Skip to content

P
Paddle

Crayon鑫 / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle
切换分支/标签
查找文件 普通视图历史永久链接Permalink
index_cn.html 35.3 KB
Newer Older
T
Deploy to GitHub Pages: a703a246428dd9f95775db102e338aebf93e9764  
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 


<!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>本地训练与预测 &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="up" title="FAQ" href="../index_cn.html"/>
        <link rel="next" title="集群训练与预测" href="../cluster/index_cn.html"/>
        <link rel="prev" title="参数设置" href="../parameter/index_cn.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 class="current">
<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 current"><a class="reference internal" href="../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 class="current">
<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
128 129 130 131 132 
<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/cluster/cluster_train_cn.html#">概述</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/usage/cluster/cluster_train_cn.html#">环境准备</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/usage/cluster/cluster_train_cn.html#">启动参数说明</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/usage/cluster/cluster_train_cn.html#">使用分布式计算平台或工具</a></li>
T
Deploy to GitHub Pages: a703a246428dd9f95775db102e338aebf93e9764  
Travis CI 已提交
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 
<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/dev/contribute_to_paddle_cn.html">如何贡献代码</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>
<li class="toctree-l1 current"><a class="reference internal" href="../index_cn.html">FAQ</a><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="../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="../model/index_cn.html">模型配置</a></li>
<li class="toctree-l2"><a class="reference internal" href="../parameter/index_cn.html">参数设置</a></li>
<li class="toctree-l2 current"><a class="current reference internal" href="#">本地训练与预测</a></li>
<li class="toctree-l2"><a class="reference internal" href="../cluster/index_cn.html">集群训练与预测</a></li>
</ul>
</li>
</ul>

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

      

 







<div role="navigation" aria-label="breadcrumbs navigation">
  <ul class="wy-breadcrumbs">
      
