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| <h1 id="module-api">Module API</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>BaseModule</code>. The most widely used module class is called <code>Module</code>. Module wraps a <code>Symbol</code> and one or more <code>Executors</code>. For a full list of functions, see <code>BaseModule</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>Module</code> namespace.</p> |
| |
| <h2 id="preparing-a-module-for-computation">Preparing a Module for Computation</h2> |
| |
| <p>To construct a module, refer to the constructors for the module class. For example, the <code>Module</code> class accepts a <code>Symbol</code> as input:</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.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="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">out</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="n">fc3</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="c1">// construct the module |
| </span> <span class="k">val</span> <span class="nv">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> |
| </code></pre></div> |
| <p>By default, <code>context</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>bind()</code> to allocate the device memory and <code>initParams()</code> or <code>SetParams()</code> to initialize the parameters. |
| If you simply want to fit a module, you don't need to call <code>bind()</code> and <code>initParams()</code> explicitly, because the fit() function automatically calls them if they are needed.</p> |
| <div class="highlight"><pre><code class="language-scala" data-lang="scala"> <span class="nv">mod</span><span class="o">.</span><span class="py">bind</span><span class="o">(</span><span class="n">dataShapes</span> <span class="k">=</span> <span class="nv">train_dataiter</span><span class="o">.</span><span class="py">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="nv">train_dataiter</span><span class="o">.</span><span class="py">provideLabel</span><span class="o">))</span> |
| <span class="nv">mod</span><span class="o">.</span><span class="py">initParams</span><span class="o">()</span> |
| </code></pre></div> |
| <p>Now you can compute with the module using functions like <code>forward()</code>, <code>backward()</code>, etc.</p> |
| |
| <h2 id="training-predicting-and-evaluating">Training, Predicting, and Evaluating</h2> |
| |
| <p>Modules provide high-level APIs for training, predicting, and evaluating. To fit a module, call the <code>fit()</code> function with some <code>DataIter</code>s:</p> |
| <div class="highlight"><pre><code class="language-scala" data-lang="scala"> <span class="k">import</span> <span class="nn">org.apache.mxnet.optimizer.SGD</span> |
| <span class="k">val</span> <span class="nv">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="nv">mod</span><span class="o">.</span><span class="py">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="nv">scala</span><span class="o">.</span><span class="py">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="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> |
| </code></pre></div> |
| <p>The interface is very similar to the old <code>FeedForward</code> class. You can pass in batch-end callbacks using <code>setBatchEndCallback</code> and epoch-end callbacks using <code>setEpochEndCallback</code>. You can also set parameters using methods like <code>setOptimizer</code> and <code>setEvalMetric</code>. To learn more about the <code>FitParams()</code>, see the <a href="/versions/1.7/api/scala/docs/api/#org.apache.mxnet.module.FitParams">API page</a>. To predict with a module, call <code>predict()</code> with a <code>DataIter</code>:</p> |
| <div class="highlight"><pre><code class="language-scala" data-lang="scala"> <span class="nv">mod</span><span class="o">.</span><span class="py">predict</span><span class="o">(</span><span class="n">val_dataiter</span><span class="o">)</span> |
| </code></pre></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 href="/versions/1.7/api/scala/docs/api/#org.apache.mxnet.module.BaseModule"><code>predict()</code> function</a>.</p> |
| |
| <p>When prediction results might be too large to fit in memory, use the <code>predictEveryBatch</code> API:</p> |
| <div class="highlight"><pre><code class="language-scala" data-lang="scala"> <span class="k">val</span> <span class="nv">preds</span> <span class="k">=</span> <span class="nv">mod</span><span class="o">.</span><span class="py">predictEveryBatch</span><span class="o">(</span><span class="n">val_dataiter</span><span class="o">)</span> |
| <span class="nv">val_dataiter</span><span class="o">.</span><span class="py">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="nf">while</span> <span class="o">(</span><span class="nv">val_dataiter</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">batch</span> <span class="k">=</span> <span class="nv">val_dataiter</span><span class="o">.</span><span class="py">next</span><span class="o">()</span> |
| <span class="k">val</span> <span class="nv">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="nv">NDArray</span><span class="o">.</span><span class="py">argmax_channel</span><span class="o">(</span><span class="nf">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="py">toArray</span><span class="o">.</span><span class="py">map</span><span class="o">(</span><span class="nv">_</span><span class="o">.</span><span class="py">toInt</span><span class="o">)</span> |
