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| |
| <h1 id="mxnet-scala-model-api">MXNet Scala Model API</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 href="ndarray">ndarray</a> and <a href="symbol">symbolic</a> |
| modules that make neural network training easy.</p> |
| |
| <p>Topics:</p> |
| |
| <ul> |
| <li><a href="#train-the-model">Train the Model</a></li> |
| <li><a href="#save-the-model">Save the Model</a></li> |
| <li><a href="#periodic-checkpointing">Periodic Checkpoint</a></li> |
| <li><a href="#use-multiple-devices">Multiple Devices</a></li> |
| <li><a href="/versions/1.9.1/api/scala/docs/api/#org.apache.mxnet.Model">Model API Reference</a></li> |
| </ul> |
| |
| <h2 id="train-the-model">Train the Model</h2> |
| |
| <p>To train a model, perform two steps: configure the model using the symbol parameter, |
| then call <code>model.Feedforward.create</code> to create the model. |
| The following example creates a two-layer neural network.</p> |
| <div class="highlight"><pre><code class="language-scala" data-lang="scala"> <span class="c1">// configure a two layer neuralnetwork |
| </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="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">softmax</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="nc">Some</span><span class="o">(</span><span class="n">fc2</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="c1">// Construct the FeedForward model and fit on the input training data |
| </span> <span class="k">val</span> <span class="nv">model</span> <span class="k">=</span> <span class="nv">FeedForward</span><span class="o">.</span><span class="py">newBuilder</span><span class="o">(</span><span class="n">softmax</span><span class="o">)</span> |
| <span class="o">.</span><span class="py">setContext</span><span class="o">(</span><span class="nv">Context</span><span class="o">.</span><span class="py">cpu</span><span class="o">())</span> |
| <span class="o">.</span><span class="py">setNumEpoch</span><span class="o">(</span><span class="n">num_epoch</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">0.01f</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="py">setTrainData</span><span class="o">(</span><span class="n">trainDataIter</span><span class="o">)</span> |
| <span class="o">.</span><span class="py">setEvalData</span><span class="o">(</span><span class="n">valDataIter</span><span class="o">)</span> |
| <span class="o">.</span><span class="py">build</span><span class="o">()</span> |
| </code></pre></div> |
| <p>You can also use the <code>scikit-learn-style</code> construct and <code>fit</code> function to create a model.</p> |
| <div class="highlight"><pre><code class="language-scala" data-lang="scala"> <span class="c1">// create a model using sklearn-style two-step way |
| </span> <span class="k">val</span> <span class="nv">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="nv">model</span><span class="o">.</span><span class="py">fit</span><span class="o">(</span><span class="n">trainData</span> <span class="k">=</span> <span class="n">train</span><span class="o">)</span> |
| </code></pre></div> |
| <p>For more information, see <a href="/versions/1.9.1/api/scala/docs/api/#package">API Reference</a>.</p> |
| |
| <h2 id="save-the-model">Save the Model</h2> |
| |
| <p>After the job is done, save your work. |
| We also provide <code>save</code> and <code>load</code> functions. You can use the <code>load</code> function to load a model checkpoint from a file.</p> |
| <div class="highlight"><pre><code class="language-scala" data-lang="scala"> <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="nv">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="nv">num_epoch</span> <span class="k">=</span> <span class="mi">100</span> |
| <span class="nv">Model</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="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="nv">model_loaded</span> <span class="k">=</span> <span class="nv">FeedForward</span><span class="o">.</span><span class="py">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> |
| </code></pre></div> |
| <p>The advantage of these two <code>save</code> and <code>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.</p> |
| |
| <h2 id="periodic-checkpointing">Periodic Checkpointing</h2> |
| |
| <p>We recommend checkpointing your model after each iteration. |
| To do this, use <code>EpochEndCallback</code> to add a <code>Model.saveCheckpoint(<parameters>)</code> checkpoint callback to the function after each iteration .</p> |
| <div class="highlight"><pre><code class="language-scala" data-lang="scala"> <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="nv">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="nv">checkpoint</span><span class="k">:</span> <span class="kt">EpochEndCallback</span> <span class="o">=</span> |
| <span class="nf">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="nf">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="nv">Model</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="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="nf">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="nv">Model</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">num_epoch</span><span class="o">)</span> |
| |
| </code></pre></div> |
| <p>You can load the model checkpoint later using <code>Model.loadCheckpoint(modelPrefix, num_epoch)</code>.</p> |
| |
| <h2 id="use-multiple-devices">Use Multiple Devices</h2> |
| |
| <p>Set <code>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.</p> |
| <div class="highlight"><pre><code class="language-scala" data-lang="scala"> <span class="k">val</span> <span class="nv">devices</span> <span class="k">=</span> <span class="nc">Array</span><span class="o">(</span><span class="nv">Context</span><span class="o">.</span><span class="py">gpu</span><span class="o">(</span><span class="mi">0</span><span class="o">),</span> <span class="nv">Context</span><span class="o">.</span><span class="py">gpu</span><span class="o">(</span><span class="mi">1</span><span class="o">))</span> |
| |
| <span class="k">val</span> <span class="nv">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> |
| </code></pre></div> |
| <p>Training occurs in parallel on the GPUs that you specify.</p> |
| |
| <h2 id="next-steps">Next Steps</h2> |
| |
| <ul> |
| <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> |
| </div> |
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