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  <div class="section" id="layers">
<span id="api-v2-layer"></span><h1>Layers<a class="headerlink" href="#layers" title="永久链接至标题"></a></h1>
<div class="section" id="data-layer">
<h2>Data layer<a class="headerlink" href="#data-layer" title="永久链接至标题"></a></h2>
<div class="section" id="data">
<span id="api-v2-layer-data"></span><h3>data<a class="headerlink" href="#data" title="永久链接至标题"></a></h3>
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<dl class="attribute">
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<dt>
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<code class="descclassname">paddle.v2.layer.</code><code class="descname">data</code></dt>
<dd><p><code class="xref py py-class docutils literal"><span class="pre">name</span></code> 的别名</p>
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</dd></dl>

</div>
</div>
<div class="section" id="fully-connected-layers">
<h2>Fully Connected Layers<a class="headerlink" href="#fully-connected-layers" title="永久链接至标题"></a></h2>
<div class="section" id="fc">
<span id="api-v2-layer-fc"></span><h3>fc<a class="headerlink" href="#fc" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">fc</code></dt>
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<dd><p>Helper for declare fully connected layer.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">fc</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">layer</span><span class="p">,</span>
              <span class="n">size</span><span class="o">=</span><span class="mi">1024</span><span class="p">,</span>
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              <span class="n">act</span><span class="o">=</span><span class="n">paddle</span><span class="o">.</span><span class="n">v2</span><span class="o">.</span><span class="n">activation</span><span class="o">.</span><span class="n">Linear</span><span class="p">(),</span>
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              <span class="n">bias_attr</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
</pre></div>
</div>
<p>which is equal to:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">mixed</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">1024</span><span class="p">)</span> <span class="k">as</span> <span class="n">fc</span><span class="p">:</span>
    <span class="n">fc</span> <span class="o">+=</span> <span class="n">full_matrix_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">)</span>
</pre></div>
</div>
<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|list|tuple</em>) &#8211; The input layer. Could be a list/tuple of input layer.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; The layer dimension.</li>
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<li><strong>act</strong> (<em>paddle.v2.activation.Base</em>) &#8211; Activation Type. Default is tanh.</li>
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<li><strong>param_attr</strong> (<em>paddle.v2.attr.ParameterAttribute</em>) &#8211; The Parameter Attribute|list.</li>
<li><strong>bias_attr</strong> (<em>paddle.v2.attr.ParameterAttribute|None|Any</em>) &#8211; The Bias Attribute. If no bias, then pass False or
something not type of paddle.v2.attr.ParameterAttribute. None will get a
default Bias.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttributeNone</em>) &#8211; Extra Layer config.</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 class="section" id="selective-fc">
<h3>selective_fc<a class="headerlink" href="#selective-fc" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">selective_fc</code></dt>
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<dd><p>Selectived fully connected layer. Different from fc, the output
of this layer maybe sparse. It requires an additional input to indicate
several selected columns for output. If the selected columns is not
specified, selective_fc acts exactly like fc.</p>
<p>The simple usage is:</p>
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<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">sel_fc</span> <span class="o">=</span> <span class="n">selective_fc</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">128</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">v2</span><span class="o">.</span><span class="n">activation</span><span class="o">.</span><span class="n">Tanh</span><span class="p">())</span>
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</pre></div>
</div>
<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|list|tuple</em>) &#8211; The input layer.</li>
<li><strong>select</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; The select layer. The output of select layer should be a
sparse binary matrix, and treat as the mask of selective fc.
If is None, acts exactly like fc.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; The layer dimension.</li>
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<li><strong>act</strong> (<em>paddle.v2.activation.Base</em>) &#8211; Activation Type. Default is tanh.</li>
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<li><strong>param_attr</strong> (<em>paddle.v2.attr.ParameterAttribute</em>) &#8211; The Parameter Attribute.</li>
<li><strong>bias_attr</strong> (<em>paddle.v2.attr.ParameterAttribute|None|Any</em>) &#8211; The Bias Attribute. If no bias, then pass False or
something not type of paddle.v2.attr.ParameterAttribute. None will get a
default Bias.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttributeNone</em>) &#8211; Extra Layer config.</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 class="section" id="conv-layers">
<h2>Conv Layers<a class="headerlink" href="#conv-layers" title="永久链接至标题"></a></h2>
<div class="section" id="conv-operator">
<h3>conv_operator<a class="headerlink" href="#conv-operator" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">conv_operator</code></dt>
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<dd><p>Different from img_conv, conv_op is an Operator, which can be used
in mixed. And conv_op takes two inputs to perform convolution.
The first input is the image and the second is filter kernel. It only
support GPU mode.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">op</span> <span class="o">=</span> <span class="n">conv_operator</span><span class="p">(</span><span class="n">img</span><span class="o">=</span><span class="n">input1</span><span class="p">,</span>
                   <span class="nb">filter</span><span class="o">=</span><span class="n">input2</span><span class="p">,</span>
                   <span class="n">filter_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
                   <span class="n">num_filters</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span>
                   <span class="n">num_channels</span><span class="o">=</span><span class="mi">64</span><span class="p">)</span>
</pre></div>
</div>
<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>img</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; input image</li>
<li><strong>filter</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; input filter</li>
<li><strong>filter_size</strong> (<em>int</em>) &#8211; The x dimension of a filter kernel.</li>
<li><strong>filter_size_y</strong> (<em>int</em>) &#8211; The y dimension of a filter kernel. Since
PaddlePaddle now supports rectangular filters,
the filter&#8217;s shape can be (filter_size, filter_size_y).</li>
<li><strong>num_filters</strong> (<em>int</em>) &#8211; channel of output data.</li>
<li><strong>num_channels</strong> (<em>int</em>) &#8211; channel of input data.</li>
<li><strong>stride</strong> (<em>int</em>) &#8211; The x dimension of the stride.</li>
<li><strong>stride_y</strong> (<em>int</em>) &#8211; The y dimension of the stride.</li>
<li><strong>padding</strong> (<em>int</em>) &#8211; The x dimension of padding.</li>
<li><strong>padding_y</strong> (<em>int</em>) &#8211; The y dimension of padding.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">A ConvOperator 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">ConvOperator</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="conv-projection">
<h3>conv_projection<a class="headerlink" href="#conv-projection" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">conv_projection</code></dt>
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<dd><p>Different from img_conv and conv_op, conv_projection is an Projection,
which can be used in mixed and conat. It use cudnn to implement
conv and only support GPU mode.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">proj</span> <span class="o">=</span> <span class="n">conv_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">input1</span><span class="p">,</span>
                       <span class="n">filter_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
                       <span class="n">num_filters</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span>
                       <span class="n">num_channels</span><span class="o">=</span><span class="mi">64</span><span class="p">)</span>
</pre></div>
</div>
<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 layer</li>
<li><strong>filter_size</strong> (<em>int</em>) &#8211; The x dimension of a filter kernel.</li>
<li><strong>filter_size_y</strong> (<em>int</em>) &#8211; The y dimension of a filter kernel. Since
PaddlePaddle now supports rectangular filters,
the filter&#8217;s shape can be (filter_size, filter_size_y).</li>
<li><strong>num_filters</strong> (<em>int</em>) &#8211; channel of output data.</li>
<li><strong>num_channels</strong> (<em>int</em>) &#8211; channel of input data.</li>
<li><strong>stride</strong> (<em>int</em>) &#8211; The x dimension of the stride.</li>
<li><strong>stride_y</strong> (<em>int</em>) &#8211; The y dimension of the stride.</li>
<li><strong>padding</strong> (<em>int</em>) &#8211; The x dimension of padding.</li>
<li><strong>padding_y</strong> (<em>int</em>) &#8211; The y dimension of padding.</li>
<li><strong>groups</strong> (<em>int</em>) &#8211; The group number.</li>
<li><strong>param_attr</strong> (<em>paddle.v2.attr.ParameterAttribute</em>) &#8211; Convolution param attribute. None means default attribute</li>
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<li><strong>trans</strong> (<em>boolean</em>) &#8211; whether it is convTrans or conv</li>
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</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">A DotMulProjection 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">DotMulProjection</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="conv-shift">
<h3>conv_shift<a class="headerlink" href="#conv-shift" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">conv_shift</code></dt>
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<dd><dl class="docutils">
<dt>This layer performs cyclic convolution for two input. For example:</dt>
<dd><ul class="first last simple">
<li>a[in]: contains M elements.</li>
<li>b[in]: contains N elements (N should be odd).</li>
<li>c[out]: contains M elements.</li>
</ul>
</dd>
</dl>
<div class="math">
\[c[i] = \sum_{j=-(N-1)/2}^{(N-1)/2}a_{i+j} * b_{j}\]</div>
<dl class="docutils">
<dt>In this formular:</dt>
<dd><ul class="first last simple">
<li>a&#8217;s index is computed modulo M. When it is negative, then get item from
the right side (which is the end of array) to the left.</li>
<li>b&#8217;s index is computed modulo N. When it is negative, then get item from
the right size (which is the end of array) to the left.</li>
</ul>
</dd>
</dl>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">conv_shift</span> <span class="o">=</span> <span class="n">conv_shift</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="n">layer1</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="n">layer2</span><span class="p">)</span>
</pre></div>
</div>
<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; layer name</li>
<li><strong>a</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Input layer a.</li>
<li><strong>b</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; input layer b.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; layer&#8217;s extra attribute.</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 class="section" id="img-conv">
<h3>img_conv<a class="headerlink" href="#img-conv" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">img_conv</code></dt>
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<dd><p>Convolution layer for image. Paddle can support both square and non-square
input currently.</p>
<p>The details of convolution layer, please refer UFLDL&#8217;s <a class="reference external" href="http://ufldl.stanford.edu/tutorial/supervised/FeatureExtractionUsingConvolution/">convolution</a> .</p>
<p>Convolution Transpose (deconv) layer for image. Paddle can support both square
and non-square input currently.</p>
<p>The details of convolution transpose layer,
please refer to the following explanation and references therein
&lt;<a class="reference external" href="http://datascience.stackexchange.com/questions/6107/">http://datascience.stackexchange.com/questions/6107/</a>
what-are-deconvolutional-layers/&gt;`_ .
The num_channel means input image&#8217;s channel number. It may be 1 or 3 when
input is raw pixels of image(mono or RGB), or it may be the previous layer&#8217;s
num_filters * num_group.</p>
<p>There are several group of filter in PaddlePaddle implementation.
Each group will process some channel of the inputs. For example, if an input
num_channel = 256, group = 4, num_filter=32, the PaddlePaddle will create
32*4 = 128 filters to process inputs. The channels will be split into 4
pieces. First 256/4 = 64 channels will process by first 32 filters. The
rest channels will be processed by rest group of filters.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">conv</span> <span class="o">=</span> <span class="n">img_conv</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">filter_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">filter_size_y</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                      <span class="n">num_channels</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
                      <span class="n">num_filters</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                      <span class="n">bias_attr</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span>
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                      <span class="n">act</span><span class="o">=</span><span class="n">paddle</span><span class="o">.</span><span class="n">v2</span><span class="o">.</span><span class="n">activation</span><span class="o">.</span><span class="n">Relu</span><span class="p">())</span>
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</pre></div>
</div>
<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; Layer name.</li>
<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Layer Input.</li>
<li><strong>filter_size</strong> (<em>int|tuple|list</em>) &#8211; The x dimension of a filter kernel. Or input a tuple for
two image dimension.</li>
<li><strong>filter_size_y</strong> (<em>int|None</em>) &#8211; The y dimension of a filter kernel. Since PaddlePaddle
currently supports rectangular filters, the filter&#8217;s
shape will be (filter_size, filter_size_y).</li>
<li><strong>num_filters</strong> &#8211; Each filter group&#8217;s number of filter</li>
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<li><strong>act</strong> (<em>paddle.v2.activation.Base</em>) &#8211; Activation type. Default is tanh</li>
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<li><strong>groups</strong> (<em>int</em>) &#8211; Group size of filters.</li>
<li><strong>stride</strong> (<em>int|tuple|list</em>) &#8211; The x dimension of the stride. Or input a tuple for two image
dimension.</li>
<li><strong>stride_y</strong> (<em>int</em>) &#8211; The y dimension of the stride.</li>
<li><strong>padding</strong> (<em>int|tuple|list</em>) &#8211; The x dimension of the padding. Or input a tuple for two
image dimension</li>
<li><strong>padding_y</strong> (<em>int</em>) &#8211; The y dimension of the padding.</li>
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<li><strong>dilation</strong> (<em>int|tuple|list</em>) &#8211; The x dimension of the dilation. Or input a tuple for two
image dimension</li>
<li><strong>dilation_y</strong> (<em>int</em>) &#8211; The y dimension of the dilation.</li>
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<li><strong>bias_attr</strong> (<em>paddle.v2.attr.ParameterAttribute|False</em>) &#8211; Convolution bias attribute. None means default bias.
False means no bias.</li>
<li><strong>num_channels</strong> (<em>int</em>) &#8211; number of input channels. If None will be set
automatically from previous output.</li>
<li><strong>param_attr</strong> (<em>paddle.v2.attr.ParameterAttribute</em>) &#8211; Convolution param attribute. None means default attribute</li>
<li><strong>shared_biases</strong> (<em>bool</em>) &#8211; Is biases will be shared between filters or not.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; Layer Extra Attribute.</li>
<li><strong>trans</strong> (<em>bool</em>) &#8211; true if it is a convTransLayer, false if it is a convLayer</li>
<li><strong>layer_type</strong> (<em>String</em>) &#8211; specify the layer_type, default is None. If trans=True,
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layer_type has to be &#8220;exconvt&#8221; or &#8220;cudnn_convt&#8221;,
otherwise layer_type has to be either &#8220;exconv&#8221; or
&#8220;cudnn_conv&#8221;</li>
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</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 class="section" id="context-projection">
<span id="api-v2-layer-context-projection"></span><h3>context_projection<a class="headerlink" href="#context-projection" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">context_projection</code></dt>
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<dd><p>Context Projection.</p>
<p>It just simply reorganizes input sequence, combines &#8220;context_len&#8221; sequence
to one context from context_start. &#8220;context_start&#8221; will be set to
-(context_len - 1) / 2 by default. If context position out of sequence
length, padding will be filled as zero if padding_attr = False, otherwise
it is trainable.</p>
<p>For example, origin sequence is [A B C D E F G], context len is 3, then
after context projection and not set padding_attr, sequence will
be [ 0AB ABC BCD CDE DEF EFG FG0 ].</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 Sequence.</li>
<li><strong>context_len</strong> (<em>int</em>) &#8211; context length.</li>
<li><strong>context_start</strong> (<em>int</em>) &#8211; context start position. Default is
-(context_len - 1)/2</li>
<li><strong>padding_attr</strong> (<em>bool|paddle.v2.attr.ParameterAttribute</em>) &#8211; Padding Parameter Attribute. If false, it means padding
always be zero. Otherwise Padding is learnable, and
parameter attribute is set by this parameter.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">Projection</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">Projection</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
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</div>
<div class="section" id="row-conv">
<h3>row_conv<a class="headerlink" href="#row-conv" 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">row_conv</code></dt>
<dd><p>The row convolution is called lookahead convolution. It is firstly
introduced in paper of <a class="reference external" href="https://arxiv.org/pdf/1512.02595v1.pdf">Deep Speech 2: End-toEnd Speech Recognition
in English and Mandarin</a> .</p>
<p>The bidirectional RNN that learns representation for a sequence by
performing a forward and a backward pass through the entire sequence.
