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  <div class="section" id="id1">
<h1>基本使用概念<a class="headerlink" href="#id1" title="永久链接至标题"></a></h1>
<p>PaddlePaddle是源于百度的一个深度学习平台。PaddlePaddle为深度学习研究人员提供了丰富的API，可以轻松地完成神经网络配置，模型训练等任务。
这里将介绍PaddlePaddle的基本使用概念，并且展示了如何利用PaddlePaddle来解决一个经典的线性回归问题。
在使用该文档之前，请参考 <a class="reference external" href="../build_and_install/index_cn.html">安装文档</a> 完成PaddlePaddle的安装。</p>
<div class="section" id="id3">
<h2>配置网络<a class="headerlink" href="#id3" title="永久链接至标题"></a></h2>
<div class="section" id="paddlepaddle">
<h3>加载PaddlePaddle<a class="headerlink" href="#paddlepaddle" title="永久链接至标题"></a></h3>
<p>在进行网络配置之前，首先需要加载相应的Python库，并进行初始化操作。</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>import paddle.v2 as paddle
import numpy as np
paddle.init<span class="o">(</span><span class="nv">use_gpu</span><span class="o">=</span>False<span class="o">)</span>
</pre></div>
</div>
</div>
<div class="section" id="id4">
<h3>搭建神经网络<a class="headerlink" href="#id4" title="永久链接至标题"></a></h3>
<p>搭建神经网络就像使用积木搭建宝塔一样。在PaddlePaddle中，layer是我们的积木，而神经网络是我们要搭建的宝塔。我们使用不同的layer进行组合，来搭建神经网络。
宝塔的底端需要坚实的基座来支撑，同样，神经网络也需要一些特定的layer作为输入接口，来完成网络的训练。</p>
<p>例如，我们可以定义如下layer来描述神经网络的输入：</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="nv">x</span> <span class="o">=</span> paddle.layer.data<span class="o">(</span><span class="nv">name</span><span class="o">=</span><span class="s1">&#39;x&#39;</span>, <span class="nv">type</span><span class="o">=</span>paddle.data_type.dense_vector<span class="o">(</span><span class="m">2</span><span class="o">))</span>
<span class="nv">y</span> <span class="o">=</span> paddle.layer.data<span class="o">(</span><span class="nv">name</span><span class="o">=</span><span class="s1">&#39;y&#39;</span>, <span class="nv">type</span><span class="o">=</span>paddle.data_type.dense_vector<span class="o">(</span><span class="m">1</span><span class="o">))</span>
</pre></div>
</div>
<p>其中x表示输入数据是一个维度为2的稠密向量，y表示输入数据是一个维度为1的稠密向量。</p>
<p>PaddlePaddle支持不同类型的输入数据，主要包括四种类型，和三种序列模式。</p>
<p>四种数据类型：</p>
<ul class="simple">
<li>dense_vector：稠密的浮点数向量。</li>
<li>sparse_binary_vector：稀疏的01向量，即大部分值为0，但有值的地方必须为1。</li>
<li>sparse_float_vector：稀疏的向量，即大部分值为0，但有值的部分可以是任何浮点数。</li>
<li>integer：整数标签。</li>
</ul>
<p>三种序列模式：</p>
<ul class="simple">
<li>SequenceType.NO_SEQUENCE：不是一条序列</li>
<li>SequenceType.SEQUENCE：是一条时间序列</li>
<li>SequenceType.SUB_SEQUENCE： 是一条时间序列，且序列的每一个元素还是一个时间序列。</li>
</ul>
<p>不同的数据类型和序列模式返回的格式不同，列表如下：</p>
<table border="1" class="docutils">
<colgroup>
<col width="17%" />
<col width="17%" />
<col width="28%" />
<col width="38%" />
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">&#160;</th>
<th class="head">NO_SEQUENCE</th>
<th class="head">SEQUENCE</th>
<th class="head">SUB_SEQUENCE</th>
</tr>
</thead>
<tbody valign="top">
<tr class="row-even"><td>dense_vector</td>
<td>[f, f, ...]</td>
<td>[[f, ...], [f, ...], ...]</td>
<td>[[[f, ...], ...], [[f, ...], ...],...]</td>
</tr>
<tr class="row-odd"><td>sparse_binary_vector</td>
<td>[i, i, ...]</td>
<td>[[i, ...], [i, ...], ...]</td>
<td>[[[i, ...], ...], [[i, ...], ...],...]</td>
</tr>
<tr class="row-even"><td>sparse_float_vector</td>
<td>[(i,f), (i,f), ...]</td>
<td>[[(i,f), ...], [(i,f), ...], ...]</td>
<td>[[[(i,f), ...], ...], [[(i,f), ...], ...],...]</td>
</tr>
<tr class="row-odd"><td>integer_value</td>
<td>i</td>
<td>[i, i, ...]</td>
<td>[[i, ...], [i, ...], ...]</td>
</tr>
</tbody>
</table>
<p>其中，f代表一个浮点数，i代表一个整数。</p>
<p>注意：对sparse_binary_vector和sparse_float_vector，PaddlePaddle存的是有值位置的索引。例如，</p>
<ul class="simple">
<li>对一个5维非序列的稀疏01向量 <code class="docutils literal"><span class="pre">[0,</span> <span class="pre">1,</span> <span class="pre">1,</span> <span class="pre">0,</span> <span class="pre">0]</span></code> ，类型是sparse_binary_vector，返回的是 <code class="docutils literal"><span class="pre">[1,</span> <span class="pre">2]</span></code> 。</li>
<li>对一个5维非序列的稀疏浮点向量 <code class="docutils literal"><span class="pre">[0,</span> <span class="pre">0.5,</span> <span class="pre">0.7,</span> <span class="pre">0,</span> <span class="pre">0]</span></code> ，类型是sparse_float_vector，返回的是 <code class="docutils literal"><span class="pre">[(1,</span> <span class="pre">0.5),</span> <span class="pre">(2,</span> <span class="pre">0.7)]</span></code> 。</li>
</ul>
<p>在定义输入layer之后，我们可以使用其他layer进行组合。在组合时，需要指定layer的输入来源。</p>
<p>例如，我们可以定义如下的layer组合：</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="nv">y_predict</span> <span class="o">=</span> paddle.layer.fc<span class="o">(</span><span class="nv">input</span><span class="o">=</span>x, <span class="nv">size</span><span class="o">=</span><span class="m">1</span>, <span class="nv">act</span><span class="o">=</span>paddle.activation.Linear<span class="o">())</span>
<span class="nv">cost</span> <span class="o">=</span> paddle.layer.mse_cost<span class="o">(</span><span class="nv">input</span><span class="o">=</span>y_predict, <span class="nv">label</span><span class="o">=</span>y<span class="o">)</span>
</pre></div>
</div>
<p>其中，x与y为之前描述的输入层；而y_predict是接收x作为输入，接上一个全连接层；cost接收y_predict与y作为输入，接上均方误差层。</p>
<p>最后一层cost中记录了神经网络的所有拓扑结构，通过组合不同的layer，我们即可完成神经网络的搭建。</p>
</div>
</div>
<div class="section" id="id5">
<h2>训练模型<a class="headerlink" href="#id5" title="永久链接至标题"></a></h2>
<p>在完成神经网络的搭建之后，我们首先需要根据神经网络结构来创建所需要优化的parameters，并创建optimizer。
之后，我们可以创建trainer来对网络进行训练。</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="nv">parameters</span> <span class="o">=</span> paddle.parameters.create<span class="o">(</span>cost<span class="o">)</span>
<span class="nv">optimizer</span> <span class="o">=</span> paddle.optimizer.Momentum<span class="o">(</span><span class="nv">momentum</span><span class="o">=</span><span class="m">0</span><span class="o">)</span>
<span class="nv">trainer</span> <span class="o">=</span> paddle.trainer.SGD<span class="o">(</span><span class="nv">cost</span><span class="o">=</span>cost,
                             <span class="nv">parameters</span><span class="o">=</span>parameters,
                             <span class="nv">update_equation</span><span class="o">=</span>optimizer<span class="o">)</span>
</pre></div>
</div>
<p>其中，trainer接收三个参数，包括神经网络拓扑结构、神经网络参数以及迭代方程。</p>
<p>在搭建神经网络的过程中，我们仅仅对神经网络的输入进行了描述。而trainer需要读取训练数据进行训练，PaddlePaddle中通过reader来加载数据。</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="c1"># define training dataset reader</span>
def train_reader<span class="o">()</span>:
    <span class="nv">train_x</span> <span class="o">=</span> np.array<span class="o">([[</span><span class="m">1</span>, <span class="m">1</span><span class="o">]</span>, <span class="o">[</span><span class="m">1</span>, <span class="m">2</span><span class="o">]</span>, <span class="o">[</span><span class="m">3</span>, <span class="m">4</span><span class="o">]</span>, <span class="o">[</span><span class="m">5</span>, <span class="m">2</span><span class="o">]])</span>
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    <span class="nv">train_y</span> <span class="o">=</span> np.array<span class="o">([[</span>-2<span class="o">]</span>, <span class="o">[</span>-3<span class="o">]</span>, <span class="o">[</span>-7<span class="o">]</span>, <span class="o">[</span>-7<span class="o">]])</span>
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    def reader<span class="o">()</span>:
        <span class="k">for</span> i in xrange<span class="o">(</span>train_y.shape<span class="o">[</span><span class="m">0</span><span class="o">])</span>:
            yield train_x<span class="o">[</span>i<span class="o">]</span>, train_y<span class="o">[</span>i<span class="o">]</span>
    <span class="k">return</span> reader
</pre></div>
</div>
<p>最终我们可以调用trainer的train方法启动训练：</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="c1"># define feeding map</span>
<span class="nv">feeding</span> <span class="o">=</span> <span class="o">{</span><span class="s1">&#39;x&#39;</span>: <span class="m">0</span>, <span class="s1">&#39;y&#39;</span>: <span class="m">1</span><span class="o">}</span>

