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
pydataprovider2_cn.html 104.9 KB
Newer Older
T
Deploy to GitHub Pages: 2c54905c3e3154327a8eb57ad96d0770dcc5f435  
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 


<!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>PyDataProvider2的使用 &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">
T
Deploy to GitHub Pages: 299e959a190017689d18af70d300f681745f4c67  
Travis CI 已提交
65 
        <a class="fork-on-github" href="https://github.com/PaddlePaddle/Paddle" target="_blank"><i class="fa fa-github"></i>Folk me on Github</a>
T
Deploy to GitHub Pages: 2c54905c3e3154327a8eb57ad96d0770dcc5f435  
Travis CI 已提交
66 67 68 69 70 71 72 73 74 75 76 77 
        <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">
T
Deploy to GitHub Pages: 18e5edf6a5b09868dbb083c312fe852cb8c29e55  
Travis CI 已提交
78 
          <li><a href="/">Home</a></li>
T
Deploy to GitHub Pages: 2c54905c3e3154327a8eb57ad96d0770dcc5f435  
Travis CI 已提交
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 
        </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="../../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>
T
Deploy to GitHub Pages: 5914c5c7488d48a412c1503ef96de2c1a6b06ed6  
Travis CI 已提交
111 
<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>
T
Deploy to GitHub Pages: 2c54905c3e3154327a8eb57ad96d0770dcc5f435  
Travis CI 已提交
112 113 114 115 
<li class="toctree-l3"><a class="reference internal" href="../../../getstarted/build_and_install/ubuntu_install_cn.html">Ubuntu部署PaddlePaddle</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>
T
Deploy to GitHub Pages: ab5ad2215d037ed6985f0830669c4bde8944a944  
Travis CI 已提交
116 
<li class="toctree-l2"><a class="reference internal" href="../../../getstarted/concepts/use_concepts_cn.html">基本使用概念</a></li>
T
Deploy to GitHub Pages: 2c54905c3e3154327a8eb57ad96d0770dcc5f435  
Travis CI 已提交
117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 
</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">运行分布式训练</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/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>
T
Deploy to GitHub Pages: 7c088b529c97f6dc3c28d60dd0510a64bce61312  
Travis CI 已提交
133 
<li class="toctree-l3"><a class="reference internal" href="../../../howto/deep_model/rnn/rnn_config_cn.html">RNN配置</a></li>
T
Deploy to GitHub Pages: 2c54905c3e3154327a8eb57ad96d0770dcc5f435  
Travis CI 已提交
134 135 136 137 138 139 140 141 
<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>
T
Deploy to GitHub Pages: 5f2cbce4190fb83883fed45668b5e4d87a577589  
Travis CI 已提交
142 
<li class="toctree-l1"><a class="reference internal" href="../../index_cn.html">API</a><ul>
T
Deploy to GitHub Pages: 362a14d777ec795f1f6b397c1039ebb594050a10  
Travis CI 已提交
143 144 145 
<li class="toctree-l2"><a class="reference internal" href="../../v2/model_configs.html">模型配置</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../v2/config/activation.html">Activation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../v2/config/layer.html">Layers</a></li>
T
Deploy to GitHub Pages: 0beda8736ffcac80a95bea20f873d8972d2b5b5e  
Travis CI 已提交
146 
<li class="toctree-l3"><a class="reference internal" href="../../v2/config/evaluators.html">Evaluators</a></li>
T
Deploy to GitHub Pages: 362a14d777ec795f1f6b397c1039ebb594050a10  
Travis CI 已提交
147 148 149 150 151 152 153 154 
<li class="toctree-l3"><a class="reference internal" href="../../v2/config/optimizer.html">Optimizer</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../v2/config/pooling.html">Pooling</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../v2/config/networks.html">Networks</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../v2/config/attr.html">Parameter Attribute</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../../v2/data.html">数据访问</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../v2/run_logic.html">训练与应用</a></li>
T
Deploy to GitHub Pages: 5f2cbce4190fb83883fed45668b5e4d87a577589  
Travis CI 已提交
155 156 
</ul>
</li>
T
Deploy to GitHub Pages: 2c54905c3e3154327a8eb57ad96d0770dcc5f435  
Travis CI 已提交
157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 
<li class="toctree-l1"><a class="reference internal" href="../../../faq/index_cn.html">FAQ</a></li>
</ul>

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

      

 







<div role="navigation" aria-label="breadcrumbs navigation">
  <ul class="wy-breadcrumbs">
      
