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class="sphinxsidebar leftsidebar" role="navigation"> <div class="sphinxsidebarwrapper"> <ul> <li class="toctree-l1"><a class="reference internal" href="../python/index.html">Python Documents</a></li> <li class="toctree-l1"><a class="reference internal" href="../r/index.html">R Documents</a></li> <li class="toctree-l1"><a class="reference internal" href="../julia/index.html">Julia Documents</a></li> <li class="toctree-l1"><a class="reference internal" href="../c++/index.html">C++ Documents</a></li> <li class="toctree-l1"><a class="reference internal" href="index.html">Scala Documents</a></li> <li class="toctree-l1"><a class="reference internal" href="../perl/index.html">Perl Documents</a></li> <li class="toctree-l1"><a class="reference internal" href="../../how_to/index.html">HowTo Documents</a></li> <li class="toctree-l1"><a class="reference internal" href="../../architecture/index.html">System Documents</a></li> <li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Tutorials</a></li> </ul> </div> </div> <div class="content"> <div class="page-tracker"></div> <div class="section" id="module-api"> <h1>Module API<a class="headerlink" href="#module-api" title="Permalink to this headline">¶</a></h1> The module API provides an intermediate and high-level interface for performing computation with neural networks in MXNet. A module is an instance of subclasses of the <code class="docutils literal">BaseModule</code>. The most widely used module class is called <code class="docutils literal">Module</code>. Module wraps a <code class="docutils literal">Symbol</code> and one or more <code class="docutils literal">Executors</code>. For a full list of functions, see <code class="docutils literal">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 class="docutils literal">Module</code> namespace. <div class="section" id="preparing-a-module-for-computation"> <h2>Preparing a Module for Computation<a class="headerlink" href="#preparing-a-module-for-computation" title="Permalink to this headline">¶</a></h2> To construct a module, refer to the constructors for the module class. For example, the <code class="docutils literal">Module</code> class accepts a <code class="docutils literal">Symbol</code> as input: <div class="highlight-scala"><div class="highlight"><pre> import ml.dmlc.mxnet._ import ml.dmlc.mxnet.module.{FitParams, Module} // construct a simple MLP val data = Symbol.Variable("data") val fc1 = Symbol.FullyConnected(name = "fc1")(data)(Map("num_hidden" -> 128)) val act1 = Symbol.Activation(name = "relu1")(fc1)(Map("act_type" -> "relu")) val fc2 = Symbol.FullyConnected(name = "fc2")(act1)(Map("num_hidden" -> 64)) val act2 = Symbol.Activation(name = "relu2")(fc2)(Map("act_type" -> "relu")) val fc3 = Symbol.FullyConnected(name = "fc3")(act2)(Map("num_hidden" -> 10)) val out = Symbol.SoftmaxOutput(name = "softmax")(fc3)() // construct the module val mod = new Module(out) </pre></div> </div> By default, <code class="docutils literal">context</code> is the CPU. If you need data parallelization, you can specify a GPU context or an array of GPU contexts. Before you can compute with a module, you need to call <code class="docutils literal">bind()</code> to allocate the device memory and <code class="docutils literal">initParams()</code> or <code class="docutils literal">SetParams()</code> to initialize the parameters. If you simply want to fit a module, you don’t need to call <code class="docutils literal">bind()</code> and <code class="docutils literal">initParams()</code> explicitly, because the fit() function automatically calls them if they are needed. <div class="highlight-scala"><div class="highlight"><pre> mod.bind(dataShapes = train_dataiter.provideData, labelShapes = Some(train_dataiter.provideLabel)) mod.initParams() </pre></div> </div> Now you can compute with the module using functions like <code class="docutils literal">forward()</code>, <code class="docutils literal">backward()</code>, etc. </div> <div class="section" id="training-predicting-and-evaluating"> <h2>Training, Predicting, and Evaluating<a class="headerlink" href="#training-predicting-and-evaluating" title="Permalink to this headline">¶</a></h2> Modules provide high-level APIs for training, predicting, and evaluating. To fit a module, call the <code class="docutils literal">fit()</code> function with some <code class="docutils literal">DataIter</code>s: <div class="highlight-scala"><div class="highlight"><pre> import ml.dmlc.mxnet.optimizer.SGD val mod = new Module(softmax) mod.fit(train_dataiter, evalData = scala.Option(eval_dataiter), \ numEpoch = n_epoch, fitParams = new FitParams()\ .setOptimizer(new SGD(learningRate = 0.1f, momentum = 0.9f, wd = 0.0001f))) </pre></div> </div> The interface is very similar to the old <code class="docutils literal">FeedForward</code> class. You can pass in batch-end callbacks using <code class="docutils literal">setBatchEndCallback</code> and epoch-end callbacks using <code class="docutils literal">setEpochEndCallback</code>. You can also set parameters using methods like <code class="docutils literal">setOptimizer</code> and <code class="docutils literal">setEvalMetric</code>. To learn more about the <code class="docutils literal">FitParams()</code>, see the <a class="reference external" href="/versions/0.11.0/api/scala/docs/index.html#ml.dmlc.mxnet.module.FitParams">API page</a>. To predict with