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  <div class="section" id="paddlepython">
<h1>Paddle的Python预测接口<a class="headerlink" href="#paddlepython" title="Permalink to this headline"></a></h1>
<p>Paddle目前使用Swig对其常用的预测接口进行了封装，使在Python环境下的预测接口更加简单。
在Python环境下预测结果，主要分为以下几个步骤。</p>
<ul class="simple">
<li>读入解析训练配置</li>
<li>构造GradientMachine</li>
<li>准备数据</li>
<li>预测</li>
</ul>
<p>典型的预测代码如下，使用mnist手写识别作为样例。</p>
<div class="highlight-python"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre>  1
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104</pre></div></td><td class="code"><div class="highlight"><pre><span></span><span class="c1"># Copyright (c) 2016 Baidu, Inc. All Rights Reserved</span>
<span class="c1">#</span>
<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
<span class="c1"># you may not use this file except in compliance with the License.</span>
<span class="c1"># You may obtain a copy of the License at</span>
<span class="c1">#</span>
<span class="c1">#     http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c1">#</span>
<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
<span class="c1"># See the License for the specific language governing permissions and</span>
<span class="c1"># limitations under the License.</span>

<span class="kn">from</span> <span class="nn">py_paddle</span> <span class="kn">import</span> <span class="n">swig_paddle</span><span class="p">,</span> <span class="n">DataProviderWrapperConverter</span>
<span class="kn">from</span> <span class="nn">paddle.trainer.PyDataProviderWrapper</span> <span class="kn">import</span> <span class="n">DenseSlot</span>
<span class="kn">from</span> <span class="nn">paddle.trainer.config_parser</span> <span class="kn">import</span> <span class="n">parse_config</span>

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                  <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]]]</span>


<span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
    <span class="n">conf</span> <span class="o">=</span> <span class="n">parse_config</span><span class="p">(</span><span class="s2">&quot;./mnist_model/trainer_config.conf.norm&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span>
    <span class="k">print</span> <span class="n">conf</span><span class="o">.</span><span class="n">data_config</span><span class="o">.</span><span class="n">load_data_args</span>
    <span class="n">network</span> <span class="o">=</span> <span class="n">swig_paddle</span><span class="o">.</span><span class="n">GradientMachine</span><span class="o">.</span><span class="n">createFromConfigProto</span><span class="p">(</span><span class="n">conf</span><span class="o">.</span><span class="n">model_config</span><span class="p">)</span>
    <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">network</span><span class="p">,</span> <span class="n">swig_paddle</span><span class="o">.</span><span class="n">GradientMachine</span><span class="p">)</span>  <span class="c1"># For code hint.</span>
    <span class="n">network</span><span class="o">.</span><span class="n">loadParameters</span><span class="p">(</span><span class="s2">&quot;./mnist_model/&quot;</span><span class="p">)</span>
    <span class="n">converter</span> <span class="o">=</span> <span class="n">DataProviderWrapperConverter</span><span class="p">(</span><span class="bp">False</span><span class="p">,</span> <span class="p">[</span><span class="n">DenseSlot</span><span class="p">(</span><span class="mi">784</span><span class="p">)])</span>
    <span class="n">inArg</span> <span class="o">=</span> <span class="n">converter</span><span class="p">(</span><span class="n">TEST_DATA</span><span class="p">)</span>
    <span class="k">print</span> <span class="n">network</span><span class="o">.</span><span class="n">forwardTest</span><span class="p">(</span><span class="n">inArg</span><span class="p">)</span>


<span class="k">if</span> <span class="n">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
    <span class="n">swig_paddle</span><span class="o">.</span><span class="n">initPaddle</span><span class="p">(</span><span class="s2">&quot;--use_gpu=0&quot;</span><span class="p">)</span>
    <span class="n">main</span><span class="p">()</span>
</pre></div>
</td></tr></table></div>
<p>主要的软件包为py_paddle.swig_paddle，这个软件包文档相对完善。可以使用python的 <code class="code docutils literal"><span class="pre">help()</span></code> 函数查询文档。主要步骤为:</p>
<ul>
<li><p class="first">在程序开始阶段，使用命令行参数初始化paddle</p>
</li>
<li><p class="first">在98行载入paddle的训练文件。读取config</p>
</li>
<li><p class="first">在100行创建神经网络，并在83行载入参数。</p>
</li>
<li><dl class="first docutils">
<dt>103行创建一个从工具类，用来转换数据。</dt>
<dd><ul class="first last simple">
<li>swig_paddle接受的原始数据是C++的Matrix，也就是直接写内存的float数组。</li>
<li>这个接口并不用户友好。所以，我们提供了一个工具类DataProviderWrapperConverter.</li>
<li>这个工具类接收和PyDataProviderWrapper一样的输入数据，请参考PyDataProviderWrapper的文档。</li>
</ul>
</dd>
</dl>
</li>
<li><p class="first">在第105行执行预测。forwardTest是一个工具类，直接提取出神经网络Output层的输出结果。典型的输出结果为:</p>
</li>
</ul>
<div class="highlight-text"><div class="highlight"><pre><span></span>[{&#39;id&#39;: None, &#39;value&#39;: array([[  5.53018653e-09,   1.12194102e-05,   1.96644767e-09,
      1.43630644e-02,   1.51111044e-13,   9.85625684e-01,
      2.08823112e-10,   2.32777140e-08,   2.00186201e-09,
      1.15501715e-08],
   [  9.99982715e-01,   1.27787406e-10,   1.72296313e-05,
      1.49316648e-09,   1.36540484e-11,   6.93137714e-10,
      2.70634608e-08,   3.48565123e-08,   5.25639710e-09,
      4.48684503e-08]], dtype=float32)}]
</pre></div>
</div>
<p>其中，value即为softmax层的输出。由于数据是两个，所以输出的value。</p>
</div>


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