    <li>PyDataProvider2的使用</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="pydataprovider2">
<span id="api-pydataprovider2"></span><h1><a class="toc-backref" href="#id11">PyDataProvider2的使用</a><a class="headerlink" href="#pydataprovider2" title="永久链接至标题"></a></h1>
<p>PyDataProvider2是PaddlePaddle使用Python提供数据的推荐接口。该接口使用多线程读取数据，并提供了简单的Cache功能；同时可以使用户只关注如何从文件中读取每一条数据，而不用关心数据如何传输，如何存储等等。</p>
<div class="contents topic" id="contents">
<p class="topic-title first">Contents</p>
<ul class="simple">
<li><a class="reference internal" href="#pydataprovider2" id="id11">PyDataProvider2的使用</a><ul>
<li><a class="reference internal" href="#mnist" id="id12">MNIST的使用场景</a><ul>
<li><a class="reference internal" href="#id1" id="id13">样例数据</a></li>
<li><a class="reference internal" href="#dataprovider" id="id14">dataprovider的使用</a></li>
<li><a class="reference internal" href="#id2" id="id15">网络配置中的调用</a></li>
<li><a class="reference internal" href="#id3" id="id16">小结</a></li>
</ul>
</li>
<li><a class="reference internal" href="#id4" id="id17">时序模型的使用场景</a><ul>
<li><a class="reference internal" href="#id5" id="id18">样例数据</a></li>
<li><a class="reference internal" href="#id6" id="id19">dataprovider的使用</a></li>
<li><a class="reference internal" href="#id7" id="id20">网络配置中的调用</a></li>
</ul>
</li>
<li><a class="reference internal" href="#reference" id="id21">参考(Reference)</a><ul>
<li><a class="reference internal" href="#provider" id="id22">&#64;provider</a></li>
<li><a class="reference internal" href="#input-types" id="id23">input_types</a></li>
<li><a class="reference internal" href="#init-hook" id="id24">init_hook</a></li>
<li><a class="reference internal" href="#cache" id="id25">cache</a></li>
</ul>
</li>
<li><a class="reference internal" href="#id8" id="id26">注意事项</a><ul>
<li><a class="reference internal" href="#id9" id="id27">可能的内存泄露问题</a></li>
<li><a class="reference internal" href="#id10" id="id28">内存不够用的情况</a></li>
</ul>
</li>
</ul>
</li>
</ul>
</div>
<div class="section" id="mnist">
<h2><a class="toc-backref" href="#id12">MNIST的使用场景</a><a class="headerlink" href="#mnist" title="永久链接至标题"></a></h2>
<p>我们以MNIST手写识别为例，来说明PyDataProvider2的简单使用场景。</p>
<div class="section" id="id1">
<h3><a class="toc-backref" href="#id13">样例数据</a><a class="headerlink" href="#id1" title="永久链接至标题"></a></h3>
<p>MNIST是一个包含有70,000张灰度图片的数字分类数据集。样例数据 <code class="docutils literal"><span class="pre">mnist_train.txt</span></code> 如下：</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="mi">5</span><span class="p">;</span><span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.215686</span> <span class="mf">0.533333</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.67451</span> <span class="mf">0.992157</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.070588</span> <span class="mf">0.886275</span> <span class="mf">0.992157</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.192157</span> <span class="mf">0.070588</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.670588</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.117647</span> <span class="mf">0.933333</span> <span class="mf">0.858824</span> <span class="mf">0.313725</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.090196</span> <span class="mf">0.858824</span> <span class="mf">0.992157</span> <span class="mf">0.831373</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.141176</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.611765</span> <span class="mf">0.054902</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.258824</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.529412</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.368627</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.419608</span> <span class="mf">0.003922</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.094118</span> <span class="mf">0.835294</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.517647</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.603922</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.603922</span> <span class="mf">0.545098</span> <span class="mf">0.043137</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.447059</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.956863</span> <span class="mf">0.062745</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.011765</span> <span class="mf">0.666667</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.745098</span> <span class="mf">0.137255</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.152941</span> <span class="mf">0.866667</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.521569</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.070588</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.803922</span> <span class="mf">0.352941</span> <span class="mf">0.745098</span> <span class="mf">0.992157</span> <span class="mf">0.945098</span> <span class="mf">0.317647</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.580392</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.764706</span> <span class="mf">0.043137</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.070588</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.776471</span> <span class="mf">0.043137</span> <span class="mi">0</span> <span class="mf">0.007843</span> <span class="mf">0.27451</span> <span class="mf">0.882353</span> <span class="mf">0.941176</span> <span class="mf">0.176471</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.180392</span> <span class="mf">0.898039</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.313725</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.070588</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.713725</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.627451</span> <span class="mf">0.992157</span> <span class="mf">0.729412</span> <span class="mf">0.062745</span> <span class="mi">0</span> <span class="mf">0.509804</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.776471</span> <span class="mf">0.035294</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.494118</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.968627</span> <span class="mf">0.168627</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.423529</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.364706</span> <span class="mi">0</span> <span class="mf">0.717647</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.317647</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.533333</span> <span class="mf">0.992157</span> <span class="mf">0.984314</span> <span class="mf">0.945098</span> <span class="mf">0.603922</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.003922</span> <span class="mf">0.466667</span> <span class="mf">0.992157</span> <span class="mf">0.988235</span> <span class="mf">0.976471</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.788235</span> <span class="mf">0.007843</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.686275</span> <span class="mf">0.882353</span> <span class="mf">0.364706</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.098039</span> <span class="mf">0.588235</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.980392</span> <span class="mf">0.305882</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.101961</span> <span class="mf">0.67451</span> <span class="mf">0.321569</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.105882</span> <span class="mf">0.733333</span> <span class="mf">0.976471</span> <span class="mf">0.811765</span> <span class="mf">0.713725</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.65098</span> <span class="mf">0.992157</span> <span class="mf">0.321569</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.25098</span> <span class="mf">0.007843</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">1</span> <span class="mf">0.94902</span> <span class="mf">0.219608</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.968627</span> <span class="mf">0.764706</span> <span class="mf">0.152941</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.498039</span> <span class="mf">0.25098</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span><span class="p">;</span>
<span class="mi">0</span><span class="p">;</span><span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.298039</span> <span class="mf">0.333333</span> <span class="mf">0.333333</span> <span class="mf">0.333333</span> <span class="mf">0.337255</span> <span class="mf">0.333333</span> <span class="mf">0.333333</span> <span class="mf">0.109804</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.027451</span> <span class="mf">0.223529</span> <span class="mf">0.776471</span> <span class="mf">0.964706</span> <span class="mf">0.988235</span> <span class="mf">0.988235</span> <span class="mf">0.988235</span> <span class="mf">0.992157</span> <span class="mf">0.988235</span> <span class="mf">0.988235</span> <span class="mf">0.780392</span> <span class="mf">0.098039</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.14902</span> <span class="mf">0.698039</span> <span class="mf">0.988235</span> <span class="mf">0.992157</span> <span class="mf">0.988235</span> <span class="mf">0.901961</span> <span class="mf">0.87451</span> <span class="mf">0.568627</span> <span class="mf">0.882353</span> <span class="mf">0.976471</span> <span class="mf">0.988235</span> <span class="mf">0.988235</span> <span class="mf">0.501961</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.188235</span> <span class="mf">0.647059</span> <span class="mf">0.988235</span> <span class="mf">0.988235</span> <span class="mf">0.745098</span> <span class="mf">0.439216</span> <span class="mf">0.098039</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.572549</span> <span class="mf">0.988235</span> <span class="mf">0.988235</span> <span class="mf">0.988235</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.2</span> <span class="mf">0.933333</span> <span class="mf">0.992157</span> <span class="mf">0.941176</span> <span