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| <h1 id="mxnet-clojure-symbolic-api">MXNet Clojure Symbolic API</h1> |
| |
| <p>Topics:</p> |
| |
| <ul> |
| <li><a href="#how-to-compose-symbols">How to Compose Symbols</a></li> |
| <li><a href="#more-complicated-compositions">More Complicated Compositions</a></li> |
| <li><a href="#group-multiple-symbols">Group Multiple Symbols</a></li> |
| <li><a href="#serialization">Serialization</a></li> |
| <li><a href="#executing-symbols">Executing Symbols</a></li> |
| <li><a href="/versions/1.9.1/api/clojure/docs/api/org.apache.clojure-mxnet.symbol">Symbol API Reference</a></li> |
| </ul> |
| |
| <p>We also highly encourage you to read <a href="symbol_in_pictures">Symbolic Configuration and Execution in Pictures</a>.</p> |
| |
| <p>To follow along with this documentation, you can use this namespace to with the following requirements:</p> |
| <div class="highlight"><pre><code class="language-clojure" data-lang="clojure"><span class="p">(</span><span class="nf">ns</span><span class="w"> </span><span class="n">docs.symbol</span><span class="w"> |
| </span><span class="p">(</span><span class="no">:require</span><span class="w"> </span><span class="p">[</span><span class="n">org.apache.clojure-mxnet.executor</span><span class="w"> </span><span class="no">:as</span><span class="w"> </span><span class="n">executor</span><span class="p">]</span><span class="w"> |
| </span><span class="p">[</span><span class="n">org.apache.clojure-mxnet.ndarray</span><span class="w"> </span><span class="no">:as</span><span class="w"> </span><span class="n">ndarray</span><span class="p">]</span><span class="w"> |
| </span><span class="p">[</span><span class="n">org.apache.clojure-mxnet.symbol</span><span class="w"> </span><span class="no">:as</span><span class="w"> </span><span class="n">sym</span><span class="p">]</span><span class="w"> |
| </span><span class="p">[</span><span class="n">org.apache.clojure-mxnet.context</span><span class="w"> </span><span class="no">:as</span><span class="w"> </span><span class="n">context</span><span class="p">]))</span><span class="w"> |
| </span></code></pre></div> |
| <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-clojure" data-lang="clojure"><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">data</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/variable</span><span class="w"> </span><span class="s">"data"</span><span class="p">))</span><span class="w"> |
| </span><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">fc1</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/fully-connected</span><span class="w"> </span><span class="s">"fc1"</span><span class="w"> </span><span class="p">{</span><span class="no">:data</span><span class="w"> </span><span class="n">data</span><span class="w"> </span><span class="no">:num-hidden</span><span class="w"> </span><span class="mi">128</span><span class="p">}))</span><span class="w"> |
| </span><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">act1</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/activation</span><span class="w"> </span><span class="s">"act1"</span><span class="w"> </span><span class="p">{</span><span class="no">:data</span><span class="w"> </span><span class="n">fc1</span><span class="w"> </span><span class="no">:act-type</span><span class="w"> </span><span class="s">"relu"</span><span class="p">}))</span><span class="w"> |
| </span><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">fc2</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/fully-connected</span><span class="w"> </span><span class="s">"fc2"</span><span class="w"> </span><span class="p">{</span><span class="no">:data</span><span class="w"> </span><span class="n">act1</span><span class="w"> </span><span class="no">:num-hidden</span><span class="w"> </span><span class="mi">64</span><span class="p">}))</span><span class="w"> |
| </span><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">net</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/softmax-output</span><span class="w"> </span><span class="s">"out"</span><span class="w"> </span><span class="p">{</span><span class="no">:data</span><span class="w"> </span><span class="n">fc2</span><span class="p">}))</span><span class="w"> |
| </span></code></pre></div> |
| <p>This can also be combined more dynamically with the <code>as-></code> Clojure threading form.</p> |
| <div class="highlight"><pre><code class="language-clojure" data-lang="clojure"><span class="p">(</span><span class="nf">as-></span><span class="w"> </span><span class="p">(</span><span class="nf">sym/variable</span><span class="w"> </span><span class="s">"data"</span><span class="p">)</span><span class="w"> </span><span class="n">data</span><span class="w"> |
| </span><span class="p">(</span><span class="nf">sym/fully-connected</span><span class="w"> </span><span class="s">"fc1"</span><span class="w"> </span><span class="p">{</span><span class="no">:data</span><span class="w"> </span><span class="n">data</span><span class="w"> </span><span class="no">:num-hidden</span><span class="w"> </span><span class="mi">128</span><span class="p">})</span><span class="w"> |
| </span><span class="p">(</span><span class="nf">sym/activation</span><span class="w"> </span><span class="s">"act1"</span><span class="w"> </span><span class="p">{</span><span class="no">:data</span><span class="w"> </span><span class="n">data</span><span class="w"> </span><span class="no">:act-type</span><span class="w"> </span><span class="s">"relu"</span><span class="p">})</span><span class="w"> |
