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<!--- under the License. --><div class="section" id="mxnet-clojure-symbolic-api">
<span id="mxnet-clojure-symbolic-api"></span><h1>MXNet Clojure Symbolic API<a class="headerlink" href="#mxnet-clojure-symbolic-api" title="Permalink to this headline"></a></h1>
<p>Topics:</p>
<ul class="simple">
<li><a class="reference external" href="#how-to-compose-symbols">How to Compose Symbols</a></li>
<li><a class="reference external" href="#more-complicated-compositions">More Complicated Compositions</a></li>
<li><a class="reference external" href="#group-multiple-symbols">Group Multiple Symbols</a></li>
<li><a class="reference external" href="#serialization">Serialization</a></li>
<li><a class="reference external" href="#executing-symbols">Executing Symbols</a></li>
<li><a class="reference external" href="/versions/1.4.1/api/clojure/docs/org.apache.clojure-mxnet.symbol.html">Symbol API Reference</a></li>
</ul>
<p>We also highly encourage you to read <a class="reference internal" href="symbol_in_pictures.html"><span class="doc">Symbolic Configuration and Execution in Pictures</span></a>.</p>
<p>To follow along with this documentation, you can use this namespace to with the following requirements:</p>
<div class="highlight-clojure"><div class="highlight"><pre><span></span><span class="p">(</span><span class="kd">ns </span><span class="nv">docs.symbol</span>
<span class="p">(</span><span class="ss">:require</span> <span class="p">[</span><span class="nv">org.apache.clojure-mxnet.executor</span> <span class="ss">:as</span> <span class="nv">executor</span><span class="p">]</span>
<span class="p">[</span><span class="nv">org.apache.clojure-mxnet.ndarray</span> <span class="ss">:as</span> <span class="nv">ndarray</span><span class="p">]</span>
<span class="p">[</span><span class="nv">org.apache.clojure-mxnet.symbol</span> <span class="ss">:as</span> <span class="nv">sym</span><span class="p">]</span>
<span class="p">[</span><span class="nv">org.apache.clojure-mxnet.context</span> <span class="ss">:as</span> <span class="nv">context</span><span class="p">]))</span>
</pre></div>
</div>
<div class="section" id="how-to-compose-symbols">
<span id="how-to-compose-symbols"></span><h2>How to Compose Symbols<a class="headerlink" href="#how-to-compose-symbols" title="Permalink to this headline"></a></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-clojure"><div class="highlight"><pre><span></span><span class="p">(</span><span class="k">def </span><span class="nv">data</span> <span class="p">(</span><span class="nf">sym/variable</span> <span class="s">"data"</span><span class="p">))</span>
<span class="p">(</span><span class="k">def </span><span class="nv">fc1</span> <span class="p">(</span><span class="nf">sym/fully-connected</span> <span class="s">"fc1"</span> <span class="p">{</span><span class="ss">:data</span> <span class="nv">data</span> <span class="ss">:num-hidden</span> <span class="mi">128</span><span class="p">}))</span>
<span class="p">(</span><span class="k">def </span><span class="nv">act1</span> <span class="p">(</span><span class="nf">sym/activation</span> <span class="s">"act1"</span> <span class="p">{</span><span class="ss">:data</span> <span class="nv">fc1</span> <span class="ss">:act-type</span> <span class="s">"relu"</span><span class="p">}))</span>
<span class="p">(</span><span class="k">def </span><span class="nv">fc2</span> <span class="p">(</span><span class="nf">sym/fully-connected</span> <span class="s">"fc2"</span> <span class="p">{</span><span class="ss">:data</span> <span class="nv">act1</span> <span class="ss">:num-hidden</span> <span class="mi">64</span><span class="p">}))</span>
<span class="p">(</span><span class="k">def </span><span class="nv">net</span> <span class="p">(</span><span class="nf">sym/softmax-output</span> <span class="s">"out"</span> <span class="p">{</span><span class="ss">:data</span> <span class="nv">fc2</span><span class="p">}))</span>
</pre></div>
</div>
<p>This can also be combined more dynamically with the <code class="docutils literal"><span class="pre">as-></span></code> Clojure threading form.</p>
