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   <div class="section" id="initializer">
 <h1>Initializer<a class="headerlink" href="#initializer" title="Permalink to this headline">¶</a></h1>
 <div class="section" id="module-singa.initializer">
 <span id="python-api"></span><h2>Python API<a class="headerlink" href="#module-singa.initializer" title="Permalink to this headline">¶</a></h2>
 <p>Popular initialization methods for parameter values (Tensor objects).</p>
 <p>Example usages:</p>
 <div class="highlight-python"><div class="highlight"><pre><span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">tensor</span>
 <span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">initializer</span>

 <span class="n">x</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">Tensor</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
 <span class="n">initializer</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span> <span class="c1"># use both fan_in and fan_out</span>
 <span class="n">initializer</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>  <span class="c1"># use only fan_in</span>
 </pre></div>
 </div>
 <dl class="function">
 <dt id="singa.initializer.uniform">
 <code class="descclassname">singa.initializer.</code><code class="descname">uniform</code><span class="sig-paren">(</span><em>t</em>, <em>fan_in=0</em>, <em>fan_out=0</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.initializer.uniform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Initialize the values of the input tensor following a uniform
 distribution with specific bounds.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
 <li><strong>fan_in</strong> (<em>int</em>) &#8211; for the weight Tensor of a convolution layer,
 fan_in = nb_channel * kh * kw; for dense layer,
 fan_in = input_feature_length</li>
 <li><strong>fan_out</strong> (<em>int</em>) &#8211; for the convolution layer weight Tensor,
 fan_out = nb_filter * kh * kw; for the weight Tensor of a dense
 layer, fan_out = output_feature_length</li>
 </ul>
 </td>
 </tr>
 </tbody>
 </table>
 <p>Ref: [Bengio and Glorot 2010]: Understanding the difficulty of
 training deep feedforward neuralnetworks.</p>
 </dd></dl>

 <dl class="function">
 <dt id="singa.initializer.gaussian">
 <code class="descclassname">singa.initializer.</code><code class="descname">gaussian</code><span class="sig-paren">(</span><em>t</em>, <em>fan_in=0</em>, <em>fan_out=0</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.initializer.gaussian" title="Permalink to this definition">¶</a></dt>
 <dd><p>Initialize the values of the input tensor following a Gaussian
 distribution with specific std.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
 <li><strong>fan_in</strong> (<em>int</em>) &#8211; for the weight Tensor of a convolution layer,
 fan_in = nb_channel * kh * kw; for dense layer,
 fan_in = input_feature_length</li>
 <li><strong>fan_out</strong> (<em>int</em>) &#8211; for the convolution layer weight Tensor,
 fan_out = nb_filter * kh * kw; for the weight Tensor of a dense
 layer, fan_out = output_feature_length</li>
 </ul>
 </td>
 </tr>
 </tbody>
 </table>
 <p>Ref Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun: Delving Deep into
 Rectifiers: Surpassing Human-Level Performance on ImageNet Classification</p>
 </dd></dl>

 </div>
 <div class="section" id="cpp-api">
 <h2>CPP API<a class="headerlink" href="#cpp-api" title="Permalink to this headline">¶</a></h2>
 </div>
 </div>


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	<div class="section" id="initializer">
	<h1>Initializer<a class="headerlink" href="#initializer" title="Permalink to this headline">¶</a></h1>
	<div class="section" id="module-singa.initializer">
	<span id="python-api"></span><h2>Python API<a class="headerlink" href="#module-singa.initializer" title="Permalink to this headline">¶</a></h2>
	<p>Popular initialization methods for parameter values (Tensor objects).</p>
	<p>Example usages:</p>
	<div class="highlight-python"><div class="highlight"><pre><span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">tensor</span>
	<span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">initializer</span>

	<span class="n">x</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">Tensor</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
	<span class="n">initializer</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span> <span class="c1"># use both fan_in and fan_out</span>
	<span class="n">initializer</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="c1"># use only fan_in</span>
	</pre></div>
	</div>
	<dl class="function">
	<dt id="singa.initializer.uniform">
	<code class="descclassname">singa.initializer.</code><code class="descname">uniform</code><span class="sig-paren">(</span><em>t</em>, <em>fan_in=0</em>, <em>fan_out=0</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.initializer.uniform" title="Permalink to this definition">¶</a></dt>
	<dd><p>Initialize the values of the input tensor following a uniform
	distribution with specific bounds.</p>
	<table class="docutils field-list" frame="void" rules="none">
	<col class="field-name" />
	<col class="field-body" />
	<tbody valign="top">
	<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
	<li><strong>fan_in</strong> (<em>int</em>) – for the weight Tensor of a convolution layer,
	fan_in = nb_channel * kh * kw; for dense layer,
	fan_in = input_feature_length</li>
	<li><strong>fan_out</strong> (<em>int</em>) – for the convolution layer weight Tensor,
	fan_out = nb_filter * kh * kw; for the weight Tensor of a dense
	layer, fan_out = output_feature_length</li>
	</ul>
	</td>
	</tr>
	</tbody>
	</table>
	<p>Ref: [Bengio and Glorot 2010]: Understanding the difficulty of
	training deep feedforward neuralnetworks.</p>
	</dd></dl>

	<dl class="function">
	<dt id="singa.initializer.gaussian">
	<code class="descclassname">singa.initializer.</code><code class="descname">gaussian</code><span class="sig-paren">(</span><em>t</em>, <em>fan_in=0</em>, <em>fan_out=0</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.initializer.gaussian" title="Permalink to this definition">¶</a></dt>
	<dd><p>Initialize the values of the input tensor following a Gaussian
	distribution with specific std.</p>
	<table class="docutils field-list" frame="void" rules="none">
	<col class="field-name" />
	<col class="field-body" />
	<tbody valign="top">
	<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
	<li><strong>fan_in</strong> (<em>int</em>) – for the weight Tensor of a convolution layer,
	fan_in = nb_channel * kh * kw; for dense layer,
	fan_in = input_feature_length</li>
	<li><strong>fan_out</strong> (<em>int</em>) – for the convolution layer weight Tensor,
	fan_out = nb_filter * kh * kw; for the weight Tensor of a dense
	layer, fan_out = output_feature_length</li>
	</ul>
	</td>
	</tr>
	</tbody>
	</table>
	<p>Ref Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun: Delving Deep into
	Rectifiers: Surpassing Human-Level Performance on ImageNet Classification</p>
	</dd></dl>

	</div>
	<div class="section" id="cpp-api">
	<h2>CPP API<a class="headerlink" href="#cpp-api" title="Permalink to this headline">¶</a></h2>
	</div>
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