| </span><span class="p">(</span><span class="nf">sym/fully-connected</span><span class="w"> </span><span class="s">"fc2"</span><span class="w"> </span><span class="p">{</span><span class="no">:data</span><span class="w"> </span><span class="n">data</span><span class="w"> </span><span class="no">:num-hidden</span><span class="w"> </span><span class="mi">64</span><span class="p">})</span><span class="w"> |
| </span><span class="p">(</span><span class="nf">sym/softmax-output</span><span class="w"> </span><span class="s">"out"</span><span class="w"> </span><span class="p">{</span><span class="no">:data</span><span class="w"> </span><span class="n">data</span><span class="p">}))</span><span class="w"> |
| |
| </span><span class="n">net</span><span class="w"> </span><span class="c1">;=> #object[org.apache.mxnet.Symbol 0x5c78c8c2 "org.apache.mxnet.Symbol@5c78c8c2"]</span><span class="w"> |
| </span></code></pre></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"><pre><code class="language-clojure" data-lang="clojure"><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">a</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/variable</span><span class="w"> </span><span class="s">"a"</span><span class="p">))</span><span class="w"> |
| </span><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">b</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/variable</span><span class="w"> </span><span class="s">"b"</span><span class="p">))</span><span class="w"> |
| </span><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">c</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/+</span><span class="w"> </span><span class="n">a</span><span class="w"> </span><span class="n">b</span><span class="p">))</span><span class="w"> |
| </span></code></pre></div> |
| <h2 id="more-complicated-compositions">More Complicated Compositions</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"><pre><code class="language-clojure" data-lang="clojure"><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">lhs</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/variable</span><span class="w"> </span><span class="s">"data1"</span><span class="p">))</span><span class="w"> |
| </span><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">rhs</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/variable</span><span class="w"> </span><span class="s">"data2"</span><span class="p">))</span><span class="w"> |
| </span><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">net</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/fully-connected</span><span class="w"> </span><span class="s">"fc1"</span><span class="w"> </span><span class="p">{</span><span class="no">:data</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/+</span><span class="w"> </span><span class="n">lhs</span><span class="w"> </span><span class="n">rhs</span><span class="p">)</span><span class="w"> </span><span class="no">:num-hidden</span><span class="w"> </span><span class="mi">128</span><span class="p">}))</span><span class="w"> |
| </span><span class="p">(</span><span class="nf">sym/list-arguments</span><span class="w"> </span><span class="n">net</span><span class="p">)</span><span class="w"> </span><span class="c1">;=> ["data1" "data2" "fc1_weight" "fc1_bias"]</span><span class="w"> |
| </span></code></pre></div> |
| <h2 id="group-multiple-symbols">Group Multiple Symbols</h2> |
| |
| <p>To construct neural networks with multiple loss layers, we can use <code>group</code> to group multiple symbols together. The following example groups two outputs:</p> |
| <div class="highlight"><pre><code class="language-clojure" data-lang="clojure"><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">net</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/variable</span><span class="w"> </span><span class="s">"data"</span><span class="p">))</span><span class="w"> |
| </span><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">fc1</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/fully-connected</span><span class="w"> </span><span class="p">{</span><span class="no">:data</span><span class="w"> </span><span class="n">net</span><span class="w"> </span><span class="no">:num-hidden</span><span class="w"> </span><span class="mi">128</span><span class="p">}))</span><span class="w"> |
| </span><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">net2</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/activation</span><span class="w"> </span><span class="p">{</span><span class="no">:data</span><span class="w"> </span><span class="n">fc1</span><span class="w"> </span><span class="no">:act-type</span><span class="w"> </span><span class="s">"relu"</span><span class="p">}))</span><span class="w"> |
| </span><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">out1</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/softmax-output</span><span class="w"> </span><span class="p">{</span><span class="no">:data</span><span class="w"> </span><span class="n">net2</span><span class="p">}))</span><span class="w"> |
| </span><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">out2</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/linear-regression-output</span><span class="w"> </span><span class="p">{</span><span class="no">:data</span><span class="w"> </span><span class="n">net2</span><span class="p">}))</span><span class="w"> |
| </span><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">group</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/group</span><span class="w"> </span><span class="p">[</span><span class="n">out1</span><span class="w"> </span><span class="n">out2</span><span class="p">]))</span><span class="w"> |
| </span><span class="p">(</span><span class="nf">sym/list-outputs</span><span class="w"> </span><span class="n">group</span><span class="p">)</span><span class="w"> |