        <li><a href="../index_cn.html">FAQ</a> > </li>
      
    <li>本地训练与预测</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="id1">
<h1><a class="toc-backref" href="#id9">本地训练与预测</a><a class="headerlink" href="#id1" title="永久链接至标题"></a></h1>
<div class="contents topic" id="contents">
<p class="topic-title first">Contents</p>
<ul class="simple">
<li><a class="reference internal" href="#id1" id="id9">本地训练与预测</a><ul>
<li><a class="reference internal" href="#id2" id="id10">1. 如何减少内存占用</a><ul>
<li><a class="reference internal" href="#dataprovider" id="id11">减少DataProvider缓冲池内存</a></li>
<li><a class="reference internal" href="#id3" id="id12">神经元激活内存</a></li>
<li><a class="reference internal" href="#id4" id="id13">参数内存</a></li>
</ul>
</li>
<li><a class="reference internal" href="#id5" id="id14">2. 如何加速训练速度</a><ul>
<li><a class="reference internal" href="#id6" id="id15">减少数据载入的耗时</a></li>
<li><a class="reference internal" href="#id7" id="id16">加速训练速度</a></li>
<li><a class="reference internal" href="#id8" id="id17">利用更多的计算资源</a></li>
</ul>
</li>
<li><a class="reference internal" href="#gpu" id="id18">3. 如何指定GPU设备</a></li>
<li><a class="reference internal" href="#floating-point-exception" id="id19">4. 训练过程中出现 <code class="code docutils literal"><span class="pre">Floating</span> <span class="pre">point</span> <span class="pre">exception</span></code>, 训练因此退出怎么办?</a></li>
<li><a class="reference internal" href="#infer-layer" id="id20">5.  如何调用 infer 接口输出多个layer的预测结果</a></li>
</ul>
</li>
</ul>
</div>
<div class="section" id="id2">
<h2><a class="toc-backref" href="#id10">1. 如何减少内存占用</a><a class="headerlink" href="#id2" title="永久链接至标题"></a></h2>
<p>神经网络的训练本身是一个非常消耗内存和显存的工作，经常会消耗数10GB的内存和数GB的显存。
PaddlePaddle的内存占用主要分为如下几个方面:</p>
<ul class="simple">
<li>DataProvider缓冲池内存（只针对内存）</li>
<li>神经元激活内存（针对内存和显存）</li>
<li>参数内存 （针对内存和显存）</li>
<li>其他内存杂项</li>
</ul>
<p>其中，其他内存杂项是指PaddlePaddle本身所用的一些内存，包括字符串分配，临时变量等等，暂不考虑在内。</p>
<div class="section" id="dataprovider">
<h3><a class="toc-backref" href="#id11">减少DataProvider缓冲池内存</a><a class="headerlink" href="#dataprovider" title="永久链接至标题"></a></h3>
<p>PyDataProvider使用的是异步加载，同时在内存里直接随即选取数据来做Shuffle。即</p>
<img src="../../_images/graphviz-9be6aad37f57c60f4b971dde0ef44ce27179cf9a.png" alt="digraph {
    rankdir=LR;
    数据文件 -&gt; 内存池 -&gt; PaddlePaddle训练
}" />
<p>所以，减小这个内存池即可减小内存占用，同时也可以加速开始训练前数据载入的过程。但是，这
个内存池实际上决定了shuffle的粒度。所以，如果将这个内存池减小，又要保证数据是随机的，
那么最好将数据文件在每次读取之前做一次shuffle。可能的代码为</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="nd">@provider</span><span class="p">(</span><span class="n">min_pool_size</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">...</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">process</span><span class="p">(</span><span class="n">settings</span><span class="p">,</span> <span class="n">filename</span><span class="p">):</span>
    <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;shuf </span><span class="si">%s</span><span class="s1"> &gt; </span><span class="si">%s</span><span class="s1">.shuf&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">filename</span><span class="p">))</span>  <span class="c1"># shuffle before.</span>
    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%s</span><span class="s1">.shuf&#39;</span> <span class="o">%</span> <span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
        <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">f</span><span class="p">:</span>
            <span class="k">yield</span> <span class="n">get_sample_from_line</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
</pre></div>
</div>
<p>这样做可以极大的减少内存占用，并且可能会加速训练过程，详细文档参考 <a class="reference internal" href="../../api/v1/data_provider/pydataprovider2_cn.html#api-pydataprovider2"><span class="std std-ref">PyDataProvider2的使用</span></a> 。</p>
</div>
<div class="section" id="id3">
<h3><a class="toc-backref" href="#id12">神经元激活内存</a><a class="headerlink" href="#id3" title="永久链接至标题"></a></h3>
<p>神经网络在训练的时候，会对每一个激活暂存一些数据，如神经元激活值等。
在反向传递的时候，这些数据会被用来更新参数。这些数据使用的内存主要和两个参数有关系，
一是batch size，另一个是每条序列(Sequence)长度。所以，其实也是和每个mini-batch中包含
的时间步信息成正比。</p>
<p>所以做法可以有两种：</p>
<ul class="simple">