class="mf">0.247059</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.188235</span> <span class="mf">0.898039</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.039216</span> <span class="mf">0.639216</span> <span class="mf">0.933333</span> <span class="mf">0.988235</span> <span class="mf">0.913725</span> <span class="mf">0.278431</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.113725</span> <span class="mf">0.843137</span> <span class="mf">0.988235</span> <span class="mf">0.988235</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.235294</span> <span class="mf">0.988235</span> <span class="mf">0.992157</span> <span class="mf">0.988235</span> <span class="mf">0.815686</span> <span class="mf">0.07451</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.333333</span> <span class="mf">0.988235</span> <span class="mf">0.988235</span> <span class="mf">0.552941</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.211765</span> <span class="mf">0.878431</span> <span class="mf">0.988235</span> <span class="mf">0.992157</span> <span class="mf">0.701961</span> <span class="mf">0.329412</span> <span class="mf">0.109804</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.698039</span> <span class="mf">0.988235</span> <span class="mf">0.913725</span> <span class="mf">0.145098</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.188235</span> <span class="mf">0.890196</span> <span class="mf">0.988235</span> <span class="mf">0.988235</span> <span class="mf">0.745098</span> <span class="mf">0.047059</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.882353</span> <span class="mf">0.988235</span> <span class="mf">0.568627</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.2</span> <span class="mf">0.933333</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.447059</span> <span class="mf">0.294118</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.447059</span> <span class="mf">0.992157</span> <span class="mf">0.768627</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.623529</span> <span class="mf">0.988235</span> <span class="mf">0.988235</span> <span class="mf">0.988235</span> <span class="mf">0.988235</span> <span class="mf">0.992157</span> <span class="mf">0.47451</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.188235</span> <span class="mf">0.933333</span> <span class="mf">0.87451</span> <span class="mf">0.509804</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.992157</span> <span class="mf">0.988235</span> <span class="mf">0.937255</span> <span class="mf">0.792157</span> <span class="mf">0.988235</span> <span class="mf">0.894118</span> <span class="mf">0.082353</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.027451</span> <span class="mf">0.647059</span> <span class="mf">0.992157</span> <span class="mf">0.654902</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.623529</span> <span class="mf">0.988235</span> <span class="mf">0.913725</span> <span class="mf">0.329412</span> <span class="mf">0.376471</span> <span class="mf">0.184314</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.027451</span> <span class="mf">0.513725</span> <span class="mf">0.988235</span> <span class="mf">0.635294</span> <span class="mf">0.219608</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.196078</span> <span class="mf">0.929412</span> <span class="mf">0.988235</span> <span class="mf">0.988235</span> <span class="mf">0.741176</span> <span class="mf">0.309804</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.529412</span> <span class="mf">0.988235</span> <span class="mf">0.678431</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.223529</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mi">1</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mi">1</span> <span class="mf">0.992157</span> <span class="mf">0.992157</span> <span class="mf">0.882353</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.023529</span> <span class="mf">0.478431</span> <span class="mf">0.654902</span> <span class="mf">0.658824</span> <span class="mf">0.952941</span> <span class="mf">0.988235</span> <span class="mf">0.988235</span> <span class="mf">0.988235</span> <span class="mf">0.992157</span> <span class="mf">0.988235</span> <span class="mf">0.729412</span> <span class="mf">0.278431</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.196078</span> <span class="mf">0.647059</span> <span class="mf">0.764706</span> <span class="mf">0.764706</span> <span class="mf">0.768627</span> <span class="mf">0.580392</span> <span class="mf">0.047059</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span><span class="p">;</span>
<span class="mi">4</span><span class="p">;</span><span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.180392</span> <span class="mf">0.470588</span> <span class="mf">0.623529</span> <span class="mf">0.623529</span> <span class="mf">0.623529</span> <span class="mf">0.588235</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.243137</span> <span class="mf">0.494118</span> <span class="mf">0.862745</span> <span class="mf">0.870588</span> <span class="mf">0.960784</span> <span class="mf">0.996078</span> <span class="mf">0.996078</span> <span class="mf">0.996078</span> <span class="mf">0.996078</span> <span class="mf">0.992157</span> <span class="mf">0.466667</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.317647</span> <span class="mf">0.639216</span> <span class="mf">0.639216</span> <span class="mf">0.639216</span> <span class="mf">0.639216</span> <span class="mf">0.639216</span> <span class="mf">0.470588</span> <span class="mf">0.262745</span> <span class="mf">0.333333</span> <span class="mf">0.929412</span> <span class="mf">0.694118</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.811765</span> <span class="mf">0.694118</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.811765</span> <span class="mf">0.694118</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.811765</span> <span class="mf">0.694118</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.184314</span> <span class="mf">0.992157</span> <span class="mf">0.694118</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.192157</span> <span class="mf">0.996078</span> <span class="mf">0.384314</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.454902</span> <span class="mf">0.980392</span> <span class="mf">0.219608</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.564706</span> <span class="mf">0.941176</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.588235</span> <span class="mf">0.776471</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.945098</span> <span class="mf">0.560784</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.054902</span> <span class="mf">0.952941</span> <span class="mf">0.356863</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.337255</span> <span class="mf">0.917647</span> <span class="mf">0.109804</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.698039</span> <span class="mf">0.701961</span> <span class="mf">0.019608</span> <span class="mf">0.4</span> <span class="mf">0.662745</span> <span class="mf">0.662745</span> <span class="mf">0.662745</span> <span class="mf">0.662745</span> <span class="mf">0.662745</span> <span class="mf">0.662745</span> <span class="mf">0.662745</span> <span class="mf">0.376471</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.090196</span> <span class="mf">0.639216</span> <span class="mf">0.972549</span> <span class="mf">0.945098</span> <span class="mf">0.913725</span> <span class="mf">0.996078</span> <span class="mf">0.996078</span> <span class="mf">0.996078</span> <span class="mf">0.996078</span> <span class="mi">1</span> <span class="mf">0.996078</span> <span class="mf">0.996078</span> <span class="mi">1</span> <span class="mf">0.996078</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.007843</span> <span class="mf">0.105882</span> <span class="mf">0.717647</span> <span class="mf">0.776471</span> <span class="mf">0.905882</span> <span class="mf">0.996078</span> <span class="mf">0.996078</span> <span class="mf">0.988235</span> <span class="mf">0.980392</span> <span class="mf">0.862745</span> <span class="mf">0.537255</span> <span class="mf">0.223529</span> <span class="mf">0.223529</span> <span class="mf">0.368627</span> <span class="mf">0.376471</span> <span class="mf">0.6</span> <span class="mf">0.6</span> <span class="mf">0.6</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.262745</span> <span class="mf">0.470588</span> <span class="mf">0.6</span> <span class="mf">0.996078</span> <span class="mf">0.996078</span> <span class="mf">0.996078</span> <span class="mf">0.996078</span> <span class="mf">0.847059</span> <span class="mf">0.356863</span> <span class="mf">0.156863</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.909804</span> <span class="mf">0.705882</span> <span class="mf">0.823529</span> <span class="mf">0.635294</span> <span class="mf">0.490196</span> <span class="mf">0.219608</span> <span class="mf">0.113725</span> <span class="mf">0.062745</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mf">0.152941</span> <span class="mf">0.152941</span> <span class="mf">0.156863</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span><span class="p">;</span>
</pre></div>
</div>
<p>其中每行数据代表一张图片，行内使用 <code class="docutils literal"><span class="pre">;</span></code> 分成两部分。第一部分是图片的标签，为0-9中的一个数字；第二部分是28*28的图片像素灰度值。 对应的 <code class="docutils literal"><span class="pre">train.list</span></code> 即为这个数据文件的名字：</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">mnist_train</span><span class="o">.</span><span class="n">txt</span>
</pre></div>
</div>
</div>
<div class="section" id="dataprovider">
<h3><a class="toc-backref" href="#id14">dataprovider的使用</a><a class="headerlink" href="#dataprovider" title="永久链接至标题"></a></h3>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">paddle.trainer.PyDataProvider2</span> <span class="k">import</span> <span class="o">*</span>


