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   <div class="section" id="module-singa.metric">
 <span id="metric"></span><h1>Metric<a class="headerlink" href="#module-singa.metric" title="Permalink to this headline">¶</a></h1>
 <p>This module includes a set of metric classes for evaluating the model&#8217;s
 performance. The specific metric classes could be converted from C++
 implmentation or implemented directly using Python.</p>
 <p>Example usage:</p>
 <div class="highlight-python"><div class="highlight"><pre><span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">tensor</span>
 <span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">metric</span>

 <span class="n">x</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">Tensor</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
 <span class="n">x</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>  <span class="c1"># randomly genearte the prediction activation</span>
 <span class="n">x</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">SoftMax</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>  <span class="c1"># normalize the prediction into probabilities</span>
 <span class="n">y</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int</span><span class="p">))</span>  <span class="c1"># set the truth</span>

 <span class="n">f</span> <span class="o">=</span> <span class="n">metric</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">()</span>
 <span class="n">acc</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>  <span class="c1"># averaged accuracy over all 3 samples in x</span>
 </pre></div>
 </div>
 <dl class="class">
 <dt id="singa.metric.Metric">
 <em class="property">class </em><code class="descclassname">singa.metric.</code><code class="descname">Metric</code><a class="headerlink" href="#singa.metric.Metric" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <code class="xref py py-class docutils literal"><span class="pre">object</span></code></p>
 <p>Base metric class.</p>
 <p>Subclasses that wrap the C++ loss classes can use the inherited foward,
 and evaluate functions of this base class. Other subclasses need
 to override these functions. Users need to feed in the <strong>predictions</strong> and
 ground truth to get the metric values.</p>
 <dl class="method">
 <dt id="singa.metric.Metric.forward">
 <code class="descname">forward</code><span class="sig-paren">(</span><em>x</em>, <em>y</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.metric.Metric.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the metric for each sample.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
 <li><strong>x</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) &#8211; predictions, one row per sample</li>
 <li><strong>y</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) &#8211; ground truth values, one row per sample</li>
 </ul>
 </td>
 </tr>
 <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a tensor of floats, one per sample</p>
 </td>
 </tr>
 </tbody>
 </table>
 </dd></dl>

 <dl class="method">
 <dt id="singa.metric.Metric.evaluate">
 <code class="descname">evaluate</code><span class="sig-paren">(</span><em>x</em>, <em>y</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.metric.Metric.evaluate" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the averaged metric over all samples.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
 <li><strong>x</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) &#8211; predictions, one row per sample</li>
 <li><strong>y</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) &#8211; ground truth values, one row per sample</li>
 </ul>
 </td>
 </tr>
 <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a float value for the averaged metric</p>
 </td>
 </tr>
 </tbody>
 </table>
 </dd></dl>

 </dd></dl>

 <dl class="class">
 <dt id="singa.metric.Accuracy">
 <em class="property">class </em><code class="descclassname">singa.metric.</code><code class="descname">Accuracy</code><a class="headerlink" href="#singa.metric.Accuracy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#singa.metric.Metric" title="singa.metric.Metric"><code class="xref py py-class docutils literal"><span class="pre">singa.metric.Metric</span></code></a></p>
 <p>Compute the top one accuracy for singel label prediction tasks.</p>
 <p>It calls the C++ functions to do the calculation.</p>
 </dd></dl>

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	<div class="section" id="module-singa.metric">
	<span id="metric"></span><h1>Metric<a class="headerlink" href="#module-singa.metric" title="Permalink to this headline">¶</a></h1>
	<p>This module includes a set of metric classes for evaluating the model’s
	performance. The specific metric classes could be converted from C++
	implmentation or implemented directly using Python.</p>
	<p>Example usage:</p>
	<div class="highlight-python"><div class="highlight"><pre><span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">tensor</span>
	<span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">metric</span>

	<span class="n">x</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">Tensor</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
	<span class="n">x</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="c1"># randomly genearte the prediction activation</span>
	<span class="n">x</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">SoftMax</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="c1"># normalize the prediction into probabilities</span>
	<span class="n">y</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int</span><span class="p">))</span> <span class="c1"># set the truth</span>

	<span class="n">f</span> <span class="o">=</span> <span class="n">metric</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">()</span>
	<span class="n">acc</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="c1"># averaged accuracy over all 3 samples in x</span>
	</pre></div>
	</div>
	<dl class="class">
	<dt id="singa.metric.Metric">
	<em class="property">class </em><code class="descclassname">singa.metric.</code><code class="descname">Metric</code><a class="headerlink" href="#singa.metric.Metric" title="Permalink to this definition">¶</a></dt>
	<dd><p>Bases: <code class="xref py py-class docutils literal"><span class="pre">object</span></code></p>
	<p>Base metric class.</p>
	<p>Subclasses that wrap the C++ loss classes can use the inherited foward,
	and evaluate functions of this base class. Other subclasses need
	to override these functions. Users need to feed in the <strong>predictions</strong> and
	ground truth to get the metric values.</p>
	<dl class="method">
	<dt id="singa.metric.Metric.forward">
	<code class="descname">forward</code><span class="sig-paren">(</span><em>x</em>, <em>y</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.metric.Metric.forward" title="Permalink to this definition">¶</a></dt>
	<dd><p>Compute the metric for each sample.</p>
	<table class="docutils field-list" frame="void" rules="none">
	<col class="field-name" />
	<col class="field-body" />
	<tbody valign="top">
	<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
	<li><strong>x</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) – predictions, one row per sample</li>
	<li><strong>y</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) – ground truth values, one row per sample</li>
	</ul>
	</td>
	</tr>
	<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a tensor of floats, one per sample</p>
	</td>
	</tr>
	</tbody>
	</table>
	</dd></dl>

	<dl class="method">
	<dt id="singa.metric.Metric.evaluate">
	<code class="descname">evaluate</code><span class="sig-paren">(</span><em>x</em>, <em>y</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.metric.Metric.evaluate" title="Permalink to this definition">¶</a></dt>
	<dd><p>Compute the averaged metric over all samples.</p>
	<table class="docutils field-list" frame="void" rules="none">
	<col class="field-name" />
	<col class="field-body" />
	<tbody valign="top">
	<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
	<li><strong>x</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) – predictions, one row per sample</li>
	<li><strong>y</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) – ground truth values, one row per sample</li>
	</ul>
	</td>
	</tr>
	<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a float value for the averaged metric</p>
	</td>
	</tr>
	</tbody>
	</table>
	</dd></dl>

	</dd></dl>

	<dl class="class">
	<dt id="singa.metric.Accuracy">
	<em class="property">class </em><code class="descclassname">singa.metric.</code><code class="descname">Accuracy</code><a class="headerlink" href="#singa.metric.Accuracy" title="Permalink to this definition">¶</a></dt>
	<dd><p>Bases: <a class="reference internal" href="#singa.metric.Metric" title="singa.metric.Metric"><code class="xref py py-class docutils literal"><span class="pre">singa.metric.Metric</span></code></a></p>
	<p>Compute the top one accuracy for singel label prediction tasks.</p>
	<p>It calls the C++ functions to do the calculation.</p>
	</dd></dl>

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