However, unlike unidirectional RNNs, bidirectional RNNs are challenging
to deploy in an online and low-latency setting. The lookahead convolution
incorporates information from future subsequences in a computationally
efficient manner to improve unidirectional recurrent neural networks.</p>
<p>The connection of row convolution is different form the 1D sequence
convolution. Assumed that, the future context-length is k, that is to say,
it can get the output at timestep t by using the the input feature from t-th
timestep to (t+k+1)-th timestep. Assumed that the hidden dim of input
activations are d, the activations r_t for the new layer at time-step t are:</p>
<div class="math">
\[r_{t,r} = \sum_{j=1}^{k + 1} {w_{i,j}h_{t+j-1, i}}
          \quad         ext{for} \quad  (1 \leq i \leq d)\]</div>
<div class="admonition note">
<p class="first admonition-title">注解</p>
<p class="last">The <cite>context_len</cite> is <cite>k + 1</cite>. That is to say, the lookahead step
number plus one equals context_len.</p>
</div>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">row_conv</span> <span class="o">=</span> <span class="n">row_conv</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span> <span class="n">context_len</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
</pre></div>
</div>
<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; The input layer.</li>
<li><strong>context_len</strong> (<em>int</em>) &#8211; The context length equals the lookahead step number
plus one.</li>
<li><strong>act</strong> (<em>paddle.v2.activation.Base</em>) &#8211; Activation Type. Default is linear activation.</li>
<li><strong>param_attr</strong> (<em>paddle.v2.attr.ParameterAttribute</em>) &#8211; The Parameter Attribute. If None, the parameter will be
initialized smartly. It&#8217;s better set it by yourself.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttributeNone</em>) &#8211; Extra Layer config.</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>
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</div>
</div>
<div class="section" id="image-pooling-layer">
<h2>Image Pooling Layer<a class="headerlink" href="#image-pooling-layer" title="永久链接至标题"></a></h2>
<div class="section" id="img-pool">
<h3>img_pool<a class="headerlink" href="#img-pool" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">img_pool</code></dt>
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<dd><p>Image pooling Layer.</p>
<p>The details of pooling layer, please refer ufldl&#8217;s <a class="reference external" href="http://ufldl.stanford.edu/tutorial/supervised/Pooling/">pooling</a> .</p>
<ul class="simple">
<li>ceil_mode=True:</li>
</ul>
<div class="math">
\[w = 1 + int(ceil(input\_width + 2 * padding - pool\_size) / float(stride))
h = 1 + int(ceil(input\_height + 2 * padding\_y - pool\_size\_y) / float(stride\_y))\]</div>
<ul class="simple">
<li>ceil_mode=False:</li>
</ul>
<div class="math">
\[w = 1 + int(floor(input\_width + 2 * padding - pool\_size) / float(stride))
h = 1 + int(floor(input\_height + 2 * padding\_y - pool\_size\_y) / float(stride\_y))\]</div>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">maxpool</span> <span class="o">=</span> <span class="n">img_pool</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">conv</span><span class="p">,</span>
                         <span class="n">pool_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
                         <span class="n">pool_size_y</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
                         <span class="n">num_channels</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
                         <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                         <span class="n">stride_y</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
                         <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                         <span class="n">padding_y</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
                         <span class="n">pool_type</span><span class="o">=</span><span class="n">MaxPooling</span><span class="p">())</span>
</pre></div>
</div>
<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>padding</strong> (<em>int</em>) &#8211; pooling padding width.</li>
<li><strong>padding_y</strong> (<em>int|None</em>) &#8211; pooling padding height. It&#8217;s equal to padding by default.</li>
<li><strong>name</strong> (<em>basestring.</em>) &#8211; name of pooling layer</li>
<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; layer&#8217;s input</li>
<li><strong>pool_size</strong> (<em>int</em>) &#8211; pooling window width</li>
<li><strong>pool_size_y</strong> (<em>int|None</em>) &#8211; pooling window height. It&#8217;s eaqual to pool_size by default.</li>
<li><strong>num_channels</strong> (<em>int</em>) &#8211; number of input channel.</li>
<li><strong>pool_type</strong> (<em>BasePoolingType</em>) &#8211; pooling type. MaxPooling or AvgPooling. Default is
MaxPooling.</li>
<li><strong>stride</strong> (<em>int</em>) &#8211; stride width of pooling.</li>
<li><strong>stride_y</strong> (<em>int|None</em>) &#8211; stride height of pooling. It is equal to stride by default.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; Extra Layer attribute.</li>
<li><strong>ceil_mode</strong> (<em>bool</em>) &#8211; Wether to use ceil mode to calculate output height and with.
Defalut is True. If set false, Otherwise use floor.</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 class="section" id="spp">
<h3>spp<a class="headerlink" href="#spp" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">spp</code></dt>
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<dd><p>Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.
The details please refer to
<a class="reference external" href="https://arxiv.org/abs/1406.4729">Kaiming He&#8217;s paper</a>.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">spp</span> <span class="o">=</span> <span class="n">spp</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">data</span><span class="p">,</span>
                <span class="n">pyramid_height</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
                <span class="n">num_channels</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span>
                <span class="n">pool_type</span><span class="o">=</span><span class="n">MaxPooling</span><span class="p">())</span>
</pre></div>
</div>
<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; layer name.</li>
<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; layer&#8217;s input.</li>
<li><strong>num_channels</strong> (<em>int</em>) &#8211; number of input channel.</li>
<li><strong>pool_type</strong> &#8211; Pooling type. MaxPooling or AveragePooling. Default is MaxPooling.</li>
<li><strong>pyramid_height</strong> (<em>int</em>) &#8211; pyramid height.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; Extra Layer Attribute.</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 class="section" id="maxout">
<h3>maxout<a class="headerlink" href="#maxout" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">maxout</code></dt>
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<dd><dl class="docutils">
<dt>A layer to do max out on conv layer output.</dt>
<dd><ul class="first last simple">
<li>Input: output of a conv layer.</li>
<li>Output: feature map size same as input. Channel is (input channel) / groups.</li>
</ul>
</dd>
</dl>
<p>So groups should be larger than 1, and the num of channels should be able
to devided by groups.</p>
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<div class="math">
\[y_{si+j} = \max_k x_{gsi + sk + j}
g = groups
s = input.size / num_channels
0 \le i &lt; num_channels / groups
0 \le j &lt; s
0 \le k &lt; groups\]</div>
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<dl class="docutils">
<dt>Please refer to Paper:</dt>
<dd><ul class="first last simple">
<li>Maxout Networks: <a class="reference external" href="http://www.jmlr.org/proceedings/papers/v28/goodfellow13.pdf">http://www.jmlr.org/proceedings/papers/v28/goodfellow13.pdf</a></li>
<li>Multi-digit Number Recognition from Street View         Imagery using Deep Convolutional Neural Networks:         <a class="reference external" href="https://arxiv.org/pdf/1312.6082v4.pdf">https://arxiv.org/pdf/1312.6082v4.pdf</a></li>
</ul>
</dd>
</dl>
<p>The simple usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">maxout</span> <span class="o">=</span> <span class="n">maxout</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span>
                      <span class="n">num_channels</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span>
                      <span class="n">groups</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
</pre></div>
</div>
<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; The input layer.</li>
<li><strong>num_channels</strong> (<em>int|None</em>) &#8211; The channel number of input layer. If None will be set
automatically from previous output.</li>
<li><strong>groups</strong> (<em>int</em>) &#8211; The group number of input layer.</li>
<li><strong>name</strong> (<em>None|basestring.</em>) &#8211; The name of this layer, which can not specify.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; Extra Layer attribute.</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 class="section" id="norm-layer">
<h2>Norm Layer<a class="headerlink" href="#norm-layer" title="永久链接至标题"></a></h2>
<div class="section" id="img-cmrnorm">
<h3>img_cmrnorm<a class="headerlink" href="#img-cmrnorm" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">img_cmrnorm</code></dt>
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<dd><p>Response normalization across feature maps.
The details please refer to
<a class="reference external" href="http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf">Alex&#8217;s paper</a>.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">norm</span> <span class="o">=</span> <span class="n">img_cmrnorm</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">net</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
</pre></div>
</div>
<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>None|basestring</em>) &#8211; layer name.</li>
<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; layer&#8217;s input.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; Normalize in number of <span class="math">\(size\)</span> feature maps.</li>
<li><strong>scale</strong> (<em>float</em>) &#8211; The hyper-parameter.</li>
<li><strong>power</strong> (<em>float</em>) &#8211; The hyper-parameter.</li>
<li><strong>num_channels</strong> &#8211; input layer&#8217;s filers number or channels. If
num_channels is None, it will be set automatically.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; Extra Layer Attribute.</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 class="section" id="batch-norm">
<h3>batch_norm<a class="headerlink" href="#batch-norm" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">batch_norm</code></dt>
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<dd><p>Batch Normalization Layer. The notation of this layer as follow.</p>
<p><span class="math">\(x\)</span> is the input features over a mini-batch.</p>
<div class="math">
\[\begin{split}\mu_{\beta} &amp;\gets \frac{1}{m} \sum_{i=1}^{m} x_i \qquad &amp;//\
\ mini-batch\ mean \\
\sigma_{\beta}^{2} &amp;\gets \frac{1}{m} \sum_{i=1}^{m}(x_i - \
\mu_{\beta})^2 \qquad &amp;//\ mini-batch\ variance \\
\hat{x_i} &amp;\gets \frac{x_i - \mu_\beta} {\sqrt{\
\sigma_{\beta}^{2} + \epsilon}} \qquad &amp;//\ normalize \\
y_i &amp;\gets \gamma \hat{x_i} + \beta \qquad &amp;//\ scale\ and\ shift\end{split}\]</div>
<p>The details of batch normalization please refer to this
<a class="reference external" href="http://arxiv.org/abs/1502.03167">paper</a>.</p>
<p>The example usage is:</p>
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<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">norm</span> <span class="o">=</span> <span class="n">batch_norm</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">net</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">v2</span><span class="o">.</span><span class="n">activation</span><span class="o">.</span><span class="n">Relu</span><span class="p">())</span>
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</pre></div>
</div>
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<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; layer name.</li>
<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; batch normalization input. Better be linear activation.
Because there is an activation inside batch_normalization.</li>
<li><strong>batch_norm_type</strong> (<em>None|string</em><em>, </em><em>None</em><em> or </em><em>&quot;batch_norm&quot;</em><em> or </em><em>&quot;cudnn_batch_norm&quot;</em>) &#8211; We have batch_norm and cudnn_batch_norm. batch_norm
supports both CPU and GPU. cudnn_batch_norm requires
cuDNN version greater or equal to v4 (&gt;=v4). But
cudnn_batch_norm is faster and needs less memory
than batch_norm. By default (None), we will
automaticly select cudnn_batch_norm for GPU and
batch_norm for CPU. Otherwise, select batch norm
type based on the specified type. If you use cudnn_batch_norm,
we suggested you use latest version, such as v5.1.</li>
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<li><strong>act</strong> (<em>paddle.v2.activation.Base</em>) &#8211; Activation Type. Better be relu. Because batch
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normalization will normalize input near zero.</li>
<li><strong>num_channels</strong> (<em>int</em>) &#8211; num of image channels or previous layer&#8217;s number of
filters. None will automatically get from layer&#8217;s
input.</li>
<li><strong>bias_attr</strong> (<em>paddle.v2.attr.ParameterAttribute</em>) &#8211; <span class="math">\(\beta\)</span>, better be zero when initialize. So the
initial_std=0, initial_mean=1 is best practice.</li>
<li><strong>param_attr</strong> (<em>paddle.v2.attr.ParameterAttribute</em>) &#8211; <span class="math">\(\gamma\)</span>, better be one when initialize. So the
initial_std=0, initial_mean=1 is best practice.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; Extra Layer Attribute.</li>
<li><strong>use_global_stats</strong> (<em>bool|None.</em>) &#8211; whether use moving mean/variance statistics
during testing peroid. If None or True,
it will use moving mean/variance statistics during
testing. If False, it will use the mean
and variance of current batch of test data for
testing.</li>
<li><strong>moving_average_fraction</strong> (<em>float.</em>) &#8211; Factor used in the moving average
computation, referred to as facotr,
<span class="math">\(runningMean = newMean*(1-factor)
+ runningMean*factor\)</span></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 class="section" id="sum-to-one-norm">
<h3>sum_to_one_norm<a class="headerlink" href="#sum-to-one-norm" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
T
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">sum_to_one_norm</code></dt>
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<dd><p>A layer for sum-to-one normalization,
which is used in NEURAL TURING MACHINE.</p>
<div class="math">
\[out[i] = \frac {in[i]} {\sum_{k=1}^N in[k]}\]</div>
<p>where <span class="math">\(in\)</span> is a (batchSize x dataDim) input vector,
and <span class="math">\(out\)</span> is a (batchSize x dataDim) output vector.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">sum_to_one_norm</span> <span class="o">=</span> <span class="n">sum_to_one_norm</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">)</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
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<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 layer.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; Layer name.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; extra layer attributes.</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>
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</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>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">cross_channel_norm</code></dt>
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<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>
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</div>
<div class="section" id="row-l2-norm">
<h3>row_l2_norm<a class="headerlink" href="#row-l2-norm" 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">row_l2_norm</code></dt>
<dd><blockquote>
<div><p>A layer for L2-normalization in each row.</p>
<div class="math">
\[out[i] =\]</div>
</div></blockquote>
<p>rac{in[i]}{sqrt{sum_{k=1}^N in[k]^{2}}}</p>
<blockquote>
<div><p>where the size of <span class="math">\(in\)</span> is (batchSize x dataDim) ,
and the size of <span class="math">\(out\)</span> is a (batchSize x dataDim) .</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">row_l2_norm</span> <span class="o">=</span> <span class="n">row_l2_norm</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">)</span>
</pre></div>
</div>
<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">param input:</th><td class="field-body">Input layer.</td>
</tr>
<tr class="field-even field"><th class="field-name">type input:</th><td class="field-body">paddle.v2.config_base.Layer</td>
</tr>
<tr class="field-odd field"><th class="field-name">param name:</th><td class="field-body">Layer name.</td>
</tr>
<tr class="field-even field"><th class="field-name">type name:</th><td class="field-body">basestring</td>
</tr>
<tr class="field-odd field"><th class="field-name" colspan="2">param layer_attr:</th></tr>
<tr class="field-odd field"><td>&#160;</td><td class="field-body">extra layer attributes.</td>
</tr>
<tr class="field-even field"><th class="field-name" colspan="2">type layer_attr:</th></tr>
<tr class="field-even field"><td>&#160;</td><td class="field-body">paddle.v2.attr.ExtraAttribute</td>
</tr>
<tr class="field-odd field"><th class="field-name">return:</th><td class="field-body">paddle.v2.config_base.Layer object.</td>
</tr>
<tr class="field-even field"><th class="field-name">rtype:</th><td class="field-body">paddle.v2.config_base.Layer</td>
</tr>
</tbody>
</table>
</div></blockquote>
</dd></dl>
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</div>
</div>
<div class="section" id="recurrent-layers">
<h2>Recurrent Layers<a class="headerlink" href="#recurrent-layers" title="永久链接至标题"></a></h2>
<div class="section" id="recurrent">
<h3>recurrent<a class="headerlink" href="#recurrent" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">recurrent</code></dt>
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<dd><p>Simple recurrent unit layer. It is just a fully connect layer through both
time and neural network.</p>
<p>For each sequence [start, end] it performs the following computation:</p>
<div class="math">
\[\begin{split}out_{i} = act(in_{i})     \      \      \text{for} \ i = start \\
out_{i} = act(in_{i} + out_{i-1} * W) \ \ \text{for} \ start &lt; i &lt;= end\end{split}\]</div>
<p>If reversed is true, the order is reversed:</p>
<div class="math">
\[\begin{split}out_{i} = act(in_{i})           \    \   \text{for} \ i = end  \\
out_{i} = act(in_{i} + out_{i+1} * W) \ \ \text{for} \ start &lt;= i &lt; end\end{split}\]</div>
<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 Layer</li>
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<li><strong>act</strong> (<em>paddle.v2.activation.Base</em>) &#8211; activation.</li>
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<li><strong>bias_attr</strong> (<em>paddle.v2.attr.ParameterAttribute</em>) &#8211; bias attribute.</li>
<li><strong>param_attr</strong> (<em>paddle.v2.attr.ParameterAttribute</em>) &#8211; parameter attribute.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; name of the layer</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; Layer Attribute.</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 class="section" id="lstmemory">
<h3>lstmemory<a class="headerlink" href="#lstmemory" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">lstmemory</code></dt>
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<dd><p>Long Short-term Memory Cell.</p>
<p>The memory cell was implemented as follow equations.</p>
<div class="math">
\[ \begin{align}\begin{aligned}i_t &amp; = \sigma(W_{xi}x_{t} + W_{hi}h_{t-1} + W_{ci}c_{t-1} + b_i)\\f_t &amp; = \sigma(W_{xf}x_{t} + W_{hf}h_{t-1} + W_{cf}c_{t-1} + b_f)\\c_t &amp; = f_tc_{t-1} + i_t tanh (W_{xc}x_t+W_{hc}h_{t-1} + b_c)\\o_t &amp; = \sigma(W_{xo}x_{t} + W_{ho}h_{t-1} + W_{co}c_t + b_o)\\h_t &amp; = o_t tanh(c_t)\end{aligned}\end{align} \]</div>
<p>NOTE: In PaddlePaddle&#8217;s implementation, the multiplications
<span class="math">\(W_{xi}x_{t}\)</span> , <span class="math">\(W_{xf}x_{t}\)</span>,
<span class="math">\(W_{xc}x_t\)</span>, <span class="math">\(W_{xo}x_{t}\)</span> are not done in the lstmemory layer,
so an additional mixed with full_matrix_projection or a fc must
be included in the configuration file to complete the input-to-hidden
mappings before lstmemory is called.</p>
<p>NOTE: This is a low level user interface. You can use network.simple_lstm
to config a simple plain lstm layer.</p>
<p>Please refer to <strong>Generating Sequences With Recurrent Neural Networks</strong> for
more details about LSTM.</p>
<p><a class="reference external" href="http://arxiv.org/abs/1308.0850">Link</a> goes as below.</p>
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<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 lstmemory layer name.</li>
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<li><strong>size</strong> (<em>int</em>) &#8211; DEPRECATED. size of the lstm cell</li>
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<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; input layer name.</li>
<li><strong>reverse</strong> (<em>bool</em>) &#8211; is sequence process reversed or not.</li>
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<li><strong>act</strong> (<em>paddle.v2.activation.Base</em>) &#8211; activation type, paddle.v2.activation.Tanh by default. <span class="math">\(h_t\)</span></li>
<li><strong>gate_act</strong> (<em>paddle.v2.activation.Base</em>) &#8211; gate activation type, paddle.v2.activation.Sigmoid by default.</li>
<li><strong>state_act</strong> (<em>paddle.v2.activation.Base</em>) &#8211; state activation type, paddle.v2.activation.Tanh by default.</li>
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<li><strong>bias_attr</strong> (<em>paddle.v2.attr.ParameterAttribute|None|False</em>) &#8211; Bias attribute. None means default bias. False means no
bias.</li>
<li><strong>param_attr</strong> (<em>paddle.v2.attr.ParameterAttribute|None|False</em>) &#8211; Parameter Attribute.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttributeNone</em>) &#8211; Extra Layer attribute</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 class="section" id="grumemory">
<h3>grumemory<a class="headerlink" href="#grumemory" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">grumemory</code></dt>
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<dd><p>Gate Recurrent Unit Layer.</p>
<p>The memory cell was implemented as follow equations.</p>
<p>1. update gate <span class="math">\(z\)</span>: defines how much of the previous memory to
keep around or the unit updates its activations. The update gate
is computed by:</p>
<div class="math">
\[z_t = \sigma(W_{z}x_{t} + U_{z}h_{t-1} + b_z)\]</div>
<p>2. reset gate <span class="math">\(r\)</span>: determines how to combine the new input with the
previous memory. The reset gate is computed similarly to the update gate:</p>
<div class="math">
\[r_t = \sigma(W_{r}x_{t} + U_{r}h_{t-1} + b_r)\]</div>
<p>3. The candidate activation <span class="math">\(\tilde{h_t}\)</span> is computed similarly to
that of the traditional recurrent unit:</p>
<div class="math">
\[{\tilde{h_t}} = tanh(W x_{t} + U (r_{t} \odot h_{t-1}) + b)\]</div>
<p>4. The hidden activation <span class="math">\(h_t\)</span> of the GRU at time t is a linear
interpolation between the previous activation <span class="math">\(h_{t-1}\)</span> and the
candidate activation <span class="math">\(\tilde{h_t}\)</span>:</p>
<div class="math">
\[h_t = (1 - z_t) h_{t-1} + z_t {\tilde{h_t}}\]</div>
<p>NOTE: In PaddlePaddle&#8217;s implementation, the multiplication operations
<span class="math">\(W_{r}x_{t}\)</span>, <span class="math">\(W_{z}x_{t}\)</span> and <span class="math">\(W x_t\)</span> are not computed in
gate_recurrent layer. Consequently, an additional mixed with
full_matrix_projection or a fc must be included before grumemory
is called.</p>
<p>More details can be found by referring to <a class="reference external" href="https://arxiv.org/abs/1412.3555">Empirical Evaluation of Gated
Recurrent Neural Networks on Sequence Modeling.</a></p>
<p>The simple usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">gru</span> <span class="o">=</span> <span class="n">grumemory</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
</pre></div>
</div>
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<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>None|basestring</em>) &#8211; The gru layer name.</li>
<li><strong>input</strong> (<em>paddle.v2.config_base.Layer.</em>) &#8211; input layer.</li>
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<li><strong>size</strong> (<em>int</em>) &#8211; DEPRECATED. size of the gru cell</li>
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<li><strong>reverse</strong> (<em>bool</em>) &#8211; Whether sequence process is reversed or not.</li>
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<li><strong>act</strong> (<em>paddle.v2.activation.Base</em>) &#8211; activation type, paddle.v2.activation.Tanh by default. This activation
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affects the <span class="math">\({\tilde{h_t}}\)</span>.</li>
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<li><strong>gate_act</strong> (<em>paddle.v2.activation.Base</em>) &#8211; gate activation type, paddle.v2.activation.Sigmoid by default.