<span class="c1"># Define a py data provider</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="s1">&#39;pixel&#39;</span><span class="p">:</span> <span class="n">dense_vector</span><span class="p">(</span><span class="mi">28</span> <span class="o">*</span> <span class="mi">28</span><span class="p">),</span>
                 <span class="s1">&#39;label&#39;</span><span class="p">:</span> <span class="n">integer_value</span><span class="p">(</span><span class="mi">10</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="c1"># settings is not used currently.</span>
    <span class="n">f</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span>  <span class="c1"># open one of training file</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="c1"># read each line</span>
        <span class="n">label</span><span class="p">,</span> <span class="n">pixel</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;;&#39;</span><span class="p">)</span>

        <span class="c1"># get features and label</span>
        <span class="n">pixels_str</span> <span class="o">=</span> <span class="n">pixel</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39; &#39;</span><span class="p">)</span>

        <span class="n">pixels_float</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">for</span> <span class="n">each_pixel_str</span> <span class="ow">in</span> <span class="n">pixels_str</span><span class="p">:</span>
            <span class="n">pixels_float</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">each_pixel_str</span><span class="p">))</span>

        <span class="c1"># give data to paddle.</span>
        <span class="k">yield</span> <span class="p">{</span><span class="s2">&quot;pixel&quot;</span><span class="p">:</span> <span class="n">pixels_float</span><span class="p">,</span> <span class="s1">&#39;label&#39;</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">label</span><span class="p">)}</span>

