><span class="o">)(</span><span class="n">fc2</span><span class="o">)(</span><span class="nc">Map</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">fc3</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">"fc3"</span><span class="o">)(</span><span class="n">act2</span><span class="o">)(</span><span class="nc">Map</span><span class="o">(</span><span class="s">"num_hidden"</span> <span class="o">-></span> <span class="mi">10</span><span class="o">))</span>
<span class="k">val</span> <span class="n">out</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">"softmax"</span><span class="o">)(</span><span class="n">fc3</span><span class="o">)()</span>
<span class="c1">// construct the module</span>
<span class="k">val</span> <span class="n">mod</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">Module</span><span class="o">(</span><span class="n">out</span><span class="o">)</span>
</pre></div>
</div>
<p>By default, <code class="docutils literal"><span class="pre">context</span></code> is the CPU. If you need data parallelization, you can specify a GPU context or an array of GPU contexts.</p>
<p>Before you can compute with a module, you need to call <code class="docutils literal"><span class="pre">bind()</span></code> to allocate the device memory and <code class="docutils literal"><span class="pre">initParams()</span></code> or <code class="docutils literal"><span class="pre">SetParams()</span></code> to initialize the parameters.
If you simply want to fit a module, you don’t need to call <code class="docutils literal"><span class="pre">bind()</span></code> and <code class="docutils literal"><span class="pre">initParams()</span></code> explicitly, because the fit() function automatically calls them if they are needed.</p>
<div class="highlight-scala"><div class="highlight"><pre><span></span> <span class="n">mod</span><span class="o">.</span><span class="n">bind</span><span class="o">(</span><span class="n">dataShapes</span> <span class="k">=</span> <span class="n">train_dataiter</span><span class="o">.</span><span class="n">provideData</span><span class="o">,</span> <span class="n">labelShapes</span> <span class="k">=</span> <span class="nc">Some</span><span class="o">(</span><span class="n">train_dataiter</span><span class="o">.</span><span class="n">provideLabel</span><span class="o">))</span>
<span class="n">mod</span><span class="o">.</span><span class="n">initParams</span><span class="o">()</span>
</pre></div>
</div>
<p>Now you can compute with the module using functions like <code class="docutils literal"><span class="pre">forward()</span></code>, <code class="docutils literal"><span class="pre">backward()</span></code>, etc.</p>
</div>
<div class="section" id="training-predicting-and-evaluating">
<span id="training-predicting-and-evaluating"></span><h2>Training, Predicting, and Evaluating<a class="headerlink" href="#training-predicting-and-evaluating" title="Permalink to this headline"></a></h2>
<p>Modules provide high-level APIs for training, predicting, and evaluating. To fit a module, call the <code class="docutils literal"><span class="pre">fit()</span></code> function with some <code class="docutils literal"><span class="pre">DataIter</span></code>s:</p>
<div class="highlight-scala"><div class="highlight"><pre><span></span> <span class="k">import</span> <span class="nn">ml.dmlc.mxnet.optimizer.SGD</span>
<span class="k">val</span> <span class="n">mod</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">Module</span><span class="o">(</span><span class="n">softmax</span><span class="o">)</span>
<span class="n">mod</span><span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">train_dataiter</span><span class="o">,</span> <span class="n">evalData</span> <span class="k">=</span> <span class="n">scala</span><span class="o">.</span><span class="nc">Option</span><span class="o">(</span><span class="n">eval_dataiter</span><span class="o">),</span> <span class="o">\</span>
<span class="n">numEpoch</span> <span class="k">=</span> <span class="n">n_epoch</span><span class="o">,</span> <span class="n">fitParams</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">FitParams</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">0.1f</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>
</pre></div>
</div>
<p>The interface is very similar to the old <code class="docutils literal"><span class="pre">FeedForward</span></code> class. You can pass in batch-end callbacks using <code class="docutils literal"><span class="pre">setBatchEndCallback</span></code> and epoch-end callbacks using <code class="docutils literal"><span class="pre">setEpochEndCallback</span></code>. You can also set parameters using methods like <code class="docutils literal"><span class="pre">setOptimizer</span></code> and <code class="docutils literal"><span class="pre">setEvalMetric</span></code>. To learn more about the <code class="docutils literal"><span class="pre">FitParams()</span></code>, see the <a class="reference external" href="http://mxnet.io/api/scala/docs/index.html#ml.dmlc.mxnet.module.FitParams">API page</a>. To predict with a module, call <code class="docutils literal"><span class="pre">predict()</span></code> with a <code class="docutils literal"><span class="pre">DataIter</span></code>:</p>