<li>减小batch size。 即在网络配置中 <code class="code docutils literal"><span class="pre">settings(batch_size=1000)</span></code> 设置成一个小一些的值。但是batch size本身是神经网络的超参数，减小batch size可能会对训练结果产生影响。</li>
<li>减小序列的长度，或者直接扔掉非常长的序列。比如，一个数据集大部分序列长度是100-200,
但是突然有一个10000长的序列，就很容易导致内存超限，特别是在LSTM等RNN中。</li>
</ul>
</div>
<div class="section" id="id4">
<h3><a class="toc-backref" href="#id13">参数内存</a><a class="headerlink" href="#id4" title="永久链接至标题"></a></h3>
<p>PaddlePaddle支持非常多的优化算法(Optimizer)，不同的优化算法需要使用不同大小的内存。
例如使用 <code class="code docutils literal"><span class="pre">adadelta</span></code> 算法，则需要使用等于权重参数规模大约5倍的内存。举例，如果参数保存下来的模型目录
文件为 <code class="code docutils literal"><span class="pre">100M</span></code>， 那么该优化算法至少需要 <code class="code docutils literal"><span class="pre">500M</span></code> 的内存。</p>
<p>可以考虑使用一些优化算法，例如 <code class="code docutils literal"><span class="pre">momentum</span></code>。</p>
</div>
</div>
<div class="section" id="id5">
<h2><a class="toc-backref" href="#id14">2. 如何加速训练速度</a><a class="headerlink" href="#id5" title="永久链接至标题"></a></h2>
<p>加速PaddlePaddle训练可以考虑从以下几个方面：</p>
<ul class="simple">
<li>减少数据载入的耗时</li>
<li>加速训练速度</li>
<li>利用分布式训练驾驭更多的计算资源</li>
</ul>
<div class="section" id="id6">
<h3><a class="toc-backref" href="#id15">减少数据载入的耗时</a><a class="headerlink" href="#id6" title="永久链接至标题"></a></h3>
<p>使用<code class="code docutils literal"><span class="pre">pydataprovider</span></code>时，可以减少缓存池的大小，同时设置内存缓存功能，即可以极大的加速数据载入流程。
<code class="code docutils literal"><span class="pre">DataProvider</span></code> 缓存池的减小，和之前减小通过减小缓存池来减小内存占用的原理一致。</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="nd">@provider</span><span class="p">(</span><span class="n">min_pool_size</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">...</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">process</span><span class="p">(</span><span class="n">settings</span><span class="p">,</span> <span class="n">filename</span><span class="p">):</span>
    <span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;shuf </span><span class="si">%s</span><span class="s1"> &gt; </span><span class="si">%s</span><span class="s1">.shuf&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">filename</span><span class="p">))</span>  <span class="c1"># shuffle before.</span>
    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%s</span><span class="s1">.shuf&#39;</span> <span class="o">%</span> <span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
        <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">f</span><span class="p">:</span>
            <span class="k">yield</span> <span class="n">get_sample_from_line</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
</pre></div>
</div>
<p>同时 <code class="code docutils literal"><span class="pre">&#64;provider</span></code> 接口有一个 <code class="code docutils literal"><span class="pre">cache</span></code> 参数来控制缓存方法，将其设置成 <code class="code docutils literal"><span class="pre">CacheType.CACHE_PASS_IN_MEM</span></code> 的话，会将第一个 <code class="code docutils literal"><span class="pre">pass</span></code> (过完所有训练数据即为一个pass)生成的数据缓存在内存里，在之后的 <code class="code docutils literal"><span class="pre">pass</span></code> 中，不会再从 <code class="code docutils literal"><span class="pre">python</span></code> 端读取数据，而是直接从内存的缓存里读取数据。这也会极大减少数据读入的耗时。</p>
</div>
<div class="section" id="id7">
<h3><a class="toc-backref" href="#id16">加速训练速度</a><a class="headerlink" href="#id7" title="永久链接至标题"></a></h3>
<p>PaddlePaddle支持Sparse的训练，sparse训练需要训练特征是 <code class="code docutils literal"><span class="pre">sparse_binary_vector</span></code> 、 <code class="code docutils literal"><span class="pre">sparse_vector</span></code> 、或者 <code class="code docutils literal"><span class="pre">integer_value</span></code> 的任一一种。同时，与这个训练数据交互的Layer，需要将其Parameter设置成 sparse 更新模式，即设置 <code class="code docutils literal"><span class="pre">sparse_update=True</span></code></p>
<p>这里使用简单的 <code class="code docutils literal"><span class="pre">word2vec</span></code> 训练语言模型距离，具体使用方法为:</p>
<p>使用一个词前两个词和后两个词，来预测这个中间的词。这个任务的DataProvider为:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">DICT_DIM</span> <span class="o">=</span> <span class="mi">3000</span>