<div class="highlight-clojure"><div class="highlight"><pre><span></span><span class="p">(</span><span class="nf">as-></span> <span class="p">(</span><span class="nf">sym/variable</span> <span class="s">"data"</span><span class="p">)</span> <span class="nv">data</span>
<span class="p">(</span><span class="nf">sym/fully-connected</span> <span class="s">"fc1"</span> <span class="p">{</span><span class="ss">:data</span> <span class="nv">data</span> <span class="ss">:num-hidden</span> <span class="mi">128</span><span class="p">})</span>
<span class="p">(</span><span class="nf">sym/activation</span> <span class="s">"act1"</span> <span class="p">{</span><span class="ss">:data</span> <span class="nv">data</span> <span class="ss">:act-type</span> <span class="s">"relu"</span><span class="p">})</span>
<span class="p">(</span><span class="nf">sym/fully-connected</span> <span class="s">"fc2"</span> <span class="p">{</span><span class="ss">:data</span> <span class="nv">data</span> <span class="ss">:num-hidden</span> <span class="mi">64</span><span class="p">})</span>
<span class="p">(</span><span class="nf">sym/softmax-output</span> <span class="s">"out"</span> <span class="p">{</span><span class="ss">:data</span> <span class="nv">data</span><span class="p">}))</span>
<span class="nv">net</span> <span class="c1">;=> #object[org.apache.mxnet.Symbol 0x5c78c8c2 "org.apache.mxnet.Symbol@5c78c8c2"] </span>
</pre></div>
</div>
<p>The basic arithmetic operators (plus, minus, div, multiplication) work as expected.</p>
<p>The following example creates a computation graph that adds two inputs together.</p>
<div class="highlight-clojure"><div class="highlight"><pre><span></span><span class="p">(</span><span class="k">def </span><span class="nv">a</span> <span class="p">(</span><span class="nf">sym/variable</span> <span class="s">"a"</span><span class="p">))</span>
<span class="p">(</span><span class="k">def </span><span class="nv">b</span> <span class="p">(</span><span class="nf">sym/variable</span> <span class="s">"b"</span><span class="p">))</span>
<span class="p">(</span><span class="k">def </span><span class="nv">c</span> <span class="p">(</span><span class="nf">sym/+</span> <span class="nv">a</span> <span class="nv">b</span><span class="p">))</span>
</pre></div>
</div>
</div>
<div class="section" id="more-complicated-compositions">
<span id="more-complicated-compositions"></span><h2>More Complicated Compositions<a class="headerlink" href="#more-complicated-compositions" title="Permalink to this headline"></a></h2>
<p>MXNet provides well-optimized symbols for layers commonly used in deep learning (see src/operator). We can also define new operators in Python. The following example first performs an element-wise add between two symbols, then feeds them to the fully connected operator:</p>
<div class="highlight-clojure"><div class="highlight"><pre><span></span><span class="p">(</span><span class="k">def </span><span class="nv">lhs</span> <span class="p">(</span><span class="nf">sym/variable</span> <span class="s">"data1"</span><span class="p">))</span>
<span class="p">(</span><span class="k">def </span><span class="nv">rhs</span> <span class="p">(</span><span class="nf">sym/variable</span> <span class="s">"data2"</span><span class="p">))</span>
<span class="p">(</span><span class="k">def </span><span class="nv">net</span> <span class="p">(</span><span class="nf">sym/fully-connected</span> <span class="s">"fc1"</span> <span class="p">{</span><span class="ss">:data</span> <span class="p">(</span><span class="nf">sym/+</span> <span class="nv">lhs</span> <span class="nv">rhs</span><span class="p">)</span> <span class="ss">:num-hidden</span> <span class="mi">128</span><span class="p">}))</span>
<span class="p">(</span><span class="nf">sym/list-arguments</span> <span class="nv">net</span><span class="p">)</span> <span class="c1">;=> ["data1" "data2" "fc1_weight" "fc1_bias"]</span>
</pre></div>
</div>
</div>
<div class="section" id="group-multiple-symbols">
<span id="group-multiple-symbols"></span><h2>Group Multiple Symbols<a class="headerlink" href="#group-multiple-symbols" title="Permalink to this headline"></a></h2>
<p>To construct neural networks with multiple loss layers, we can use <code class="docutils literal"><span class="pre">group</span></code> to group multiple symbols together. The following example groups two outputs:</p>