<div class="highlight-scala"><div class="highlight"><pre><span></span> <span class="n">mod</span><span class="o">.</span><span class="n">predict</span><span class="o">(</span><span class="n">val_dataiter</span><span class="o">)</span>
</pre></div>
</div>
<p>The module collects and returns all of the prediction results. For more details about the format of the return values, see the documentation for the <a class="reference external" href="http://mxnet.io/api/scala/docs/index.html#ml.dmlc.mxnet.module.BaseModule"><code class="docutils literal"><span class="pre">predict()</span></code> function</a>.</p>
<p>When prediction results might be too large to fit in memory, use the <code class="docutils literal"><span class="pre">predictEveryBatch</span></code> API:</p>
<div class="highlight-scala"><div class="highlight"><pre><span></span> <span class="k">val</span> <span class="n">preds</span> <span class="k">=</span> <span class="n">mod</span><span class="o">.</span><span class="n">predictEveryBatch</span><span class="o">(</span><span class="n">val_dataiter</span><span class="o">)</span>
<span class="n">val_dataiter</span><span class="o">.</span><span class="n">reset</span><span class="o">()</span>
<span class="k">var</span> <span class="n">i</span> <span class="k">=</span> <span class="mi">0</span>
<span class="k">while</span> <span class="o">(</span><span class="n">val_dataiter</span><span class="o">.</span><span class="n">hasNext</span><span class="o">)</span> <span class="o">{</span>
<span class="k">val</span> <span class="n">batch</span> <span class="k">=</span> <span class="n">val_dataiter</span><span class="o">.</span><span class="n">next</span><span class="o">()</span>
<span class="k">val</span> <span class="n">predLabel</span><span class="k">:</span> <span class="kt">Array</span><span class="o">[</span><span class="kt">Int</span><span class="o">]</span> <span class="k">=</span> <span class="nc">NDArray</span><span class="o">.</span><span class="n">argmax_channel</span><span class="o">(</span><span class="n">preds</span><span class="o">(</span><span class="n">i</span><span class="o">)(</span><span class="mi">0</span><span class="o">)).</span><span class="n">toArray</span><span class="o">.</span><span class="n">map</span><span class="o">(</span><span class="k">_</span><span class="o">.</span><span class="n">toInt</span><span class="o">)</span>
<span class="k">val</span> <span class="n">label</span> <span class="k">=</span> <span class="n">batch</span><span class="o">.</span><span class="n">label</span><span class="o">(</span><span class="mi">0</span><span class="o">).</span><span class="n">toArray</span><span class="o">.</span><span class="n">map</span><span class="o">(</span><span class="k">_</span><span class="o">.</span><span class="n">toInt</span><span class="o">)</span>
<span class="c1">//do something...</span>
<span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="o">}</span>
</pre></div>
</div>
<p>If you need to evaluate on a test set and don’t need the prediction output, call the <code class="docutils literal"><span class="pre">score()</span></code> function with a <code class="docutils literal"><span class="pre">DataIter</span></code> and an <code class="docutils literal"><span class="pre">EvalMetric</span></code>:</p>
<div class="highlight-scala"><div class="highlight"><pre><span></span> <span class="n">mod</span><span class="o">.</span><span class="n">score</span><span class="o">(</span><span class="n">val_dataiter</span><span class="o">,</span> <span class="n">metric</span><span class="o">)</span>
</pre></div>
</div>
<p>This runs predictions on each batch in the provided <code class="docutils literal"><span class="pre">DataIter</span></code> and computes the evaluation score using the provided <code class="docutils literal"><span class="pre">EvalMetric</span></code>. The evaluation results are stored in <code class="docutils literal"><span class="pre">metric</span></code> so that you can query later.</p>
</div>
<div class="section" id="saving-and-loading-module-parameters">
<span id="saving-and-loading-module-parameters"></span><h2>Saving and Loading Module Parameters<a class="headerlink" href="#saving-and-loading-module-parameters" title="Permalink to this headline"></a></h2>
<p>To save the module parameters in each training epoch, use a <code class="docutils literal"><span class="pre">checkpoint</span></code> callback:</p>
<div class="highlight-scala"><div class="highlight"><pre><span></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">"mymodel"</span>
<span class="k">for</span> <span class="o">(</span><span class="n">epoch</span> <span class="k"><-</span> <span class="mi">0</span> <span class="n">until</span> <span class="mi">5</span><span class="o">)</span> <span class="o">{</span>
<span class="k">while</span><span class="o">(</span><span class="n">train_dataiter</span><span class="o">.</span><span class="n">hasNext</span><span class="o">){</span>
<span class="c1">// forward backward pass</span>
<span class="c1">//do something...</span>