<span class="nd">@provider</span><span class="p">(</span><span class="n">input_types</span><span class="o">=</span><span class="p">[</span><span class="n">integer_sequence</span><span class="p">(</span><span class="n">DICT_DIM</span><span class="p">),</span> <span class="n">integer_value</span><span class="p">(</span><span class="n">DICT_DIM</span><span class="p">)])</span>
<span class="k">def</span> <span class="nf">process</span><span class="p">(</span><span class="n">settings</span><span class="p">,</span> <span class="n">filename</span><span class="p">):</span>
    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
        <span class="c1"># yield word ids to predict inner word id</span>
        <span class="c1"># such as [28, 29, 10, 4], 4</span>
        <span class="c1"># It means the sentance is  28, 29, 4, 10, 4.</span>
        <span class="k">yield</span> <span class="n">read_next_from_file</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
</pre></div>
</div>
<p>这个任务的配置为:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">...</span>  <span class="c1"># the settings and define data provider is omitted.</span>
<span class="n">DICT_DIM</span> <span class="o">=</span> <span class="mi">3000</span>  <span class="c1"># dictionary dimension.</span>
<span class="n">word_ids</span> <span class="o">=</span> <span class="n">data_layer</span><span class="p">(</span><span class="s1">&#39;word_ids&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">DICT_DIM</span><span class="p">)</span>