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This activation affects the <span class="math">\(z_t\)</span> and <span class="math">\(r_t\)</span>. It is the
<span class="math">\(\sigma\)</span> in the above formula.</li>
<li><strong>bias_attr</strong> (<em>paddle.v2.attr.ParameterAttribute|None|False</em>) &#8211; Bias attribute. None means default bias. False means no
bias.</li>
<li><strong>param_attr</strong> (<em>paddle.v2.attr.ParameterAttribute|None|False</em>) &#8211; Parameter Attribute.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttributeNone</em>) &#8211; Extra Layer attribute</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 class="section" id="recurrent-layer-group">
<h2>Recurrent Layer Group<a class="headerlink" href="#recurrent-layer-group" title="永久链接至标题"></a></h2>
<div class="section" id="memory">
<h3>memory<a class="headerlink" href="#memory" title="永久链接至标题"></a></h3>
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<dl class="class">
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<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">memory</code></dt>
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<dd><p>The memory layers is a layer cross each time step. Reference this output
as previous time step layer <code class="code docutils literal"><span class="pre">name</span></code> &#8216;s output.</p>
<p>The default memory is zero in first time step, previous time step&#8217;s
output in the rest time steps.</p>
<p>If boot_bias, the first time step value is this bias and
with activation.</p>
<p>If boot_with_const_id, then the first time stop is a IndexSlot, the
Arguments.ids()[0] is this <code class="code docutils literal"><span class="pre">cost_id</span></code>.</p>
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<p>If boot is not null, the memory is just the boot&#8217;s output.
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Set <code class="code docutils literal"><span class="pre">is_seq</span></code> is true boot layer is sequence.</p>
<p>The same name layer in recurrent group will set memory on each time
step.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">mem</span> <span class="o">=</span> <span class="n">memory</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;state&#39;</span><span class="p">)</span>
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<span class="n">state</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">mem</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;state&#39;</span><span class="p">)</span>
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</pre></div>
</div>
<p>If you do not want to specify the name, you can equivalently use set_input()
to specify the layer needs to be remembered as the following:</p>
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<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">mem</span> <span class="o">=</span> <span class="n">memory</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">256</span><span class="p">)</span>
<span class="n">state</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">mem</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">256</span><span class="p">)</span>
<span class="n">mem</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">mem</span><span class="p">)</span>
</pre></div>
</div>
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<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 name of the layer which this memory remembers.
If name is None, user should call set_input() to specify the
name of the layer which this memory remembers.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; size of memory.</li>
<li><strong>memory_name</strong> (<em>basestring</em>) &#8211; the name of the memory.
It is ignored when name is provided.</li>
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<li><strong>is_seq</strong> (<em>bool</em>) &#8211; DEPRECATED. is sequence for boot</li>
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<li><strong>boot</strong> (<em>paddle.v2.config_base.Layer|None</em>) &#8211; boot layer of memory.</li>
<li><strong>boot_bias</strong> (<em>paddle.v2.attr.ParameterAttribute|None</em>) &#8211; boot layer&#8217;s bias</li>
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<li><strong>boot_bias_active_type</strong> (<em>paddle.v2.activation.Base</em>) &#8211; boot layer&#8217;s active type.</li>
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<li><strong>boot_with_const_id</strong> (<em>int</em>) &#8211; boot layer&#8217;s id.</li>
</ul>
</td>
</tr>
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<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 which is a memory.</p>
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</td>
</tr>
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<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>
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</td>
</tr>
</tbody>
</table>
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</dd></dl>
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</div>
<div class="section" id="recurrent-group">
<h3>recurrent_group<a class="headerlink" href="#recurrent-group" title="永久链接至标题"></a></h3>
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<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">recurrent_group</code></dt>
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<dd><p>Recurrent layer group is an extremely flexible recurrent unit in
PaddlePaddle. As long as the user defines the calculation done within a
time step, PaddlePaddle will iterate such a recurrent calculation over
sequence input. This is extremely usefull for attention based model, or
Neural Turning Machine like models.</p>
<p>The basic usage (time steps) is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">step</span><span class="p">(</span><span class="nb">input</span><span class="p">):</span>
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    <span class="n">output</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">layer</span><span class="p">,</span>
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                      <span class="n">size</span><span class="o">=</span><span class="mi">1024</span><span class="p">,</span>
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                      <span class="n">act</span><span class="o">=</span><span class="n">paddle</span><span class="o">.</span><span class="n">v2</span><span class="o">.</span><span class="n">activation</span><span class="o">.</span><span class="n">Linear</span><span class="p">(),</span>
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                      <span class="n">bias_attr</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">output</span>

<span class="n">group</span> <span class="o">=</span> <span class="n">recurrent_group</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">,</span>
                        <span class="n">step</span><span class="o">=</span><span class="n">step</span><span class="p">)</span>
</pre></div>
</div>
<p>You can see following configs for further usages:</p>
<ul class="simple">
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<li>time steps: lstmemory_group, paddle/gserver/tests/sequence_group.conf,                   demo/seqToseq/seqToseq_net.py</li>
<li>sequence steps: paddle/gserver/tests/sequence_nest_group.conf</li>
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</ul>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
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<tr class="field-odd field"><th class="field-name">参数:</th><td class="field-body"><ul class="first simple">
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<li><strong>step</strong> (<em>callable</em>) &#8211; <p>recurrent one time step function.The input of this function is
input of the group. The return of this function will be
recurrent group&#8217;s return value.</p>
<p>The recurrent group scatter a sequence into time steps. And
for each time step, will invoke step function, and return
a time step result. Then gather each time step of output into
layer group&#8217;s output.</p>
</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; recurrent_group&#8217;s name.</li>
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<li><strong>input</strong> (<em>paddle.v2.config_base.Layer|StaticInput|SubsequenceInput|list|tuple</em>) &#8211; <p>Input links array.</p>
<p>paddle.v2.config_base.Layer will be scattered into time steps.
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SubsequenceInput will be scattered into sequence steps.
StaticInput will be imported to each time step, and doesn&#8217;t change
through time. It&#8217;s a mechanism to access layer outside step function.</p>
</li>
<li><strong>reverse</strong> (<em>bool</em>) &#8211; If reverse is set true, the recurrent unit will process the
input sequence in a reverse order.</li>
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<li><strong>targetInlink</strong> (<em>paddle.v2.config_base.Layer|SubsequenceInput</em>) &#8211; <p>DEPRECATED.
The input layer which share info with layer group&#8217;s output</p>
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<p>Param input specifies multiple input layers. For
SubsequenceInput inputs, config should assign one input
layer that share info(the number of sentences and the number
of words in each sentence) with all layer group&#8217;s outputs.
targetInlink should be one of the layer group&#8217;s input.</p>
</li>
</ul>
</td>
</tr>
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<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>
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</tr>
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<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>
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</tr>
</tbody>
</table>
</dd></dl>
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</div>
<div class="section" id="lstm-step">
<h3>lstm_step<a class="headerlink" href="#lstm-step" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">lstm_step</code></dt>
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<dd><p>LSTM Step Layer. This function is used only in recurrent_group.
The lstm equations are shown as follows.</p>
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<div class="math">
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\[ \begin{align}\begin{aligned}i_t &amp; = \sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + W_{c_i}c_{t-1} + b_i)\\f_t &amp; = \sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + W_{c_f}c_{t-1} + b_f)\\c_t &amp; = f_tc_{t-1} + i_t tanh (W_{x_c}x_t+W_{h_c}h_{t-1} + b_c)\\o_t &amp; = \sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + W_{c_o}c_t + b_o)\\h_t &amp; = o_t tanh(c_t)\end{aligned}\end{align} \]</div>
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<p>The input of lstm step is <span class="math">\(Wx_t + Wh_{t-1}\)</span>, and user should use
<code class="code docutils literal"><span class="pre">mixed</span></code> and <code class="code docutils literal"><span class="pre">full_matrix_projection</span></code> to calculate these
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input vectors.</p>
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<p>The state of lstm step is <span class="math">\(c_{t-1}\)</span>. And lstm step layer will do</p>
<div class="math">
\[ \begin{align}\begin{aligned}i_t = \sigma(input + W_{ci}c_{t-1} + b_i)\\...\end{aligned}\end{align} \]</div>
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<p>This layer has two outputs. Default output is <span class="math">\(h_t\)</span>. The other
output is <span class="math">\(o_t\)</span>, whose name is &#8216;state&#8217; and can use
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<code class="code docutils literal"><span class="pre">get_output</span></code> to extract this output.</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; Layer&#8217;s name.</li>
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<li><strong>size</strong> (<em>int</em>) &#8211; Layer&#8217;s size. NOTE: lstm layer&#8217;s size, should be equal to
<code class="code docutils literal"><span class="pre">input.size/4</span></code>, and should be equal to
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<code class="code docutils literal"><span class="pre">state.size</span></code>.</li>
<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; input layer. <span class="math">\(Wx_t + Wh_{t-1}\)</span></li>
<li><strong>state</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; State Layer. <span class="math">\(c_{t-1}\)</span></li>
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<li><strong>act</strong> (<em>paddle.v2.activation.Base</em>) &#8211; Activation type. Default is tanh</li>
<li><strong>gate_act</strong> (<em>paddle.v2.activation.Base</em>) &#8211; Gate Activation Type. Default is sigmoid, and should
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be sigmoid only.</li>
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<li><strong>state_act</strong> (<em>paddle.v2.activation.Base</em>) &#8211; State Activation Type. Default is sigmoid, and should
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be sigmoid only.</li>
<li><strong>bias_attr</strong> (<em>paddle.v2.attr.ParameterAttribute</em>) &#8211; Bias Attribute.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; layer&#8217;s extra attribute.</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 class="section" id="gru-step">
<h3>gru_step<a class="headerlink" href="#gru-step" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">gru_step</code></dt>
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<dd><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; </li>
<li><strong>output_mem</strong> &#8211; </li>
<li><strong>size</strong> &#8211; </li>
<li><strong>act</strong> &#8211; </li>
<li><strong>name</strong> &#8211; </li>
<li><strong>gate_act</strong> &#8211; </li>
<li><strong>bias_attr</strong> &#8211; </li>
<li><strong>param_attr</strong> &#8211; the parameter_attribute for transforming the output_mem
from previous step.</li>
<li><strong>layer_attr</strong> &#8211; </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 class="section" id="beam-search">
<h3>beam_search<a class="headerlink" href="#beam-search" title="永久链接至标题"></a></h3>
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<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">beam_search</code></dt>
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<dd><p>Beam search is a heuristic search algorithm used in sequence generation.
It explores a graph by expanding the most promising nodes in a limited set
to maintain tractability.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">rnn_step</span><span class="p">(</span><span class="nb">input</span><span class="p">):</span>
    <span class="n">last_time_step_output</span> <span class="o">=</span> <span class="n">memory</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;rnn&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">512</span><span class="p">)</span>
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    <span class="k">with</span> <span class="n">mixed</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">512</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;rnn&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">simple_rnn</span><span class="p">:</span>
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        <span class="n">simple_rnn</span> <span class="o">+=</span> <span class="n">full_matrix_projection</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
        <span class="n">simple_rnn</span> <span class="o">+=</span> <span class="n">last_time_step_output</span>
    <span class="k">return</span> <span class="n">simple_rnn</span>

<span class="n">generated_word_embedding</span> <span class="o">=</span> <span class="n">GeneratedInput</span><span class="p">(</span>
                       <span class="n">size</span><span class="o">=</span><span class="n">target_dictionary_dim</span><span class="p">,</span>
                       <span class="n">embedding_name</span><span class="o">=</span><span class="s2">&quot;target_language_embedding&quot;</span><span class="p">,</span>
                       <span class="n">embedding_size</span><span class="o">=</span><span class="n">word_vector_dim</span><span class="p">)</span>

<span class="n">beam_gen</span> <span class="o">=</span> <span class="n">beam_search</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;decoder&quot;</span><span class="p">,</span>
                       <span class="n">step</span><span class="o">=</span><span class="n">rnn_step</span><span class="p">,</span>
                       <span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">StaticInput</span><span class="p">(</span><span class="n">encoder_last</span><span class="p">),</span>
                              <span class="n">generated_word_embedding</span><span class="p">],</span>
                       <span class="n">bos_id</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
                       <span class="n">eos_id</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                       <span class="n">beam_size</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
</pre></div>
</div>
<p>Please see the following demo for more details:</p>
<ul class="simple">
<li>machine translation : demo/seqToseq/translation/gen.conf                             demo/seqToseq/seqToseq_net.py</li>
</ul>
<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>base string</em>) &#8211; Name of the recurrent unit that generates sequences.</li>
<li><strong>step</strong> (<em>callable</em>) &#8211; <p>A callable function that defines the calculation in a time
step, and it is applied to sequences with arbitrary length by
sharing a same set of weights.</p>
<p>You can refer to the first parameter of recurrent_group, or
demo/seqToseq/seqToseq_net.py for more details.</p>
</li>
<li><strong>input</strong> (<em>list</em>) &#8211; Input data for the recurrent unit, which should include the
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previously generated words as a GeneratedInput object.
