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 <h1>Train Char-RNN over plain text<a class="headerlink" href="#train-char-rnn-over-plain-text" title="Permalink to this headline">¶</a></h1>
 <p>Recurrent neural networks (RNN) are widely used for modelling sequential data,
 e.g., natural language sentences. This example describes how to implement a RNN
 application (or model) using SINGA&#8216;s RNN layers.
 We will use the <a class="reference external" href="https://github.com/karpathy/char-rnn">char-rnn</a> model as an
 example, which trains over sentences or
 source code, with each character as an input unit. Particularly, we will train
 a RNN using GRU over Linux kernel source code. After training, we expect to
 generate meaningful code from the model.</p>
 <div class="section" id="instructions">
 <h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2>
 <ul>
 <li><p class="first">Compile and install SINGA. Currently the RNN implementation depends on Cudnn with version &gt;= 5.05.</p>
 </li>
 <li><p class="first">Prepare the dataset. Download the <a class="reference external" href="http://cs.stanford.edu/people/karpathy/char-rnn/">kernel source code</a>.
 Other plain text files can also be used.</p>
 </li>
 <li><p class="first">Start the training,</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>Some hyper-parameters could be set through command line,</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">Sample characters from the model by providing the number of characters to sample and the seed string.</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">&#39;model.bin&#39;</span> <span class="mi">100</span> <span class="o">--</span><span class="n">seed</span> <span class="s1">&#39;#include &lt;std&#39;</span>
 </pre></div>
 </div>
 <p>Please replace &#8216;model.bin&#8216; with the path to one of the checkpoint paths.</p>
 </li>
 </ul>
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	under the License.
	--><div class="section" id="train-char-rnn-over-plain-text">
	<h1>Train Char-RNN over plain text<a class="headerlink" href="#train-char-rnn-over-plain-text" title="Permalink to this headline">¶</a></h1>
	<p>Recurrent neural networks (RNN) are widely used for modelling sequential data,
	e.g., natural language sentences. This example describes how to implement a RNN
	application (or model) using SINGA‘s RNN layers.
	We will use the <a class="reference external" href="https://github.com/karpathy/char-rnn">char-rnn</a> model as an
	example, which trains over sentences or
	source code, with each character as an input unit. Particularly, we will train
	a RNN using GRU over Linux kernel source code. After training, we expect to
	generate meaningful code from the model.</p>
	<div class="section" id="instructions">
	<h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2>
	<ul>
	<li><p class="first">Compile and install SINGA. Currently the RNN implementation depends on Cudnn with version >= 5.05.</p>
	</li>
	<li><p class="first">Prepare the dataset. Download the <a class="reference external" href="http://cs.stanford.edu/people/karpathy/char-rnn/">kernel source code</a>.
	Other plain text files can also be used.</p>
	</li>
	<li><p class="first">Start the training,</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>Some hyper-parameters could be set through command line,</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">Sample characters from the model by providing the number of characters to sample and the seed string.</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>Please replace ‘model.bin‘ with the path to one of the checkpoint paths.</p>
	</li>
	</ul>
	</div>
	</div>


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