<span class="n">emb</span> <span class="o">=</span> <span class="n">embedding_layer</span><span class="p">(</span>
    <span class="nb">input</span><span class="o">=</span><span class="n">word_ids</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">param_attr</span><span class="o">=</span><span class="n">ParamAttr</span><span class="p">(</span><span class="n">sparse_update</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
<span class="n">emb_sum</span> <span class="o">=</span> <span class="n">pooling_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">emb</span><span class="p">,</span> <span class="n">pooling_type</span><span class="o">=</span><span class="n">SumPooling</span><span class="p">())</span>
<span class="n">predict</span> <span class="o">=</span> <span class="n">fc_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">emb_sum</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">DICT_DIM</span><span class="p">,</span> <span class="n">act</span><span class="o">=</span><span class="n">Softmax</span><span class="p">())</span>
<span class="n">outputs</span><span class="p">(</span>
    <span class="n">classification_cost</span><span class="p">(</span>
        <span class="nb">input</span><span class="o">=</span><span class="n">predict</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">data_layer</span><span class="p">(</span>
            <span class="s1">&#39;label&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">DICT_DIM</span><span class="p">)))</span>
</pre></div>
</div>
</div>
<div class="section" id="id8">
<h3><a class="toc-backref" href="#id17">利用更多的计算资源</a><a class="headerlink" href="#id8" title="永久链接至标题"></a></h3>
<p>利用更多的计算资源可以分为一下几个方式来进行:</p>
<ul class="simple">
<li>单机CPU训练<ul>
<li>使用多线程训练。设置命令行参数 <code class="code docutils literal"><span class="pre">trainer_count</span></code>。</li>
</ul>
</li>
<li>单机GPU训练<ul>
<li>使用显卡训练。设置命令行参数 <code class="code docutils literal"><span class="pre">use_gpu</span></code>。</li>
<li>使用多块显卡训练。设置命令行参数 <code class="code docutils literal"><span class="pre">use_gpu</span></code> 和 <code class="code docutils literal"><span class="pre">trainer_count</span></code> 。</li>
</ul>
</li>
<li>多机训练<ul>
T
Deploy to GitHub Pages: 63ffe5250a120ff430469b8d000deb2b031c4881  
Travis CI 已提交
349 
<li>请参考 <span class="xref std std-ref">cluster_train</span> 。</li>
T
Deploy to GitHub Pages: a703a246428dd9f95775db102e338aebf93e9764  
Travis CI 已提交
350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 
</ul>
</li>
</ul>
</div>
</div>
<div class="section" id="gpu">
<h2><a class="toc-backref" href="#id18">3. 如何指定GPU设备</a><a class="headerlink" href="#gpu" title="永久链接至标题"></a></h2>
<p>例如机器上有4块GPU，编号从0开始，指定使用2、3号GPU：</p>
<ul class="simple">
<li>方式1：通过 <a class="reference external" href="http://www.acceleware.com/blog/cudavisibledevices-masking-gpus">CUDA_VISIBLE_DEVICES</a> 环境变量来指定特定的GPU。</li>
</ul>
<div class="highlight-bash"><div class="highlight"><pre><span></span>env <span class="nv">CUDA_VISIBLE_DEVICES</span><span class="o">=</span><span class="m">2</span>,3 paddle train --use_gpu<span class="o">=</span><span class="nb">true</span> --trainer_count<span class="o">=</span><span class="m">2</span>
</pre></div>
</div>
<ul class="simple">
<li>方式2：通过命令行参数 <code class="docutils literal"><span class="pre">--gpu_id</span></code> 指定。</li>
</ul>
<div class="highlight-bash"><div class="highlight"><pre><span></span>paddle train --use_gpu<span class="o">=</span><span class="nb">true</span> --trainer_count<span class="o">=</span><span class="m">2</span> --gpu_id<span class="o">=</span><span class="m">2</span>
</pre></div>
</div>
</div>
<div class="section" id="floating-point-exception">
<h2><a class="toc-backref" href="#id19">4. 训练过程中出现 <code class="code docutils literal"><span class="pre">Floating</span> <span class="pre">point</span> <span class="pre">exception</span></code>, 训练因此退出怎么办?</a><a class="headerlink" href="#floating-point-exception" title="永久链接至标题"></a></h2>
<p>Paddle二进制在运行时捕获了浮点数异常，只要出现浮点数异常(即训练过程中出现NaN或者Inf)，立刻退出。浮点异常通常的原因是浮点数溢出、除零等问题。
主要原因包括两个方面:</p>
<ul class="simple">
<li>训练过程中参数或者训练过程中的梯度尺度过大，导致参数累加，乘除等时候，导致了浮点数溢出。</li>
<li>模型一直不收敛，发散到了一个数值特别大的地方。</li>
<li>训练数据有问题，导致参数收敛到了一些奇异的情况。或者输入数据尺度过大，有些特征的取值达到数百万，这时进行矩阵乘法运算就可能导致浮点数溢出。</li>
</ul>
<p>这里有两种有效的解决方法：</p>
<ol class="arabic simple">
<li>设置 <code class="code docutils literal"><span class="pre">gradient_clipping_threshold</span></code> 参数，示例代码如下：</li>
</ol>
<div class="highlight-python"><div class="highlight"><pre><span></span>
</pre></div>
</div>
<dl class="docutils">
