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55 56 57 
  <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。
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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>
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<li><p class="first">在程序开始阶段，使用命令行参数初始化PaddlePaddle</p>
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278 
</li>
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279 
<li><p class="first">在98行载入PaddlePaddle的训练文件。读取config</p>
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280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 
</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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  <p class="topless"><a href="../data_provider/write_new_dataprovider.html"
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