| <span class="k">val</span> <span class="nv">label</span> <span class="k">=</span> <span class="nv">batch</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">toArray</span><span class="o">.</span><span class="py">map</span><span class="o">(</span><span class="nv">_</span><span class="o">.</span><span class="py">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> |
| </code></pre></div> |
| <p>If you need to evaluate on a test set and don't need the prediction output, call the <code>score()</code> function with a <code>DataIter</code> and an <code>EvalMetric</code>:</p> |
| <div class="highlight"><pre><code class="language-scala" data-lang="scala"> <span class="nv">mod</span><span class="o">.</span><span class="py">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> |
| </code></pre></div> |
| <p>This runs predictions on each batch in the provided <code>DataIter</code> and computes the evaluation score using the provided <code>EvalMetric</code>. The evaluation results are stored in <code>metric</code> so that you can query later.</p> |
| |
| <h2 id="saving-and-loading-module-parameters">Saving and Loading Module Parameters</h2> |
| |
| <p>To save the module parameters in each training epoch, use a <code>checkpoint</code> callback:</p> |
| <div class="highlight"><pre><code class="language-scala" data-lang="scala"> <span class="k">val</span> <span class="nv">modelPrefix</span><span class="k">:</span> <span class="kt">String</span> <span class="o">=</span> <span class="s">"mymodel"</span> |
| |
| <span class="nf">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="nf">while</span><span class="o">(</span><span class="nv">train_dataiter</span><span class="o">.</span><span class="py">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="nv">checkpoint</span> <span class="k">=</span> <span class="nv">mod</span><span class="o">.</span><span class="py">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> |
| </code></pre></div> |
| <p>To load the saved module parameters, call the <code>loadCheckpoint</code> function:</p> |
| <div class="highlight"><pre><code class="language-scala" data-lang="scala"> <span class="k">val</span> <span class="nv">mod</span> <span class="k">=</span> <span class="nv">Module</span><span class="o">.</span><span class="py">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> |
| </code></pre></div> |
| <p>To initialize parameters, Bind the symbols to construct executors first with <code>bind</code> method. Then, initialize the parameters and auxiliary states by calling <code>initParams()</code> method.</p> |
| <div class="highlight"><pre><code class="language-scala" data-lang="scala"> <span class="nv">mod</span><span class="o">.</span><span class="py">bind</span><span class="o">(</span><span class="n">dataShapes</span> <span class="k">=</span> <span class="nv">train_dataiter</span><span class="o">.</span><span class="py">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="nv">train_dataiter</span><span class="o">.</span><span class="py">provideLabel</span><span class="o">))</span> |
| <span class="nv">mod</span><span class="o">.</span><span class="py">initParams</span><span class="o">()</span> |
| </code></pre></div> |
| <p>To get current parameters, use <code>getParams</code> method.</p> |
| <div class="highlight"><pre><code class="language-scala" data-lang="scala"> <span class="nf">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="nv">mod</span><span class="o">.</span><span class="py">getParams</span> |
| </code></pre></div> |
| <p>To assign parameter and aux state values, use <code>setParams</code> method.</p> |
| <div class="highlight"><pre><code class="language-scala" data-lang="scala"> <span class="nv">mod</span><span class="o">.</span><span class="py">setParams</span><span class="o">(</span><span class="n">argParams</span><span class="o">,</span> <span class="n">auxParams</span><span class="o">)</span> |
| </code></pre></div> |
| <p>To resume training from a saved checkpoint, instead of calling <code>setParams()</code>, directly call <code>fit()</code>, passing the loaded parameters, so that <code>fit()</code> knows to start from those parameters instead of initializing randomly:</p> |
| <div class="highlight"><pre><code class="language-scala" data-lang="scala"> <span class="nv">mod</span><span class="o">.</span><span class="py">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="py">setArgParams</span><span class="o">(</span><span class="n">argParams</span><span class="o">).\</span> |
| <span class="nf">setAuxParams</span><span class="o">(</span><span class="n">auxParams</span><span class="o">).</span><span class="py">setBeginEpoch</span><span class="o">(</span><span class="n">beginEpoch</span><span class="o">))</span> |
| </code></pre></div> |
| <p>Create an object of the <code>FitParams()</code> class, and then use it to call the <code>setBeginEpoch()</code> method to pass <code>beginEpoch</code> so that <code>fit()</code> knows to resume from a saved epoch.</p> |
| |
| <h2 id="next-steps">Next Steps</h2> |
| |
| <ul> |
| <li>See <a href="model">Model API</a> for an alternative simple high-level interface for training neural networks.</li> |
| <li>See <a href="symbol">Symbolic API</a> for operations on NDArrays that assemble neural networks from layers.</li> |
| <li>See <a href="io">IO Data Loading API</a> for parsing and loading data.</li> |
| <li>See <a href="ndarray">NDArray API</a> for vector/matrix/tensor operations.</li> |
| <li>See <a href="kvstore">KVStore API</a> for multi-GPU and multi-host distributed training.</li> |
| </ul> |
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