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  <div class="section" id="networks">
<h1>Networks<a class="headerlink" href="#networks" title="永久链接至标题">¶</a></h1>
<p>The v2.networks module contains pieces of neural network that combine multiple layers.</p>
<div class="section" id="nlp">
<h2>NLP<a class="headerlink" href="#nlp" title="永久链接至标题">¶</a></h2>
<div class="section" id="sequence-conv-pool">
<h3>sequence_conv_pool<a class="headerlink" href="#sequence-conv-pool" title="永久链接至标题">¶</a></h3>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.networks.</code><code class="descname">sequence_conv_pool</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Text convolution pooling layers helper.</p>
<p>Text input =&gt; Context Projection =&gt; FC Layer =&gt; Pooling =&gt; 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; name of output layer(pooling layer name)</li>
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; name of input layer</li>
<li><strong>context_len</strong> (<em>int</em>) &#8211; context projection length. See
context_projection&#8217;s document.</li>
<li><strong>hidden_size</strong> (<em>int</em>) &#8211; FC Layer size.</li>
<li><strong>context_start</strong> (<em>int</em><em> or </em><em>None</em>) &#8211; context projection length. See
context_projection&#8217;s context_start.</li>
<li><strong>pool_type</strong> (<em>BasePoolingType.</em>) &#8211; pooling layer type. See pooling_layer&#8217;s document.</li>
<li><strong>context_proj_layer_name</strong> (<em>basestring</em>) &#8211; context projection layer name.
None if user don&#8217;t care.</li>
<li><strong>context_proj_param_attr</strong> (<em>ParameterAttribute</em><em> or </em><em>None.</em>) &#8211; context projection parameter attribute.
None if user don&#8217;t care.</li>
<li><strong>fc_layer_name</strong> (<em>basestring</em>) &#8211; fc layer name. None if user don&#8217;t care.</li>
<li><strong>fc_param_attr</strong> (<em>ParameterAttribute</em><em> or </em><em>None</em>) &#8211; fc layer parameter attribute. None if user don&#8217;t care.</li>
<li><strong>fc_bias_attr</strong> (<em>ParameterAttribute</em><em> or </em><em>None</em>) &#8211; fc bias parameter attribute. False if no bias,
None if user don&#8217;t care.</li>
<li><strong>fc_act</strong> (<em>BaseActivation</em>) &#8211; fc layer activation type. None means tanh</li>
<li><strong>pool_bias_attr</strong> (<em>ParameterAttribute</em><em> or </em><em>None.</em>) &#8211; pooling layer bias attr. None if don&#8217;t care.
False if no bias.</li>
<li><strong>fc_attr</strong> (<em>ExtraLayerAttribute</em>) &#8211; fc layer extra attribute.</li>
<li><strong>context_attr</strong> (<em>ExtraLayerAttribute</em>) &#8211; context projection layer extra attribute.</li>
<li><strong>pool_attr</strong> (<em>ExtraLayerAttribute</em>) &#8211; pooling layer 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">output 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">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="text-conv-pool">
<span id="api-trainer-config-helpers-network-text-conv-pool"></span><h3>text_conv_pool<a class="headerlink" href="#text-conv-pool" title="永久链接至标题">¶</a></h3>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.networks.</code><code class="descname">text_conv_pool</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Text convolution pooling layers helper.</p>
<p>Text input =&gt; Context Projection =&gt; FC Layer =&gt; Pooling =&gt; 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; name of output layer(pooling layer name)</li>
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; name of input layer</li>
<li><strong>context_len</strong> (<em>int</em>) &#8211; context projection length. See
context_projection&#8217;s document.</li>
<li><strong>hidden_size</strong> (<em>int</em>) &#8211; FC Layer size.</li>
<li><strong>context_start</strong> (<em>int</em><em> or </em><em>None</em>) &#8211; context projection length. See
context_projection&#8217;s context_start.</li>
<li><strong>pool_type</strong> (<em>BasePoolingType.</em>) &#8211; pooling layer type. See pooling_layer&#8217;s document.</li>
<li><strong>context_proj_layer_name</strong> (<em>basestring</em>) &#8211; context projection layer name.