<div class="highlight-clojure"><div class="highlight"><pre><span></span><span class="p">(</span><span class="k">def </span><span class="nv">net</span> <span class="p">(</span><span class="nf">sym/variable</span> <span class="s">"data"</span><span class="p">))</span>
<span class="p">(</span><span class="k">def </span><span class="nv">fc1</span> <span class="p">(</span><span class="nf">sym/fully-connected</span> <span class="p">{</span><span class="ss">:data</span> <span class="nv">net</span> <span class="ss">:num-hidden</span> <span class="mi">128</span><span class="p">}))</span>
<span class="p">(</span><span class="k">def </span><span class="nv">net2</span> <span class="p">(</span><span class="nf">sym/activation</span> <span class="p">{</span><span class="ss">:data</span> <span class="nv">fc1</span> <span class="ss">:act-type</span> <span class="s">"relu"</span><span class="p">}))</span>
<span class="p">(</span><span class="k">def </span><span class="nv">out1</span> <span class="p">(</span><span class="nf">sym/softmax-output</span> <span class="p">{</span><span class="ss">:data</span> <span class="nv">net2</span><span class="p">}))</span>
<span class="p">(</span><span class="k">def </span><span class="nv">out2</span> <span class="p">(</span><span class="nf">sym/linear-regression-output</span> <span class="p">{</span><span class="ss">:data</span> <span class="nv">net2</span><span class="p">}))</span>
<span class="p">(</span><span class="k">def </span><span class="nv">group</span> <span class="p">(</span><span class="nf">sym/group</span> <span class="p">[</span><span class="nv">out1</span> <span class="nv">out2</span><span class="p">]))</span>
<span class="p">(</span><span class="nf">sym/list-outputs</span> <span class="nv">group</span><span class="p">)</span>
<span class="c1">;=> ["softmaxoutput0_output" "linearregressionoutput0_output"]</span>
</pre></div>
</div>
</div>
<div class="section" id="serialization">
<span id="serialization"></span><h2>Serialization<a class="headerlink" href="#serialization" title="Permalink to this headline"></a></h2>
<p>You can use the <a class="reference external" href="docs/org.apache.clojure-mxnet.symbol.html#var-save"><code class="docutils literal"><span class="pre">save</span></code></a> and <a class="reference external" href="docs/org.apache.clojure-mxnet.symbol.html#var-load"><code class="docutils literal"><span class="pre">load</span></code></a> functions to serialize the Symbol objects. The advantage of using save and load 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 mxnet.Symbol.toJson. Refer to API documentation for more details.</p>
<p>The following example shows how to save a symbol to a file, load it back, and compare two symbols using a JSON string. You can also save to S3 as well</p>
<div class="highlight-clojure"><div class="highlight"><pre><span></span><span class="p">(</span><span class="k">def </span><span class="nv">a</span> <span class="p">(</span><span class="nf">sym/variable</span> <span class="s">"a"</span><span class="p">))</span>
<span class="p">(</span><span class="k">def </span><span class="nv">b</span> <span class="p">(</span><span class="nf">sym/variable</span> <span class="s">"b"</span><span class="p">))</span>
<span class="p">(</span><span class="k">def </span><span class="nv">c</span> <span class="p">(</span><span class="nf">sym/+</span> <span class="nv">a</span> <span class="nv">b</span><span class="p">))</span>
<span class="p">(</span><span class="nf">sym/save</span> <span class="nv">c</span> <span class="s">"symbol-c.json"</span><span class="p">)</span>
<span class="p">(</span><span class="k">def </span><span class="nv">c2</span> <span class="p">(</span><span class="nf">sym/load</span> <span class="s">"symbol-c.json"</span><span class="p">))</span>
<span class="p">(</span><span class="nb">= </span><span class="p">(</span><span class="nf">sym/to-json</span> <span class="nv">c</span><span class="p">)</span> <span class="p">(</span><span class="nf">sym/to-json</span> <span class="nv">c2</span><span class="p">))</span> <span class="c1">;=>true</span>
</pre></div>
</div>
</div>
<div class="section" id="executing-symbols">
<span id="executing-symbols"></span><h2>Executing Symbols<a class="headerlink" href="#executing-symbols" title="Permalink to this headline"></a></h2>
