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develop/doc_cn/_sources/getstarted/concepts/use_concepts_cn.rst.txt

@@ -147,4 +147,9 @@ PaddlePaddle支持不同类型的输入数据，主要包括四种类型，和
 ..  literalinclude:: src/train.py
     :linenos:
 
+使用以上训练好的模型进行预测，取其中一个模型params_pass_90.tar，输入需要预测的向量组，然后打印输出：
+
+..  literalinclude:: src/infer.py
+    :linenos:
+
 有关线性回归的实际应用，可以参考PaddlePaddle book的 `第一章节 <http://book.paddlepaddle.org/index.html>`_。

develop/doc_cn/getstarted/concepts/use_concepts_cn.html

@@ -400,7 +400,12 @@ trainer.train<span class="o">(</span>
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-52</pre></div></td><td class="code"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">paddle.v2</span> <span class="k">as</span> <span class="nn">paddle</span>
+52
+53
+54
+55
+56
+57</pre></div></td><td class="code"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">paddle.v2</span> <span class="k">as</span> <span class="nn">paddle</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 
 <span class="c1"># init paddle</span>
@@ -428,6 +433,11 @@ trainer.train<span class="o">(</span>
         <span class="k">if</span> <span class="n">event</span><span class="o">.</span><span class="n">batch_id</span> <span class="o">%</span> <span class="mi">1</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
             <span class="nb">print</span> <span class="s2">&quot;Pass </span><span class="si">%d</span><span class="s2">, Batch </span><span class="si">%d</span><span class="s2">, Cost </span><span class="si">%f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">pass_id</span><span class="p">,</span> <span class="n">event</span><span class="o">.</span><span class="n">batch_id</span><span class="p">,</span>
                                                   <span class="n">event</span><span class="o">.</span><span class="n">cost</span><span class="p">)</span>
+    <span class="c1"># product model every 10 pass</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">event</span><span class="p">,</span> <span class="n">paddle</span><span class="o">.</span><span class="n">event</span><span class="o">.</span><span class="n">EndPass</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">event</span><span class="o">.</span><span class="n">pass_id</span> <span class="o">%</span> <span class="mi">10</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;params_pass_</span><span class="si">%d</span><span class="s1">.tar&#39;</span> <span class="o">%</span> <span class="n">event</span><span class="o">.</span><span class="n">pass_id</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+                <span class="n">trainer</span><span class="o">.</span><span class="n">save_parameter_to_tar</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
 
 
 <span class="c1"># define training dataset reader</span>
@@ -454,6 +464,44 @@ trainer.train<span class="o">(</span>
     <span class="n">num_passes</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
 </pre></div>
 </td></tr></table></div>
+<p>使用以上训练好的模型进行预测，取其中一个模型params_pass_90.tar，输入需要预测的向量组，然后打印输出：</p>
+<div class="highlight-default"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18</pre></div></td><td class="code"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">paddle.v2</span> <span class="k">as</span> <span class="nn">paddle</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="n">paddle</span><span class="o">.</span><span class="n">init</span><span class="p">(</span><span class="n">use_gpu</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="n">paddle</span><span class="o">.</span><span class="n">data_type</span><span class="o">.</span><span class="n">dense_vector</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span>
+<span class="n">y_predict</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">act</span><span class="o">=</span><span class="n">paddle</span><span class="o">.</span><span class="n">activation</span><span class="o">.</span><span class="n">Linear</span><span class="p">())</span>
+
+<span class="c1"># loading the model which generated by training</span>
+<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;params_pass_90.tar&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</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">Parameters</span><span class="o">.</span><span class="n">from_tar</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
+
+<span class="c1"># Input multiple sets of data，Output the infer result in a array.</span>
+<span class="n">i</span> <span class="o">=</span> <span class="p">[[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]],</span> <span class="p">[[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]],</span> <span class="p">[[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]]]</span>
+<span class="nb">print</span> <span class="n">paddle</span><span class="o">.</span><span class="n">infer</span><span class="p">(</span><span class="n">output_layer</span><span class="o">=</span><span class="n">y_predict</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">i</span><span class="p">)</span>
+<span class="c1"># Will print:</span>
+<span class="c1"># [[ -3.24491572]</span>
+<span class="c1">#  [ -6.94668722]</span>
+<span class="c1">#  [-10.64845848]]</span>
+</pre></div>
+</td></tr></table></div>
 <p>有关线性回归的实际应用，可以参考PaddlePaddle book的 <a class="reference external" href="http://book.paddlepaddle.org/index.html">第一章节</a>。</p>
 </div>
 </div>