In beam_search, none of the input&#8217;s type should be paddle.v2.config_base.Layer.</li>
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<li><strong>bos_id</strong> (<em>int</em>) &#8211; Index of the start symbol in the dictionary. The start symbol
is a special token for NLP task, which indicates the
beginning of a sequence. In the generation task, the start
symbol is essential, since it is used to initialize the RNN
internal state.</li>
<li><strong>eos_id</strong> (<em>int</em>) &#8211; Index of the end symbol in the dictionary. The end symbol is
a special token for NLP task, which indicates the end of a
sequence. The generation process will stop once the end
symbol is generated, or a pre-defined max iteration number
is exceeded.</li>
<li><strong>max_length</strong> (<em>int</em>) &#8211; Max generated sequence length.</li>
<li><strong>beam_size</strong> (<em>int</em>) &#8211; Beam search for sequence generation is an iterative search
algorithm. To maintain tractability, every iteration only
only stores a predetermined number, called the beam_size,
of the most promising next words. The greater the beam
size, the fewer candidate words are pruned.</li>
<li><strong>num_results_per_sample</strong> (<em>int</em>) &#8211; Number of the generated results per input
sequence. This number must always be less than
beam size.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">The generated word index.</p>
</td>
</tr>
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<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>
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</td>
</tr>
</tbody>
</table>
</dd></dl>
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</div>
<div class="section" id="get-output">
<h3>get_output<a class="headerlink" href="#get-output" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">get_output</code></dt>
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<dd><p>Get layer&#8217;s output by name. In PaddlePaddle, a layer might return multiple
values, but returns one layer&#8217;s output. If the user wants to use another
output besides the default one, please use get_output first to get
the output from input.</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; Layer&#8217;s name.</li>
<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; get output layer&#8217;s input. And this layer should contains
multiple outputs.</li>
<li><strong>arg_name</strong> (<em>basestring</em>) &#8211; Output name from input.</li>
<li><strong>layer_attr</strong> &#8211; Layer&#8217;s extra attribute.</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 class="section" id="mixed-layer">
<h2>Mixed Layer<a class="headerlink" href="#mixed-layer" title="永久链接至标题"></a></h2>
<div class="section" id="mixed">
<span id="api-v2-layer-mixed"></span><h3>mixed<a class="headerlink" href="#mixed" title="永久链接至标题"></a></h3>
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<dl class="class">
<dt>
<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">mixed</code></dt>
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<dd><p>Mixed Layer. A mixed layer will add all inputs together, then activate.
Each inputs is a projection or operator.</p>
<p>There are two styles of usages.</p>
<ol class="arabic simple">
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<li>When not set inputs parameter, use mixed like this:</li>
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</ol>
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<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">mixed</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">256</span><span class="p">)</span> <span class="k">as</span> <span class="n">m</span><span class="p">:</span>
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    <span class="n">m</span> <span class="o">+=</span> <span class="n">full_matrix_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer1</span><span class="p">)</span>
    <span class="n">m</span> <span class="o">+=</span> <span class="n">identity_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer2</span><span class="p">)</span>
</pre></div>
</div>
<ol class="arabic simple" start="2">
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<li>You can also set all inputs when invoke mixed as follows:</li>
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</ol>
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<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">m</span> <span class="o">=</span> <span class="n">mixed</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span>
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                <span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">full_matrix_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer1</span><span class="p">),</span>
                       <span class="n">full_matrix_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer2</span><span class="p">)])</span>
</pre></div>
</div>
<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; mixed layer name. Can be referenced by other layer.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; layer size.</li>
<li><strong>input</strong> &#8211; inputs layer. It is an optional parameter. If set,
then this function will just return layer&#8217;s name.</li>
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<li><strong>act</strong> (<em>paddle.v2.activation.Base</em>) &#8211; Activation Type.</li>
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<li><strong>bias_attr</strong> (<em>paddle.v2.attr.ParameterAttribute</em><em> or </em><em>None</em><em> or </em><em>bool</em>) &#8211; The Bias Attribute. If no bias, then pass False or
something not type of paddle.v2.attr.ParameterAttribute. None will get a
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default Bias.</li>
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<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; The extra layer config. Default is None.</li>
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</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">MixedLayerType object can add inputs or layer name.</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">MixedLayerType</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
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</div>
<div class="section" id="embedding">
<span id="api-v2-layer-embedding"></span><h3>embedding<a class="headerlink" href="#embedding" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">embedding</code></dt>
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<dd><p>Define a embedding Layer.</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; Name of this embedding layer.</li>
<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; The input layer for this embedding. NOTE: must be Index Data.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; The embedding dimension.</li>
<li><strong>param_attr</strong> (<em>paddle.v2.attr.ParameterAttribute|None</em>) &#8211; The embedding parameter attribute. See paddle.v2.attr.ParameterAttribute
for details.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttributeNone</em>) &#8211; Extra layer Config. Default is None.</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 class="section" id="scaling-projection">
<h3>scaling_projection<a class="headerlink" href="#scaling-projection" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">scaling_projection</code></dt>
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<dd><p>scaling_projection multiplies the input with a scalar parameter and add to
the output.</p>
<div class="math">
\[out += w * in\]</div>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">proj</span> <span class="o">=</span> <span class="n">scaling_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">)</span>
</pre></div>
</div>
<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 Layer.</li>
<li><strong>param_attr</strong> (<em>paddle.v2.attr.ParameterAttribute</em>) &#8211; Parameter config, None if use default.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">A ScalingProjection 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">ScalingProjection</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="dotmul-projection">
<h3>dotmul_projection<a class="headerlink" href="#dotmul-projection" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">dotmul_projection</code></dt>
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<dd><p>DotMulProjection with a layer as input.
It performs element-wise multiplication with weight.</p>
<div class="math">
\[out.row[i] += in.row[i] .* weight\]</div>
<p>where <span class="math">\(.*\)</span> means element-wise multiplication.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">proj</span> <span class="o">=</span> <span class="n">dotmul_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">)</span>
</pre></div>
</div>
<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 layer.</li>
<li><strong>param_attr</strong> (<em>paddle.v2.attr.ParameterAttribute</em>) &#8211; Parameter config, None if use default.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">A DotMulProjection 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">DotMulProjection</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="dotmul-operator">
<h3>dotmul_operator<a class="headerlink" href="#dotmul-operator" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">dotmul_operator</code></dt>
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<dd><p>DotMulOperator takes two inputs and performs element-wise multiplication:</p>
<div class="math">
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\[out.row[i] += scale * (a.row[i] .* b.row[i])\]</div>
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<p>where <span class="math">\(.*\)</span> means element-wise multiplication, and
scale is a config scalar, its default value is one.</p>
<p>The example usage is:</p>
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<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">op</span> <span class="o">=</span> <span class="n">dotmul_operator</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="n">layer1</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="n">layer2</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
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</pre></div>
</div>
<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>a</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Input layer1</li>
<li><strong>b</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Input layer2</li>
<li><strong>scale</strong> (<em>float</em>) &#8211; config scalar, default value is one.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">A DotMulOperator 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">DotMulOperator</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="full-matrix-projection">
<h3>full_matrix_projection<a class="headerlink" href="#full-matrix-projection" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">full_matrix_projection</code></dt>
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<dd><p>Full Matrix Projection. It performs full matrix multiplication.</p>
<div class="math">
\[out.row[i] += in.row[i] * weight\]</div>
<p>There are two styles of usage.</p>
<ol class="arabic simple">
<li>When used in mixed like this, you can only set the input:</li>
</ol>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">mixed</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span> <span class="k">as</span> <span class="n">m</span><span class="p">:</span>
    <span class="n">m</span> <span class="o">+=</span> <span class="n">full_matrix_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">)</span>
</pre></div>
</div>
<ol class="arabic simple" start="2">
<li>When used as an independant object like this, you must set the size:</li>
</ol>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">proj</span> <span class="o">=</span> <span class="n">full_matrix_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">,</span>
                              <span class="n">size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
                              <span class="n">param_attr</span><span class="o">=</span><span class="n">ParamAttr</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;_proj&#39;</span><span class="p">))</span>
</pre></div>
</div>
<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 layer</li>
<li><strong>size</strong> (<em>int</em>) &#8211; The parameter size. Means the width of parameter.</li>
<li><strong>param_attr</strong> (<em>paddle.v2.attr.ParameterAttribute</em>) &#8211; Parameter config, None if use default.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">A FullMatrixProjection 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">FullMatrixProjection</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="identity-projection">
<h3>identity_projection<a class="headerlink" href="#identity-projection" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">identity_projection</code></dt>
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<dd><ol class="arabic simple">
<li>IdentityProjection if offset=None. It performs:</li>
</ol>
<div class="math">
\[out.row[i] += in.row[i]\]</div>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">proj</span> <span class="o">=</span> <span class="n">identity_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">)</span>
</pre></div>
</div>
<p>2. IdentityOffsetProjection if offset!=None. It likes IdentityProjection,
but layer size may be smaller than input size.
It select dimesions [offset, offset+layer_size) from input:</p>
<div class="math">
\[out.row[i] += in.row[i + \textrm{offset}]\]</div>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">proj</span> <span class="o">=</span> <span class="n">identity_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">,</span>
                           <span class="n">offset</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
</pre></div>
</div>
<p>Note that both of two projections should not have any parameter.</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 Layer.</li>
<li><strong>offset</strong> (<em>int</em>) &#8211; Offset, None if use default.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">A IdentityProjection or IdentityOffsetProjection 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">IdentityProjection or IdentityOffsetProjection</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
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</div>
<div class="section" id="slice-projection">
<h3>slice_projection<a class="headerlink" href="#slice-projection" 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">slice_projection</code></dt>
<dd><p>slice_projection can slice the input value into multiple parts,
and then select some of them to merge into a new output.</p>
<div class="math">
\[output = [input.slices()]\]</div>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">proj</span> <span class="o">=</span> <span class="n">slice_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">,</span> <span class="n">slices</span><span class="o">=</span><span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">)])</span>
</pre></div>
</div>
<p>Note that slice_projection should not have any parameter.</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 Layer.</li>
<li><strong>slices</strong> (<em>pair of int</em>) &#8211; An array of slice parameters.
Each slice contains the start and end offsets based
on the input.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">A SliceProjection 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">SliceProjection</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
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</div>
<div class="section" id="table-projection">
<h3>table_projection<a class="headerlink" href="#table-projection" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">table_projection</code></dt>
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<dd><p>Table Projection. It selects rows from parameter where row_id
is in input_ids.</p>
<div class="math">
\[out.row[i] += table.row[ids[i]]\]</div>
<p>where <span class="math">\(out\)</span> is output, <span class="math">\(table\)</span> is parameter, <span class="math">\(ids\)</span> is input_ids,
and <span class="math">\(i\)</span> is row_id.</p>
<p>There are two styles of usage.</p>
<ol class="arabic simple">
<li>When used in mixed like this, you can only set the input:</li>
</ol>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">mixed</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span> <span class="k">as</span> <span class="n">m</span><span class="p">:</span>
    <span class="n">m</span> <span class="o">+=</span> <span class="n">table_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">)</span>
</pre></div>
</div>
<ol class="arabic simple" start="2">
<li>When used as an independant object like this, you must set the size:</li>
</ol>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">proj</span> <span class="o">=</span> <span class="n">table_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">,</span>
                        <span class="n">size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
                        <span class="n">param_attr</span><span class="o">=</span><span class="n">ParamAttr</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;_proj&#39;</span><span class="p">))</span>
</pre></div>
</div>
<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 layer, which must contains id fields.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; The parameter size. Means the width of parameter.</li>
<li><strong>param_attr</strong> (<em>paddle.v2.attr.ParameterAttribute</em>) &#8211; Parameter config, None if use default.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">A TableProjection 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">TableProjection</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="trans-full-matrix-projection">
<h3>trans_full_matrix_projection<a class="headerlink" href="#trans-full-matrix-projection" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">trans_full_matrix_projection</code></dt>
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<dd><p>Different from full_matrix_projection, this projection performs matrix
multiplication, using transpose of weight.</p>
<div class="math">
\[out.row[i] += in.row[i] * w^\mathrm{T}\]</div>
<p><span class="math">\(w^\mathrm{T}\)</span> means transpose of weight.
The simply usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">proj</span> <span class="o">=</span> <span class="n">trans_full_matrix_projection</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">,</span>
                                    <span class="n">size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
                                    <span class="n">param_attr</span><span class="o">=</span><span class="n">ParamAttr</span><span class="p">(</span>
                                         <span class="n">name</span><span class="o">=</span><span class="s1">&#39;_proj&#39;</span><span class="p">,</span>
                                         <span class="n">initial_mean</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
                                         <span class="n">initial_std</span><span class="o">=</span><span class="mf">0.01</span><span class="p">))</span>
</pre></div>
</div>
<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 layer</li>
<li><strong>size</strong> (<em>int</em>) &#8211; The parameter size. Means the width of parameter.</li>
<li><strong>param_attr</strong> (<em>paddle.v2.attr.ParameterAttribute</em>) &#8211; Parameter config, None if use default.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">A TransposedFullMatrixProjection 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">TransposedFullMatrixProjection</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
</div>
<div class="section" id="aggregate-layers">
<h2>Aggregate Layers<a class="headerlink" href="#aggregate-layers" title="永久链接至标题"></a></h2>
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<div class="section" id="aggregatelevel">
<h3>AggregateLevel<a class="headerlink" href="#aggregatelevel" 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">AggregateLevel</code></dt>
<dd><p>PaddlePaddle supports three sequence types:</p>
<ul class="simple">
<li><code class="code docutils literal"><span class="pre">SequenceType.NO_SEQUENCE</span></code> means the sample is not a sequence.</li>
<li><code class="code docutils literal"><span class="pre">SequenceType.SEQUENCE</span></code> means the sample is a sequence.</li>
<li><code class="code docutils literal"><span class="pre">SequenceType.SUB_SEQUENCE</span></code> means the sample is a nested sequence,
each timestep of which is also a sequence.</li>
</ul>
<p>Accordingly, AggregateLevel supports two modes:</p>
<ul class="simple">
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<li><code class="code docutils literal"><span class="pre">AggregateLevel.TO_NO_SEQUENCE</span></code> means the aggregation acts on each
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timestep of a sequence, both <code class="code docutils literal"><span class="pre">SUB_SEQUENCE</span></code> and <code class="code docutils literal"><span class="pre">SEQUENCE</span></code> will
be aggregated to <code class="code docutils literal"><span class="pre">NO_SEQUENCE</span></code>.</li>
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<li><code class="code docutils literal"><span class="pre">AggregateLevel.TO_SEQUENCE</span></code> means the aggregation acts on each
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sequence of a nested sequence, <code class="code docutils literal"><span class="pre">SUB_SEQUENCE</span></code> will be aggregated to
<code class="code docutils literal"><span class="pre">SEQUENCE</span></code>.</li>
</ul>
</dd></dl>

</div>
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<div class="section" id="api-v2-layer-pooling">
<span id="id1"></span><h3>pooling<a class="headerlink" href="#api-v2-layer-pooling" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">pooling</code></dt>
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<dd><p>Pooling layer for sequence inputs, not used for Image.</p>
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<p>If stride &gt; 0, this layer slides a window whose size is determined by stride,
and return the pooling value of the window as the output. Thus, a long sequence
will be shorten.</p>
<p>The parameter stride specifies the intervals at which to apply the pooling
operation. Note that for sequence with sub-sequence, the default value
of stride is -1.</p>
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<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">seq_pool</span> <span class="o">=</span> <span class="n">pooling</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">,</span>
                         <span class="n">pooling_type</span><span class="o">=</span><span class="n">AvgPooling</span><span class="p">(),</span>
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                         <span class="n">agg_level</span><span class="o">=</span><span class="n">AggregateLevel</span><span class="o">.</span><span class="n">TO_NO_SEQUENCE</span><span class="p">)</span>
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</pre></div>
</div>
<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">
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<li><strong>agg_level</strong> (<em>AggregateLevel</em>) &#8211; AggregateLevel.TO_NO_SEQUENCE or
AggregateLevel.TO_SEQUENCE</li>
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<li><strong>name</strong> (<em>basestring</em>) &#8211; layer name.</li>
<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; input layer name.</li>
<li><strong>pooling_type</strong> (<em>BasePoolingType|None</em>) &#8211; Type of pooling, MaxPooling(default), AvgPooling,
SumPooling, SquareRootNPooling.</li>
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<li><strong>stride</strong> (<em>Int</em>) &#8211; The step size between successive pooling regions.</li>
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<li><strong>bias_attr</strong> (<em>paddle.v2.attr.ParameterAttribute|None|False</em>) &#8211; Bias parameter attribute. False if no bias.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttributeNone</em>) &#8211; The Extra Attributes for layer, such as dropout.</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 class="section" id="last-seq">
<span id="api-v2-layer-last-seq"></span><h3>last_seq<a class="headerlink" href="#last-seq" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">last_seq</code></dt>
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<dd><p>Get Last Timestamp Activation of a sequence.</p>
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<p>If stride &gt; 0, this layer slides a window whose size is determined by stride,
and return the last value of the window as the output. Thus, a long sequence
will be shorten. Note that for sequence with sub-sequence, the default value
of stride is -1.</p>
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<p>The simple usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">seq</span> <span class="o">=</span> <span class="n">last_seq</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">)</span>
</pre></div>
</div>
<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>agg_level</strong> &#8211; Aggregated level</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; Layer name.</li>
<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Input layer name.</li>
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<li><strong>stride</strong> (<em>Int</em>) &#8211; The step size between successive pooling regions.</li>
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<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; extra layer attributes.</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 class="section" id="first-seq">
<span id="api-v2-layer-first-seq"></span><h3>first_seq<a class="headerlink" href="#first-seq" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">first_seq</code></dt>
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<dd><p>Get First Timestamp Activation of a sequence.</p>
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<p>If stride &gt; 0, this layer slides a window whose size is determined by stride,
and return the first value of the window as the output. Thus, a long sequence
will be shorten. Note that for sequence with sub-sequence, the default value
of stride is -1.</p>
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<p>The simple usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">seq</span> <span class="o">=</span> <span class="n">first_seq</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">)</span>
</pre></div>
</div>
<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>agg_level</strong> &#8211; aggregation level</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; Layer name.</li>
<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Input layer name.</li>
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<li><strong>stride</strong> (<em>Int</em>) &#8211; The step size between successive pooling regions.</li>
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<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; extra layer attributes.</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 class="section" id="concat">
<h3>concat<a class="headerlink" href="#concat" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">concat</code></dt>
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<dd><p>Concat all input vector into one huge vector.
