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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>
    <span class="nv">train_y</span> <span class="o">=</span> np.array<span class="o">([</span>-2, -3, -7, -7<span class="o">])</span>
    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>
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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>
    <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="o">-</span><span class="mi">3</span><span class="p">,</span> <span class="o">-</span><span class="mi">7</span><span class="p">,</span> <span class="o">-</span><span class="mi">7</span><span class="p">])</span>

    <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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