    <span class="n">f</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>  <span class="c1"># close file</span>
</pre></div>
</div>
<ul>
<li><p class="first">首先，引入PaddlePaddle的PyDataProvider2包。</p>
</li>
<li><p class="first">其次，定义一个Python的 <a class="reference external" href="http://www.learnpython.org/en/Decorators">Decorator</a> <a class="reference internal" href="#provider">&#64;provider</a> 。用于将下一行的数据输入函数标记成一个PyDataProvider2，同时设置它的input_types属性。</p>
<ul>
<li><p class="first"><a class="reference internal" href="#input-types">input_types</a>：设置这个PyDataProvider2返回什么样的数据。本例根据网络配置中 <code class="docutils literal"><span class="pre">data_layer</span></code> 的名字，显式指定返回的是一个28*28维的稠密浮点数向量和一个[0-9]的10维整数标签。</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">img</span> <span class="o">=</span> <span class="n">data_layer</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;pixel&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">784</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="n">name</span><span class="o">=</span><span class="s1">&#39;label&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
</pre></div>
</div>
</li>
<li><p class="first">注意：如果用户不显示指定返回数据的对应关系，那么PaddlePaddle会根据layer的声明顺序，来确定对应关系。但这个关系可能不正确，所以推荐使用显式指定的方式来设置input_types。</p>
</li>
</ul>
</li>
<li><p class="first">最后，实现数据输入函数（如本例的 <code class="docutils literal"><span class="pre">process</span></code> 函数）。</p>
<ul class="simple">
<li>该函数的功能是：打开文本文件，读取每一行，将行中的数据转换成与input_types一致的格式，然后返回给PaddlePaddle进程。注意，<ul>
<li>返回的顺序需要和input_types中定义的顺序一致。</li>
<li>返回时，必须使用Python关键词 <code class="docutils literal"><span class="pre">yield</span></code> ，相关概念是 <code class="docutils literal"><span class="pre">generator</span></code> 。</li>
<li>一次yield调用，返回一条完整的样本。如果想为一个数据文件返回多条样本，只需要在函数中调用多次yield即可（本例中使用for循环进行多次调用）。</li>
</ul>
</li>
<li>该函数具有两个参数：<ul>
<li>settings：在本例中没有使用，具体可以参考 <a class="reference internal" href="#init-hook">init_hook</a> 中的说明。</li>
<li>filename：为 <code class="docutils literal"><span class="pre">train.list</span></code> 或 <code class="docutils literal"><span class="pre">test.list</span></code> 中的一行，即若干数据文件路径的某一个。</li>
</ul>
</li>
</ul>
</li>
</ul>
</div>
<div class="section" id="id2">
<h3><a class="toc-backref" href="#id15">网络配置中的调用</a><a class="headerlink" href="#id2" title="永久链接至标题"></a></h3>
<p>在网络配置里，只需要一行代码就可以调用这个PyDataProvider2，如，</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">paddle.trainer_config_helpers</span> <span class="k">import</span> <span class="o">*</span>

<span class="n">define_py_data_sources2</span><span class="p">(</span>
    <span class="n">train_list</span><span class="o">=</span><span class="s1">&#39;train.list&#39;</span><span class="p">,</span>
    <span class="n">test_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
    <span class="n">module</span><span class="o">=</span><span class="s1">&#39;mnist_provider&#39;</span><span class="p">,</span>
    <span class="n">obj</span><span class="o">=</span><span class="s1">&#39;process&#39;</span><span class="p">)</span>
</pre></div>
</div>
<p>训练数据是 <code class="docutils literal"><span class="pre">train.list</span></code> ，没有测试数据，调用的PyDataProvider2是 <code class="docutils literal"><span class="pre">mnist_provider</span></code> 模块中的 <code class="docutils literal"><span class="pre">process</span></code> 函数。</p>
</div>
<div class="section" id="id3">
<h3><a class="toc-backref" href="#id16">小结</a><a class="headerlink" href="#id3" title="永久链接至标题"></a></h3>
<p>至此，简单的PyDataProvider2样例就说明完毕了。对用户来说，仅需要知道如何从 <strong>一个文件</strong> 中读取 <strong>一条样本</strong> ，就可以将数据传送给PaddlePaddle了。而PaddlePaddle则会帮用户做以下工作：</p>
<ul class="simple">
<li>将数据组合成Batch进行训练</li>
<li>对训练数据进行Shuffle</li>
<li>多线程的数据读取</li>
<li>缓存训练数据到内存(可选)</li>
<li>CPU-&gt;GPU双缓存</li>
</ul>
<p>是不是很简单呢？</p>
</div>
</div>
<div class="section" id="id4">
<h2><a class="toc-backref" href="#id17">时序模型的使用场景</a><a class="headerlink" href="#id4" title="永久链接至标题"></a></h2>
<div class="section" id="id5">
<h3><a class="toc-backref" href="#id18">样例数据</a><a class="headerlink" href="#id5" title="永久链接至标题"></a></h3>
<p>时序模型是指数据的某一维度是一个序列形式，即包含时间步信息。所谓时间步信息，不一定和时间有关系，只是说明数据的顺序是重要的。例如，文本信息就是一个序列数据。</p>
<p>本例采用英文情感分类的数据，即将一段英文文本数据，分类成正面情绪和负面情绪两类(用0和1表示)。样例数据 <code class="docutils literal"><span class="pre">sentimental_train.txt</span></code> 如下：</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="mi">0</span>       <span class="n">I</span> <span class="n">saw</span> <span class="n">this</span> <span class="n">movie</span> <span class="n">at</span> <span class="n">the</span> <span class="n">AFI</span> <span class="n">Dallas</span> <span class="n">festival</span> <span class="o">.</span> <span class="n">It</span> <span class="nb">all</span> <span class="n">takes</span> <span class="n">place</span> <span class="n">at</span> <span class="n">a</span> <span class="n">lake</span> <span class="n">house</span> <span class="ow">and</span> <span class="n">it</span> <span class="n">looks</span> <span class="n">wonderful</span> <span class="o">.</span>
<span class="mi">1</span>       <span class="n">This</span> <span class="n">documentary</span> <span class="n">makes</span> <span class="n">you</span> <span class="n">travel</span> <span class="nb">all</span> <span class="n">around</span> <span class="n">the</span> <span class="n">globe</span> <span class="o">.</span> <span class="n">It</span> <span class="n">contains</span> <span class="n">rare</span> <span class="ow">and</span> <span class="n">stunning</span> <span class="n">sequels</span> <span class="kn">from</span> <span class="nn">the</span> <span class="n">wilderness</span> <span class="o">.</span>
<span class="o">...</span>
</pre></div>
</div>
</div>
<div class="section" id="id6">
<h3><a class="toc-backref" href="#id19">dataprovider的使用</a><a class="headerlink" href="#id6" title="永久链接至标题"></a></h3>
<p>相对MNIST而言，这个dataprovider较复杂，主要原因是增加了初始化机制 <a class="reference internal" href="#init-hook">init_hook</a>。本例的 <code class="docutils literal"><span class="pre">on_init</span></code> 函数就是根据该机制配置的，它会在dataprovider创建的时候执行。</p>
<ul class="simple">
<li>其中 <code class="docutils literal"><span class="pre">input_types</span></code> 和在 <a class="reference internal" href="#provider">&#64;provider</a> 中配置的效果一致。本例中的输入特征是词ID的序列，因此使用 <code class="docutils literal"><span class="pre">integer_value_sequence</span></code> 类型来设置。</li>
<li>将 <code class="docutils literal"><span class="pre">dictionary</span></code> 存入settings对象，在 <code class="docutils literal"><span class="pre">process</span></code> 函数中使用。 dictionary是从网络配置中传入的dict对象，即一个将单词字符串映射到单词ID的字典。</li>
</ul>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">paddle.trainer.PyDataProvider2</span> <span class="k">import</span> <span class="o">*</span>