None if user don&#8217;t care.</li>
<li><strong>context_proj_param_attr</strong> (<em>ParameterAttribute</em><em> or </em><em>None.</em>) &#8211; context projection parameter attribute.
None if user don&#8217;t care.</li>
<li><strong>fc_layer_name</strong> (<em>basestring</em>) &#8211; fc layer name. None if user don&#8217;t care.</li>
<li><strong>fc_param_attr</strong> (<em>ParameterAttribute</em><em> or </em><em>None</em>) &#8211; fc layer parameter attribute. None if user don&#8217;t care.</li>
<li><strong>fc_bias_attr</strong> (<em>ParameterAttribute</em><em> or </em><em>None</em>) &#8211; fc bias parameter attribute. False if no bias,
None if user don&#8217;t care.</li>
<li><strong>fc_act</strong> (<em>BaseActivation</em>) &#8211; fc layer activation type. None means tanh</li>
<li><strong>pool_bias_attr</strong> (<em>ParameterAttribute</em><em> or </em><em>None.</em>) &#8211; pooling layer bias attr. None if don&#8217;t care.
False if no bias.</li>
<li><strong>fc_attr</strong> (<em>ExtraLayerAttribute</em>) &#8211; fc layer extra attribute.</li>
<li><strong>context_attr</strong> (<em>ExtraLayerAttribute</em>) &#8211; context projection layer extra attribute.</li>
<li><strong>pool_attr</strong> (<em>ExtraLayerAttribute</em>) &#8211; pooling layer 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">output 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">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
</div>
<div class="section" id="images">
<h2>Images<a class="headerlink" href="#images" title="永久链接至标题">¶</a></h2>
<div class="section" id="img-conv-bn-pool">
<h3>img_conv_bn_pool<a class="headerlink" href="#img-conv-bn-pool" title="永久链接至标题">¶</a></h3>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.networks.</code><code class="descname">img_conv_bn_pool</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Convolution, batch normalization, pooling group.</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; group name</li>
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; layer&#8217;s input</li>
<li><strong>filter_size</strong> (<em>int</em>) &#8211; see img_conv_layer&#8217;s document</li>
<li><strong>num_filters</strong> (<em>int</em>) &#8211; see img_conv_layer&#8217;s document</li>
<li><strong>pool_size</strong> (<em>int</em>) &#8211; see img_pool_layer&#8217;s document.</li>
<li><strong>pool_type</strong> (<em>BasePoolingType</em>) &#8211; see img_pool_layer&#8217;s document.</li>
<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; see batch_norm_layer&#8217;s document.</li>
<li><strong>groups</strong> (<em>int</em>) &#8211; see img_conv_layer&#8217;s document</li>
<li><strong>conv_stride</strong> (<em>int</em>) &#8211; see img_conv_layer&#8217;s document.</li>
<li><strong>conv_padding</strong> (<em>int</em>) &#8211; see img_conv_layer&#8217;s document.</li>
<li><strong>conv_bias_attr</strong> (<em>ParameterAttribute</em>) &#8211; see img_conv_layer&#8217;s document.</li>
<li><strong>num_channel</strong> (<em>int</em>) &#8211; see img_conv_layer&#8217;s document.</li>
<li><strong>conv_param_attr</strong> (<em>ParameterAttribute</em>) &#8211; see img_conv_layer&#8217;s document.</li>
<li><strong>shared_bias</strong> (<em>bool</em>) &#8211; see img_conv_layer&#8217;s document.</li>
<li><strong>conv_layer_attr</strong> (<em>ExtraLayerOutput</em>) &#8211; see img_conv_layer&#8217;s document.</li>
<li><strong>bn_param_attr</strong> (<em>ParameterAttribute.</em>) &#8211; see batch_norm_layer&#8217;s document.</li>
<li><strong>bn_bias_attr</strong> &#8211; see batch_norm_layer&#8217;s document.</li>
<li><strong>bn_layer_attr</strong> &#8211; ParameterAttribute.</li>
<li><strong>pool_stride</strong> (<em>int</em>) &#8211; see img_pool_layer&#8217;s document.</li>
<li><strong>pool_padding</strong> (<em>int</em>) &#8211; see img_pool_layer&#8217;s document.</li>
<li><strong>pool_layer_attr</strong> (<em>ExtraLayerAttribute</em>) &#8211; see img_pool_layer&#8217;s document.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">Layer groups output</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">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="img-conv-group">