<p>To execute symbols, first we need to define the data that they should run on. We can do it by using the bind method, which accepts device context and a dict mapping free variable names to NDArrays as arguments and returns an executor. The executor provides forward method for evaluation and an attribute outputs to get all the results.</p>
<div class="highlight-clojure"><div class="highlight"><pre><span></span><span class="p">(</span><span class="k">def </span><span class="nv">a</span> <span class="p">(</span><span class="nf">sym/variable</span> <span class="s">"a"</span><span class="p">))</span>
<span class="p">(</span><span class="k">def </span><span class="nv">b</span> <span class="p">(</span><span class="nf">sym/variable</span> <span class="s">"b"</span><span class="p">))</span>
<span class="p">(</span><span class="k">def </span><span class="nv">c</span> <span class="p">(</span><span class="nf">sym/+</span> <span class="nv">a</span> <span class="nv">b</span><span class="p">))</span>
<span class="p">(</span><span class="k">def </span><span class="nv">ex</span> <span class="p">(</span><span class="nf">sym/bind</span> <span class="nv">c</span> <span class="p">{</span><span class="s">"a"</span> <span class="p">(</span><span class="nf">ndarray/ones</span> <span class="p">[</span><span class="mi">2</span> <span class="mi">2</span><span class="p">])</span> <span class="s">"b"</span> <span class="p">(</span><span class="nf">ndarray/ones</span> <span class="p">[</span><span class="mi">2</span> <span class="mi">2</span><span class="p">])}))</span>
<span class="p">(</span><span class="nb">-> </span><span class="p">(</span><span class="nf">executor/forward</span> <span class="nv">ex</span><span class="p">)</span>
<span class="p">(</span><span class="nf">executor/outputs</span><span class="p">)</span>
<span class="p">(</span><span class="nf">first</span><span class="p">)</span>
<span class="p">(</span><span class="nf">ndarray/->vec</span><span class="p">))</span><span class="c1">;=> [2.0 2.0 2.0 2.0]</span>
</pre></div>
</div>
<p>We can evaluate the same symbol on GPU with different data.
<em>To do this you must have the correct native library jar defined as a dependency</em></p>
<p><strong>Note In order to execute the following section on a cpu set gpu_device to (cpu)</strong></p>
<div class="highlight-clojure"><div class="highlight"><pre><span></span><span class="p">(</span><span class="k">def </span><span class="nv">ex</span> <span class="p">(</span><span class="nf">sym/bind</span> <span class="nv">c</span> <span class="p">(</span><span class="nf">context/gpu</span> <span class="mi">0</span><span class="p">)</span> <span class="p">{</span><span class="s">"a"</span> <span class="p">(</span><span class="nf">ndarray/ones</span> <span class="p">[</span><span class="mi">2</span> <span class="mi">2</span><span class="p">])</span> <span class="s">"b"</span> <span class="p">(</span><span class="nf">ndarray/ones</span> <span class="p">[</span><span class="mi">2</span> <span class="mi">2</span><span class="p">])}))</span>
</pre></div>
</div>
</div>
<div class="section" id="next-steps">
<span id="next-steps"></span><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="ndarray.html"><span class="doc">NDArray API</span></a> for vector/matrix/tensor operations.</li>
<li>See <a class="reference internal" href="kvstore.html"><span class="doc">KVStore API</span></a> for multi-GPU and multi-host distributed training.</li>
</ul>
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<h3><a href="../../index.html">Table Of Contents</a></h3>
<ul>
<li><a class="reference internal" href="#">MXNet Clojure Symbolic API</a><ul>
<li><a class="reference internal" href="#how-to-compose-symbols">How to Compose Symbols</a></li>
<li><a class="reference internal" href="#more-complicated-compositions">More Complicated Compositions</a></li>
<li><a class="reference internal" href="#group-multiple-symbols">Group Multiple Symbols</a></li>
<li><a class="reference internal" href="#serialization">Serialization</a></li>
<li><a class="reference internal" href="#executing-symbols">Executing Symbols</a></li>
<li><a class="reference internal" href="#next-steps">Next Steps</a></li>
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