<span class="c1"># event_handler to print training info</span>
def event_handler<span class="o">(</span>event<span class="o">)</span>:
    <span class="k">if</span> isinstance<span class="o">(</span>event, paddle.event.EndIteration<span class="o">)</span>:
        <span class="k">if</span> event.batch_id % <span class="nv">1</span> <span class="o">==</span> <span class="m">0</span>:
            print <span class="s2">&quot;Pass %d, Batch %d, Cost %f&quot;</span> % <span class="o">(</span>
                event.pass_id, event.batch_id, event.cost<span class="o">)</span>
<span class="c1"># training</span>
trainer.train<span class="o">(</span>
    <span class="nv">reader</span><span class="o">=</span>paddle.batch<span class="o">(</span>train_reader<span class="o">()</span>, <span class="nv">batch_size</span><span class="o">=</span><span class="m">1</span><span class="o">)</span>,
    <span class="nv">feeding</span><span class="o">=</span>feeding,
    <span class="nv">event_handler</span><span class="o">=</span>event_handler,
    <span class="nv">num_passes</span><span class="o">=</span><span class="m">100</span><span class="o">)</span>
</pre></div>
</div>
<p>关于PaddlePaddle的更多使用方法请参考 <a class="reference external" href="../../howto/index_cn.html">进阶指南</a>。</p>
</div>
<div class="section" id="id7">
<h2>线性回归完整示例<a class="headerlink" href="#id7" title="永久链接至标题"></a></h2>
<p>下面给出在三维空间中使用线性回归拟合一条直线的例子：</p>
<div class="highlight-default"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
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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>
<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>
<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="c1"># network config</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="n">y</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;y&#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">1</span><span class="p">))</span>
<span class="n">cost</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">mse_cost</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">y_predict</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">y</span><span class="p">)</span>

