Skip to content

P
Paddle

PaddlePaddle / Paddle
大约 1 年 前同步成功

通知 2299
Star 20931
Fork 5422
P
Paddle
切换分支/标签
查找文件 普通视图历史永久链接Permalink
rnn.html 25.2 KB
Newer Older
T
Deploy to GitHub Pages: b3f6b5a9599c548324dd02e225db6a9e1a3e402e  
Travis CI 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 


<!DOCTYPE html>
<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
<head>
  <meta charset="utf-8">
  
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  
  <title>RNNOp design &mdash; PaddlePaddle  文档</title>
  

  
  

  

  
  
    

  

  
  
    <link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
  

  
  
        <link rel="index" title="索引"
              href="../../genindex.html"/>
        <link rel="search" title="搜索" href="../../search.html"/>
    <link rel="top" title="PaddlePaddle  文档" href="../../index.html"/> 

  <link rel="stylesheet" href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" type="text/css" />
  <link rel="stylesheet" href="../../_static/css/override.css" type="text/css" />
  <script>
  var _hmt = _hmt || [];
  (function() {
    var hm = document.createElement("script");
    hm.src = "//hm.baidu.com/hm.js?b9a314ab40d04d805655aab1deee08ba";
    var s = document.getElementsByTagName("script")[0]; 
    s.parentNode.insertBefore(hm, s);
  })();
  </script>

  

  
  <script src="../../_static/js/modernizr.min.js"></script>

</head>

<body class="wy-body-for-nav" role="document">

  
  <header class="site-header">
    <div class="site-logo">
      <a href="/"><img src="../../_static/images/PP_w.png"></a>
    </div>
    <div class="site-nav-links">
      <div class="site-menu">
        <a class="fork-on-github" href="https://github.com/PaddlePaddle/Paddle" target="_blank"><i class="fa fa-github"></i>Fork me on Github</a>
        <div class="language-switcher dropdown">
          <a type="button" data-toggle="dropdown">
            <span>English</span>
            <i class="fa fa-angle-up"></i>
            <i class="fa fa-angle-down"></i>
          </a>
          <ul class="dropdown-menu">
            <li><a href="/doc_cn">中文</a></li>
            <li><a href="/doc">English</a></li>
          </ul>
        </div>
        <ul class="site-page-links">
          <li><a href="/">Home</a></li>
        </ul>
      </div>
      <div class="doc-module">
        
        <ul>
<li class="toctree-l1"><a class="reference internal" href="../../getstarted/index_cn.html">新手入门</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../howto/index_cn.html">进阶指南</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../api/index_cn.html">API</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../faq/index_cn.html">FAQ</a></li>
</ul>

        
<div role="search">
  <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
    <input type="text" name="q" placeholder="Search docs" />
    <input type="hidden" name="check_keywords" value="yes" />
    <input type="hidden" name="area" value="default" />
  </form>
</div>        
      </div>
    </div>
  </header>
  
  <div class="main-content-wrap">

    
    <nav class="doc-menu-vertical" role="navigation">
        
          
          <ul>
<li class="toctree-l1"><a class="reference internal" href="../../getstarted/index_cn.html">新手入门</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../getstarted/build_and_install/index_cn.html">安装与编译</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../getstarted/build_and_install/docker_install_cn.html">PaddlePaddle的Docker容器使用方式</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../getstarted/build_and_install/cmake/build_from_source_cn.html">PaddlePaddle的编译选项</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../../getstarted/concepts/use_concepts_cn.html">基本使用概念</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../../howto/index_cn.html">进阶指南</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../howto/usage/cmd_parameter/index_cn.html">设置命令行参数</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../howto/usage/cmd_parameter/use_case_cn.html">使用案例</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../howto/usage/cmd_parameter/arguments_cn.html">参数概述</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../howto/usage/cmd_parameter/detail_introduction_cn.html">细节描述</a></li>
</ul>
</li>
T
Deploy to GitHub Pages: 63ffe5250a120ff430469b8d000deb2b031c4881  
Travis CI 已提交
125 
<li class="toctree-l2"><a class="reference internal" href="../../howto/usage/cluster/cluster_train_cn.html">PaddlePaddle分布式训练</a></li>
T
Deploy to GitHub Pages: b3f6b5a9599c548324dd02e225db6a9e1a3e402e  
Travis CI 已提交
126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 
<li class="toctree-l2"><a class="reference internal" href="../../howto/usage/k8s/k8s_basis_cn.html">Kubernetes 简介</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/usage/k8s/k8s_cn.html">Kubernetes单机训练</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/usage/k8s/k8s_distributed_cn.html">Kubernetes分布式训练</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/dev/build_cn.html">编译PaddlePaddle和运行单元测试</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/dev/write_docs_cn.html">如何贡献/修改文档</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/deep_model/rnn/index_cn.html">RNN相关模型</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../howto/deep_model/rnn/rnn_config_cn.html">RNN配置</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../howto/deep_model/rnn/recurrent_group_cn.html">Recurrent Group教程</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../howto/deep_model/rnn/hierarchical_layer_cn.html">支持双层序列作为输入的Layer</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../howto/deep_model/rnn/hrnn_rnn_api_compare_cn.html">单双层RNN API对比介绍</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/optimization/gpu_profiling_cn.html">GPU性能分析与调优</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../../api/index_cn.html">API</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">模型配置</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/attr.html">Parameter Attribute</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../../api/v2/data.html">数据访问</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../api/v2/run_logic.html">训练与应用</a></li>
</ul>
</li>
T
Deploy to GitHub Pages: a703a246428dd9f95775db102e338aebf93e9764  
Travis CI 已提交
156 157 158 159 160 161 162 163 
<li class="toctree-l1"><a class="reference internal" href="../../faq/index_cn.html">FAQ</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../faq/build_and_install/index_cn.html">编译安装与单元测试</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../faq/model/index_cn.html">模型配置</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../faq/parameter/index_cn.html">参数设置</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../faq/local/index_cn.html">本地训练与预测</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../faq/cluster/index_cn.html">集群训练与预测</a></li>
</ul>
</li>
T
Deploy to GitHub Pages: b3f6b5a9599c548324dd02e225db6a9e1a3e402e  
Travis CI 已提交
164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 
</ul>

