Deploy to GitHub Pages: 5961b52b

develop/doc/_sources/api/v2/config/layer.rst.txt

@@ -109,6 +109,12 @@ sum_to_one_norm
     :members: sum_to_one_norm
     :noindex:
     
+cross_channel_norm
+------------------
+..  automodule:: paddle.v2.layer
+    :members: cross_channel_norm
+    :noindex:
+    
 Recurrent Layers
 ================
 

develop/doc/api/v2/config/layer.html

@@ -203,6 +203,7 @@
 <li><a class="reference internal" href="#img-cmrnorm">img_cmrnorm</a></li>
 <li><a class="reference internal" href="#batch-norm">batch_norm</a></li>
 <li><a class="reference internal" href="#sum-to-one-norm">sum_to_one_norm</a></li>
+<li><a class="reference internal" href="#cross-channel-norm">cross_channel_norm</a></li>
 </ul>
 </li>
 <li><a class="reference internal" href="#recurrent-layers">Recurrent Layers</a><ul>
@@ -1245,6 +1246,49 @@ and <span class="math">\(out\)</span> is a (batchSize x dataDim) output vector.<
 </table>
 </dd></dl>
 
+</div>
+<div class="section" id="cross-channel-norm">
+<h3>cross_channel_norm<a class="headerlink" href="#cross-channel-norm" title="Permalink to this headline">¶</a></h3>
+<p><cite>paddle.v2.layer</cite> is a part of model config packages in paddle.v2. In API v2,
+we want to make Paddle a plain Python package. The model config package defined
+the way how to configure a neural network topology in Paddle Python code.</p>
+<p>The primary usage shows below.</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">paddle.v2</span> <span class="kn">as</span> <span class="nn">paddle</span>
+
+<span class="n">img</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;img&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="n">paddle</span><span class="o">.</span><span class="n">data_type</span><span class="o">.</span><span class="n">dense_vector</span><span class="p">(</span><span class="mi">784</span><span class="p">))</span>
+<span class="n">hidden</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">img</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
+<span class="n">prediction</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">hidden</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+                             <span class="n">act</span><span class="o">=</span><span class="n">paddle</span><span class="o">.</span><span class="n">activation</span><span class="o">.</span><span class="n">Softmax</span><span class="p">())</span>
+
+<span class="c1"># use prediction instance where needed.</span>
+<span class="n">parameters</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">parameters</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">cost</span><span class="p">)</span>
+</pre></div>
+</div>
+<dl class="class">
+<dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">cross_channel_norm</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<dd><p>Normalize a layer&#8217;s output. This layer is necessary for ssd.
+This layer applys normalize across the channels of each sample to
+a conv layer&#8217;s output and scale the output by a group of trainable
+factors which dimensions equal to the channel&#8217;s number.</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>name</strong> (<em>basestring</em>) &#8211; The Layer Name.</li>
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; The input layer.</li>
+<li><strong>param_attr</strong> (<em>paddle.v2.attr.ParameterAttribute</em>) &#8211; The Parameter Attribute|list.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">paddle.v2.config_base.Layer</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
 </div>
 </div>
 <div class="section" id="recurrent-layers">

develop/doc_cn/_sources/api/v2/config/layer.rst.txt

@@ -109,6 +109,12 @@ sum_to_one_norm
     :members: sum_to_one_norm
     :noindex:
     
+cross_channel_norm
+------------------
+..  automodule:: paddle.v2.layer
+    :members: cross_channel_norm
+    :noindex:
+    
 Recurrent Layers
 ================
 

develop/doc_cn/api/v2/config/layer.html

@@ -210,6 +210,7 @@
 <li><a class="reference internal" href="#img-cmrnorm">img_cmrnorm</a></li>
 <li><a class="reference internal" href="#batch-norm">batch_norm</a></li>
 <li><a class="reference internal" href="#sum-to-one-norm">sum_to_one_norm</a></li>
+<li><a class="reference internal" href="#cross-channel-norm">cross_channel_norm</a></li>
 </ul>
 </li>
 <li><a class="reference internal" href="#recurrent-layers">Recurrent Layers</a><ul>
@@ -1252,6 +1253,49 @@ and <span class="math">\(out\)</span> is a (batchSize x dataDim) output vector.<
 </table>
 </dd></dl>
 
+</div>
+<div class="section" id="cross-channel-norm">
+<h3>cross_channel_norm<a class="headerlink" href="#cross-channel-norm" title="永久链接至标题">¶</a></h3>
+<p><cite>paddle.v2.layer</cite> is a part of model config packages in paddle.v2. In API v2,
+we want to make Paddle a plain Python package. The model config package defined
+the way how to configure a neural network topology in Paddle Python code.</p>
+<p>The primary usage shows below.</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">paddle.v2</span> <span class="kn">as</span> <span class="nn">paddle</span>
+
+<span class="n">img</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;img&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="n">paddle</span><span class="o">.</span><span class="n">data_type</span><span class="o">.</span><span class="n">dense_vector</span><span class="p">(</span><span class="mi">784</span><span class="p">))</span>
+<span class="n">hidden</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">img</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
+<span class="n">prediction</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">hidden</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+                             <span class="n">act</span><span class="o">=</span><span class="n">paddle</span><span class="o">.</span><span class="n">activation</span><span class="o">.</span><span class="n">Softmax</span><span class="p">())</span>
+
+<span class="c1"># use prediction instance where needed.</span>
+<span class="n">parameters</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">parameters</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">cost</span><span class="p">)</span>
+</pre></div>
+</div>
+<dl class="class">
+<dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">cross_channel_norm</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<dd><p>Normalize a layer&#8217;s output. This layer is necessary for ssd.
+This layer applys normalize across the channels of each sample to
+a conv layer&#8217;s output and scale the output by a group of trainable
+factors which dimensions equal to the channel&#8217;s number.</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>name</strong> (<em>basestring</em>) &#8211; The Layer Name.</li>
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; The input layer.</li>
+<li><strong>param_attr</strong> (<em>paddle.v2.attr.ParameterAttribute</em>) &#8211; The Parameter Attribute|list.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first last">paddle.v2.config_base.Layer</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
 </div>
 </div>
 <div class="section" id="recurrent-layers">