Skip to content

P
Paddle

PaddlePaddle / Paddle
大约 1 年 前同步成功

通知 2298
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
graph_survey.html 35.6 KB
Newer Older
T
Deploy to GitHub Pages: 1ff47b284c565d030b198705d5f18b4bd4ce53e5  
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 


<!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>Survey on Graph &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>
<li class="toctree-l1"><a class="reference internal" href="../mobile/index_cn.html">MOBILE</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/pip_install_cn.html">使用pip安装</a></li>
<li class="toctree-l3"><a class="reference internal" href="../getstarted/build_and_install/docker_install_cn.html">使用Docker安装运行</a></li>
<li class="toctree-l3"><a class="reference internal" href="../howto/dev/build_cn.html">用Docker编译和测试PaddlePaddle</a></li>
<li class="toctree-l3"><a class="reference internal" href="../getstarted/build_and_install/build_from_source_cn.html">从源码编译</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>
<li class="toctree-l2"><a class="reference internal" href="../howto/usage/cluster/cluster_train_cn.html">PaddlePaddle分布式训练</a></li>
<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/contribute_to_paddle_cn.html">如何贡献代码</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><ul>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/data/data_reader.html">Data Reader Interface</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/data/image.html">Image Interface</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/data/dataset.html">Dataset</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../api/v2/run_logic.html">训练与应用</a></li>
</ul>
</li>
<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>
<li class="toctree-l1"><a class="reference internal" href="../mobile/index_cn.html">MOBILE</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../mobile/cross_compiling_for_android_cn.html">Android平台编译指南</a></li>
<li class="toctree-l2"><a class="reference internal" href="../mobile/cross_compiling_for_ios_cn.html">iOS平台编译指南</a></li>
<li class="toctree-l2"><a class="reference internal" href="../mobile/cross_compiling_for_raspberry_cn.html">Raspberry Pi平台编译指南</a></li>
</ul>
</li>
</ul>

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

      

 







<div role="navigation" aria-label="breadcrumbs navigation">
  <ul class="wy-breadcrumbs">
      
    <li>Survey on Graph</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="survey-on-graph">
<span id="survey-on-graph"></span><h1>Survey on Graph<a class="headerlink" href="#survey-on-graph" title="永久链接至标题"></a></h1>
<p>Neural network framework often provides symbolic API for users to write network topology conveniently. This doc manily focus on symbolic API in most popular neural network frameworks, and try to find out how to parse symbolic configuration to a portable file, such as protobuf or json.</p>
<div class="section" id="mxnet">
<span id="mxnet"></span><h2>Mxnet<a class="headerlink" href="#mxnet" title="永久链接至标题"></a></h2>
<p>The core concept of symbolic API is <code class="docutils literal"><span class="pre">Symbol</span></code>. Mxnet implements <code class="docutils literal"><span class="pre">Symbol</span></code> class in C++, and export to Python using C-API. Please refer to the comments in Mxnet:</p>
<p><code class="docutils literal"><span class="pre">Symbol</span></code> is help class used to represent the operator node in Graph.
<code class="docutils literal"><span class="pre">Symbol</span></code> acts as an interface for building graphs from different components like Variable, Functor and Group. <code class="docutils literal"><span class="pre">Symbol</span></code> is also exported to python front-end (while Graph is not) to enable quick test and deployment. Conceptually, symbol is the final operation of a graph and thus including all the information required (the graph) to evaluate its output value.</p>
<p>A simple network topology wrote by Symbol is as follows:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">get_symbol</span><span class="p">(</span><span class="n">num_classes</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">&#39;data&#39;</span><span class="p">)</span>
    <span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Flatten</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">)</span>
    <span class="n">fc1</span>  <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">FullyConnected</span><span class="p">(</span><span class="n">data</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;fc1&#39;</span><span class="p">,</span> <span class="n">num_hidden</span><span class="o">=</span><span class="mi">128</span><span class="p">)</span>
    <span class="n">act1</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Activation</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">fc1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;relu1&#39;</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="s2">&quot;relu&quot;</span><span class="p">)</span>
    <span class="n">fc2</span>  <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">FullyConnected</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">act1</span><span class="p">,</span> <span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;fc2&#39;</span><span class="p">,</span> <span class="n">num_hidden</span> <span class="o">=</span> <span class="mi">64</span><span class="p">)</span>
    <span class="n">act2</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Activation</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">fc2</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;relu2&#39;</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="s2">&quot;relu&quot;</span><span class="p">)</span>
    <span class="n">fc3</span>  <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">FullyConnected</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">act2</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;fc3&#39;</span><span class="p">,</span> <span class="n">num_hidden</span><span class="o">=</span><span class="n">num_classes</span><span class="p">)</span>
    <span class="n">mlp</span>  <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">SoftmaxOutput</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">fc3</span><span class="p">,</span> <span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;softmax&#39;</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">mlp</span>
</pre></div>
</div>
<p>Varible here is actually a Symbol. Every basic Symbol will correspond to one Node, and every Node has its own NodeAttr. There is a op field in NodeAttr class, when a Symbol represents Variable(often input data), the op field is null.</p>
<p>Symbol contains a data member, std::vector<NodeEntry> outputs, and NodeEntry cantains a poniter to Node. We can follow the Node pointer to get all the Graph.</p>
<p>And Symbol can be saved to a Json file.</p>
<p>Here is a detailed example:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">mxnet</span> <span class="k">as</span> <span class="nn">mx</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">&#39;data&#39;</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span> <span class="n">data</span><span class="o">.</span><span class="n">debug_str</span><span class="p">()</span>
<span class="go">Variable:data</span>