        
    </nav>
    
    <section class="doc-content-wrap">

      

 







<div role="navigation" aria-label="breadcrumbs navigation">
  <ul class="wy-breadcrumbs">
      
    <li>RNNOp design</li>
  </ul>
</div>
      
      <div class="wy-nav-content" id="doc-content">
        <div class="rst-content">
          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
           <div itemprop="articleBody">
            
  <div class="section" id="rnnop-design">
<span id="rnnop-design"></span><h1>RNNOp design<a class="headerlink" href="#rnnop-design" title="永久链接至标题"></a></h1>
<p>This document is about an RNN operator which requires that instances in a mini-batch have the same length.  We will have a more flexible RNN operator.</p>
<div class="section" id="rnn-algorithm-implementation">
<span id="rnn-algorithm-implementation"></span><h2>RNN Algorithm Implementation<a class="headerlink" href="#rnn-algorithm-implementation" title="永久链接至标题"></a></h2>
<p aligh="center">
<img src="./images/rnn.jpg"/>
</p><p>The above diagram shows an RNN unrolled into a full network.</p>
<p>There are several important concepts:</p>
<ul class="simple">
<li><em>step-net</em>: the sub-graph to run at each step,</li>
<li><em>memory</em>, $h_t$, the state of the current step,</li>
<li><em>ex-memory</em>, $h_{t-1}$, the state of the previous step,</li>
<li><em>initial memory value</em>, the ex-memory of the first step.</li>
</ul>
<div class="section" id="step-scope">
<span id="step-scope"></span><h3>Step-scope<a class="headerlink" href="#step-scope" title="永久链接至标题"></a></h3>
<p>There could be local variables defined in step-nets.  PaddlePaddle runtime realizes these variables in <em>step-scopes</em> &#8211; scopes created for each step.</p>
<p aligh="center">
<img src="./images/rnn.png"/><br/>
Figure 2 the RNN's data flow
</p><p>Please be aware that all steps run the same step-net.  Each step</p>
<ol class="simple">
<li>creates the step-scope,</li>
<li>realizes local variables, including step-outputs, in the step-scope, and</li>
<li>runs the step-net, which could use these variables.</li>
</ol>
<p>The RNN operator will compose its output from step outputs in step scopes.</p>
</div>
<div class="section" id="memory-and-ex-memory">
<span id="memory-and-ex-memory"></span><h3>Memory and Ex-memory<a class="headerlink" href="#memory-and-ex-memory" title="永久链接至标题"></a></h3>
<p>Let&#8217;s give more details about memory and ex-memory via a simply example:</p>
<p>$$
h_t = U h_{t-1} + W x_t
$$,</p>
<p>where $h_t$ and $h_{t-1}$ are the memory and ex-memory of step $t$&#8217;s respectively.</p>
<p>In the implementation, we can make an ex-memory variable either &#8220;refers to&#8221; the memory variable of the previous step,
or copy the value of the previous memory value to the current ex-memory variable.</p>
</div>
<div class="section" id="usage-in-python">
<span id="usage-in-python"></span><h3>Usage in Python<a class="headerlink" href="#usage-in-python" title="永久链接至标题"></a></h3>
<p>For more information on Block, please refer to the <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/block.md">design doc</a>.</p>
<p>We can define an RNN&#8217;s step-net using Block:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">paddle</span> <span class="kn">as</span> <span class="nn">pd</span>

