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<!DOCTYPE html>
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<li class="toctree-l1"><a class="reference internal" href="about.html">About N2D2-IP</a></li>
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<li class="toctree-l1"><a class="reference internal" href="quant_qat.html">[NEW] Quantization-Aware Training</a></li>
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<div class="section" id="deepnet">
<h1>DeepNet<a class="headerlink" href="#deepnet" title="Permalink to this headline">¶</a></h1>
<div class="section" id="introduction">
<h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">¶</a></h2>
<p>In order to create a neural network in N2D2 using an INI file, you can use the
DeepNetGenerator:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="n">net</span> <span class="o">=</span> <span class="n">N2D2</span><span class="o">.</span><span class="n">Network</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">deepNet</span> <span class="o">=</span> <span class="n">N2D2</span><span class="o">.</span><span class="n">DeepNetGenerator</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="s2">"../models/mnist24_16c4s2_24c5s2_150_10.ini"</span><span class="p">)</span>
</pre></div>
</div>
<p>Before executing the model, the network must first be initialized:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="n">deepNet</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span>
</pre></div>
</div>
<p>In order to test the first batch sample from the dataset, we retrieve the
StimuliProvider and read the first batch from the test set:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="n">sp</span> <span class="o">=</span> <span class="n">deepNet</span><span class="o">.</span><span class="n">getStimuliProvider</span><span class="p">()</span>
<span class="n">sp</span><span class="o">.</span><span class="n">readBatch</span><span class="p">(</span><span class="n">N2D2</span><span class="o">.</span><span class="n">Database</span><span class="o">.</span><span class="n">Test</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
</pre></div>
</div>
<p>We can now run the network on this data:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="n">deepNet</span><span class="o">.</span><span class="n">test</span><span class="p">(</span><span class="n">N2D2</span><span class="o">.</span><span class="n">Database</span><span class="o">.</span><span class="n">Test</span><span class="p">,</span> <span class="p">[])</span>
</pre></div>
</div>
<p>Finally, in order to retrieve the estimated outputs, one has to retrieve the
first and unique target of the model and get the estimated labels and values:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="n">target</span> <span class="o">=</span> <span class="n">deepNet</span><span class="o">.</span><span class="n">getTargets</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">labels</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">target</span><span class="o">.</span><span class="n">getEstimatedLabels</span><span class="p">())</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
<span class="n">values</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">target</span><span class="o">.</span><span class="n">getEstimatedLabelsValue</span><span class="p">())</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
<span class="n">results</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">values</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
</pre></div>
</div>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>[(1, 0.15989691), (1, 0.1617092), (9, 0.14962792), (9, 0.16899541), (1, 0.16261548), (1, 0.17289816), (1, 0.13728766), (1, 0.15315214), (1, 0.14424478), (9, 0.17937173), (9, 0.1518211), (1, 0.12860793), (9, 0.17310674), (9, 0.14563303), (1, 0.1782302), (9, 0.14206158), (1, 0.18292117), (9, 0.14831853), (1, 0.2224524), (9, 0.1745578), (1, 0.20414244), (1, 0.26987872), (1, 0.16570412), (9, 0.17435187)]
</pre></div>
</div>
</div>
<div class="section" id="api-reference">
<h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to this headline">¶</a></h2>
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