<span class="k">def</span> <span class="nf">on_init</span><span class="p">(</span><span class="n">settings</span><span class="p">,</span> <span class="n">dictionary</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="c1"># on_init will invoke when data provider is initialized. The dictionary</span>
    <span class="c1"># is passed from trainer_config, and is a dict object with type</span>
    <span class="c1"># (word string =&gt; word id).</span>

    <span class="c1"># set input types in runtime. It will do the same thing as</span>
    <span class="c1"># @provider(input_types) will do, but it is set dynamically during runtime.</span>
    <span class="n">settings</span><span class="o">.</span><span class="n">input_types</span> <span class="o">=</span> <span class="p">{</span>
        <span class="c1"># The text is a sequence of integer values, and each value is a word id.</span>
        <span class="c1"># The whole sequence is the sentences that we want to predict its</span>
        <span class="c1"># sentimental.</span>
        <span class="s1">&#39;data&#39;</span><span class="p">:</span> <span class="n">integer_value_sequence</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dictionary</span><span class="p">)),</span>  <span class="c1"># text input</span>
        <span class="s1">&#39;label&#39;</span><span class="p">:</span> <span class="n">integer_value</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># label positive/negative</span>
    <span class="p">}</span>

    <span class="c1"># save dictionary as settings.dictionary. </span>
    <span class="c1"># It will be used in process method.</span>
    <span class="n">settings</span><span class="o">.</span><span class="n">dictionary</span> <span class="o">=</span> <span class="n">dictionary</span>


<span class="nd">@provider</span><span class="p">(</span><span class="n">init_hook</span><span class="o">=</span><span class="n">on_init</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">f</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;r&#39;</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="c1"># read each line of file</span>
        <span class="n">label</span><span class="p">,</span> <span class="n">sentence</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">)</span>  <span class="c1"># get label and sentence</span>
        <span class="n">words</span> <span class="o">=</span> <span class="n">sentence</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39; &#39;</span><span class="p">)</span>  <span class="c1"># get words</span>

        <span class="c1"># convert word string to word id</span>
        <span class="c1"># the word not in dictionary will be ignored.</span>
        <span class="n">word_ids</span> <span class="o">=</span> <span class="p">[]</span>

        <span class="k">for</span> <span class="n">each_word</span> <span class="ow">in</span> <span class="n">words</span><span class="p">:</span>
            <span class="k">if</span> <span class="n">each_word</span> <span class="ow">in</span> <span class="n">settings</span><span class="o">.</span><span class="n">dictionary</span><span class="p">:</span>
                <span class="n">word_ids</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">settings</span><span class="o">.</span><span class="n">dictionary</span><span class="p">[</span><span class="n">each_word</span><span class="p">])</span>

        <span class="c1"># give data to paddle.</span>
        <span class="k">yield</span> <span class="n">word_ids</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">label</span><span class="p">)</span>

    <span class="n">f</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
</pre></div>
</div>
</div>
<div class="section" id="id7">
<h3><a class="toc-backref" href="#id20">网络配置中的调用</a><a class="headerlink" href="#id7" title="永久链接至标题"></a></h3>
<p>调用这个PyDataProvider2的方法，基本上和MNIST样例一致，除了</p>
<ul class="simple">
<li>在配置中需要读取外部字典。</li>
<li>在声明DataProvider的时候传入dictionary作为参数。</li>
</ul>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">paddle.trainer_config_helpers</span> <span class="k">import</span> <span class="o">*</span>

<span class="n">dictionary</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
<span class="o">...</span>  <span class="c1">#  read dictionary from outside</span>

