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  <div class="section" id="initializer">
<h1>Initializer<a class="headerlink" href="#initializer" title="永久链接至标题">¶</a></h1>
<div class="section" id="id1">
<h2>Initializer<a class="headerlink" href="#id1" title="永久链接至标题">¶</a></h2>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.fluid.initializer.</code><code class="descname">Initializer</code></dt>
<dd><p>Base class for variable initializers</p>
<p>Defines the common interface of variable initializers.
They add operations to the init program that are used
to initialize variables. Users should not use this class
directly, but need to use one of its implementations.</p>
</dd></dl>

</div>
<div class="section" id="constantinitializer">
<h2>ConstantInitializer<a class="headerlink" href="#constantinitializer" title="永久链接至标题">¶</a></h2>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.fluid.initializer.</code><code class="descname">ConstantInitializer</code><span class="sig-paren">(</span><em>value=0.0</em><span class="sig-paren">)</span></dt>
<dd><p>Implements the constant initializer</p>
</dd></dl>

</div>
<div class="section" id="uniforminitializer">
<h2>UniformInitializer<a class="headerlink" href="#uniforminitializer" title="永久链接至标题">¶</a></h2>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.fluid.initializer.</code><code class="descname">UniformInitializer</code><span class="sig-paren">(</span><em>low=-1.0</em>, <em>high=1.0</em>, <em>seed=0</em><span class="sig-paren">)</span></dt>
<dd><p>Implements the random uniform distribution initializer</p>
</dd></dl>

</div>
<div class="section" id="normalinitializer">
<h2>NormalInitializer<a class="headerlink" href="#normalinitializer" title="永久链接至标题">¶</a></h2>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.fluid.initializer.</code><code class="descname">NormalInitializer</code><span class="sig-paren">(</span><em>loc=0.0</em>, <em>scale=1.0</em>, <em>seed=0</em><span class="sig-paren">)</span></dt>
<dd><p>Implements the  random Normal(Gaussian) distribution initializer</p>
</dd></dl>

</div>
<div class="section" id="xavierinitializer">
<h2>XavierInitializer<a class="headerlink" href="#xavierinitializer" title="永久链接至标题">¶</a></h2>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.fluid.initializer.</code><code class="descname">XavierInitializer</code><span class="sig-paren">(</span><em>uniform=True</em>, <em>fan_in=None</em>, <em>fan_out=None</em>, <em>seed=0</em><span class="sig-paren">)</span></dt>
<dd><p>Implements the Xavier initializer</p>
<p>This class implements the Xavier weight initializer from the paper
Understanding the difficulty of training deep feedforward neural
networks[1] by Xavier Glorot and Yoshua Bengio.</p>
<p>This initializer is designed to keep the scale of the gradients
approximately same in all the layers. In case of Uniform distribution,
the range is [-x, x], where x = sqrt(6 / (fan_in + fan_out)).
In case of Normal distribution, the mean is 0 and the standard deviation
is sqrt(2/ (fan_in + fan_out)).</p>
<p class="rubric">References</p>
<dl class="docutils">
<dt>[1] Understanding the difficulty of training deep feedforward neural</dt>
<dd>networks. International conference on artificial intelligence and
statistics.
(<a class="reference external" href="http://proceedings.mlr.press/v9/glorot10a.html">http://proceedings.mlr.press/v9/glorot10a.html</a>)</dd>
</dl>
</dd></dl>

</div>
<div class="section" id="msrainitializer">
<h2>MSRAInitializer<a class="headerlink" href="#msrainitializer" title="永久链接至标题">¶</a></h2>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.fluid.initializer.</code><code class="descname">MSRAInitializer</code><span class="sig-paren">(</span><em>uniform=True</em>, <em>fan_in=None</em>, <em>seed=0</em><span class="sig-paren">)</span></dt>
<dd><p>Implements the MSRA initializer a.k.a. Kaiming Initializer</p>
<p>This class implements the weight initialization from the paper
Delving Deep into Rectifiers: Surpassing Human-Level Performance on
ImageNet Classification[1] by Kaiming He, Xiangyu Zhang, Shaoqing Ren
and Jian Sun. This is a robust initialization method that particularly
considers the rectifier nonlinearities. In case of Uniform distribution,
the range is [-x, x], where x = sqrt(6 / fan_in). In case of Normal
distribution, the mean is 0 and the standard deviation
is sqrt(2/ fan_in).</p>
<p class="rubric">References</p>
<dl class="docutils">
<dt>[1] Delving Deep into Rectifiers: Surpassing Human-Level Performance</dt>
<dd>on ImageNet Classification
(<a class="reference external" href="https://arxiv.org/abs/1502.01852">https://arxiv.org/abs/1502.01852</a>)</dd>
</dl>
</dd></dl>

</div>
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