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class="row"> <div aria-label="main navigation" 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="mxnet-scala-model-api"> <h1>MXNet Scala Model API<a class="headerlink" href="#mxnet-scala-model-api" title="Permalink to this headline">¶</a></h1> 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">ndarray</a> and <a class="reference internal" href="symbol.html">symbolic</a> modules that make neural network training easy. Topics: <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="#/versions/0.11.0/api/scala/docs/index.html#ml.dmlc.mxnet.Model">Model API Reference</a></li> </ul> <div class="section" id="train-the-model"> <h2>Train the Model<a class="headerlink" href="#train-the-model" title="Permalink to this headline">¶</a></h2> To train a model, perform two steps: configure the model using the symbol parameter, then call <code class="docutils literal">model.Feedforward.create</code> to create the model. The following example creates a two-layer neural network. <div class="highlight-scala"><div class="highlight"><pre> // configure a two layer neuralnetwork val data = Symbol.Variable("data") val fc1 = Symbol.FullyConnected(name = "fc1")()(Map("data" -> data, "num_hidden" -> 128)) val act1 = Symbol.Activation(name = "relu1")()(Map("data" -> fc1, "act_type" -> "relu")) val fc2 = Symbol.FullyConnected(name = "fc2")()(Map("data" -> act1, "num_hidden" -> 64)) val softmax = Symbol.SoftmaxOutput(name = "sm")()(Map("data" -> fc2)) // Construct the FeedForward model and fit on the input training data val model = FeedForward.newBuilder(softmax) .setContext(Context.cpu()) .setNumEpoch(num_epoch) .setOptimizer(new SGD(learningRate = 0.01f, momentum = 0.9f, wd = 0.0001f)) .setTrainData(trainDataIter) .setEvalData(valDataIter) .build() </pre></div> </div> You can also use the <code class="docutils literal">scikit-learn-style</code> construct and <code class="docutils literal">fit</code> function to create a model. <div class="highlight-scala"><div class="highlight"><pre> // create a model using sklearn-style two-step way val model = new FeedForward(softmax, numEpoch = numEpochs, argParams = argParams, auxParams = auxParams, beginEpoch = beginEpoch, epochSize = epochSize) model.fit(trainData = train) </pre></div> </div> For more information, see <a class="reference external" href="/versions/0.11.0/api/scala/docs/index.html">API Reference</a>. </div> <div class="section" id="save-the-model"> <h2>Save the Model<a class="headerlink" href="#save-the-model" title="Permalink to this headline">¶</a></h2> After the job is done, save your work. We also provide <code class="docutils literal">save</code> and <code class="docutils literal">load</code> functions. You can use the <code class="docutils literal">load</code> function to load a model checkpoint from a file. <div class="highlight-scala"><div class="highlight"><pre> // checkpoint the model data into file, // save a model to modelPrefix-symbol.json and modelPrefix-0100.params val modelPrefix: String = "checkpt" val num_epoch = 100 Model.saveCheckpoint(modelPrefix, epoch + 1, symbol, argParams, auxStates) // load model back val model_loaded = FeedForward.load(modelPrefix, num_epoch) </pre></div> </div> The advantage of these two <code class="docutils literal">save</code> and <code class="docutils literal">load</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. </div> <div class="section" id="periodic-checkpointing"> <h2>Periodic Checkpointing<a class="headerlink" href="#periodic-checkpointing" title="Permalink to this headline">¶</a></h2> We recommend checkpointing your model after each iteration. To do this, use <code class="docutils literal">EpochEndCallback</code> to add a <code class="docutils literal">Model.saveCheckpoint(<parameters>)</code> checkpoint callback to the function after each iteration . <div class="highlight-scala"><div class="highlight"><pre> // modelPrefix-symbol.json will be saved for symbol. // modelPrefix-epoch.params will be saved for parameters. // Checkpoint the model into file. Can specify parameters. // For more information, check API doc. val modelPrefix: String = "checkpt" val checkpoint: EpochEndCallback = if (modelPrefix == null) null else new EpochEndCallback { override def invoke(epoch: Int, symbol: Symbol, argParams: Map[String, NDArray], auxStates: Map[String, NDArray]): Unit = { Model.saveCheckpoint(modelPrefix, epoch + 1, symbol, argParams, auxParams) } } // Load model checkpoint from file. Returns symbol, argParams, auxParams. val (_, argParams, _) = Model.loadCheckpoint(modelPrefix, num_epoch) </pre></div> </div> You can load the model checkpoint later using <code class="docutils literal">Model.loadCheckpoint(modelPrefix, num_epoch)</code>. </div> <div class="section" id="use-multiple-devices"> <h2>Use Multiple Devices<a class="headerlink" href="#use-multiple-devices" title="Permalink to this headline">¶</a></h2> Set <code class="docutils literal">ctx</code> to the list of devices that you want to train on. You can create a list of devices in any way you want. <div class="highlight-scala"><div class="highlight"><pre> val devices = Array(Context.gpu(0), Context.gpu(1)) val model = new FeedForward(ctx = devices, symbol = network, numEpoch = numEpochs, optimizer = optimizer, epochSize = epochSize, ...) </pre></div> </div> Training occurs in parallel on the GPUs that you specify. </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="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="#">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> </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. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF. 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