<span class="n">define_py_data_sources2</span><span class="p">(</span>
    <span class="n">train_list</span><span class="o">=</span><span class="s1">&#39;train.list&#39;</span><span class="p">,</span>
    <span class="n">test_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
    <span class="n">module</span><span class="o">=</span><span class="s1">&#39;sentimental_provider&#39;</span><span class="p">,</span>
    <span class="n">obj</span><span class="o">=</span><span class="s1">&#39;process&#39;</span><span class="p">,</span>
    <span class="c1"># above codes same as mnist sample.</span>
<span class="hll">    <span class="n">args</span><span class="o">=</span><span class="p">{</span>  <span class="c1"># pass to provider.</span>
</span><span class="hll">        <span class="s1">&#39;dictionary&#39;</span><span class="p">:</span> <span class="n">dictionary</span>
</span><span class="hll">    <span class="p">})</span>
</span></pre></div>
</div>
</div>
</div>
<div class="section" id="reference">
<h2><a class="toc-backref" href="#id21">参考(Reference)</a><a class="headerlink" href="#reference" title="永久链接至标题"></a></h2>
<div class="section" id="provider">
<h3><a class="toc-backref" href="#id22">&#64;provider</a><a class="headerlink" href="#provider" title="永久链接至标题"></a></h3>
<p><code class="docutils literal"><span class="pre">&#64;provider</span></code> 是一个Python的 <a class="reference external" href="http://www.learnpython.org/en/Decorators">Decorator</a> ，可以将某一个函数标记成一个PyDataProvider2。如果不了解 <a class="reference external" href="http://www.learnpython.org/en/Decorators">Decorator</a> 是什么也没关系，只需知道这是一个标记属性的方法就可以了。它包含的属性参数如下:</p>
<ul class="simple">
<li>input_types：数据输入格式。具体的格式说明，请参考 <a class="reference internal" href="#input-types">input_types</a> 。</li>
<li>should_shuffle：是不是要对数据做Shuffle。训练时默认shuffle，测试时默认不shuffle。</li>
<li>min_pool_size：设置内存中最小暂存的数据条数，也是PaddlePaddle所能够保证的shuffle粒度。如果为-1，则会预先读取全部数据到内存中。</li>
<li>pool_size： 设置内存中暂存的数据条数。如果为-1（默认），则不在乎内存暂存多少条数据。如果设置，则推荐大于训练时batch size的值，并且在内存足够的情况下越大越好。</li>
<li>can_over_batch_size：是否允许暂存略微多余pool_size的数据。由于这样做可以避免很多死锁问题，一般推荐设置成True。</li>
<li>calc_batch_size：可以传入一个函数，用于自定义每条数据的batch size（默认为1）。</li>
<li>cache： 数据缓存的策略，具体请参考 <a class="reference internal" href="#cache">cache</a> 。</li>
<li>init_hook：初始化时调用的函数，具体请参考 <a class="reference internal" href="#init-hook">init_hook</a> 。</li>
<li>check：如果为true，会根据input_types检查数据的合法性。</li>
<li>check_fail_continue：如果为true，那么当check出数据不合法时，会扔到这条数据，继续训练或预测。（对check=false的情况，没有作用）</li>
</ul>
</div>
<div class="section" id="input-types">
<h3><a class="toc-backref" href="#id23">input_types</a><a class="headerlink" href="#input-types" title="永久链接至标题"></a></h3>
<p>PaddlePaddle的数据包括四种主要类型，和三种序列模式。</p>
<p>四种数据类型：</p>
<ul class="simple">
<li>dense_vector：稠密的浮点数向量。</li>
<li>sparse_binary_vector：稀疏的01向量，即大部分值为0，但有值的地方必须为1。</li>
<li>sparse_float_vector：稀疏的向量，即大部分值为0，但有值的部分可以是任何浮点数。</li>
<li>integer：整数标签。</li>
</ul>
<p>三种序列模式：</p>
<ul class="simple">
<li>SequenceType.NO_SEQUENCE：不是一条序列</li>
<li>SequenceType.SEQUENCE：是一条时间序列</li>
<li>SequenceType.SUB_SEQUENCE： 是一条时间序列，且序列的每一个元素还是一个时间序列。</li>
</ul>
<p>不同的数据类型和序列模式返回的格式不同，列表如下：</p>
<table border="1" class="docutils">
<colgroup>
<col width="17%" />
<col width="17%" />
<col width="28%" />
<col width="38%" />
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">&#160;</th>
<th class="head">NO_SEQUENCE</th>
<th class="head">SEQUENCE</th>
<th class="head">SUB_SEQUENCE</th>
</tr>
</thead>
<tbody valign="top">
<tr class="row-even"><td>dense_vector</td>
<td>[f, f, ...]</td>
<td>[[f, ...], [f, ...], ...]</td>
<td>[[[f, ...], ...], [[f, ...], ...],...]</td>
</tr>
<tr class="row-odd"><td>sparse_binary_vector</td>
<td>[i, i, ...]</td>
<td>[[i, ...], [i, ...], ...]</td>
<td>[[[i, ...], ...], [[i, ...], ...],...]</td>
</tr>
<tr class="row-even"><td>sparse_float_vector</td>
<td>[(i,f), (i,f), ...]</td>
<td>[[(i,f), ...], [(i,f), ...], ...]</td>
<td>[[[(i,f), ...], ...], [[(i,f), ...], ...],...]</td>
</tr>
<tr class="row-odd"><td>integer_value</td>
<td>i</td>
<td>[i, i, ...]</td>
<td>[[i, ...], [i, ...], ...]</td>
</tr>
</tbody>
</table>
<p>其中，f代表一个浮点数，i代表一个整数。</p>
<p>注意：对sparse_binary_vector和sparse_float_vector，PaddlePaddle存的是有值位置的索引。例如，</p>
<ul class="simple">
<li>对一个5维非序列的稀疏01向量 <code class="docutils literal"><span class="pre">[0,</span> <span class="pre">1,</span> <span class="pre">1,</span> <span class="pre">0,</span> <span class="pre">0]</span></code> ，类型是sparse_binary_vector，返回的是 <code class="docutils literal"><span class="pre">[1,</span> <span class="pre">2]</span></code> 。</li>
<li>对一个5维非序列的稀疏浮点向量 <code class="docutils literal"><span class="pre">[0,</span> <span class="pre">0.5,</span> <span class="pre">0.7,</span> <span class="pre">0,</span> <span class="pre">0]</span></code> ，类型是sparse_float_vector，返回的是 <code class="docutils literal"><span class="pre">[(1,</span> <span class="pre">0.5),</span> <span class="pre">(2,</span> <span class="pre">0.7)]</span></code> 。</li>
</ul>
</div>
<div class="section" id="init-hook">
<h3><a class="toc-backref" href="#id24">init_hook</a><a class="headerlink" href="#init-hook" title="永久链接至标题"></a></h3>
<p>init_hook可以传入一个函数。该函数在初始化的时候会被调用，其参数如下:</p>
<ul class="simple">
<li><dl class="first docutils">
<dt>第一个参数是settings对象，它和数据传入函数的第一个参数（如本例中 <code class="docutils literal"><span class="pre">process</span></code> 函数的 <code class="docutils literal"><span class="pre">settings</span></code> 参数）必须一致。该对象具有以下两个属性：</dt>
<dd><ul class="first last">
<li>settings.input_types：数据输入格式，具体请参考 <a class="reference internal" href="#input-types">input_types</a> 。</li>
<li>settings.logger：一个logging对象。</li>
</ul>
</dd>
</dl>
</li>
<li><dl class="first docutils">
<dt>其他参数使用 <code class="docutils literal"><span class="pre">kwargs</span></code> （key word arguments）传入，包括以下两种：</dt>
<dd><ul class="first last">
<li>PaddlePaddle定义的参数: 1）is_train：bool型参数，表示用于训练或预测；2）file_list：所有文件列表。</li>
<li>用户定义的参数：使用args在网络配置中设置。</li>
</ul>
</dd>
</dl>
</li>
</ul>
<p>注意：PaddlePaddle保留添加参数的权力，因此init_hook尽量使用 <code class="docutils literal"><span class="pre">**kwargs</span></code> 来接受不使用的函数以保证兼容性。</p>
</div>
<div class="section" id="cache">
<h3><a class="toc-backref" href="#id25">cache</a><a class="headerlink" href="#cache" title="永久链接至标题"></a></h3>
<p>PyDataProvider2提供了两种简单的Cache策略：</p>
<ul class="simple">
<li>CacheType.NO_CACHE：不缓存任何数据，每次都会从python端读取数据</li>
<li>CacheType.CACHE_PASS_IN_MEM：第一个pass会从python端读取数据，剩下的pass会直接从内存里
读取数据。</li>
</ul>
</div>
</div>
<div class="section" id="id8">
<h2><a class="toc-backref" href="#id26">注意事项</a><a class="headerlink" href="#id8" title="永久链接至标题"></a></h2>
<div class="section" id="id9">
<h3><a class="toc-backref" href="#id27">可能的内存泄露问题</a><a class="headerlink" href="#id9" title="永久链接至标题"></a></h3>
<p>PaddlePaddle将train.list中的每一行都传递给process函数，从而生成多个generator。当训练数据非常多时，就会生成非常多的generator。</p>
<p>虽然每个generator在没有调用的时候，是几乎不占内存的；但当调用过一次后，generator便会存下当前的上下文(Context)，而这个Context可能会非常大。并且，generator至少需要调用两次才会知道是否停止。所以，即使process函数里面只有一个yield，也需要两次随机选择到相同generator的时候，才会释放该段内存。</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">func</span><span class="p">():</span>
    <span class="k">yield</span> <span class="mi">0</span>