| </span><span class="c1">;=> ["softmaxoutput0_output" "linearregressionoutput0_output"]</span><span class="w"> |
| </span></code></pre></div> |
| <h2 id="serialization">Serialization</h2> |
| |
| <p>You can use the <a href="/versions/1.9.1/api/clojure/docs/api/org.apache.clojure-mxnet.symbol.html#var-save"><code>save</code></a> and <a href="/versions/1.9.1/api/clojure/docs/api/org.apache.clojure-mxnet.symbol.html#var-load"><code>load</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"><pre><code class="language-clojure" data-lang="clojure"><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">a</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/variable</span><span class="w"> </span><span class="s">"a"</span><span class="p">))</span><span class="w"> |
| </span><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">b</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/variable</span><span class="w"> </span><span class="s">"b"</span><span class="p">))</span><span class="w"> |
| </span><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">c</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/+</span><span class="w"> </span><span class="n">a</span><span class="w"> </span><span class="n">b</span><span class="p">))</span><span class="w"> |
| </span><span class="p">(</span><span class="nf">sym/save</span><span class="w"> </span><span class="n">c</span><span class="w"> </span><span class="s">"symbol-c.json"</span><span class="p">)</span><span class="w"> |
| </span><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">c2</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/load</span><span class="w"> </span><span class="s">"symbol-c.json"</span><span class="p">))</span><span class="w"> |
| </span><span class="p">(</span><span class="nb">=</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/to-json</span><span class="w"> </span><span class="n">c</span><span class="p">)</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/to-json</span><span class="w"> </span><span class="n">c2</span><span class="p">))</span><span class="w"> </span><span class="c1">;=>true</span><span class="w"> |
| </span></code></pre></div> |
| <h2 id="executing-symbols">Executing Symbols</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"><pre><code class="language-clojure" data-lang="clojure"><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">a</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/variable</span><span class="w"> </span><span class="s">"a"</span><span class="p">))</span><span class="w"> |
| </span><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">b</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/variable</span><span class="w"> </span><span class="s">"b"</span><span class="p">))</span><span class="w"> |
| </span><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">c</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/+</span><span class="w"> </span><span class="n">a</span><span class="w"> </span><span class="n">b</span><span class="p">))</span><span class="w"> |
| |
| </span><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">ex</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/bind</span><span class="w"> </span><span class="n">c</span><span class="w"> </span><span class="p">{</span><span class="s">"a"</span><span class="w"> </span><span class="p">(</span><span class="nf">ndarray/ones</span><span class="w"> </span><span class="p">[</span><span class="mi">2</span><span class="w"> </span><span class="mi">2</span><span class="p">])</span><span class="w"> </span><span class="s">"b"</span><span class="w"> </span><span class="p">(</span><span class="nf">ndarray/ones</span><span class="w"> </span><span class="p">[</span><span class="mi">2</span><span class="w"> </span><span class="mi">2</span><span class="p">])}))</span><span class="w"> |
| </span><span class="p">(</span><span class="nb">-></span><span class="w"> </span><span class="p">(</span><span class="nf">executor/forward</span><span class="w"> </span><span class="n">ex</span><span class="p">)</span><span class="w"> |
| </span><span class="p">(</span><span class="nf">executor/outputs</span><span class="p">)</span><span class="w"> |
| </span><span class="p">(</span><span class="nb">first</span><span class="p">)</span><span class="w"> |
| </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><span class="w"> |
| </span></code></pre></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"><pre><code class="language-clojure" data-lang="clojure"><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">ex</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/bind</span><span class="w"> </span><span class="n">c</span><span class="w"> </span><span class="p">(</span><span class="nf">context/gpu</span><span class="w"> </span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="s">"a"</span><span class="w"> </span><span class="p">(</span><span class="nf">ndarray/ones</span><span class="w"> </span><span class="p">[</span><span class="mi">2</span><span class="w"> </span><span class="mi">2</span><span class="p">])</span><span class="w"> </span><span class="s">"b"</span><span class="w"> </span><span class="p">(</span><span class="nf">ndarray/ones</span><span class="w"> </span><span class="p">[</span><span class="mi">2</span><span class="w"> </span><span class="mi">2</span><span class="p">])}))</span><span class="w"> |
| </span></code></pre></div> |
| <h2 id="next-steps">Next Steps</h2> |
| |
| <ul> |
| <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> |
| </div> |
| |
| </div> |
| </div> |
| |
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