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  <div class="section" id="paddlepaddlepython">
<h1>PaddlePaddle的Python预测接口<a class="headerlink" href="#paddlepaddlepython" title="Permalink to this headline">¶</a></h1>
<p>PaddlePaddle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, 完整代码见
<code class="code docutils literal"><span class="pre">src_root/doc/ui/predict/predict_sample.py</span></code> 。</p>
<div class="highlight-python"><div class="highlight"><pre><span></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">DataProviderConverter</span>
<span class="kn">from</span> <span class="nn">paddle.trainer.PyDataProvider2</span> <span class="kn">import</span> <span class="n">dense_vector</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>


<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.py&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">DataProviderConverter</span><span class="p">([</span><span class="n">dense_vector</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>
</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 <code class="code docutils literal"><span class="pre">swig_paddle.initPaddle()</span></code> 传入命令行参数初始化
PaddlePaddle。详细的命令行参数请参考
<a class="reference external" href="../cmd_argument/detail_introduction.html">命令行参数</a> 。</p>
</li>
<li><p class="first">接下来使用 <code class="code docutils literal"><span class="pre">parse_config()</span></code> 解析训练时的配置文件。这里要注意预测数据通常
不包含label, 而且预测网络通常直接输出最后一层的结果而不是像训练时一样以cost
layer作为输出，所以用于预测的配置文件要做相应的修改。</p>
</li>
<li><p class="first">使用 <code class="code docutils literal"><span class="pre">swig_paddle.GradientMachine.createFromConfigproto()</span></code> 根据上一步解
析好的配置创建神经网络。</p>
</li>
<li><dl class="first docutils">
<dt>创建一个 <code class="code docutils literal"><span class="pre">DataProviderConverter</span></code> 对象converter。</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。
这个接口并不用户友好。所以，我们提供了一个工具类DataProviderConverter。
这个工具类接收和PyDataProvider2一样的输入数据，详情请参考
<a class="reference external" href="../../../doc/ui/data_provider/pydataprovider2.html">PyDataProvider2文档</a> 。</li>
</ul>
</dd>
</dl>
</li>
<li><p class="first">最后使用 <code class="code docutils literal"><span class="pre">forwardTest()</span></code> 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。由于数据是两条，所以输出的value包含两个向量&nbsp;。</p>
</div>


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