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| <h1 id="mxnet-scala-symbolic-api">MXNet Scala Symbolic API</h1> |
| |
| <p>Topics:</p> |
| |
| <ul> |
| <li><a href="#how-to-compose-symbols">How to Compose Symbols</a> introduces operator overloading of symbols.</li> |
| <li><a href="#symbol-attributes">Symbol Attributes</a> describes how to attach attributes to symbols.</li> |
| <li><a href="#serialization">Serialization</a> explains how to save and load symbols.</li> |
| <li><a href="#executing-symbols">Executing Symbols</a> explains how to evaluate the symbols with data.</li> |
| <li><a href="/versions/1.8.0/api/scala/docs/api/#org.apache.mxnet.Executor">Execution API Reference</a> documents the execution APIs.</li> |
| <li><a href="#multiple-outputs">Multiple Outputs</a> explains how to configure multiple outputs.</li> |
| <li><a href="/versions/1.8.0/api/scala/docs/api/#org.apache.mxnet.Symbol">Symbol Creation API Reference</a> documents functions.</li> |
| </ul> |
| |
| <p>We also highly encourage you to read <a href="symbol_in_pictures">Symbolic Configuration and Execution in Pictures</a>.</p> |
| |
| <h2 id="how-to-compose-symbols">How to Compose Symbols</h2> |
| |
| <p>The symbolic API provides a way to configure computation graphs. |
| You can configure the graphs either at the level of neural network layer operations or as fine-grained operations.</p> |
| |
| <p>The following example configures a two-layer neural network.</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">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">net</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">"out"</span><span class="o">)</span> |
| <span class="k">:type</span> <span class="kt">net</span> |
| <span class="c1">// org.apache.mxnet.Symbol |
| </span></code></pre></div> |
| <p>The basic arithmetic operators (plus, minus, div, multiplication) are overloaded for |
| <em>element-wise operations</em> of symbols.</p> |
| |
| <p>The following example creates a computation graph that adds two inputs together.</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">val</span> <span class="nv">a</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">"a"</span><span class="o">)</span> |
| <span class="k">val</span> <span class="nv">b</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">"b"</span><span class="o">)</span> |
| <span class="k">val</span> <span class="nv">c</span> <span class="k">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span> |
| </code></pre></div> |
| <h2 id="symbol-attributes">Symbol Attributes</h2> |
| |
| <p>You can add an attribute to a symbol by providing an attribute dictionary when you create a symbol.</p> |
| <div class="highlight"><pre><code class="language-scala" data-lang="scala"> <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="nc">Map</span><span class="o">(</span><span class="s">"mood"</span><span class="o">-></span> <span class="s">"angry"</span><span class="o">))</span> |
| <span class="k">val</span> <span class="nv">op</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">Convolution</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">kernel</span> <span class="k">=</span> <span class="nc">Shape</span><span class="o">(</span><span class="mi">1</span><span class="o">,</span> <span class="mi">1</span><span class="o">),</span> <span class="n">num_filter</span> <span class="k">=</span> <span class="mi">1</span><span class="o">,</span> <span class="n">attr</span> <span class="k">=</span> <span class="nc">Map</span><span class="o">(</span><span class="s">"mood"</span> <span class="o">-></span> <span class="s">"so so"</span><span class="o">))</span> |
| </code></pre></div> |
| <p>For proper communication with the C++ backend, both the key and values of the attribute dictionary should be strings. To retrieve the attributes, use <code>attr(key)</code>:</p> |
| <div class="highlight"><pre><code class="language-" data-lang=""> data.attr("mood") |
| // Option[String] = Some(angry) |
| </code></pre></div> |
| <p>To attach attributes, you can use <code>AttrScope</code>. <code>AttrScope</code> automatically adds the specified attributes to all of the symbols created within that scope. The user can also inherit this object to change naming behavior. For example:</p> |
| <div class="highlight"><pre><code class="language-scala" data-lang="scala"> <span class="nf">val</span> <span class="o">(</span><span class="n">data</span><span class="o">,</span> <span class="n">gdata</span><span class="o">)</span> <span class="k">=</span> |
| <span class="nc">AttrScope</span><span class="o">(</span><span class="nc">Map</span><span class="o">(</span><span class="s">"group"</span> <span class="o">-></span> <span class="s">"4"</span><span class="o">,</span> <span class="s">"data"</span> <span class="o">-></span> <span class="s">"great"</span><span class="o">)).</span><span class="py">withScope</span> <span class="o">{</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="n">attr</span> <span class="k">=</span> <span class="nc">Map</span><span class="o">(</span><span class="s">"dtype"</span> <span class="o">-></span> <span class="s">"data"</span><span class="o">,</span> <span class="s">"group"</span> <span class="o">-></span> <span class="s">"1"</span><span class="o">))</span> |
| <span class="k">val</span> <span class="nv">gdata</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">"data2"</span><span class="o">)</span> |