Inputs can be list of paddle.v2.config_base.Layer or list of projection.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">concat</span> <span class="o">=</span> <span class="n">concat</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">layer1</span><span class="p">,</span> <span class="n">layer2</span><span class="p">])</span>
</pre></div>
</div>
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<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; Layer name.</li>
<li><strong>input</strong> (<em>list|tuple|collections.Sequence</em>) &#8211; input layers or projections</li>
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<li><strong>act</strong> (<em>paddle.v2.activation.Base</em>) &#8211; Activation type.</li>
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<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; Extra Layer Attribute.</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 class="section" id="seq-concat">
<h3>seq_concat<a class="headerlink" href="#seq-concat" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">seq_concat</code></dt>
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<dd><p>Concat sequence a with sequence b.</p>
<dl class="docutils">
<dt>Inputs:</dt>
<dd><ul class="first last simple">
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<li>a = [a1, a2, ..., am]</li>
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<li>b = [b1, b2, ..., bn]</li>
</ul>
</dd>
</dl>
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<p>Output: [a1, ..., am, b1, ..., bn]</p>
<p>Note that the above computation is for one sample. Multiple samples are
processed in one batch.</p>
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<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">concat</span> <span class="o">=</span> <span class="n">seq_concat</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="n">layer1</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="n">layer2</span><span class="p">)</span>
</pre></div>
</div>
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<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; Layer name.</li>
<li><strong>a</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; input sequence layer</li>
<li><strong>b</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; input sequence layer</li>
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<li><strong>act</strong> (<em>paddle.v2.activation.Base</em>) &#8211; Activation type.</li>
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<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; Extra Layer Attribute.</li>
<li><strong>bias_attr</strong> (<em>paddle.v2.attr.ParameterAttribute</em><em> or </em><em>None</em><em> or </em><em>bool</em>) &#8211; The Bias Attribute. If no bias, then pass False or
something not type of paddle.v2.attr.ParameterAttribute. None will get a
default Bias.</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>
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</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 class="section" id="seq-slice">
<h3>seq_slice<a class="headerlink" href="#seq-slice" 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">seq_slice</code></dt>
<dd><p>seq_slice will return one or several sub-sequences from the
input sequence layer given start and end indices.</p>
<blockquote>
<div><ul class="simple">
<li>If only start indices are given, and end indices are set to None,
this layer slices the input sequence from the given start indices
to its end.</li>
<li>If only end indices are given, and start indices are set to None,
this layer slices the input sequence from its beginning to the
given end indices.</li>
<li>If start and end indices are both given, they should have the same
number of elements.</li>
</ul>
</div></blockquote>
<p>If start or end indices contains more than one elements, the input sequence
will be sliced for multiple times.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">seq_silce</span> <span class="o">=</span> <span class="n">seq_slice</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">input_seq</span><span class="p">,</span>
                            <span class="n">starts</span><span class="o">=</span><span class="n">start_pos</span><span class="p">,</span> <span class="n">ends</span><span class="o">=</span><span class="n">end_pos</span><span class="p">)</span>
</pre></div>
</div>
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<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; name of this layer.</li>
<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; input for this layer, it should be a sequence.</li>
<li><strong>starts</strong> (<em>paddle.v2.config_base.Layer|None</em>) &#8211; start indices to slice the input sequence.</li>
<li><strong>ends</strong> (<em>paddle.v2.config_base.Layer|None</em>) &#8211; end indices to slice the input sequence.</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>
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</td>
</tr>
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<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>
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</div>
<div class="section" id="kmax-sequence-score">
<h3>kmax_sequence_score<a class="headerlink" href="#kmax-sequence-score" title="永久链接至标题"></a></h3>
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</div>
<div class="section" id="sub-nested-seq">
<h3>sub_nested_seq<a class="headerlink" href="#sub-nested-seq" 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">sub_nested_seq</code></dt>
<dd><p>The sub_nested_seq accepts two inputs: the first one is a nested
sequence; the second one is a set of selceted indices in the nested sequence.</p>
<p>Then sub_nest_seq trims the first nested sequence input according
to the selected indices to form a new output. This layer is useful in
beam training.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">sub_nest_seq</span> <span class="o">=</span> <span class="n">sub_nested_seq</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">data</span><span class="p">,</span> <span class="n">selected_indices</span><span class="p">])</span>
</pre></div>
</div>
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<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; A nested sequence.</li>
<li><strong>selected_indices</strong> &#8211; a set of sequence indices in the nested sequence.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; name of this layer.</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>
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<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 class="section" id="reshaping-layers">
<h2>Reshaping Layers<a class="headerlink" href="#reshaping-layers" title="永久链接至标题"></a></h2>
<div class="section" id="block-expand">
<h3>block_expand<a class="headerlink" href="#block-expand" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">block_expand</code></dt>
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<dd><dl class="docutils">
<dt>Expand feature map to minibatch matrix.</dt>
<dd><ul class="first last simple">
<li>matrix width is: block_y * block_x * num_channels</li>
<li>matirx height is: outputH * outputW</li>
</ul>
</dd>
</dl>
<div class="math">
\[ \begin{align}\begin{aligned}outputH = 1 + (2 * padding_y + imgSizeH - block_y + stride_y - 1) / stride_y\\outputW = 1 + (2 * padding_x + imgSizeW - block_x + stride_x - 1) / stride_x\end{aligned}\end{align} \]</div>
<p>The expand method is the same with ExpandConvLayer, but saved the transposed
value. After expanding, output.sequenceStartPositions will store timeline.
The number of time steps are outputH * outputW and the dimension of each
time step is block_y * block_x * num_channels. This layer can be used after
convolution neural network, and before recurrent neural network.</p>
<p>The simple usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">block_expand</span> <span class="o">=</span> <span class="n">block_expand</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">,</span>
                                  <span class="n">num_channels</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span>
                                  <span class="n">stride_x</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                                  <span class="n">stride_y</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                                  <span class="n">block_x</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                                  <span class="n">block_x</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
</pre></div>
</div>
<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; The input layer.</li>
<li><strong>num_channels</strong> (<em>int|None</em>) &#8211; The channel number of input layer.</li>
<li><strong>block_x</strong> (<em>int</em>) &#8211; The width of sub block.</li>
<li><strong>block_y</strong> (<em>int</em>) &#8211; The width of sub block.</li>
<li><strong>stride_x</strong> (<em>int</em>) &#8211; The stride size in horizontal direction.</li>
<li><strong>stride_y</strong> (<em>int</em>) &#8211; The stride size in vertical direction.</li>
<li><strong>padding_x</strong> (<em>int</em>) &#8211; The padding size in horizontal direction.</li>
<li><strong>padding_y</strong> (<em>int</em>) &#8211; The padding size in vertical direction.</li>
<li><strong>name</strong> (<em>None|basestring.</em>) &#8211; The name of this layer, which can not specify.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttributeNone</em>) &#8211; Extra Layer config.</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>
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</div>
<div class="section" id="expandlevel">
<span id="api-v2-layer-expand"></span><h3>ExpandLevel<a class="headerlink" href="#expandlevel" 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">ExpandLevel</code></dt>
<dd><p>Please refer to AggregateLevel first.</p>
<p>ExpandLevel supports two modes:</p>
<ul class="simple">
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<li><code class="code docutils literal"><span class="pre">ExpandLevel.FROM_NO_SEQUENCE</span></code> means the expansion acts on
<code class="code docutils literal"><span class="pre">NO_SEQUENCE</span></code>, which will be expanded to
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<code class="code docutils literal"><span class="pre">SEQUENCE</span></code> or <code class="code docutils literal"><span class="pre">SUB_SEQUENCE</span></code>.</li>
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<li><code class="code docutils literal"><span class="pre">ExpandLevel.FROM_SEQUENCE</span></code> means the expansion acts on
<code class="code docutils literal"><span class="pre">SEQUENCE</span></code>, which will be expanded to
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<code class="code docutils literal"><span class="pre">SUB_SEQUENCE</span></code>.</li>
</ul>
</dd></dl>
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</div>
<div class="section" id="expand">
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<h3>expand<a class="headerlink" href="#expand" title="永久链接至标题"></a></h3>
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<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">expand</code></dt>
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<dd><p>A layer for &#8220;Expand Dense data or (sequence data where the length of each
sequence is one) to sequence data.&#8221;</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">expand</span> <span class="o">=</span> <span class="n">expand</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer1</span><span class="p">,</span>
                      <span class="n">expand_as</span><span class="o">=</span><span class="n">layer2</span><span class="p">,</span>
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                      <span class="n">expand_level</span><span class="o">=</span><span class="n">ExpandLevel</span><span class="o">.</span><span class="n">FROM_NO_SEQUENCE</span><span class="p">)</span>
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</pre></div>
</div>
<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 layer</li>
<li><strong>expand_as</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Expand as this layer&#8217;s sequence info.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; Layer name.</li>
<li><strong>bias_attr</strong> (<em>paddle.v2.attr.ParameterAttribute|None|False</em>) &#8211; Bias attribute. None means default bias. False means no
bias.</li>
<li><strong>expand_level</strong> (<em>ExpandLevel</em>) &#8211; whether input layer is timestep(default) or sequence.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; extra layer attributes.</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 class="section" id="repeat">
<h3>repeat<a class="headerlink" href="#repeat" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">repeat</code></dt>
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<dd><p>A layer for repeating the input for num_repeats times.</p>
<p>If as_row_vector:
.. math:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">y</span>  <span class="o">=</span> <span class="p">[</span><span class="n">x_1</span><span class="p">,</span>\<span class="n">cdots</span><span class="p">,</span> <span class="n">x_n</span><span class="p">,</span> \<span class="n">cdots</span><span class="p">,</span> <span class="n">x_1</span><span class="p">,</span> \<span class="n">cdots</span><span class="p">,</span> <span class="n">x_n</span><span class="p">]</span>
</pre></div>
</div>
<p>If not as_row_vector:
.. math:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">y</span>  <span class="o">=</span> <span class="p">[</span><span class="n">x_1</span><span class="p">,</span>\<span class="n">cdots</span><span class="p">,</span> <span class="n">x_1</span><span class="p">,</span> \<span class="n">cdots</span><span class="p">,</span> <span class="n">x_n</span><span class="p">,</span> \<span class="n">cdots</span><span class="p">,</span> <span class="n">x_n</span><span class="p">]</span>
</pre></div>
</div>
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<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">expand</span> <span class="o">=</span> <span class="n">repeat</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">,</span> <span class="n">num_repeats</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
</pre></div>
</div>
<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 layer</li>
<li><strong>num_repeats</strong> (<em>int</em>) &#8211; Repeat the input so many times</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; Layer name.</li>
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<li><strong>as_row_vector</strong> (<em>bool</em>) &#8211; True for treating input as row vector and repeating
in the column direction.  This is equivalent to apply
concat() with num_repeats same input.
False for treating input as column vector and repeating
in the row direction.</li>
<li><strong>act</strong> (<em>paddle.v2.activation.Base</em>) &#8211; Activation type.</li>
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<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; extra layer attributes.</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 class="section" id="rotate">
<h3>rotate<a class="headerlink" href="#rotate" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">rotate</code></dt>
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<dd><p>A layer for rotating 90 degrees (clock-wise) for each feature channel,
usually used when the input sample is some image or feature map.</p>
<div class="math">
\[y(j,i,:) = x(M-i-1,j,:)\]</div>
<p>where <span class="math">\(x\)</span> is (M x N x C) input, and <span class="math">\(y\)</span> is (N x M x C) output.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">rot</span> <span class="o">=</span> <span class="n">rotate</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">,</span>
                   <span class="n">height</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
                   <span class="n">width</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
</pre></div>
</div>
<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 layer.</li>
<li><strong>height</strong> (<em>int</em>) &#8211; The height of the sample matrix</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; Layer name.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; extra layer attributes.</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 class="section" id="seq-reshape">
<h3>seq_reshape<a class="headerlink" href="#seq-reshape" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">seq_reshape</code></dt>
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<dd><p>A layer for reshaping the sequence. Assume the input sequence has T instances,
the dimension of each instance is M, and the input reshape_size is N, then the
output sequence has T*M/N instances, the dimension of each instance is N.</p>
<p>Note that T*M/N must be an integer.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">reshape</span> <span class="o">=</span> <span class="n">seq_reshape</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">,</span> <span class="n">reshape_size</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
</pre></div>
</div>
<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 layer.</li>
<li><strong>reshape_size</strong> (<em>int</em>) &#8211; the size of reshaped sequence.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; Layer name.</li>
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<li><strong>act</strong> (<em>paddle.v2.activation.Base</em>) &#8211; Activation type.</li>
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<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; extra layer attributes.</li>
<li><strong>bias_attr</strong> (<em>paddle.v2.attr.ParameterAttribute</em><em> or </em><em>None</em><em> or </em><em>bool</em>) &#8211; The Bias Attribute. If no bias, then pass False or
something not type of paddle.v2.attr.ParameterAttribute. None will get a
default Bias.</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 class="section" id="math-layers">
<h2>Math Layers<a class="headerlink" href="#math-layers" title="永久链接至标题"></a></h2>
<div class="section" id="addto">
<h3>addto<a class="headerlink" href="#addto" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">addto</code></dt>
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<dd><p>AddtoLayer.</p>
<div class="math">
\[y = f(\sum_{i} x_i + b)\]</div>
<p>where <span class="math">\(y\)</span> is output, <span class="math">\(x\)</span> is input, <span class="math">\(b\)</span> is bias,
and <span class="math">\(f\)</span> is activation function.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">addto</span> <span class="o">=</span> <span class="n">addto</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">layer1</span><span class="p">,</span> <span class="n">layer2</span><span class="p">],</span>
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                    <span class="n">act</span><span class="o">=</span><span class="n">paddle</span><span class="o">.</span><span class="n">v2</span><span class="o">.</span><span class="n">activation</span><span class="o">.</span><span class="n">Relu</span><span class="p">(),</span>
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                    <span class="n">bias_attr</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
</pre></div>
</div>
<p>This layer just simply add all input layers together, then activate the sum
inputs. Each input of this layer should be the same size, which is also the
output size of this layer.</p>
<p>There is no weight matrix for each input, because it just a simple add
operation. If you want a complicated operation before add, please use
mixed.</p>
<p>It is a very good way to set dropout outside the layers. Since not all
PaddlePaddle layer support dropout, you can add an add_to layer, set
dropout here.