<span class="n">X</span> <span class="o">=</span> <span class="n">some_op</span><span class="p">()</span> <span class="c1"># x is some operator&#39;s output, and is a LoDTensor</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">some_op</span><span class="p">()</span>

<span class="c1"># declare parameters</span>
<span class="n">W</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">])</span>
<span class="n">U</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">])</span>

<span class="n">rnn</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">create_rnn_op</span><span class="p">(</span><span class="n">output_num</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="k">with</span> <span class="n">rnn</span><span class="o">.</span><span class="n">stepnet</span><span class="p">():</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">rnn</span><span class="o">.</span><span class="n">add_input</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
    <span class="c1"># declare a memory (rnn&#39;s step)</span>
    <span class="n">h</span> <span class="o">=</span> <span class="n">rnn</span><span class="o">.</span><span class="n">add_memory</span><span class="p">(</span><span class="n">init</span><span class="o">=</span><span class="n">a</span><span class="p">)</span>
    <span class="c1"># h.pre_state() means previous memory of rnn</span>
    <span class="n">new_state</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">add_two</span><span class="p">(</span> <span class="n">pd</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">W</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span> <span class="o">+</span> <span class="n">pd</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">U</span><span class="p">,</span> <span class="n">h</span><span class="o">.</span><span class="n">pre_state</span><span class="p">()))</span>
    <span class="c1"># update current memory</span>
    <span class="n">h</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">new_state</span><span class="p">)</span>
    <span class="c1"># indicate that h variables in all step scopes should be merged</span>
    <span class="n">rnn</span><span class="o">.</span><span class="n">add_outputs</span><span class="p">(</span><span class="n">h</span><span class="p">)</span>

<span class="n">out</span> <span class="o">=</span> <span class="n">rnn</span><span class="p">()</span>
</pre></div>
</div>
<p>Python API functions in above example:</p>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">rnn.add_input</span></code> indicates the parameter is a variable that will be segmented into step-inputs.</li>
<li><code class="docutils literal"><span class="pre">rnn.add_memory</span></code> creates a variable used as the memory.</li>
<li><code class="docutils literal"><span class="pre">rnn.add_outputs</span></code> mark the variables that will be concatenated across steps into the RNN output.</li>
</ul>
</div>
<div class="section" id="nested-rnn-and-lodtensor">
<span id="nested-rnn-and-lodtensor"></span><h3>Nested RNN and LoDTensor<a class="headerlink" href="#nested-rnn-and-lodtensor" title="永久链接至标题"></a></h3>
<p>An RNN whose step-net includes other RNN operators is known as an <em>nested RNN</em>.</p>
<p>For example, we could have a 2-level RNN, where the top level corresponds to paragraphs, and the lower level corresponds to sentences.</p>
<p>The following figure illustrates the feeding of text into the lower level, one sentence each step, and the feeding of step outputs to the top level. The final top level output is about the whole text.</p>
<p aligh="center">
<img src="./images/2_level_rnn.png"/>
</p><div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">paddle</span> <span class="kn">as</span> <span class="nn">pd</span>

<span class="n">W</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">])</span>
<span class="n">U</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">])</span>

<span class="n">W0</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">])</span>
<span class="n">U0</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">])</span>

<span class="c1"># a is output of some op</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">some_op</span><span class="p">()</span>

<span class="c1"># chapter_data is a set of 128-dim word vectors</span>
<span class="c1"># the first level of LoD is sentence</span>
<span class="c1"># the second level of LoD is chapter</span>
<span class="n">chapter_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="bp">None</span><span class="p">,</span> <span class="mi">128</span><span class="p">],</span> <span class="nb">type</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">lod_tensor</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>

<span class="k">def</span> <span class="nf">lower_level_rnn</span><span class="p">(</span><span class="n">paragraph</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    x: the input</span>
<span class="sd">    &#39;&#39;&#39;</span>
    <span class="n">rnn</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">create_rnn_op</span><span class="p">(</span><span class="n">output_num</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
    <span class="k">with</span> <span class="n">rnn</span><span class="o">.</span><span class="n">stepnet</span><span class="p">():</span>
        <span class="n">sentence</span> <span class="o">=</span> <span class="n">rnn</span><span class="o">.</span><span class="n">add_input</span><span class="p">(</span><span class="n">paragraph</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
        <span class="n">h</span> <span class="o">=</span> <span class="n">rnn</span><span class="o">.</span><span class="n">add_memory</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">])</span>
        <span class="n">h</span><span class="o">.</span><span class="n">update</span><span class="p">(</span>
            <span class="n">pd</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">W</span><span class="p">,</span> <span class="n">sentence</span><span class="p">)</span> <span class="o">+</span> <span class="n">pd</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">U</span><span class="p">,</span> <span class="n">h</span><span class="o">.</span><span class="n">pre_state</span><span class="p">()))</span>
        <span class="c1"># get the last state as sentence&#39;s info</span>
        <span class="n">rnn</span><span class="o">.</span><span class="n">add_outputs</span><span class="p">(</span><span class="n">h</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">rnn</span>

