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  <div class="section" id="training-and-inference">
<h1>Training and Inference<a class="headerlink" href="#training-and-inference" title="Permalink to this headline">¶</a></h1>
<div class="section" id="parameters">
<h2>Parameters<a class="headerlink" href="#parameters" title="Permalink to this headline">¶</a></h2>
</div>
<div class="section" id="trainer">
<h2>Trainer<a class="headerlink" href="#trainer" title="Permalink to this headline">¶</a></h2>
</div>
<div class="section" id="event">
<h2>Event<a class="headerlink" href="#event" title="Permalink to this headline">¶</a></h2>
<p>All training events.</p>
<p>There are:</p>
<ul class="simple">
<li>BeginIteration</li>
<li>EndIteration</li>
<li>BeginPass</li>
<li>EndPass</li>
</ul>
<p>TODO(yuyang18): Complete it!</p>
</div>
<div class="section" id="inference">
<h2>Inference<a class="headerlink" href="#inference" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.</code><code class="descname">infer</code><span class="sig-paren">(</span><em>output_layer</em>, <em>parameters</em>, <em>input</em>, <em>feeding=None</em>, <em>field='value'</em><span class="sig-paren">)</span></dt>
<dd><p>Infer a neural network by given neural network output and parameters.  The
user should pass either a batch of input data or reader method.</p>
<p>Example usages:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">result</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">infer</span><span class="p">(</span><span class="n">prediction</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="nb">input</span><span class="o">=</span><span class="n">SomeData</span><span class="p">,</span>
                      <span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">)</span>
<span class="k">print</span> <span class="n">result</span>
</pre></div>
</div>
<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>output_layer</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; output of the neural network that would be inferred</li>
<li><strong>parameters</strong> (<em>paddle.v2.parameters.Parameters</em>) &#8211; parameters of the neural network.</li>
<li><strong>input</strong> (<em>collections.Iterable</em>) &#8211; input data batch. Should be a python iterable object, and each
element is the data batch.</li>
<li><strong>feeding</strong> &#8211; Reader dictionary. Default could generate from input
value.</li>
<li><strong>field</strong> (<em>str</em>) &#8211; The prediction field. It should in [<cite>value</cite>, <cite>ids</cite>]. <cite>value</cite>
means return the prediction probabilities, <cite>ids</cite> means return
the prediction labels. Default is <cite>value</cite></li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">a numpy array</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">numpy.ndarray</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

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