<span class="c1"># create parameters</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">cost</span><span class="p">)</span>
<span class="c1"># create optimizer</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">Momentum</span><span class="p">(</span><span class="n">momentum</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="c1"># create trainer</span>
<span class="n">trainer</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">trainer</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">cost</span><span class="o">=</span><span class="n">cost</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="n">update_equation</span><span class="o">=</span><span class="n">optimizer</span><span class="p">)</span>


<span class="c1"># event_handler to print training info</span>
<span class="k">def</span> <span class="nf">event_handler</span><span class="p">(</span><span class="n">event</span><span class="p">):</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">EndIteration</span><span class="p">):</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"># define training dataset reader</span>
<span class="k">def</span> <span class="nf">train_reader</span><span class="p">():</span>
    <span class="n">train_x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</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">2</span><span class="p">]])</span>
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416 
    <span class="n">train_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="o">-</span><span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mi">7</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mi">7</span><span class="p">]])</span>
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417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 

    <span class="k">def</span> <span class="nf">reader</span><span class="p">():</span>
        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">xrange</span><span class="p">(</span><span class="n">train_y</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
            <span class="k">yield</span> <span class="n">train_x</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">train_y</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>

    <span class="k">return</span> <span class="n">reader</span>


<span class="c1"># define feeding map</span>
<span class="n">feeding</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;x&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">}</span>

<span class="c1"># training</span>
<span class="n">trainer</span><span class="o">.</span><span class="n">train</span><span class="p">(</span>
    <span class="n">reader</span><span class="o">=</span><span class="n">paddle</span><span class="o">.</span><span class="n">batch</span><span class="p">(</span>
        <span class="n">train_reader</span><span class="p">(),</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
    <span class="n">feeding</span><span class="o">=</span><span class="n">feeding</span><span class="p">,</span>
    <span class="n">event_handler</span><span class="o">=</span><span class="n">event_handler</span><span class="p">,</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>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>
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