<span class="n">top_level_rnn</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">create_rnn_op</span><span class="p">(</span><span class="n">output_num</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="k">with</span> <span class="n">top_level_rnn</span><span class="o">.</span><span class="n">stepnet</span><span class="p">():</span>
    <span class="n">paragraph_data</span> <span class="o">=</span> <span class="n">rnn</span><span class="o">.</span><span class="n">add_input</span><span class="p">(</span><span class="n">chapter_data</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">low_rnn</span> <span class="o">=</span> <span class="n">lower_level_rnn</span><span class="p">(</span><span class="n">paragraph_data</span><span class="p">)</span>
    <span class="n">paragraph_out</span> <span class="o">=</span> <span class="n">low_rnn</span><span class="p">()</span>

    <span class="n">h</span> <span class="o">=</span> <span class="n">rnn</span><span class="o">.</span><span class="n">add_memory</span><span class="p">(</span><span class="n">init</span><span class="o">=</span><span class="n">a</span><span class="p">)</span>
    <span class="n">h</span><span class="o">.</span><span class="n">update</span><span class="p">(</span>
        <span class="n">pd</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">W0</span><span class="p">,</span> <span class="n">paragraph_data</span><span class="p">)</span> <span class="o">+</span> <span class="n">pd</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">U0</span><span class="p">,</span> <span class="n">h</span><span class="o">.</span><span class="n">pre_state</span><span class="p">()))</span>
    <span class="n">top_level_rnn</span><span class="o">.</span><span class="n">add_outputs</span><span class="p">(</span><span class="n">h</span><span class="p">)</span>

<span class="c1"># just output the last step</span>
<span class="n">chapter_out</span> <span class="o">=</span> <span class="n">top_level_rnn</span><span class="p">(</span><span class="n">output_all_steps</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
</pre></div>
</div>
<p>in above example, the construction of the <code class="docutils literal"><span class="pre">top_level_rnn</span></code> calls  <code class="docutils literal"><span class="pre">lower_level_rnn</span></code>.  The input is a LoD Tensor. The top level RNN segments input text data into paragraphs, and the lower level RNN segments each paragraph into sentences.</p>
<p>By default, the <code class="docutils literal"><span class="pre">RNNOp</span></code> will concatenate the outputs from all the time steps,
if the <code class="docutils literal"><span class="pre">output_all_steps</span></code> set to False, it will only output the final time step.</p>
<p align="center">
<img src="images/rnn_2level_data.png"/>
</p></div>
</div>
</div>


           </div>
          </div>
          <footer>
  

  <hr/>

  <div role="contentinfo">
    <p>
        &copy; Copyright 2016, PaddlePaddle developers.

    </p>
  </div>
  Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/snide/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>. 

</footer>

        </div>
      </div>

    </section>

  </div>
  


  

    <script type="text/javascript">
        var DOCUMENTATION_OPTIONS = {
            URL_ROOT:'../../',
            VERSION:'',
            COLLAPSE_INDEX:false,
            FILE_SUFFIX:'.html',
            HAS_SOURCE:  true,
            SOURCELINK_SUFFIX: ".txt",
        };
    </script>
      <script type="text/javascript" src="../../_static/jquery.js"></script>
      <script type="text/javascript" src="../../_static/underscore.js"></script>
      <script type="text/javascript" src="../../_static/doctools.js"></script>
      <script type="text/javascript" src="../../_static/translations.js"></script>
      <script type="text/javascript" src="https://cdn.bootcss.com/mathjax/2.7.0/MathJax.js"></script>
       
  

  
  
    <script type="text/javascript" src="../../_static/js/theme.js"></script>
  
  
  <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha384-Tc5IQib027qvyjSMfHjOMaLkfuWVxZxUPnCJA7l2mCWNIpG9mGCD8wGNIcPD7Txa" crossorigin="anonymous"></script>
  <script src="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/js/perfect-scrollbar.jquery.min.js"></script>
  <script src="../../_static/js/paddle_doc_init.js"></script> 

</body>
</html>