<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Flatten</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span> <span class="n">data</span><span class="o">.</span><span class="n">debug_str</span><span class="p">()</span>
<span class="go">Symbol Outputs:</span>
<span class="go">    output[0]=flatten0(0)</span>
<span class="go">Variable:data</span>
<span class="go">--------------------</span>
<span class="go">Op:Flatten, Name=flatten0</span>
<span class="go">Inputs:</span>
<span class="go">    arg[0]=data(0) version=0</span>

<span class="gp">&gt;&gt;&gt; </span><span class="n">fc1</span>  <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">FullyConnected</span><span class="p">(</span><span class="n">data</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;fc1&#39;</span><span class="p">,</span> <span class="n">num_hidden</span><span class="o">=</span><span class="mi">128</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span> <span class="n">fc1</span><span class="o">.</span><span class="n">debug_str</span><span class="p">()</span>
<span class="go">Symbol Outputs:</span>
<span class="go">    output[0]=fc1(0)</span>
<span class="go">Variable:data</span>
<span class="go">--------------------</span>
<span class="go">Op:Flatten, Name=flatten0</span>
<span class="go">Inputs:</span>
<span class="go">    arg[0]=data(0) version=0</span>
<span class="go">Variable:fc1_weight</span>
<span class="go">Variable:fc1_bias</span>
<span class="go">--------------------</span>
<span class="go">Op:FullyConnected, Name=fc1</span>
<span class="go">Inputs:</span>
<span class="go">    arg[0]=flatten0(0)</span>
<span class="go">    arg[1]=fc1_weight(0) version=0</span>
<span class="go">    arg[2]=fc1_bias(0) version=0</span>
<span class="go">Attrs:</span>
<span class="go">    num_hidden=128</span>
</pre></div>
</div>
</div>
<div class="section" id="tensorflow">
<span id="tensorflow"></span><h2>TensorFlow<a class="headerlink" href="#tensorflow" title="永久链接至标题"></a></h2>
<p>The core concept of symbolic API is <code class="docutils literal"><span class="pre">Tensor</span></code>. Tensorflow defines <code class="docutils literal"><span class="pre">Tensor</span></code> in Python. Please refer to the comments in TensorFlow:</p>
<p>A <code class="docutils literal"><span class="pre">Tensor</span></code> is a symbolic handle to one of the outputs of an <code class="docutils literal"><span class="pre">Operation</span></code>. It does not hold the values of that operation&#8217;s output, but instead provides a means of computing those values in a TensorFlow <a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/Session">Session</a>.</p>
<p>A simple example is as follows:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span>  <span class="c1"># Build a dataflow graph.</span>
  <span class="n">c</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">([[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">]])</span>
  <span class="n">d</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">([[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]])</span>
  <span class="n">e</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span>

  <span class="c1"># Construct a `Session` to execute the graph.</span>
  <span class="n">sess</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span>

  <span class="c1"># Execute the graph and store the value that `e` represents in `result`.</span>
  <span class="n">result</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
</pre></div>
</div>
<p>The main method of <code class="docutils literal"><span class="pre">Tensor</span></code> is as follows:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="nd">@property</span>
<span class="k">def</span> <span class="nf">op</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
  <span class="sd">&quot;&quot;&quot;The `Operation` that produces this tensor as an output.&quot;&quot;&quot;</span>
  <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_op</span>

<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">dtype</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
   <span class="sd">&quot;&quot;&quot;The `DType` of elements in this tensor.&quot;&quot;&quot;</span>
  <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dtype</span>