Please refer to dropout for details.</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; Layer name.</li>
<li><strong>input</strong> (<em>paddle.v2.config_base.Layer|list|tuple</em>) &#8211; Input layers. It could be a paddle.v2.config_base.Layer or list/tuple of
paddle.v2.config_base.Layer.</li>
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<li><strong>act</strong> (<em>paddle.v2.activation.Base</em>) &#8211; Activation Type, default is tanh.</li>
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<li><strong>bias_attr</strong> (<em>paddle.v2.attr.ParameterAttribute|bool</em>) &#8211; Bias attribute. If False, means no bias. None is default
bias.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; Extra Layer attribute.</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 class="section" id="linear-comb">
<h3>linear_comb<a class="headerlink" href="#linear-comb" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">linear_comb</code></dt>
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<dd><dl class="docutils">
<dt>A layer for weighted sum of vectors takes two inputs.</dt>
<dd><ul class="first last simple">
<li><dl class="first docutils">
<dt>Input: size of weights is M</dt>
<dd>size of vectors is M*N</dd>
</dl>
</li>
<li>Output: a vector of size=N</li>
</ul>
</dd>
</dl>
<div class="math">
\[z(i) = \sum_{j=0}^{M-1} x(j) y(i+Nj)\]</div>
<p>where <span class="math">\(0 \le i \le N-1\)</span></p>
<p>Or in the matrix notation:</p>
<div class="math">
\[z = x^\mathrm{T} Y\]</div>
<dl class="docutils">
<dt>In this formular:</dt>
<dd><ul class="first last simple">
<li><span class="math">\(x\)</span>: weights</li>
<li><span class="math">\(y\)</span>: vectors.</li>
<li><span class="math">\(z\)</span>: the output.</li>
</ul>
</dd>
</dl>
<p>Note that the above computation is for one sample. Multiple samples are
processed in one batch.</p>
<p>The simple usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">linear_comb</span> <span class="o">=</span> <span class="n">linear_comb</span><span class="p">(</span><span class="n">weights</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> <span class="n">vectors</span><span class="o">=</span><span class="n">vectors</span><span class="p">,</span>
                                <span class="n">size</span><span class="o">=</span><span class="n">elem_dim</span><span class="p">)</span>
</pre></div>
</div>
<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>weights</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; The weight layer.</li>
<li><strong>vectors</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; The vector layer.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; the dimension of this layer.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; The Layer Name.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttributeNone</em>) &#8211; Extra Layer config.</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 class="section" id="interpolation">
<h3>interpolation<a class="headerlink" href="#interpolation" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">interpolation</code></dt>
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<dd><p>This layer is for linear interpolation with two inputs,
which is used in NEURAL TURING MACHINE.</p>
<div class="math">
\[y.row[i] = w[i] * x_1.row[i] + (1 - w[i]) * x_2.row[i]\]</div>
<p>where <span class="math">\(x_1\)</span> and <span class="math">\(x_2\)</span> are two (batchSize x dataDim) inputs,
<span class="math">\(w\)</span> is (batchSize x 1) weight vector, and <span class="math">\(y\)</span> is
(batchSize x dataDim) output.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">interpolation</span> <span class="o">=</span> <span class="n">interpolation</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">layer1</span><span class="p">,</span> <span class="n">layer2</span><span class="p">],</span> <span class="n">weight</span><span class="o">=</span><span class="n">layer3</span><span class="p">)</span>
</pre></div>
</div>
<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>list|tuple</em>) &#8211; Input layer.</li>
<li><strong>weight</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Weight layer.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; Layer name.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; extra layer attributes.</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 class="section" id="bilinear-interp">
<h3>bilinear_interp<a class="headerlink" href="#bilinear-interp" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">bilinear_interp</code></dt>
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<dd><p>This layer is to implement bilinear interpolation on conv layer output.</p>
<p>Please refer to Wikipedia: <a class="reference external" href="https://en.wikipedia.org/wiki/Bilinear_interpolation">https://en.wikipedia.org/wiki/Bilinear_interpolation</a></p>
<p>The simple usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">bilinear</span> <span class="o">=</span> <span class="n">bilinear_interp</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer1</span><span class="p">,</span> <span class="n">out_size_x</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">out_size_y</span><span class="o">=</span><span class="mi">64</span><span class="p">)</span>
</pre></div>
</div>
<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; A input layer.</li>
<li><strong>out_size_x</strong> (<em>int|None</em>) &#8211; bilinear interpolation output width.</li>
<li><strong>out_size_y</strong> (<em>int|None</em>) &#8211; bilinear interpolation output height.</li>
<li><strong>name</strong> (<em>None|basestring</em>) &#8211; The layer&#8217;s name, which cna not be specified.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; Extra Layer attribute.</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 class="section" id="power">
<h3>power<a class="headerlink" href="#power" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">power</code></dt>
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<dd><p>This layer applies a power function to a vector element-wise,
which is used in NEURAL TURING MACHINE.</p>
<div class="math">
\[y = x^w\]</div>
<p>where <span class="math">\(x\)</span> is a input vector, <span class="math">\(w\)</span> is scalar weight,
and <span class="math">\(y\)</span> is a output vector.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">power</span> <span class="o">=</span> <span class="n">power</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer1</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="n">layer2</span><span class="p">)</span>
</pre></div>
</div>
<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 layer.</li>
<li><strong>weight</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Weight layer.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; Layer name.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; extra layer attributes.</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 class="section" id="scaling">
<h3>scaling<a class="headerlink" href="#scaling" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">scaling</code></dt>
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<dd><p>A layer for multiplying input vector by weight scalar.</p>
<div class="math">
\[y  = w x\]</div>
<p>where <span class="math">\(x\)</span> is size=dataDim input, <span class="math">\(w\)</span> is size=1 weight,
and <span class="math">\(y\)</span> is size=dataDim output.</p>
<p>Note that the above computation is for one sample. Multiple samples are
processed in one batch.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">scale</span> <span class="o">=</span> <span class="n">scaling</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer1</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="n">layer2</span><span class="p">)</span>
</pre></div>
</div>
<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 layer.</li>
<li><strong>weight</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Weight layer.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; Layer name.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; extra layer attributes.</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>
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</div>
<div class="section" id="clip">
<h3>clip<a class="headerlink" href="#clip" 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">clip</code></dt>
<dd><blockquote>
<div><p>A layer for clipping the input value by the threshold.</p>
<div class="math">
\[out[i] = \min\left(\max\left(in[i],p_{1}\]</div>
</div></blockquote>
<p>ight),p_{2}
ight)</p>
<blockquote>
<div><div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">clip</span> <span class="o">=</span> <span class="n">clip</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span> <span class="nb">min</span><span class="o">=-</span><span class="mi">10</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
</pre></div>
</div>
<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">param name:</th><td class="field-body">The Layer Name.</td>
</tr>
<tr class="field-even field"><th class="field-name">type name:</th><td class="field-body">basestring</td>
</tr>
<tr class="field-odd field"><th class="field-name">param input:</th><td class="field-body">The input layer.</td>
</tr>
<tr class="field-even field"><th class="field-name">type input:</th><td class="field-body">paddle.v2.config_base.Layer.</td>
</tr>
<tr class="field-odd field"><th class="field-name">param min:</th><td class="field-body">The lower threshold for clipping.</td>
</tr>
<tr class="field-even field"><th class="field-name">type min:</th><td class="field-body">double</td>
</tr>
<tr class="field-odd field"><th class="field-name">param max:</th><td class="field-body">The upper threshold for clipping.</td>
</tr>
<tr class="field-even field"><th class="field-name">type max:</th><td class="field-body">double</td>
</tr>
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<tr class="field-odd field"><th class="field-name">return:</th><td class="field-body">paddle.v2.config_base.Layer object.</td>
</tr>
<tr class="field-even field"><th class="field-name">rtype:</th><td class="field-body">paddle.v2.config_base.Layer</td>
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</tr>
</tbody>
</table>
</div></blockquote>
</dd></dl>
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</div>
<div class="section" id="slope-intercept">
<h3>slope_intercept<a class="headerlink" href="#slope-intercept" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">slope_intercept</code></dt>
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<dd><p>This layer for applying a slope and an intercept to the input
element-wise. There is no activation and weight.</p>
<div class="math">
\[y = slope * x + intercept\]</div>
<p>The simple usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">scale</span> <span class="o">=</span> <span class="n">slope_intercept</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span> <span class="n">slope</span><span class="o">=-</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">intercept</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
</pre></div>
</div>
<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; The input layer.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; The Layer Name.</li>
<li><strong>slope</strong> (<em>float.</em>) &#8211; the scale factor.</li>
<li><strong>intercept</strong> (<em>float.</em>) &#8211; the offset.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttributeNone</em>) &#8211; Extra Layer config.</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 class="section" id="tensor">
<h3>tensor<a class="headerlink" href="#tensor" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">tensor</code></dt>
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<dd><p>This layer performs tensor operation for two input.
For example, each sample:</p>
<div class="math">
\[y_{i} = a * W_{i} * {b^\mathrm{T}}, i=0,1,...,K-1\]</div>
<dl class="docutils">
<dt>In this formular:</dt>
<dd><ul class="first last simple">
<li><span class="math">\(a\)</span>: the first input contains M elements.</li>
<li><span class="math">\(b\)</span>: the second input contains N elements.</li>
<li><span class="math">\(y_{i}\)</span>: the i-th element of y.</li>
<li><span class="math">\(W_{i}\)</span>: the i-th learned weight, shape if [M, N]</li>
<li><span class="math">\(b^\mathrm{T}\)</span>: the transpose of <span class="math">\(b_{2}\)</span>.</li>
</ul>
</dd>
</dl>
<p>The simple usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">tensor</span> <span class="o">=</span> <span class="n">tensor</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="n">layer1</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="n">layer2</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
</pre></div>
</div>
<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; layer name</li>
<li><strong>a</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Input layer a.</li>
<li><strong>b</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; input layer b.</li>
<li><strong>size</strong> (<em>int.</em>) &#8211; the layer dimension.</li>
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<li><strong>act</strong> (<em>paddle.v2.activation.Base</em>) &#8211; Activation Type. Default is tanh.</li>
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<li><strong>param_attr</strong> (<em>paddle.v2.attr.ParameterAttribute</em>) &#8211; The Parameter Attribute.</li>
<li><strong>bias_attr</strong> (<em>paddle.v2.attr.ParameterAttribute|None|Any</em>) &#8211; The Bias Attribute. If no bias, then pass False or
something not type of paddle.v2.attr.ParameterAttribute. None will get a
default Bias.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttributeNone</em>) &#8211; Extra Layer config.</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 class="section" id="cos-sim">
<span id="api-v2-layer-cos-sim"></span><h3>cos_sim<a class="headerlink" href="#cos-sim" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">cos_sim</code></dt>
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<dd><p>Cosine Similarity Layer. The cosine similarity equation is here.</p>
<div class="math">
\[similarity = cos(\theta) = {\mathbf{a} \cdot \mathbf{b}
\over \|\mathbf{a}\| \|\mathbf{b}\|}\]</div>
<p>The size of a is M, size of b is M*N,
Similarity will be calculated N times by step M. The output size is
N. The scale will be multiplied to similarity.</p>
<p>Note that the above computation is for one sample. Multiple samples are
processed in one batch.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cos</span> <span class="o">=</span> <span class="n">cos_sim</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="n">layer1</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="n">layer2</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
</pre></div>
</div>
<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; layer name</li>
<li><strong>a</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; input layer a</li>
<li><strong>b</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; input layer b</li>
<li><strong>scale</strong> (<em>float</em>) &#8211; scale for cosine value. default is 5.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; layer size. NOTE size_a * size should equal size_b.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; Extra Layer Attribute.</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 class="section" id="trans">
<h3>trans<a class="headerlink" href="#trans" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">trans</code></dt>
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<dd><p>A layer for transposing a minibatch matrix.</p>
<div class="math">
\[y = x^\mathrm{T}\]</div>
<p>where <span class="math">\(x\)</span> is (M x N) input, and <span class="math">\(y\)</span> is (N x M) output.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">trans</span> <span class="o">=</span> <span class="n">trans</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">)</span>
</pre></div>
</div>
<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 layer.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; Layer name.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; extra layer attributes.</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>
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</div>
<div class="section" id="scale-shift">
<h3>scale_shift<a class="headerlink" href="#scale-shift" 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">scale_shift</code></dt>
<dd><p>A layer applies a linear transformation to each element in each row of
the input matrix. For each element, the layer first re-scale it and then
adds a bias to it.</p>
<p>This layer is very like the SlopeInterceptLayer, except the scale and
bias are trainable.</p>
<div class="math">
\[y = w * x + b\]</div>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">scale_shift</span> <span class="o">=</span> <span class="n">scale_shift</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span> <span class="n">bias_attr</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
</pre></div>
</div>
<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 of scaling.</li>
<li><strong>bias_attr</strong> (<em>paddle.v2.attr.ParameterAttribute</em>) &#8211; The parameter attribute of shifting.</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>
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</div>
</div>
<div class="section" id="sampling-layers">
<h2>Sampling Layers<a class="headerlink" href="#sampling-layers" title="永久链接至标题"></a></h2>
<div class="section" id="maxid">
<h3>maxid<a class="headerlink" href="#maxid" title="永久链接至标题"></a></h3>
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<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">max_id</code></dt>
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<dd><p>A layer for finding the id which has the maximal value for each sample.
The result is stored in output.ids.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">maxid</span> <span class="o">=</span> <span class="n">maxid</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">)</span>
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</pre></div>
</div>
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<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 layer name.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; Layer name.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; extra layer attributes.</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>
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</div>
<div class="section" id="sampling-id">
<h3>sampling_id<a class="headerlink" href="#sampling-id" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">sampling_id</code></dt>
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<dd><p>A layer for sampling id from multinomial distribution from the input layer.
Sampling one id for one sample.</p>
<p>The simple usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">samping_id</span> <span class="o">=</span> <span class="n">sampling_id</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">)</span>
</pre></div>
</div>
<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; The input layer.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; The Layer Name.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttributeNone</em>) &#8211; Extra Layer config.</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>
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</div>
<div class="section" id="multiplex">
<h3>multiplex<a class="headerlink" href="#multiplex" 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">multiplex</code></dt>
<dd><p>This layer multiplex multiple layers according to the index,
which is provided by the first input layer.
inputs[0]: the index of the layer to output of size batchSize.
inputs[1:N]; the candidate output data.