| <span class="o">(</span><span class="n">data</span><span class="o">,</span> <span class="n">gdata</span><span class="o">)</span> |
| <span class="o">}</span> |
| <span class="nf">assert</span><span class="o">(</span><span class="nv">gdata</span><span class="o">.</span><span class="py">attr</span><span class="o">(</span><span class="s">"group"</span><span class="o">).</span><span class="py">get</span> <span class="o">===</span> <span class="s">"4"</span><span class="o">)</span> |
| <span class="nf">assert</span><span class="o">(</span><span class="nv">data</span><span class="o">.</span><span class="py">attr</span><span class="o">(</span><span class="s">"group"</span><span class="o">).</span><span class="py">get</span> <span class="o">===</span> <span class="s">"1"</span><span class="o">)</span> |
| |
| <span class="k">val</span> <span class="nv">exceedScopeData</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">"data3"</span><span class="o">)</span> |
| <span class="nf">assert</span><span class="o">(</span><span class="nv">exceedScopeData</span><span class="o">.</span><span class="py">attr</span><span class="o">(</span><span class="s">"group"</span><span class="o">)</span> <span class="o">===</span> <span class="nc">None</span><span class="o">,</span> <span class="s">"No group attr in global attr scope"</span><span class="o">)</span> |
| </code></pre></div> |
| <h2 id="serialization">Serialization</h2> |
| |
| <p>There are two ways to save and load the symbols. You can use the <code>mxnet.Symbol.save</code> and <code>mxnet.Symbol.load</code> functions to serialize the <code>Symbol</code> objects. |
| The advantage of using <code>save</code> and <code>load</code> functions is that it is language agnostic and cloud friendly. |
| The symbol is saved in JSON format. You can also get a JSON string directly using <code>mxnet.Symbol.toJson</code>. |
| Refer to <a href="/versions/1.8.0/api/scala/docs/api/#org.apache.mxnet.Symbol">API documentation</a> for more details.</p> |
| |
| <p>The following example shows how to save a symbol to an S3 bucket, load it back, and compare two symbols using a JSON string.</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">val</span> <span class="nv">a</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">"a"</span><span class="o">)</span> |
| <span class="k">val</span> <span class="nv">b</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">"b"</span><span class="o">)</span> |
| <span class="k">val</span> <span class="nv">c</span> <span class="k">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span> |
| <span class="nv">c</span><span class="o">.</span><span class="py">save</span><span class="o">(</span><span class="s">"s3://my-bucket/symbol-c.json"</span><span class="o">)</span> |
| <span class="k">val</span> <span class="nv">c2</span> <span class="k">=</span> <span class="nv">Symbol</span><span class="o">.</span><span class="py">load</span><span class="o">(</span><span class="s">"s3://my-bucket/symbol-c.json"</span><span class="o">)</span> |
| <span class="nv">c</span><span class="o">.</span><span class="py">toJson</span> <span class="o">==</span> <span class="nv">c2</span><span class="o">.</span><span class="py">toJson</span> |
| <span class="c1">// Boolean = true |
| </span></code></pre></div> |
| <h2 id="executing-symbols">Executing Symbols</h2> |
| |
| <p>After you have assembled a set of symbols into a computation graph, the MXNet engine can evaluate them. |
| If you are training a neural network, this is typically |
| handled by the high-level <a href="model">Model class</a> and the [<code>fit()</code>] function.</p> |
| |
| <p>For neural networks used in "feed-forward", "prediction", or "inference" mode (all terms for the same |
| thing: running a trained network), the input arguments are the |
| input data, and the weights of the neural network that were learned during training.</p> |
| |
| <p>To manually execute a set of symbols, you need to create an [<code>Executor</code>] object, |
| which is typically constructed by calling the [<code>simpleBind(<parameters>)</code>] method on a symbol.</p> |
| |
| <h2 id="multiple-outputs">Multiple Outputs</h2> |
| |
| <p>To group the symbols together, use the <a href="#mxnet.symbol.Group">mxnet.symbol.Group</a> function.</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">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">net</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">"out"</span><span class="o">)</span> |
| <span class="k">val</span> <span class="nv">group</span> <span class="k">=</span> <span class="nv">Symbol</span><span class="o">.</span><span class="py">Group</span><span class="o">(</span><span class="n">fc1</span><span class="o">,</span> <span class="n">net</span><span class="o">)</span> |
| <span class="nv">group</span><span class="o">.</span><span class="py">listOutputs</span><span class="o">()</span> |
| <span class="c1">// IndexedSeq[String] = ArrayBuffer(fc1_output, out_output) |
| </span></code></pre></div> |
| <p>After you get the <code>group</code>, you can bind on <code>group</code> instead. |
| The resulting executor will have two outputs, one for fc1_output and one for softmax_output.</p> |
| |
| <h2 id="next-steps">Next Steps</h2> |
| |
| <ul> |
| <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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