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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>
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<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.parameters.</code><code class="descname">Parameters</code></dt>
<dd><p>Parameters is a dictionary contains Paddle&#8217;s parameter. The key of
Parameters is the name of parameter. The value of Parameters is a plain
<code class="code docutils literal"><span class="pre">numpy.ndarry</span></code> .</p>
<p>Basically usage is</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="o">...</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>

<span class="n">parameters</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">parameters</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>

<span class="n">parameter_names</span> <span class="o">=</span> <span class="n">parameters</span><span class="o">.</span><span class="n">names</span><span class="p">()</span>
<span class="n">fc_mat</span> <span class="o">=</span> <span class="n">parameters</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fc&#39;</span><span class="p">)</span>
<span class="k">print</span> <span class="n">fc_mat</span>
</pre></div>
</div>
<dl class="method">
<dt>
<code class="descname">keys</code><span class="sig-paren">(</span><span class="sig-paren">)</span></dt>
<dd><p>keys are the names of each parameter.</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">Returns:</th><td class="field-body">list of parameter name</td>
</tr>
<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body">list</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="method">
<dt>
<code class="descname">names</code><span class="sig-paren">(</span><span class="sig-paren">)</span></dt>
<dd><p>names of each parameter.</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">Returns:</th><td class="field-body">list of parameter name</td>
</tr>
<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body">list</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="method">
<dt>
<code class="descname">has_key</code><span class="sig-paren">(</span><em>key</em><span class="sig-paren">)</span></dt>
<dd><p>has_key return true if there are such parameter name == key</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"><strong>key</strong> (<em>basestring</em>) &#8211; Parameter name</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">True if contains such key</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="method">
<dt>
<code class="descname">get_shape</code><span class="sig-paren">(</span><em>key</em><span class="sig-paren">)</span></dt>
<dd><p>get shape of the parameter.</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"><strong>key</strong> (<em>basestring</em>) &#8211; parameter name</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">parameter&#8217;s shape</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">tuple</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="method">
<dt>
<code class="descname">get</code><span class="sig-paren">(</span><em>parameter_name</em><span class="sig-paren">)</span></dt>
<dd><p>Get parameter by parameter name.</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">Note:</th><td class="field-body">It will always copy the parameter from C++ side.</td>
</tr>
<tr class="field-even field"><th class="field-name">Parameters:</th><td class="field-body"><strong>parameter_name</strong> (<em>basestring</em>) &#8211; parameter name</td>
</tr>
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">The parameter matrix.</td>
</tr>
<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body">np.ndarray</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="method">
<dt>
<code class="descname">set</code><span class="sig-paren">(</span><em>parameter_name</em>, <em>value</em><span class="sig-paren">)</span></dt>
<dd><p>Set parameter by parameter name &amp; matrix.</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>parameter_name</strong> (<em>basestring</em>) &#8211; parameter name</li>
<li><strong>value</strong> (<em>np.ndarray</em>) &#8211; parameter matrix</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">Nothing.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="method">
<dt>
<code class="descname">append_gradient_machine</code><span class="sig-paren">(</span><em>gradient_machine</em><span class="sig-paren">)</span></dt>
<dd><p>append gradient machine to parameters. This method is used internally in
Trainer.train.</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"><strong>gradient_machine</strong> (<em>api.GradientMachine</em>) &#8211; Paddle C++ GradientMachine object.</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"></td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="method">
<dt>
<code class="descname">serialize</code><span class="sig-paren">(</span><em>name</em>, <em>f</em><span class="sig-paren">)</span></dt>
<dd><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>name</strong> &#8211; </li>
<li><strong>f</strong> (<em>file</em>) &#8211; </li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"></p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="method">
<dt>
<code class="descname">deserialize</code><span class="sig-paren">(</span><em>name</em>, <em>f</em><span class="sig-paren">)</span></dt>
<dd><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>name</strong> &#8211; </li>
<li><strong>f</strong> (<em>file</em>) &#8211; </li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"></p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</dd></dl>
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<div class="section" id="trainer">
<h2>Trainer<a class="headerlink" href="#trainer" title="Permalink to this headline"></a></h2>
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<p>Module Trainer</p>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.trainer.</code><code class="descname">SGD</code><span class="sig-paren">(</span><em>cost</em>, <em>parameters</em>, <em>update_equation</em>, <em>extra_layers=None</em>, <em>is_local=True</em><span class="sig-paren">)</span></dt>
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<dd><p>Simple SGD Trainer.
SGD Trainer combines data reader, network topolopy and update_equation together
to train/test a neural network.</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 last simple">
<li><strong>update_equation</strong> (<em>paddle.v2.optimizer.Optimizer</em>) &#8211; The optimizer object.</li>
<li><strong>cost</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Target cost that neural network should be optimized.</li>
<li><strong>parameters</strong> (<em>paddle.v2.parameters.Parameters</em>) &#8211; The parameters dictionary.</li>
<li><strong>extra_layers</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Some layers in the neural network graph are not
in the path of cost layer.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt>
<code class="descname">train</code><span class="sig-paren">(</span><em>reader</em>, <em>num_passes=1</em>, <em>event_handler=None</em>, <em>feeding=None</em><span class="sig-paren">)</span></dt>
<dd><p>Training method. Will train num_passes of input data.</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>reader</strong> (<em>collections.Iterable</em>) &#8211; A reader that reads and yeilds data items. Usually we use a
batched reader to do mini-batch training.</li>
<li><strong>num_passes</strong> &#8211; The total train passes.</li>
<li><strong>event_handler</strong> (<em></em><em>(</em><em>BaseEvent</em><em>) </em><em>=&gt; None</em>) &#8211; Event handler. A method will be invoked when event
occurred.</li>
<li><strong>feeding</strong> (<em>dict|list</em>) &#8211; Feeding is a map of neural network input name and array
index that reader returns.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"></p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="method">
<dt>
<code class="descname">test</code><span class="sig-paren">(</span><em>reader</em>, <em>feeding=None</em><span class="sig-paren">)</span></dt>
<dd><p>Testing method. Will test input data.</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>reader</strong> (<em>collections.Iterable</em>) &#8211; A reader that reads and yeilds data items.</li>
<li><strong>feeding</strong> (<em>dict</em>) &#8211; Feeding is a map of neural network input name and array
index that reader returns.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"></p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</dd></dl>
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</div>
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<div class="section" id="event">
<h2>Event<a class="headerlink" href="#event" title="Permalink to this headline"></a></h2>
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<p>Testing and training events.</p>
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<p>There are:</p>
<ul class="simple">
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<li>TestResult</li>
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<li>BeginIteration</li>
<li>EndIteration</li>
<li>BeginPass</li>
<li>EndPass</li>
</ul>
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<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.event.</code><code class="descname">TestResult</code><span class="sig-paren">(</span><em>evaluator</em>, <em>cost</em><span class="sig-paren">)</span></dt>
<dd><p>Result that trainer.test return.</p>
</dd></dl>