For each index i from 0 to batchSize -1, the output is the i-th row of the
(index[i] + 1)-th layer.</p>
<p>For each i-th row of output:
.. math:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">y</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">x_</span><span class="p">{</span><span class="n">x_</span><span class="p">{</span><span class="mi">0</span><span class="p">}[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">}[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">],</span> <span class="n">j</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span> <span class="o">...</span> <span class="p">,</span> <span class="p">(</span><span class="n">x_</span><span class="p">{</span><span class="mi">1</span><span class="p">}</span><span class="o">.</span><span class="n">width</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
<p>where, y is output. <span class="math">\(x_{k}\)</span> is the k-th input layer and
<span class="math">\(k = x_{0}[i] + 1\)</span>.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">maxid</span> <span class="o">=</span> <span class="n">multiplex</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layers</span><span class="p">)</span>
</pre></div>
</div>
<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>list of paddle.v2.config_base.Layer</em>) &#8211; Input layers.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; Layer name.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; extra layer attributes.</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>
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</div>
<div class="section" id="slicing-and-joining-layers">
<h2>Slicing and Joining Layers<a class="headerlink" href="#slicing-and-joining-layers" title="永久链接至标题"></a></h2>
<div class="section" id="pad">
<h3>pad<a class="headerlink" href="#pad" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">pad</code></dt>
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<dd><p>This operation pads zeros to the input data according to pad_c,pad_h
and pad_w. pad_c, pad_h, pad_w specifies the which dimension and size
of padding. And the input data shape is NCHW.</p>
<p>For example, pad_c=[2,3] means padding 2 zeros before the
input data and 3 zeros after the input data in channel dimension.
pad_h means padding zeros in height dimension. pad_w means padding zeros
in width dimension.</p>
<p>For example,</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="nb">input</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>  <span class="o">=</span> <span class="p">[</span>
                    <span class="p">[</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">]],</span>
                      <span class="p">[[</span><span class="mi">2</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="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">7</span><span class="p">]]</span> <span class="p">],</span>
                    <span class="p">[</span> <span class="p">[[</span><span class="mi">4</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">7</span><span class="p">]],</span>
                      <span class="p">[[</span><span class="mi">3</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">9</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</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="p">]</span>
                  <span class="p">]</span>

<span class="n">pad_c</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">pad_h</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="n">pad_w</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span>

<span class="n">output</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span>
                    <span class="p">[</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]],</span>
                      <span class="p">[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">]],</span>
                      <span class="p">[[</span><span class="mi">2</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="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">7</span><span class="p">]],</span>
                      <span class="p">[[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]]</span> <span class="p">],</span>
                    <span class="p">[</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]],</span>
                      <span class="p">[[</span><span class="mi">4</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">7</span><span class="p">]],</span>
                      <span class="p">[[</span><span class="mi">3</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">9</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</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="p">[[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]]</span> <span class="p">]</span>
                  <span class="p">]</span>
</pre></div>
</div>
<p>The simply usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">pad</span> <span class="o">=</span> <span class="n">pad</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">ipt</span><span class="p">,</span>
                <span class="n">pad_c</span><span class="o">=</span><span class="p">[</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">],</span>
                <span class="n">pad_h</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span>
                <span class="n">pad_w</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">])</span>
</pre></div>
</div>
<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; layer&#8217;s input.</li>
<li><strong>pad_c</strong> (<em>list|None</em>) &#8211; padding size in channel dimension.</li>
<li><strong>pad_h</strong> (<em>list|None</em>) &#8211; padding size in height dimension.</li>
<li><strong>pad_w</strong> (<em>list|None</em>) &#8211; padding size in width dimension.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; Extra Layer Attribute.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; layer name.</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 class="section" id="cost-layers">
<span id="api-v2-layer-costs"></span><h2>Cost Layers<a class="headerlink" href="#cost-layers" title="永久链接至标题"></a></h2>
<div class="section" id="cross-entropy-cost">
<h3>cross_entropy_cost<a class="headerlink" href="#cross-entropy-cost" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">cross_entropy_cost</code></dt>
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<dd><p>A loss layer for multi class entropy.</p>
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<p>The example usage is:</p>
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<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cost</span> <span class="o">=</span> <span class="n">cross_entropy</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span>
                     <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">)</span>
</pre></div>
</div>
<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; The first input layer.</li>
<li><strong>label</strong> &#8211; The input label.</li>
<li><strong>name</strong> (<em>None|basestring.</em>) &#8211; The name of this layers. It is not necessary.</li>
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<li><strong>coeff</strong> (<em>float.</em>) &#8211; The cost is multiplied with coeff.
The coefficient affects the gradient in the backward.</li>
<li><strong>weight</strong> (<em>LayerOutout</em>) &#8211; The cost of each sample is multiplied with each weight.
The weight should be a layer with size=1. Note that gradient
will not be calculated for weight.</li>
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<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; Extra Layer Attribute.</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 class="section" id="cross-entropy-with-selfnorm-cost">
<h3>cross_entropy_with_selfnorm_cost<a class="headerlink" href="#cross-entropy-with-selfnorm-cost" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">cross_entropy_with_selfnorm_cost</code></dt>
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<dd><p>A loss layer for multi class entropy with selfnorm.
Input should be a vector of positive numbers, without normalization.</p>
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<p>The example usage is:</p>
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<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cost</span> <span class="o">=</span> <span class="n">cross_entropy_with_selfnorm</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span>
                                   <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">)</span>
</pre></div>
</div>
<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; The first input layer.</li>
<li><strong>label</strong> &#8211; The input label.</li>
<li><strong>name</strong> (<em>None|basestring.</em>) &#8211; The name of this layers. It is not necessary.</li>
<li><strong>coeff</strong> (<em>float.</em>) &#8211; The coefficient affects the gradient in the backward.</li>
<li><strong>softmax_selfnorm_alpha</strong> (<em>float.</em>) &#8211; The scale factor affects the cost.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; Extra Layer Attribute.</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 class="section" id="multi-binary-label-cross-entropy-cost">
<h3>multi_binary_label_cross_entropy_cost<a class="headerlink" href="#multi-binary-label-cross-entropy-cost" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">multi_binary_label_cross_entropy_cost</code></dt>
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<dd><p>A loss layer for multi binary label cross entropy.</p>
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<p>The example usage is:</p>
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<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cost</span> <span class="o">=</span> <span class="n">multi_binary_label_cross_entropy</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span>
                                        <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">)</span>
</pre></div>
</div>
<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; The first input layer.</li>
<li><strong>label</strong> &#8211; The input label.</li>
<li><strong>name</strong> (<em>None|basestring</em>) &#8211; The name of this layers. It is not necessary.</li>
<li><strong>coeff</strong> (<em>float</em>) &#8211; The coefficient affects the gradient in the backward.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; Extra Layer Attribute.</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>
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<div class="section" id="huber-regression-cost">
<h3>huber_regression_cost<a class="headerlink" href="#huber-regression-cost" title="永久链接至标题"></a></h3>
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<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">huber_regression_cost</code></dt>
<dd><blockquote>
<div>In statistics, the Huber loss is a loss function used in robust regression,
that is less sensitive to outliers in data than the squared error loss.
Given a prediction f(x), a label y and <span class="math">\(\delta\)</span>, the loss function
is defined as:</div></blockquote>
<p>ight )^2, left | y-f(x)
ight <a href="#id2"><span class="problematic" id="id3">|</span></a>leq delta</p>
<blockquote>
<div>loss = delta left | y-f(x)</div></blockquote>
<p>ight <a href="#id4"><span class="problematic" id="id5">|</span></a>-0.5delta ^2, otherwise</p>
<blockquote>
<div><p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cost</span> <span class="o">=</span> <span class="n">huber_regression_cost</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">)</span>
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</pre></div>
</div>
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<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
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<tr class="field-odd field"><th class="field-name">param input:</th><td class="field-body">The first input layer.</td>
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</tr>
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<tr class="field-even field"><th class="field-name">type input:</th><td class="field-body">paddle.v2.config_base.Layer.</td>
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</tr>
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<tr class="field-odd field"><th class="field-name">param label:</th><td class="field-body">The input label.</td>
</tr>
<tr class="field-even field"><th class="field-name">type input:</th><td class="field-body">paddle.v2.config_base.Layer.</td>
</tr>
<tr class="field-odd field"><th class="field-name">param name:</th><td class="field-body">The name of this layers. It is not necessary.</td>
</tr>
<tr class="field-even field"><th class="field-name">type name:</th><td class="field-body">None|basestring.</td>
</tr>
<tr class="field-odd field"><th class="field-name">param delta:</th><td class="field-body">The difference between the observed and predicted values.</td>
</tr>
<tr class="field-even field"><th class="field-name">type delta:</th><td class="field-body">float.</td>
</tr>
<tr class="field-odd field"><th class="field-name">param coeff:</th><td class="field-body">The coefficient affects the gradient in the backward.</td>
</tr>
<tr class="field-even field"><th class="field-name">type coeff:</th><td class="field-body">float.</td>
</tr>
<tr class="field-odd field"><th class="field-name" colspan="2">param layer_attr:</th></tr>
<tr class="field-odd field"><td>&#160;</td><td class="field-body">Extra Layer Attribute.</td>
</tr>
<tr class="field-even field"><th class="field-name" colspan="2">type layer_attr:</th></tr>
<tr class="field-even field"><td>&#160;</td><td class="field-body">paddle.v2.attr.ExtraAttribute</td>
</tr>
<tr class="field-odd field"><th class="field-name">return:</th><td class="field-body">paddle.v2.config_base.Layer object.</td>
</tr>
<tr class="field-even field"><th class="field-name">rtype:</th><td class="field-body">paddle.v2.config_base.Layer.</td>
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</tr>
</tbody>
</table>
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</div></blockquote>
</dd></dl>

</div>
<div class="section" id="huber-classification-cost">
<h3>huber_classification_cost<a class="headerlink" href="#huber-classification-cost" 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">huber_classification_cost</code></dt>
<dd><blockquote>
<div>For classification purposes, a variant of the Huber loss called modified Huber
is sometimes used. Given a prediction f(x) (a real-valued classifier score) and
a true binary class label :math:<a href="#id6"><span class="problematic" id="id7">`</span></a>yin left {-1, 1</div></blockquote>
<dl class="docutils">
<dt>ight }`, the modified Huber</dt>
<dd>loss is defined as:</dd>
<dt>ight )^2, yf(x)geq 1</dt>
<dd><blockquote class="first">
<div>loss = -4yf(x),  ext{otherwise}</div></blockquote>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cost</span> <span class="o">=</span> <span class="n">huber_classification_cost</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">)</span>
</pre></div>
</div>
<table class="last 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">param input:</th><td class="field-body">The first input layer.</td>
</tr>
<tr class="field-even field"><th class="field-name">type input:</th><td class="field-body">paddle.v2.config_base.Layer.</td>
</tr>
<tr class="field-odd field"><th class="field-name">param label:</th><td class="field-body">The input label.</td>
</tr>
<tr class="field-even field"><th class="field-name">type input:</th><td class="field-body">paddle.v2.config_base.Layer.</td>
</tr>
<tr class="field-odd field"><th class="field-name">param name:</th><td class="field-body">The name of this layers. It is not necessary.</td>
</tr>
<tr class="field-even field"><th class="field-name">type name:</th><td class="field-body">None|basestring.</td>
</tr>
<tr class="field-odd field"><th class="field-name">param coeff:</th><td class="field-body">The coefficient affects the gradient in the backward.</td>
</tr>
<tr class="field-even field"><th class="field-name">type coeff:</th><td class="field-body">float.</td>
</tr>
<tr class="field-odd field"><th class="field-name" colspan="2">param layer_attr:</th></tr>
<tr class="field-odd field"><td>&#160;</td><td class="field-body">Extra Layer Attribute.</td>
</tr>
<tr class="field-even field"><th class="field-name" colspan="2">type layer_attr:</th></tr>
<tr class="field-even field"><td>&#160;</td><td class="field-body">paddle.v2.attr.ExtraAttribute</td>
</tr>
<tr class="field-odd field"><th class="field-name">return:</th><td class="field-body">paddle.v2.config_base.Layer object.</td>
</tr>
<tr class="field-even field"><th class="field-name">rtype:</th><td class="field-body">paddle.v2.config_base.Layer.</td>
</tr>
</tbody>
</table>
</dd>
</dl>
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</dd></dl>

</div>
<div class="section" id="lambda-cost">
<h3>lambda_cost<a class="headerlink" href="#lambda-cost" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">lambda_cost</code></dt>
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<dd><p>lambdaCost for lambdaRank LTR approach.</p>
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<p>The example usage is:</p>
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<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cost</span> <span class="o">=</span> <span class="n">lambda_cost</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span>
                   <span class="n">score</span><span class="o">=</span><span class="n">score</span><span class="p">,</span>
                   <span class="n">NDCG_num</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
                   <span class="n">max_sort_size</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
<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; Samples of the same query should be loaded as sequence.</li>
<li><strong>score</strong> &#8211; The 2nd input. Score of each sample.</li>
<li><strong>NDCG_num</strong> (<em>int</em>) &#8211; The size of NDCG (Normalized Discounted Cumulative Gain),
e.g., 5 for NDCG&#64;5. It must be less than for equal to the
minimum size of lists.</li>
<li><strong>max_sort_size</strong> (<em>int</em>) &#8211; The size of partial sorting in calculating gradient.
If max_sort_size = -1, then for each list, the
algorithm will sort the entire list to get gradient.
In other cases, max_sort_size must be greater than or
equal to NDCG_num. And if max_sort_size is greater
than the size of a list, the algorithm will sort the
entire list of get gradient.</li>
<li><strong>name</strong> (<em>None|basestring</em>) &#8211; The name of this layers. It is not necessary.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; Extra Layer Attribute.</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>
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<div class="section" id="mse-cost">
<h3>mse_cost<a class="headerlink" href="#mse-cost" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">mse_cost</code></dt>
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<dd><p>mean squared error cost:</p>
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<div class="math">
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\[\frac{1}{N}\sum_{i=1}^N(t_i-y_i)^2\]</div>
<table class="docutils field-list" frame="void" rules="none">
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<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
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<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; layer name.</li>
<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Network prediction.</li>
<li><strong>label</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Data label.</li>
<li><strong>weight</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; The weight affects the cost, namely the scale of cost.
It is an optional argument.</li>
<li><strong>coeff</strong> (<em>float</em>) &#8211; The coefficient affects the gradient in the backward.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; layer&#8217;s extra attribute.</li>
</ul>
</td>
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</tr>
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<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>
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</tr>
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<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>
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</tr>
</tbody>
</table>
</dd></dl>
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</div>
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<div class="section" id="rank-cost">
<h3>rank_cost<a class="headerlink" href="#rank-cost" title="永久链接至标题"></a></h3>
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<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">rank_cost</code></dt>
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<dd><p>A cost Layer for learning to rank using gradient descent. Details can refer
to <a class="reference external" href="http://research.microsoft.com/en-us/um/people/cburges/papers/ICML_ranking.pdf">papers</a>.
This layer contains at least three inputs. The weight is an optional
argument, which affects the cost.</p>
<div class="math">
\[ \begin{align}\begin{aligned}C_{i,j} &amp; = -\tilde{P_{ij}} * o_{i,j} + log(1 + e^{o_{i,j}})\\o_{i,j} &amp; =  o_i - o_j\\\tilde{P_{i,j}} &amp; = \{0, 0.5, 1\} \ or \ \{0, 1\}\end{aligned}\end{align} \]</div>
<dl class="docutils">
<dt>In this formula:</dt>
<dd><ul class="first last simple">
<li><span class="math">\(C_{i,j}\)</span> is the cross entropy cost.</li>
<li><span class="math">\(\tilde{P_{i,j}}\)</span> is the label. 1 means positive order
and 0 means reverse order.</li>
<li><span class="math">\(o_i\)</span> and <span class="math">\(o_j\)</span>: the left output and right output.
Their dimension is one.</li>
</ul>
</dd>
</dl>
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<p>The example usage is:</p>
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<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cost</span> <span class="o">=</span> <span class="n">rank_cost</span><span class="p">(</span><span class="n">left</span><span class="o">=</span><span class="n">out_left</span><span class="p">,</span>
                 <span class="n">right</span><span class="o">=</span><span class="n">out_right</span><span class="p">,</span>
                 <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">)</span>
</pre></div>
</div>
<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>left</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; The first input, the size of this layer is 1.</li>
<li><strong>right</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; The right input, the size of this layer is 1.</li>
<li><strong>label</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Label is 1 or 0, means positive order and reverse order.</li>
<li><strong>weight</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; The weight affects the cost, namely the scale of cost.
It is an optional argument.</li>
<li><strong>name</strong> (<em>None|basestring</em>) &#8211; The name of this layers. It is not necessary.</li>
<li><strong>coeff</strong> (<em>float</em>) &#8211; The coefficient affects the gradient in the backward.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; Extra Layer Attribute.</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 class="section" id="sum-cost">
<h3>sum_cost<a class="headerlink" href="#sum-cost" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">sum_cost</code></dt>
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<dd><p>A loss layer which calculate the sum of the input as loss</p>
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<p>The example usage is:</p>
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<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cost</span> <span class="o">=</span> <span class="n">sum_cost</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">)</span>
</pre></div>
</div>
<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; The first input layer.</li>
<li><strong>name</strong> (<em>None|basestring.</em>) &#8211; The name of this layers. It is not necessary.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; Extra Layer Attribute.</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 class="section" id="crf">
<h3>crf<a class="headerlink" href="#crf" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">crf</code></dt>
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<dd><p>A layer for calculating the cost of sequential conditional random
field model.</p>
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<p>The example usage is:</p>
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<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">crf</span> <span class="o">=</span> <span class="n">crf</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span>
                <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span>
                <span class="n">size</span><span class="o">=</span><span class="n">label_dim</span><span class="p">)</span>
</pre></div>
</div>
<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; The first input layer is the feature.</li>
<li><strong>label</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; The second input layer is label.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; The category number.</li>
<li><strong>weight</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; The third layer is &#8220;weight&#8221; of each sample, which is an
optional argument.</li>
<li><strong>param_attr</strong> (<em>paddle.v2.attr.ParameterAttribute</em>) &#8211; Parameter attribute. None means default attribute</li>
<li><strong>name</strong> (<em>None|basestring</em>) &#8211; The name of this layers. It is not necessary.</li>
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<li><strong>coeff</strong> (<em>float</em>) &#8211; The coefficient affects the gradient in the backward.</li>
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<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttributeNone</em>) &#8211; Extra Layer config.</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 class="section" id="crf-decoding">
<h3>crf_decoding<a class="headerlink" href="#crf-decoding" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">crf_decoding</code></dt>
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<dd><p>A layer for calculating the decoding sequence of sequential conditional
random field model. The decoding sequence is stored in output.ids.
