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| <h1 class="post-title">Visualize Neural Networks</h1> |
| <h3></h3></header> |
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| |
| <h1 id="how-to-visualize-neural-networks-as-computation-graph">How to visualize Neural Networks as computation graph</h1> |
| |
| <p>Here, we'll demonstrate how to use <code>mx.viz.plot_network</code> |
| for visualizing your neural networks. <code>mx.viz.plot_network</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> |
| |
| <h2 id="prerequisites">Prerequisites</h2> |
| |
| <p>You need the <a href="http://jupyter.readthedocs.io/en/latest/">Jupyter Notebook</a> |
| and <a href="https://www.graphviz.org/">Graphviz</a> libraries to visualize the network. |
| Please make sure you have followed <a href="/versions/1.9.1/get_started">installation instructions</a> |
| in setting up above dependencies along with setting up MXNet.</p> |
| |
| <h2 id="visualize-the-sample-neural-network">Visualize the sample Neural Network</h2> |
| |
| <p><code>mx.viz.plot_network</code> takes <a href="/api/python/docs/api/symbol/index">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: |
| - Start Jupyter notebook server |
| <code>bash |
| $ jupyter notebook |
| </code> |
| - Access Jupyter notebook in your browser - <a href="http://localhost:8888/">http://localhost:8888/</a>. |
| - Create a new notebook - "File -> New Notebook -> Python 2" |
| - Copy and run below code to visualize a simple network.</p> |
| <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">import</span> <span class="nn">mxnet</span> <span class="k">as</span> <span class="n">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="s">'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="s">'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="s">'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> |
| </code></pre></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> |
| |
| <h1 id="references">References</h1> |
| |
| <ul> |
| <li><a href="https://github.com/apache/mxnet/blob/v1.x/example/recommenders/demo1-MF.ipynb">Example MXNet Matrix Factorization</a></li> |
| <li><a href="http://josephpcohen.com/w/visualizing-cnn-architectures-side-by-side-with-mxnet/">Visualizing CNN Architecture of MXNet Tutorials</a></li> |
| </ul> |
| |
| </div> |
| </div> |
| |
| </div> |
| </div> |
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