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| --><div class="section" id="char-rnn"> |
| <h1>在文本上训练Char-RNN<a class="headerlink" href="#char-rnn" title="Permalink to this headline">¶</a></h1> |
| <p>递归神经网络（RNN）被广泛用于建模顺序数据，例如自然语言句子。 本示例介绍如何使用SINGA的RNN层实现RNN应用程序（或模型）。 我们将使用<a class="reference external" href="https://github.com/karpathy/char-rnn">char-rnn</a>模型作为示例，它将训练语句或源代码，并将每个字符作为输入单位。 特别是，我们将使用GRU在Linux内核源代码上训练一个RNN。 经过训练，我们希望从模型中生成有意义的代码。</p> |
| <div class="section" id="id1"> |
| <h2>操作说明<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2> |
| <ul> |
| <li><p class="first">编译并安装SINGA。目前，RNN的实现是基于CuDNN（>=5.05）。.</p> |
| </li> |
| <li><p class="first">准备数据集。下载<a class="reference external" href="http://cs.stanford.edu/people/karpathy/char-rnn/">内核源代码</a>。其他文本数据也可被使用。</p> |
| </li> |
| <li><p class="first">开始训练，</p> |
| <div class="highlight-default"><div class="highlight"><pre><span></span> <span class="n">python</span> <span class="n">train</span><span class="o">.</span><span class="n">py</span> <span class="n">linux_input</span><span class="o">.</span><span class="n">txt</span> |
| </pre></div> |
| </div> |
| <p>一些超参数可以在命令行参数中设置，</p> |
| <div class="highlight-default"><div class="highlight"><pre><span></span> <span class="n">python</span> <span class="n">train</span><span class="o">.</span><span class="n">py</span> <span class="o">-</span><span class="n">h</span> |
| </pre></div> |
| </div> |
| </li> |
| <li><p class="first">通过提供要采样的字符数和种子字符串来从模型中采样字符。</p> |
| <div class="highlight-default"><div class="highlight"><pre><span></span> <span class="n">python</span> <span class="n">sample</span><span class="o">.</span><span class="n">py</span> <span class="s1">'model.bin'</span> <span class="mi">100</span> <span class="o">--</span><span class="n">seed</span> <span class="s1">'#include <std'</span> |
| </pre></div> |
| </div> |
| <p>请用其中一个checkpoint路径替换‘model.bin‘的路径。</p> |
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