If a second input is provided, it is treated as the ground-truth label, and
this layer will also calculate error. output.value[i] is 1 for incorrect
decoding or 0 for correct decoding.</p>
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<p>The example usage is:</p>
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<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">crf_decoding</span> <span class="o">=</span> <span class="n">crf_decoding</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span>
                                  <span class="n">size</span><span class="o">=</span><span class="n">label_dim</span><span class="p">)</span>
</pre></div>
</div>
<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; The first input layer.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; size of this layer.</li>
<li><strong>label</strong> (<em>paddle.v2.config_base.Layer</em><em> or </em><em>None</em>) &#8211; None or ground-truth label.</li>
<li><strong>param_attr</strong> (<em>paddle.v2.attr.ParameterAttribute</em>) &#8211; Parameter attribute. None means default attribute</li>
<li><strong>name</strong> (<em>None|basestring</em>) &#8211; The name of this layers. It is not necessary.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttributeNone</em>) &#8211; Extra Layer config.</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 class="section" id="ctc">
<h3>ctc<a class="headerlink" href="#ctc" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">ctc</code></dt>
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<dd><p>Connectionist Temporal Classification (CTC) is designed for temporal
classication task. That is, for sequence labeling problems where the
alignment between the inputs and the target labels is unknown.</p>
<p>More details can be found by referring to <a class="reference external" href="http://machinelearning.wustl.edu/mlpapers/paper_files/icml2006_GravesFGS06.pdf">Connectionist Temporal
Classification: Labelling Unsegmented Sequence Data with Recurrent
Neural Networks</a></p>
<div class="admonition note">
<p class="first admonition-title">注解</p>
<p class="last">Considering the &#8216;blank&#8217; label needed by CTC, you need to use
(num_classes + 1) as the input size. num_classes is the category number.
And the &#8216;blank&#8217; is the last category index. So the size of &#8216;input&#8217; layer, such as
fc with softmax activation, should be num_classes + 1. The size of ctc
should also be num_classes + 1.</p>
</div>
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<p>The example usage is:</p>
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<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">ctc</span> <span class="o">=</span> <span class="n">ctc</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span>
                <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span>
                <span class="n">size</span><span class="o">=</span><span class="mi">9055</span><span class="p">,</span>
                <span class="n">norm_by_times</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
</pre></div>
</div>
<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; The input layer.</li>
<li><strong>label</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; The data layer of label with variable length.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; category numbers + 1.</li>
<li><strong>name</strong> (<em>basestring|None</em>) &#8211; The name of this layer</li>
<li><strong>norm_by_times</strong> (<em>bool</em>) &#8211; Whether to normalization by times. False by default.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttributeNone</em>) &#8211; Extra Layer config.</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 class="section" id="warp-ctc">
<h3>warp_ctc<a class="headerlink" href="#warp-ctc" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">warp_ctc</code></dt>
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<dd><p>A layer intergrating the open-source <a class="reference external" href="https://github.com/baidu-research/warp-ctc">warp-ctc</a> library, which is used in
<a class="reference external" href="https://arxiv.org/pdf/1512.02595v1.pdf">Deep Speech 2: End-toEnd Speech Recognition in English and Mandarin</a>, to compute Connectionist Temporal
Classification (CTC) loss. Besides, another <a class="reference external" href="https://github.com/gangliao/warp-ctc">warp-ctc</a> repository, which is forked from
the official one, is maintained to enable more compiling options. During the
building process, PaddlePaddle will clone the source codes, build and
install it to <code class="code docutils literal"><span class="pre">third_party/install/warpctc</span></code> directory.</p>
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<p>More details of CTC can be found by referring to <a class="reference external" href="http://machinelearning.wustl.edu/mlpapers/paper_files/icml2006_GravesFGS06.pdf">Connectionist Temporal
Classification: Labelling Unsegmented Sequence Data with Recurrent
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Neural Networks</a>.</p>
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<div class="admonition note">
<p class="first admonition-title">注解</p>
<ul class="last simple">
<li>Let num_classes represent the category number. Considering the &#8216;blank&#8217;
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label needed by CTC, you need to use (num_classes + 1) as the input size.
Thus, the size of both warp_ctc layer and &#8216;input&#8217; layer should be set to
num_classes + 1.</li>
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<li>You can set &#8216;blank&#8217; to any value ranged in [0, num_classes], which
should be consistent as that used in your labels.</li>
<li>As a native &#8216;softmax&#8217; activation is interated to the warp-ctc library,
&#8216;linear&#8217; activation is expected instead in the &#8216;input&#8217; layer.</li>
</ul>
</div>
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<p>The example usage is:</p>
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<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">ctc</span> <span class="o">=</span> <span class="n">warp_ctc</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span>
                     <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span>
                     <span class="n">size</span><span class="o">=</span><span class="mi">1001</span><span class="p">,</span>
                     <span class="n">blank</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
                     <span class="n">norm_by_times</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
</pre></div>
</div>
<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; The input layer.</li>
<li><strong>label</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; The data layer of label with variable length.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; category numbers + 1.</li>
<li><strong>name</strong> (<em>basestring|None</em>) &#8211; The name of this layer, which can not specify.</li>
<li><strong>blank</strong> (<em>int</em>) &#8211; the &#8216;blank&#8217; label used in ctc</li>
<li><strong>norm_by_times</strong> (<em>bool</em>) &#8211; Whether to normalization by times. False by default.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttributeNone</em>) &#8211; Extra Layer config.</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 class="section" id="nce">
<h3>nce<a class="headerlink" href="#nce" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">nce</code></dt>
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<dd><p>Noise-contrastive estimation.
Implements the method in the following paper:
A fast and simple algorithm for training neural probabilistic language models.</p>
<p>The example usage is:</p>
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<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cost</span> <span class="o">=</span> <span class="n">nce</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">layer1</span><span class="p">,</span> <span class="n">layer2</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="n">layer2</span><span class="p">,</span>
                 <span class="n">param_attr</span><span class="o">=</span><span class="p">[</span><span class="n">attr1</span><span class="p">,</span> <span class="n">attr2</span><span class="p">],</span> <span class="n">weight</span><span class="o">=</span><span class="n">layer3</span><span class="p">,</span>
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                 <span class="n">num_classes</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">neg_distribution</span><span class="o">=</span><span class="p">[</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">0.3</span><span class="p">,</span><span class="mf">0.6</span><span class="p">])</span>
</pre></div>
</div>
<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; layer name</li>
<li><strong>input</strong> (<em>paddle.v2.config_base.Layer|list|tuple|collections.Sequence</em>) &#8211; input layers. It could be a paddle.v2.config_base.Layer of list/tuple of paddle.v2.config_base.Layer.</li>
<li><strong>label</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; label layer</li>
<li><strong>weight</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; weight layer, can be None(default)</li>
<li><strong>num_classes</strong> (<em>int</em>) &#8211; number of classes.</li>
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<li><strong>act</strong> (<em>paddle.v2.activation.Base</em>) &#8211; Activation, default is Sigmoid.</li>
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<li><strong>param_attr</strong> (<em>paddle.v2.attr.ParameterAttribute</em>) &#8211; The Parameter Attribute|list.</li>
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<li><strong>num_neg_samples</strong> (<em>int</em>) &#8211; number of negative samples. Default is 10.</li>
<li><strong>neg_distribution</strong> (<em>list|tuple|collections.Sequence|None</em>) &#8211; The distribution for generating the random negative labels.
A uniform distribution will be used if not provided.
If not None, its length must be equal to num_classes.</li>
<li><strong>bias_attr</strong> (<em>paddle.v2.attr.ParameterAttribute|None|False</em>) &#8211; Bias parameter attribute. True if no bias.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; Extra Layer Attribute.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">layer name.</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 class="section" id="hsigmoid">
<h3>hsigmoid<a class="headerlink" href="#hsigmoid" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">hsigmoid</code></dt>
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<dd><p>Organize the classes into a binary tree. At each node, a sigmoid function
is used to calculate the probability of belonging to the right branch.
This idea is from &#8220;F. Morin, Y. Bengio (AISTATS 05):
Hierarchical Probabilistic Neural Network Language Model.&#8221;</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cost</span> <span class="o">=</span> <span class="n">hsigmoid</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">layer1</span><span class="p">,</span> <span class="n">layer2</span><span class="p">],</span>
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                <span class="n">label</span><span class="o">=</span><span class="n">data</span><span class="p">)</span>
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</pre></div>
</div>
<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|list|tuple</em>) &#8211; Input layers. It could be a paddle.v2.config_base.Layer or list/tuple of
paddle.v2.config_base.Layer.</li>
<li><strong>label</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Label layer.</li>
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<li><strong>num_classes</strong> (<em>int|None</em>) &#8211; number of classes.</li>
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<li><strong>name</strong> (<em>basestring</em>) &#8211; layer name</li>
<li><strong>bias_attr</strong> (<em>paddle.v2.attr.ParameterAttribute|False</em>) &#8211; Bias attribute. None means default bias.
False means no bias.</li>
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<li><strong>param_attr</strong> (<em>paddle.v2.attr.ParameterAttribute|None</em>) &#8211; Parameter Attribute. None means default parameter.</li>
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<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; Extra Layer Attribute.</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>
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</div>
<div class="section" id="smooth-l1-cost">
<h3>smooth_l1_cost<a class="headerlink" href="#smooth-l1-cost" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">smooth_l1_cost</code></dt>
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<dd><p>This is a L1 loss but more smooth. It requires that the
size of input and label are equal. The formula is as follows,</p>
<div class="math">
\[L = \sum_{i} smooth_{L1}(input_i - label_i)\]</div>
<p>in which</p>
<div class="math">
\[\begin{split}smooth_{L1}(x) = \begin{cases} 0.5x^2&amp; \text{if}  \ |x| &lt; 1 \\ |x|-0.5&amp; \text{otherwise} \end{cases}\end{split}\]</div>
<p>More details can be found by referring to <a class="reference external" href="https://arxiv.org/pdf/1504.08083v2.pdf">Fast R-CNN</a></p>
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<p>The example usage is:</p>
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<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cost</span> <span class="o">=</span> <span class="n">smooth_l1_cost</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span>
                      <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">)</span>
</pre></div>
</div>
<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; The input layer.</li>
<li><strong>label</strong> &#8211; The input label.</li>
<li><strong>name</strong> (<em>None|basestring</em>) &#8211; The name of this layers. It is not necessary.</li>
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<li><strong>coeff</strong> (<em>float</em>) &#8211; The coefficient affects the gradient in the backward.</li>
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<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; Extra Layer Attribute.</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>
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</div>
<div class="section" id="multibox-loss">
<h3>multibox_loss<a class="headerlink" href="#multibox-loss" 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">multibox_loss</code></dt>
<dd><p>Compute the location loss and the confidence loss for ssd.</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_loc</strong> (<em>paddle.v2.config_base.Layer | List of paddle.v2.config_base.Layer</em>) &#8211; The input predict locations.</li>
<li><strong>input_conf</strong> (<em>paddle.v2.config_base.Layer | List of paddle.v2.config_base.Layer</em>) &#8211; The input priorbox confidence.</li>
<li><strong>priorbox</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; The input priorbox location and the variance.</li>
<li><strong>label</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; The input label.</li>
<li><strong>num_classes</strong> (<em>int</em>) &#8211; The number of the classification.</li>
<li><strong>overlap_threshold</strong> (<em>float</em>) &#8211; The threshold of the overlap.</li>
<li><strong>neg_pos_ratio</strong> (<em>float</em>) &#8211; The ratio of the negative bbox to the positive bbox.</li>
<li><strong>neg_overlap</strong> (<em>float</em>) &#8211; The negative bbox overlap threshold.</li>
<li><strong>background_id</strong> (<em>int</em>) &#8211; The background class index.</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>
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</div>
</div>
<div class="section" id="check-layer">
<h2>Check Layer<a class="headerlink" href="#check-layer" title="永久链接至标题"></a></h2>
<div class="section" id="eos">
<h3>eos<a class="headerlink" href="#eos" title="永久链接至标题"></a></h3>
<dl class="class">
<dt>
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<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">eos</code></dt>
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<dd><p>A layer for checking EOS for each sample:
- output_id = (input_id == conf.eos_id)</p>
<p>The result is stored in output_.ids.
It is used by recurrent layer group.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">eos</span> <span class="o">=</span> <span class="n">eos</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">,</span> <span class="n">eos_id</span><span class="o">=</span><span class="nb">id</span><span class="p">)</span>
</pre></div>
</div>
<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; Layer name.</li>
<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; Input layer name.</li>
<li><strong>eos_id</strong> (<em>int</em>) &#8211; end id of sequence</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttribute</em>) &#8211; extra layer attributes.</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>
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</dd></dl>

</div>
</div>
<div class="section" id="miscs">
<h2>Miscs<a class="headerlink" href="#miscs" title="永久链接至标题"></a></h2>
<div class="section" id="dropout">
<h3>dropout<a class="headerlink" href="#dropout" 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">dropout</code></dt>
<dd><p>&#64;TODO(yuyang18): Add comments.</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> &#8211; </li>
<li><strong>input</strong> &#8211; </li>
<li><strong>dropout_rate</strong> &#8211; </li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first last"></p>
</td>
</tr>
</tbody>
</table>
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</dd></dl>
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</div>
</div>
<div class="section" id="activation-with-learnable-parameter">
<h2>Activation with learnable parameter<a class="headerlink" href="#activation-with-learnable-parameter" title="永久链接至标题"></a></h2>
<div class="section" id="prelu">
<h3>prelu<a class="headerlink" href="#prelu" 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">prelu</code></dt>
<dd><p>The Parameter Relu activation that actives outputs with a learnable weight.</p>
<dl class="docutils">
<dt>Reference:</dt>
<dd>Delving Deep into Rectifiers: Surpassing Human-Level Performance on
ImageNet Classification <a class="reference external" href="http://arxiv.org/pdf/1502.01852v1.pdf">http://arxiv.org/pdf/1502.01852v1.pdf</a></dd>
</dl>
<div class="math">
\[\begin{split}z_i &amp;\quad if \quad z_i &gt; 0 \\
a_i * z_i  &amp;\quad \mathrm{otherwise}\end{split}\]</div>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">prelu</span> <span class="o">=</span> <span class="n">prelu</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layers</span><span class="p">,</span> <span class="n">partial_sum</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
<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; Name of this layer.</li>
<li><strong>input</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; The input layer.</li>
<li><strong>partial_sum</strong> (<em>int</em>) &#8211; <p>this parameter makes a group of inputs share a same weight.</p>
<ul>
<li>partial_sum = 1, indicates the element-wise activation: each element has a weight.</li>
<li>partial_sum = number of elements in one channel, indicates the channel-wise activation, elements in a channel share a same weight.</li>
<li>partial_sum = number of outputs, indicates all elements share a same weight.</li>
</ul>
</li>
<li><strong>param_attr</strong> (<em>paddle.v2.attr.ParameterAttribute|None</em>) &#8211; The parameter attribute. See paddle.v2.attr.ParameterAttribute for details.</li>
<li><strong>layer_attr</strong> (<em>paddle.v2.attr.ExtraAttributeNone</em>) &#8211; Extra layer configurations. Default is None.</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>
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</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
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prodict between <a href="#id12"><span class="problematic" id="id13">:match:`X&#8217;`</span></a> and <span class="math">\(\sigma\)</span> is finally returned.</p>
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<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>
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</div>
</div>
<div class="section" id="detection-output-layer">
<h2>Detection output Layer<a class="headerlink" href="#detection-output-layer" title="永久链接至标题"></a></h2>
<div class="section" id="detection-output">
<h3>detection_output<a class="headerlink" href="#detection-output" 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">detection_output</code></dt>
<dd><p>Apply the NMS to the output of network and compute the predict bounding
box location.</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_loc</strong> (<em>paddle.v2.config_base.Layer | List of paddle.v2.config_base.Layer.</em>) &#8211; The input predict locations.</li>
<li><strong>input_conf</strong> (<em>paddle.v2.config_base.Layer | List of paddle.v2.config_base.Layer.</em>) &#8211; The input priorbox confidence.</li>
<li><strong>priorbox</strong> (<em>paddle.v2.config_base.Layer</em>) &#8211; The input priorbox location and the variance.</li>
<li><strong>num_classes</strong> (<em>int</em>) &#8211; The number of the classification.</li>
<li><strong>nms_threshold</strong> (<em>float</em>) &#8211; The Non-maximum suppression threshold.</li>
<li><strong>nms_top_k</strong> (<em>int</em>) &#8211; The bbox number kept of the NMS&#8217;s output</li>
<li><strong>keep_top_k</strong> (<em>int</em>) &#8211; The bbox number kept of the layer&#8217;s output</li>
<li><strong>confidence_threshold</strong> (<em>float</em>) &#8211; The classification confidence threshold</li>
<li><strong>background_id</strong> (<em>int</em>) &#8211; The background class index.</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>
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