<span class="o">}</span>
<span class="k">val</span> <span class="n">checkpoint</span> <span class="k">=</span> <span class="n">mod</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="n">saveOptStates</span> <span class="k">=</span> <span class="kc">true</span><span class="o">)</span>
<span class="o">}</span>
</pre></div>
</div>
<p>To load the saved module parameters, call the <code class="docutils literal"><span class="pre">loadCheckpoint</span></code> function:</p>
<div class="highlight-scala"><div class="highlight"><pre><span></span> <span class="k">val</span> <span class="n">mod</span> <span class="k">=</span> <span class="nc">Module</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">loadModelEpoch</span><span class="o">,</span> <span class="n">loadOptimizerStates</span> <span class="k">=</span> <span class="kc">true</span><span class="o">)</span>
</pre></div>
</div>
<p>To initialize parameters, Bind the symbols to construct executors first with <code class="docutils literal"><span class="pre">bind</span></code> method. Then, initialize the parameters and auxiliary states by calling <code class="docutils literal"><span class="pre">initParams()</span></code> method.</p>
<div class="highlight-scala"><div class="highlight"><pre><span></span> <span class="n">mod</span><span class="o">.</span><span class="n">bind</span><span class="o">(</span><span class="n">dataShapes</span> <span class="k">=</span> <span class="n">train_dataiter</span><span class="o">.</span><span class="n">provideData</span><span class="o">,</span> <span class="n">labelShapes</span> <span class="k">=</span> <span class="nc">Some</span><span class="o">(</span><span class="n">train_dataiter</span><span class="o">.</span><span class="n">provideLabel</span><span class="o">))</span>
<span class="n">mod</span><span class="o">.</span><span class="n">initParams</span><span class="o">()</span>
</pre></div>
</div>
<p>To get current parameters, use <code class="docutils literal"><span class="pre">getParams</span></code> method.</p>
<div class="highlight-scala"><div class="highlight"><pre><span></span> <span class="k">val</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="k">=</span> <span class="n">mod</span><span class="o">.</span><span class="n">getParams</span>
</pre></div>
</div>
<p>To assign parameter and aux state values, use <code class="docutils literal"><span class="pre">setParams</span></code> method.</p>
<div class="highlight-scala"><div class="highlight"><pre><span></span> <span class="n">mod</span><span class="o">.</span><span class="n">setParams</span><span class="o">(</span><span class="n">argParams</span><span class="o">,</span> <span class="n">auxParams</span><span class="o">)</span>
</pre></div>
</div>
<p>To resume training from a saved checkpoint, instead of calling <code class="docutils literal"><span class="pre">setParams()</span></code>, directly call <code class="docutils literal"><span class="pre">fit()</span></code>, passing the loaded parameters, so that <code class="docutils literal"><span class="pre">fit()</span></code> knows to start from those parameters instead of initializing randomly:</p>
<div class="highlight-scala"><div class="highlight"><pre><span></span> <span class="n">mod</span><span class="o">.</span><span class="n">fit</span><span class="o">(...,</span> <span class="n">fitParams</span><span class="k">=new</span> <span class="nc">FitParams</span><span class="o">().</span><span class="n">setArgParams</span><span class="o">(</span><span class="n">argParams</span><span class="o">).\</span>
<span class="n">setAuxParams</span><span class="o">(</span><span class="n">auxParams</span><span class="o">).</span><span class="n">setBeginEpoch</span><span class="o">(</span><span class="n">beginEpoch</span><span class="o">))</span>
</pre></div>
</div>
<p>Create an object of the <code class="docutils literal"><span class="pre">FitParams()</span></code> class, and then use it to call the <code class="docutils literal"><span class="pre">setBeginEpoch()</span></code> method to pass <code class="docutils literal"><span class="pre">beginEpoch</span></code> so that <code class="docutils literal"><span class="pre">fit()</span></code> knows to resume from a saved epoch.</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="model.html"><em>Model API</em></a> for an alternative simple high-level interface for training neural networks.</li>
<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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<h3><a href="../../index.html">Table Of Contents</a></h3>
<ul>
<li><a class="reference internal" href="#">Module API</a><ul>
<li><a class="reference internal" href="#preparing-a-module-for-computation">Preparing a Module for Computation</a></li>
<li><a class="reference internal" href="#training-predicting-and-evaluating">Training, Predicting, and Evaluating</a></li>
<li><a class="reference internal" href="#saving-and-loading-module-parameters">Saving and Loading Module Parameters</a></li>
<li><a class="reference internal" href="#next-steps">Next Steps</a></li>
</ul>
</li>
</ul>
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