a module, call <code class="docutils literal">predict()</code> with a <code class="docutils literal">DataIter</code>: <div class="highlight-scala"><div class="highlight"><pre> mod.predict(val_dataiter) </pre></div> </div> 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="/versions/0.11.0/api/scala/docs/index.html#ml.dmlc.mxnet.module.BaseModule"><code class="docutils literal">predict()</code> function</a>. When prediction results might be too large to fit in memory, use the <code class="docutils literal">predictEveryBatch</code> API: <div class="highlight-scala"><div class="highlight"><pre> val preds = mod.predictEveryBatch(val_dataiter) val_dataiter.reset() var i = 0 while (val_dataiter.hasNext) { val batch = val_dataiter.next() val predLabel: Array[Int] = NDArray.argmax_channel(preds(i)(0)).toArray.map(_.toInt) val label = batch.label(0).toArray.map(_.toInt) //do something... i += 1 } </pre></div> </div> If you need to evaluate on a test set and don’t need the prediction output, call the <code class="docutils literal">score()</code> function with a <code class="docutils literal">DataIter</code> and an <code class="docutils literal">EvalMetric</code>: <div class="highlight-scala"><div class="highlight"><pre> mod.score(val_dataiter, metric) </pre></div> </div> This runs predictions on each batch in the provided <code class="docutils literal">DataIter</code> and computes the evaluation score using the provided <code class="docutils literal">EvalMetric</code>. The evaluation results are stored in <code class="docutils literal">metric</code> so that you can query later. </div> <div class="section" id="saving-and-loading-module-parameters"> <h2>Saving and Loading Module Parameters<a class="headerlink" href="#saving-and-loading-module-parameters" title="Permalink to this headline">¶</a></h2> To save the module parameters in each training epoch, use a <code class="docutils literal">checkpoint</code> callback: <div class="highlight-scala"><div class="highlight"><pre> val modelPrefix: String = "mymodel" for (epoch <- 0 until 5) { while(train_dataiter.hasNext){ // forward backward pass //do something... } val checkpoint = mod.saveCheckpoint(modelPrefix, epoch, saveOptStates = true) } </pre></div> </div> To load the saved module parameters, call the <code class="docutils literal">loadCheckpoint</code> function: <div class="highlight-scala"><div class="highlight"><pre> val mod = Module.loadCheckpoint(modelPrefix, loadModelEpoch, loadOptimizerStates = true) </pre></div> </div> To initialize parameters, Bind the symbols to construct executors first with <code class="docutils literal">bind</code> method. Then, initialize the parameters and auxiliary states by calling <code class="docutils literal">initParams()</code> method. <div class="highlight-scala"><div class="highlight"><pre> mod.bind(dataShapes = train_dataiter.provideData, labelShapes = Some(train_dataiter.provideLabel)) mod.initParams() </pre></div> </div> To get current parameters, use <code class="docutils literal">getParams</code> method. <div class="highlight-scala"><div class="highlight"><pre> val (argParams, auxParams) = mod.getParams </pre></div> </div> To assign parameter and aux state values, use <code class="docutils literal">setParams</code> method. <div class="highlight-scala"><div class="highlight"><pre> mod.setParams(argParams, auxParams) </pre></div> </div> To resume training from a saved checkpoint, instead of calling <code class="docutils literal">setParams()</code>, directly call <code class="docutils literal">fit()</code>, passing the loaded parameters, so that <code class="docutils literal">fit()</code> knows to start from those parameters instead of initializing randomly: <div class="highlight-scala"><div class="highlight"><pre> mod.fit(..., fitParams=new FitParams().setArgParams(argParams).\ setAuxParams(auxParams).setBeginEpoch(beginEpoch)) </pre></div> </div> Create an object of the <code class="docutils literal">FitParams()</code> class, and then use it to call the <code class="docutils literal">setBeginEpoch()</code> method to pass <code class="docutils literal">beginEpoch</code> so that <code class="docutils literal">fit()</code> knows to resume from a saved epoch. </div> <div class="section" id="next-steps"> <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">Model API</a> for an alternative simple high-level interface for training neural networks.</li> <li>See <a class="reference internal" href="symbol.html">Symbolic API</a> for operations on NDArrays that assemble neural networks from layers.</li> <li>See <a class="reference internal" href="io.html">IO Data Loading API</a> for parsing and loading data.</li> <li>See <a class="reference internal" href="ndarray.html">NDArray API</a> for vector/matrix/tensor operations.</li> <li>See <a class="reference internal" href="kvstore.html">KVStore API</a> for multi-GPU and multi-host distributed training.</li> </ul> </div> </div> </div> </div> <div aria-label="main navigation" class="sphinxsidebar rightsidebar" role="navigation"> <div class="sphinxsidebarwrapper"> <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> </div> </div> </div><div class="footer"> <div class="section-disclaimer"> <div class="container"> <div> <img height="60" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/apache_incubator_logo.png"/> Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. 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