<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">graph</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
  <span class="sd">&quot;&quot;&quot;The `Graph` that contains this tensor.&quot;&quot;&quot;</span>
  <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_op</span><span class="o">.</span><span class="n">graph</span>

<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">name</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
  <span class="sd">&quot;&quot;&quot;The string name of this tensor.&quot;&quot;&quot;</span>
  <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_op</span><span class="o">.</span><span class="n">name</span><span class="p">:</span>
    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Operation was not named: </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">_op</span><span class="p">)</span>
  <span class="k">return</span> <span class="s2">&quot;</span><span class="si">%s</span><span class="s2">:</span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_op</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_value_index</span><span class="p">)</span>

<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">device</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
  <span class="sd">&quot;&quot;&quot;The name of the device on which this tensor will be produced, or None.&quot;&quot;&quot;</span>
  <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_op</span><span class="o">.</span><span class="n">device</span>
</pre></div>
</div>
<p>Tensor can be taken as target to run by session. Tensor contains all the information of Graph, and tracks data dependency.</p>
<p>Here is a detailed example:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">c</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">([[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span> <span class="n">c</span><span class="o">.</span><span class="n">graph</span>
<span class="go">&lt;tensorflow.python.framework.ops.Graph object at 0x10f256d50&gt;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">d</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">([[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span> <span class="n">d</span><span class="o">.</span><span class="n">graph</span>
<span class="go">&lt;tensorflow.python.framework.ops.Graph object at 0x10f256d50&gt;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">e</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span> <span class="n">e</span><span class="o">.</span><span class="n">graph</span>
<span class="go">&lt;tensorflow.python.framework.ops.Graph object at 0x10f256d50&gt;</span>
</pre></div>
</div>
</div>
<div class="section" id="dynet">
<span id="dynet"></span><h2>Dynet<a class="headerlink" href="#dynet" title="永久链接至标题"></a></h2>
<p>The core concept of symbolic API is <code class="docutils literal"><span class="pre">Expression</span></code>, and Dynet defines <code class="docutils literal"><span class="pre">Expression</span></code> class in C++.</p>
<p>A simple example is as follows:</p>
<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="n">ComputationGraph</span> <span class="n">cg</span><span class="p">;</span>
<span class="n">Expression</span> <span class="n">W</span> <span class="o">=</span> <span class="n">parameter</span><span class="p">(</span><span class="n">cg</span><span class="p">,</span> <span class="n">pW</span><span class="p">);</span>

<span class="n">Expression</span> <span class="n">in</span> <span class="o">=</span> <span class="n">input</span><span class="p">(</span><span class="n">cg</span><span class="p">,</span> <span class="n">xs</span><span class="p">[</span><span class="n">i</span><span class="p">]);</span>
<span class="n">Expression</span> <span class="n">label</span> <span class="o">=</span> <span class="n">input</span><span class="p">(</span><span class="n">cg</span><span class="p">,</span> <span class="n">ys</span><span class="p">[</span><span class="n">i</span><span class="p">]);</span>
<span class="n">Expression</span> <span class="n">pred</span> <span class="o">=</span> <span class="n">W</span> <span class="o">*</span> <span class="n">in</span><span class="p">;</span>
<span class="n">Expression</span> <span class="n">loss</span> <span class="o">=</span> <span class="n">square</span><span class="p">(</span><span class="n">pred</span> <span class="o">-</span> <span class="n">label</span><span class="p">);</span>
</pre></div>
</div>
<p>The input data and parameter are also represented by Expression. Every basci Expression corresponds to a Node. And input data is also a Node.</p>
<p>Expression has a data member ComputationGraph, and ComputationGraph will be modified in users&#8217; configuring process. Expression can be a running target, beacuse Expression contains all dependency.</p>
<p>Here is a detailed example:</p>
<p>write topology in C++</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">ComputationGraph</span> <span class="n">cg</span><span class="p">;</span>
<span class="n">Expression</span> <span class="n">W</span> <span class="o">=</span> <span class="n">parameter</span><span class="p">(</span><span class="n">cg</span><span class="p">,</span> <span class="n">pW</span><span class="p">);</span>
<span class="n">cg</span><span class="o">.</span><span class="n">print_graphviz</span><span class="p">();</span>

<span class="n">Expression</span> <span class="n">pred</span> <span class="o">=</span> <span class="n">W</span> <span class="o">*</span> <span class="n">xs</span><span class="p">[</span><span class="n">i</span><span class="p">];</span>
<span class="n">cg</span><span class="o">.</span><span class="n">print_graphviz</span><span class="p">();</span>