<dt>optimizer = paddle.optimizer.RMSProp(</dt>
<dd>learning_rate=1e-3,
gradient_clipping_threshold=10.0,
regularization=paddle.optimizer.L2Regularization(rate=8e-4))</dd>
</dl>
<p>具体可以参考  <a class="reference external" href="https://github.com/PaddlePaddle/models/blob/develop/nmt_without_attention/train.py#L35">nmt_without_attention</a> 示例。</p>
<ol class="arabic simple" start="2">
<li>设置 <code class="code docutils literal"><span class="pre">error_clipping_threshold</span></code> 参数，示例代码如下：</li>
</ol>
<div class="highlight-python"><div class="highlight"><pre><span></span>
</pre></div>
</div>
<dl class="docutils">
<dt>decoder_inputs = paddle.layer.fc(</dt>
<dd><p class="first">act=paddle.activation.Linear(),
size=decoder_size * 3,
bias_attr=False,
input=[context, current_word],
layer_attr=paddle.attr.ExtraLayerAttribute(</p>
<blockquote class="last">
<div>error_clipping_threshold=100.0))</div></blockquote>
</dd>
</dl>
<p>完整代码可以参考示例 <a class="reference external" href="https://github.com/PaddlePaddle/book/blob/develop/08.machine_translation/train.py#L66">machine translation</a> 。</p>
<p>两种方法的区别：</p>
<ol class="arabic simple">
<li>两者都是对梯度的截断，但截断时机不同，前者在 <code class="code docutils literal"><span class="pre">optimzier</span></code> 更新网络参数时应用；后者在激活函数反向计算时被调用；</li>
<li>截断对象不同：前者截断可学习参数的梯度，后者截断回传给前层的梯度;</li>
</ol>
<p>除此之外，还可以通过减小学习律或者对数据进行归一化处理来解决这类问题。</p>
</div>
<div class="section" id="infer-layer">
<h2><a class="toc-backref" href="#id20">5.  如何调用 infer 接口输出多个layer的预测结果</a><a class="headerlink" href="#infer-layer" title="永久链接至标题"></a></h2>
<ul class="simple">
<li>将需要输出的层作为 <code class="code docutils literal"><span class="pre">paddle.inference.Inference()</span></code> 接口的 <code class="code docutils literal"><span class="pre">output_layer</span></code> 参数输入，代码如下：</li>
</ul>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">inferer</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">inference</span><span class="o">.</span><span class="n">Inference</span><span class="p">(</span><span class="n">output_layer</span><span class="o">=</span><span class="p">[</span><span class="n">layer1</span><span class="p">,</span> <span class="n">layer2</span><span class="p">],</span> <span class="n">parameters</span><span class="o">=</span><span class="n">parameters</span><span class="p">)</span>
</pre></div>
</div>
<ul class="simple">
<li>指定要输出的字段进行输出。以输出 <code class="code docutils literal"><span class="pre">value</span></code> 字段为例，代码如下：</li>
</ul>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">out</span> <span class="o">=</span> <span class="n">inferer</span><span class="o">.</span><span class="n">infer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">data_batch</span><span class="p">,</span> <span class="n">field</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;value&quot;</span><span class="p">])</span>
</pre></div>
</div>
<p>需要注意的是：</p>
<ul class="simple">
<li>如果指定了2个layer作为输出层，实际上需要的输出结果是两个矩阵；</li>
<li>假设第一个layer的输出A是一个 N1 * M1 的矩阵，第二个 Layer 的输出B是一个 N2 * M2 的矩阵；</li>
<li>paddle.v2 默认会将A和B 横向拼接，当N1 和 N2 大小不一样时，会报如下的错误：</li>
</ul>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="ne">ValueError</span><span class="p">:</span> <span class="nb">all</span> <span class="n">the</span> <span class="nb">input</span> <span class="n">array</span> <span class="n">dimensions</span> <span class="k">except</span> <span class="k">for</span> <span class="n">the</span> <span class="n">concatenation</span> <span class="n">axis</span> <span class="n">must</span> <span class="n">match</span> <span class="n">exactly</span>
</pre></div>
</div>
<p>多个层的输出矩阵的高度不一致导致拼接失败，这种情况常常发生在：</p>
<ul class="simple">
<li>同时输出序列层和非序列层；</li>
<li>多个输出层处理多个不同长度的序列;</li>
</ul>
<p>此时可以在调用infer接口时通过设置 <code class="code docutils literal"><span class="pre">flatten_result=False</span></code> , 跳过“拼接”步骤，来解决上面的问题。这时，infer接口的返回值是一个python list:</p>
<ul class="simple">
<li>list 中元素的个数等于网络中输出层的个数；</li>
<li>list 中每个元素是一个layer的输出结果矩阵，类型是numpy的ndarray；</li>
<li>每一个layer输出矩阵的高度，在非序列输入时：等于样本数；序列输入时等于：输入序列中元素的总数；宽度等于配置中layer的size；</li>
</ul>
</div>
</div>


           </div>
          </div>
          <footer>
  
    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
      
        <a href="../cluster/index_cn.html" class="btn btn-neutral float-right" title="集群训练与预测" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
      
      
        <a href="../parameter/index_cn.html" class="btn btn-neutral" title="参数设置" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
      
    </div>
  

  <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>