<span class="n">f</span> <span class="o">=</span> <span class="n">func</span><span class="p">()</span>  <span class="c1"># 创建generator</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>  <span class="c1"># 调用一次，返回0</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>  <span class="c1"># 调用第二次的时候，才会Stop Iteration</span>
</pre></div>
</div>
<p>由于顺序调用这些generator不会出现上述问题，因此有两种解决方案：</p>
<ol class="arabic simple">
<li><strong>最佳推荐</strong>：将样本的地址放入另一个文本文件，train.list写入那个文本文件的地址。即不要将每一个样本都放入train.list。</li>
<li>在generator的上下文中尽量留下非常少的变量引用，例如</li>
</ol>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">real_process</span><span class="p">(</span><span class="n">fn</span><span class="p">):</span>
    <span class="c1"># ... read from fn</span>
    <span class="k">return</span> <span class="n">result</span>   <span class="c1"># 当函数返回的时候，python可以解除掉内部变量的引用。</span>

<span class="k">def</span> <span class="nf">process</span><span class="p">(</span><span class="n">fn</span><span class="p">):</span>
    <span class="k">yield</span> <span class="n">real_process</span><span class="p">(</span><span class="n">fn</span><span class="p">)</span>
</pre></div>
</div>
<p>注意：这个问题是PyDataProvider读数据时候的逻辑问题，很难整体修正。</p>
</div>
<div class="section" id="id10">
<h3><a class="toc-backref" href="#id28">内存不够用的情况</a><a class="headerlink" href="#id10" title="永久链接至标题"></a></h3>
<p>PyDataProvider2会尽可能多的使用内存。因此，对于内存较小的机器，推荐使用 <code class="docutils literal"><span class="pre">pool_size</span></code> 变量来设置内存中暂存的数据条。具体请参考 <a class="reference internal" href="#provider">&#64;provider</a> 中的说明。</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',
T
Deploy to GitHub Pages: 18e5edf6a5b09868dbb083c312fe852cb8c29e55  
Travis CI 已提交
601 602 
            HAS_SOURCE:  true,
            SOURCELINK_SUFFIX: ".txt",
T
Deploy to GitHub Pages: 2c54905c3e3154327a8eb57ad96d0770dcc5f435  
Travis CI 已提交
603 604 605 606 607 608 
        };
    </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>
T
Deploy to GitHub Pages: 93007fea7f57cbb3d1297a7628d5604374393096  
Travis CI 已提交
609 
      <script type="text/javascript" src="https://cdn.bootcss.com/mathjax/2.7.0/MathJax.js"></script>
T
Deploy to GitHub Pages: 2c54905c3e3154327a8eb57ad96d0770dcc5f435  
Travis CI 已提交
610 611 612 613 614 615 616 617 618 619 620 621 622 
       
  

  
  
    <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>
T
Deploy to GitHub Pages: 299e959a190017689d18af70d300f681745f4c67  
Travis CI 已提交
623 
</html>