<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.event.</code><code class="descname">BeginPass</code><span class="sig-paren">(</span><em>pass_id</em><span class="sig-paren">)</span></dt>
<dd><p>Event On One Pass Training Start.</p>
</dd></dl>

<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.event.</code><code class="descname">EndPass</code><span class="sig-paren">(</span><em>pass_id</em>, <em>evaluator</em><span class="sig-paren">)</span></dt>
<dd><p>Event On One Pass Training Complete.</p>
</dd></dl>

<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.event.</code><code class="descname">BeginIteration</code><span class="sig-paren">(</span><em>pass_id</em>, <em>batch_id</em><span class="sig-paren">)</span></dt>
<dd><p>Event On One Batch Training Start.</p>
</dd></dl>

<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.event.</code><code class="descname">EndIteration</code><span class="sig-paren">(</span><em>pass_id</em>, <em>batch_id</em>, <em>cost</em>, <em>evaluator</em><span class="sig-paren">)</span></dt>
<dd><p>Event On One Batch Training Complete.</p>
</dd></dl>
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</div>
<div class="section" id="inference">
<h2>Inference<a class="headerlink" href="#inference" title="Permalink to this headline"></a></h2>
<dl class="function">
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<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>
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<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>
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<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">outptut_layer</span><span class="o">=</span><span class="n">prediction</span><span class="p">,</span>
                      <span class="n">parameters</span><span class="o">=</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>
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<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">
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<li><strong>output_layer</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; output of the neural network that would be inferred</li>
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<li><strong>parameters</strong> (<em>paddle.v2.parameters.Parameters</em>) &#8211; parameters of the neural network.</li>
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<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>
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<li><strong>field</strong> (<em>str</em>) &#8211; The prediction field. It should in [<cite>value</cite>, <cite>id</cite>, <cite>prob</cite>].
<cite>value</cite> and <cite>prob</cite> mean return the prediction probabilities,
<cite>id</cite> means return the prediction labels. Default is <cite>value</cite>.
Note that <cite>prob</cite> only used when output_layer is beam_search
or max_id.</li>
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</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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