<span class="n">Expression</span> <span class="n">loss</span> <span class="o">=</span> <span class="n">square</span><span class="p">(</span><span class="n">pred</span> <span class="o">-</span> <span class="n">ys</span><span class="p">[</span><span class="n">i</span><span class="p">]);</span>
<span class="n">cg</span><span class="o">.</span><span class="n">print_graphviz</span><span class="p">();</span>
</pre></div>
</div>
<p>compile and print</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="c1"># first print</span>
<span class="n">digraph</span> <span class="n">G</span> <span class="p">{</span>
  <span class="n">rankdir</span><span class="o">=</span><span class="n">LR</span><span class="p">;</span>
  <span class="n">nodesep</span><span class="o">=.</span><span class="mi">05</span><span class="p">;</span>
  <span class="n">N0</span> <span class="p">[</span><span class="n">label</span><span class="o">=</span><span class="s2">&quot;v0 = parameters(</span><span class="si">{1}</span><span class="s2">) @ 0x7ffe4de00110&quot;</span><span class="p">];</span>
<span class="p">}</span>
<span class="c1"># second print</span>
<span class="n">digraph</span> <span class="n">G</span> <span class="p">{</span>
  <span class="n">rankdir</span><span class="o">=</span><span class="n">LR</span><span class="p">;</span>
  <span class="n">nodesep</span><span class="o">=.</span><span class="mi">05</span><span class="p">;</span>
  <span class="n">N0</span> <span class="p">[</span><span class="n">label</span><span class="o">=</span><span class="s2">&quot;v0 = parameters(</span><span class="si">{1}</span><span class="s2">) @ 0x7ffe4de00110&quot;</span><span class="p">];</span>
  <span class="n">N1</span> <span class="p">[</span><span class="n">label</span><span class="o">=</span><span class="s2">&quot;v1 = v0 * -0.98&quot;</span><span class="p">];</span>
  <span class="n">N0</span> <span class="o">-&gt;</span> <span class="n">N1</span><span class="p">;</span>
<span class="p">}</span>
<span class="c1"># third print</span>
<span class="n">digraph</span> <span class="n">G</span> <span class="p">{</span>
  <span class="n">rankdir</span><span class="o">=</span><span class="n">LR</span><span class="p">;</span>
  <span class="n">nodesep</span><span class="o">=.</span><span class="mi">05</span><span class="p">;</span>
  <span class="n">N0</span> <span class="p">[</span><span class="n">label</span><span class="o">=</span><span class="s2">&quot;v0 = parameters(</span><span class="si">{1}</span><span class="s2">) @ 0x7ffe4de00110&quot;</span><span class="p">];</span>
  <span class="n">N1</span> <span class="p">[</span><span class="n">label</span><span class="o">=</span><span class="s2">&quot;v1 = v0 * -0.98&quot;</span><span class="p">];</span>
  <span class="n">N0</span> <span class="o">-&gt;</span> <span class="n">N1</span><span class="p">;</span>
  <span class="n">N2</span> <span class="p">[</span><span class="n">label</span><span class="o">=</span><span class="s2">&quot;v2 = -1.88387 - v1&quot;</span><span class="p">];</span>
  <span class="n">N1</span> <span class="o">-&gt;</span> <span class="n">N2</span><span class="p">;</span>
  <span class="n">N3</span> <span class="p">[</span><span class="n">label</span><span class="o">=</span><span class="s2">&quot;v3 = -v2&quot;</span><span class="p">];</span>
  <span class="n">N2</span> <span class="o">-&gt;</span> <span class="n">N3</span><span class="p">;</span>
  <span class="n">N4</span> <span class="p">[</span><span class="n">label</span><span class="o">=</span><span class="s2">&quot;v4 = square(v3)&quot;</span><span class="p">];</span>
  <span class="n">N3</span> <span class="o">-&gt;</span> <span class="n">N4</span><span class="p">;</span>
<span class="p">}</span>
</pre></div>
</div>
</div>
<div class="section" id="conclusion">
<span id="conclusion"></span><h2>Conclusion<a class="headerlink" href="#conclusion" title="永久链接至标题"></a></h2>
<p>Actually, Symbol/Tensor/Expression in Mxnet/TensorFlow/Dynet are the same level concepts. We use a unified name Expression here, this level concept has following features:</p>
<ul class="simple">
<li>Users wirte topoloy with symbolic API, and all return value is Expression, including input data and parameter.</li>
<li>Expression corresponds with a global Graph, and Expression can also be composed.</li>
<li>Expression tracks all dependency and can be taken as a run target</li>
</ul>
</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>