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...@@ -474,6 +474,11 @@ prelu ...@@ -474,6 +474,11 @@ prelu
.. autoclass:: paddle.v2.layer.prelu .. autoclass:: paddle.v2.layer.prelu
:noindex: :noindex:
gated_unit
-----------
.. autoclass:: paddle.v2.layer.gated_unit
:noindex:
Detection output Layer Detection output Layer
====================== ======================
......
...@@ -3724,6 +3724,62 @@ a_i * z_i &amp;\quad \mathrm{otherwise}\end{split}\]</div> ...@@ -3724,6 +3724,62 @@ a_i * z_i &amp;\quad \mathrm{otherwise}\end{split}\]</div>
</table> </table>
</dd></dl> </dd></dl>
</div>
<div class="section" id="gated-unit">
<h3>gated_unit<a class="headerlink" href="#gated-unit" title="Permalink to this headline"></a></h3>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">gated_unit</code></dt>
<dd><p>The gated unit layer implements a simple gating mechanism over the input.
The input <span class="math">\(X\)</span> is first projected into a new space <span class="math">\(X'\)</span>, and
it is also used to produce a gate weight <span class="math">\(\sigma\)</span>. Element-wise
prodict between <a href="#id6"><span class="problematic" id="id7">:match:`X&#8217;`</span></a> and <span class="math">\(\sigma\)</span> is finally returned.</p>
<dl class="docutils">
<dt>Reference:</dt>
<dd>Language Modeling with Gated Convolutional Networks
<a class="reference external" href="https://arxiv.org/abs/1612.08083">https://arxiv.org/abs/1612.08083</a></dd>
</dl>
<div class="math">
\[y=\text{act}(X \cdot W + b)\otimes \sigma(X \cdot V + c)\]</div>
<p>The example usage is:</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 simple">
<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; input for this layer.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; output size of the gated unit.</li>
<li><strong>act</strong> (<em>paddle.v2.activation.Base</em>) &#8211; activation type of the projected input.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; name of this layer.</li>
<li><strong>gate_attr</strong> (<em>paddle.v2.attr.ExtraAttributeNone</em>) &#8211; Attributes to tune the gate output, for example, error
clipping threshold, dropout and so on. See paddle.v2.attr.ExtraAttribute for
more details.</li>
<li><strong>gate_param_attr</strong> (<em>paddle.v2.attr.ParameterAttribute|None</em>) &#8211; Attributes to tune the learnable projected matrix
parameter of the gate.</li>
<li><strong>gate_bias_attr</strong> (<em>paddle.v2.attr.ParameterAttribute|None</em>) &#8211; Attributes to tune the learnable bias of the gate.</li>
<li><strong>inproj_attr</strong> (<em>paddle.v2.attr.ExtraAttributeNone</em>) &#8211; Attributes to the tune the projected input, for
example, error clipping threshold, dropout and so on. See
paddle.v2.attr.ExtraAttribute for more details.</li>
<li><strong>inproj_param_attr</strong> (<em>paddle.v2.attr.ParameterAttribute|None</em>) &#8211; Attributes to tune the learnable parameter of
the projection of input.</li>
<li><strong>inproj_bias_attr</strong> (<em>paddle.v2.attr.ParameterAttribute|None</em>) &#8211; Attributes to tune the learnable bias of
projection of the input.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttributeNone</em>) &#8211; Attributes to tune the final output of the gated unit,
for example, error clipping threshold, dropout and so on. See
paddle.v2.attr.ExtraAttribute for more details.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">paddle.v2.config_base.Layer object.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">paddle.v2.config_base.Layer</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div> </div>
</div> </div>
<div class="section" id="detection-output-layer"> <div class="section" id="detection-output-layer">
......
因为 它太大了无法显示 source diff 。你可以改为 查看blob
...@@ -474,6 +474,11 @@ prelu ...@@ -474,6 +474,11 @@ prelu
.. autoclass:: paddle.v2.layer.prelu .. autoclass:: paddle.v2.layer.prelu
:noindex: :noindex:
gated_unit
-----------
.. autoclass:: paddle.v2.layer.gated_unit
:noindex:
Detection output Layer Detection output Layer
====================== ======================
......
...@@ -3729,6 +3729,62 @@ a_i * z_i &amp;\quad \mathrm{otherwise}\end{split}\]</div> ...@@ -3729,6 +3729,62 @@ a_i * z_i &amp;\quad \mathrm{otherwise}\end{split}\]</div>
</table> </table>
</dd></dl> </dd></dl>
</div>
<div class="section" id="gated-unit">
<h3>gated_unit<a class="headerlink" href="#gated-unit" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">gated_unit</code></dt>
<dd><p>The gated unit layer implements a simple gating mechanism over the input.
The input <span class="math">\(X\)</span> is first projected into a new space <span class="math">\(X'\)</span>, and
it is also used to produce a gate weight <span class="math">\(\sigma\)</span>. Element-wise
prodict between <a href="#id6"><span class="problematic" id="id7">:match:`X&#8217;`</span></a> and <span class="math">\(\sigma\)</span> is finally returned.</p>
<dl class="docutils">
<dt>Reference:</dt>
<dd>Language Modeling with Gated Convolutional Networks
<a class="reference external" href="https://arxiv.org/abs/1612.08083">https://arxiv.org/abs/1612.08083</a></dd>
</dl>
<div class="math">
\[y=\text{act}(X \cdot W + b)\otimes \sigma(X \cdot V + c)\]</div>
<p>The example usage is:</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">参数:</th><td class="field-body"><ul class="first simple">
<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; input for this layer.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; output size of the gated unit.</li>
<li><strong>act</strong> (<em>paddle.v2.activation.Base</em>) &#8211; activation type of the projected input.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; name of this layer.</li>
<li><strong>gate_attr</strong> (<em>paddle.v2.attr.ExtraAttributeNone</em>) &#8211; Attributes to tune the gate output, for example, error
clipping threshold, dropout and so on. See paddle.v2.attr.ExtraAttribute for
more details.</li>
<li><strong>gate_param_attr</strong> (<em>paddle.v2.attr.ParameterAttribute|None</em>) &#8211; Attributes to tune the learnable projected matrix
parameter of the gate.</li>
<li><strong>gate_bias_attr</strong> (<em>paddle.v2.attr.ParameterAttribute|None</em>) &#8211; Attributes to tune the learnable bias of the gate.</li>
<li><strong>inproj_attr</strong> (<em>paddle.v2.attr.ExtraAttributeNone</em>) &#8211; Attributes to the tune the projected input, for
example, error clipping threshold, dropout and so on. See
paddle.v2.attr.ExtraAttribute for more details.</li>
<li><strong>inproj_param_attr</strong> (<em>paddle.v2.attr.ParameterAttribute|None</em>) &#8211; Attributes to tune the learnable parameter of
the projection of input.</li>
<li><strong>inproj_bias_attr</strong> (<em>paddle.v2.attr.ParameterAttribute|None</em>) &#8211; Attributes to tune the learnable bias of
projection of the input.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttributeNone</em>) &#8211; Attributes to tune the final output of the gated unit,
for example, error clipping threshold, dropout and so on. See
paddle.v2.attr.ExtraAttribute for more details.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">paddle.v2.config_base.Layer object.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><p class="first last">paddle.v2.config_base.Layer</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div> </div>
</div> </div>
<div class="section" id="detection-output-layer"> <div class="section" id="detection-output-layer">
......
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