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<!--- under the License. --><div class="section" id="how-to-visualize-neural-networks-as-computation-graph">
<span id="how-to-visualize-neural-networks-as-computation-graph"></span><h1>How to visualize Neural Networks as computation graph<a class="headerlink" href="#how-to-visualize-neural-networks-as-computation-graph" title="Permalink to this headline"></a></h1>
<p>Here, we’ll demonstrate how to use <code class="docutils literal"><span class="pre">mx.viz.plot_network</span></code>
for visualizing your neural networks. <code class="docutils literal"><span class="pre">mx.viz.plot_network</span></code>
represents the neural network as a computation graph consisting of nodes and edges.
The visualizations make clear which nodes correspond to inputs,
where the computation starts,
and which correspond to output nodes,
from which the result can be read.</p>
<div class="section" id="prerequisites">
<span id="prerequisites"></span><h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to this headline"></a></h2>
<p>You need the <a class="reference external" href="http://jupyter.readthedocs.io/en/latest/">Jupyter Notebook</a>
and <a class="reference external" href="http://www.graphviz.org/">Graphviz</a> libraries to visualize the network.
Please make sure you have followed <a class="reference external" href="/versions/1.4.1/install/index.html">installation instructions</a>
in setting up above dependencies along with setting up MXNet.</p>
</div>
<div class="section" id="visualize-the-sample-neural-network">
<span id="visualize-the-sample-neural-network"></span><h2>Visualize the sample Neural Network<a class="headerlink" href="#visualize-the-sample-neural-network" title="Permalink to this headline"></a></h2>
<p><code class="docutils literal"><span class="pre">mx.viz.plot_network</span></code> takes <a class="reference external" href="/versions/1.4.1/api/python/symbol/symbol.html">Symbol</a>, with your Network definition, and optional node_attrs, parameters for the shape of the node in the graph, as input and generates a computation graph.</p>
<p>We will now try to visualize a sample Neural Network for linear matrix factorization:</p>
<ul class="simple">
<li>Start Jupyter notebook server</li>
</ul>
<div class="highlight-bash"><div class="highlight"><pre><span></span> $ jupyter notebook
</pre></div>
</div>
<ul class="simple">
<li>Access Jupyter notebook in your browser - http://localhost:8888/.</li>
<li>Create a new notebook - “File -> New Notebook -> Python 2”</li>
<li>Copy and run below code to visualize a simple network.</li>
</ul>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mxnet</span> <span class="kn">as</span> <span class="nn">mx</span>
<span class="n">user</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'user'</span><span class="p">)</span>
<span class="n">item</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'item'</span><span class="p">)</span>
<span class="n">score</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'score'</span><span class="p">)</span>
<span class="c1"># Set dummy dimensions</span>
<span class="n">k</span> <span class="o">=</span> <span class="mi">64</span>
<span class="n">max_user</span> <span class="o">=</span> <span class="mi">100</span>
<span class="n">max_item</span> <span class="o">=</span> <span class="mi">50</span>
<span class="c1"># user feature lookup</span>
<span class="n">user</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Embedding</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">user</span><span class="p">,</span> <span class="n">input_dim</span> <span class="o">=</span> <span class="n">max_user</span><span class="p">,</span> <span class="n">output_dim</span> <span class="o">=</span> <span class="n">k</span><span class="p">)</span>
<span class="c1"># item feature lookup</span>
<span class="n">item</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Embedding</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">item</span><span class="p">,</span> <span class="n">input_dim</span> <span class="o">=</span> <span class="n">max_item</span><span class="p">,</span> <span class="n">output_dim</span> <span class="o">=</span> <span class="n">k</span><span class="p">)</span>
<span class="c1"># predict by the inner product, which is elementwise product and then sum</span>
<span class="n">net</span> <span class="o">=</span> <span class="n">user</span> <span class="o">*</span> <span class="n">item</span>
<span class="n">net</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">sum_axis</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">net</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">net</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Flatten</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">net</span><span class="p">)</span>
<span class="c1"># loss layer</span>
<span class="n">net</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">LinearRegressionOutput</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">net</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="n">score</span><span class="p">)</span>
<span class="c1"># Visualize your network</span>
<span class="n">mx</span><span class="o">.</span><span class="n">viz</span><span class="o">.</span><span class="n">plot_network</span><span class="p">(</span><span class="n">net</span><span class="p">)</span>
</pre></div>
</div>
<p>You should see computation graph something like the following image:
<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/SampleNetworkVisualization.png" width="400/"/></p>
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<span id="references"></span><h1>References<a class="headerlink" href="#references" title="Permalink to this headline"></a></h1>
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<li class="toctree-l1"><a class="reference external" href="https://github.com/dmlc/mxnet/blob/master/example/recommenders/demo1-MF.ipynb">Example MXNet Matrix Factorization</a></li>
<li class="toctree-l1"><a class="reference external" href="http://josephpcohen.com/w/visualizing-cnn-architectures-side-by-side-with-mxnet/">Visualizing CNN Architecture of MXNet Tutorials</a></li>
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<li><a class="reference internal" href="#">How to visualize Neural Networks as computation graph</a><ul>
<li><a class="reference internal" href="#prerequisites">Prerequisites</a></li>
<li><a class="reference internal" href="#visualize-the-sample-neural-network">Visualize the sample Neural Network</a></li>
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<li><a class="reference internal" href="#references">References</a></li>
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