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 --><div class="section" id="initializer">
 <h1>初始化器(Initializer)<a class="headerlink" href="#initializer" title="Permalink to this headline">¶</a></h1>
 <div class="section" id="python-api">
 <h2>Python API<a class="headerlink" href="#python-api" title="Permalink to this headline">¶</a></h2>
 <p>普遍使用的参数初始化方法（tensor对象）。</p>
 <p>示例用法：</p>
 <div class="highlight-python"><div class="highlight"><pre><span></span><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>
 <hr class="docutils" />
 <div class="section" id="singa-initializer-uniform-t-fan-in-0-fan-out-0">
 <h3>singa.initializer.uniform(t, fan_in=0, fan_out=0)<a class="headerlink" href="#singa-initializer-uniform-t-fan-in-0-fan-out-0" title="Permalink to this headline">¶</a></h3>
 <p>按照指定均匀分布对输入tensor初始化。</p>
 <p><strong>参数：</strong></p>
 <ul class="simple">
 <li><strong>fan_in (int)</strong> – 对于卷积层权重tensor，fan_in = nb_channel * kh * kw;对于全连接层，fan_in = input_feature_length</li>
 <li><strong>fan_out (int)</strong> – 对于卷积层权重tensor，fan_out = nb_filter * kh * kw;对于全连接层，fan_out = output_feature_length</li>
 </ul>
 <p><strong>参考文献</strong> [Bengio and Glorot 2010]： Understanding the difficulty of training deep feedforward neuralnetworks.</p>
 </div>
 <hr class="docutils" />
 <div class="section" id="singa-initializer-gaussian-t-fan-in-0-fan-out-0">
 <h3>singa.initializer.gaussian(t, fan_in=0, fan_out=0)<a class="headerlink" href="#singa-initializer-gaussian-t-fan-in-0-fan-out-0" title="Permalink to this headline">¶</a></h3>
 <p>按照指定高斯分布对输入tensor初始化。</p>
 <p><strong>参数：</strong></p>
 <ul class="simple">
 <li><strong>fan_in (int)</strong> – 对于卷积层权重tensor，fan_in = nb_channel * kh * kw;对于全连接层，fan_in = input_feature_length</li>
 <li><strong>fan_out (int)</strong> – 对于卷积层权重tensor，fan_out = nb_filter * kh * kw;对于全连接层，fan_out = output_feature_length</li>
 </ul>
 <p><strong>参考文献</strong> Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification</p>
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	<h1>初始化器(Initializer)<a class="headerlink" href="#initializer" title="Permalink to this headline">¶</a></h1>
	<div class="section" id="python-api">
	<h2>Python API<a class="headerlink" href="#python-api" title="Permalink to this headline">¶</a></h2>
	<p>普遍使用的参数初始化方法（tensor对象）。</p>
	<p>示例用法：</p>
	<div class="highlight-python"><div class="highlight"><pre><span></span><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>
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	<hr class="docutils" />
	<div class="section" id="singa-initializer-uniform-t-fan-in-0-fan-out-0">
	<h3>singa.initializer.uniform(t, fan_in=0, fan_out=0)<a class="headerlink" href="#singa-initializer-uniform-t-fan-in-0-fan-out-0" title="Permalink to this headline">¶</a></h3>
	<p>按照指定均匀分布对输入tensor初始化。</p>
	<p><strong>参数：</strong></p>
	<ul class="simple">
	<li><strong>fan_in (int)</strong> – 对于卷积层权重tensor，fan_in = nb_channel * kh * kw;对于全连接层，fan_in = input_feature_length</li>
	<li><strong>fan_out (int)</strong> – 对于卷积层权重tensor，fan_out = nb_filter * kh * kw;对于全连接层，fan_out = output_feature_length</li>
	</ul>
	<p><strong>参考文献</strong> [Bengio and Glorot 2010]： Understanding the difficulty of training deep feedforward neuralnetworks.</p>
	</div>
	<hr class="docutils" />
	<div class="section" id="singa-initializer-gaussian-t-fan-in-0-fan-out-0">
	<h3>singa.initializer.gaussian(t, fan_in=0, fan_out=0)<a class="headerlink" href="#singa-initializer-gaussian-t-fan-in-0-fan-out-0" title="Permalink to this headline">¶</a></h3>
	<p>按照指定高斯分布对输入tensor初始化。</p>
	<p><strong>参数：</strong></p>
	<ul class="simple">
	<li><strong>fan_in (int)</strong> – 对于卷积层权重tensor，fan_in = nb_channel * kh * kw;对于全连接层，fan_in = input_feature_length</li>
	<li><strong>fan_out (int)</strong> – 对于卷积层权重tensor，fan_out = nb_filter * kh * kw;对于全连接层，fan_out = output_feature_length</li>
	</ul>
	<p><strong>参考文献</strong> Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification</p>
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