<h3>img_conv_group<a class="headerlink" href="#img-conv-group" title="永久链接至标题">¶</a></h3>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.networks.</code><code class="descname">img_conv_group</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Image Convolution Group, Used for vgg net.</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>conv_batchnorm_drop_rate</strong> (<em>list</em>) &#8211; if conv_with_batchnorm[i] is true,
conv_batchnorm_drop_rate[i] represents the drop rate of each batch norm.</li>
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; layer&#8217;s input.</li>
<li><strong>conv_num_filter</strong> (<em>int</em>) &#8211; output channels num.</li>
<li><strong>pool_size</strong> (<em>int</em>) &#8211; pooling filter size.</li>
<li><strong>num_channels</strong> (<em>int</em>) &#8211; input channels num.</li>
<li><strong>conv_padding</strong> (<em>int</em>) &#8211; convolution padding size.</li>
<li><strong>conv_filter_size</strong> (<em>int</em>) &#8211; convolution filter size.</li>
<li><strong>conv_act</strong> (<em>BaseActivation</em>) &#8211; activation funciton after convolution.</li>
<li><strong>conv_with_batchnorm</strong> (<em>list</em>) &#8211; conv_with_batchnorm[i] represents
if there is a batch normalization after each convolution.</li>
<li><strong>pool_stride</strong> (<em>int</em>) &#8211; pooling stride size.</li>
<li><strong>pool_type</strong> (<em>BasePoolingType</em>) &#8211; pooling type.</li>
<li><strong>param_attr</strong> (<em>ParameterAttribute</em>) &#8211; Convolution param attribute.
None means default 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&#8217;s output</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="simple-img-conv-pool">
<span id="api-trainer-config-helpers-network-simple-img-conv-pool"></span><h3>simple_img_conv_pool<a class="headerlink" href="#simple-img-conv-pool" title="永久链接至标题">¶</a></h3>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.networks.</code><code class="descname">simple_img_conv_pool</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Simple image convolution and pooling group.</p>
<p>Input =&gt; conv =&gt; pooling</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; group name</li>
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; input layer name.</li>
<li><strong>filter_size</strong> (<em>int</em>) &#8211; see img_conv_layer for details</li>
<li><strong>num_filters</strong> (<em>int</em>) &#8211; see img_conv_layer for details</li>
<li><strong>pool_size</strong> (<em>int</em>) &#8211; see img_pool_layer for details</li>
<li><strong>pool_type</strong> (<em>BasePoolingType</em>) &#8211; see img_pool_layer for details</li>
<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; see img_conv_layer for details</li>
<li><strong>groups</strong> (<em>int</em>) &#8211; see img_conv_layer for details</li>
<li><strong>conv_stride</strong> (<em>int</em>) &#8211; see img_conv_layer for details</li>
<li><strong>conv_padding</strong> (<em>int</em>) &#8211; see img_conv_layer for details</li>
<li><strong>bias_attr</strong> (<em>ParameterAttribute</em>) &#8211; see img_conv_layer for details</li>
<li><strong>num_channel</strong> (<em>int</em>) &#8211; see img_conv_layer for details</li>
<li><strong>param_attr</strong> (<em>ParameterAttribute</em>) &#8211; see img_conv_layer for details</li>
<li><strong>shared_bias</strong> (<em>bool</em>) &#8211; see img_conv_layer for details</li>
<li><strong>conv_layer_attr</strong> (<em>ExtraLayerAttribute</em>) &#8211; see img_conv_layer for details</li>
<li><strong>pool_stride</strong> (<em>int</em>) &#8211; see img_pool_layer for details</li>
<li><strong>pool_padding</strong> (<em>int</em>) &#8211; see img_pool_layer for details</li>
<li><strong>pool_layer_attr</strong> (<em>ExtraLayerAttribute</em>) &#8211; see img_pool_layer for details</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">Layer&#8217;s output</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">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="small-vgg">
<h3>small_vgg<a class="headerlink" href="#small-vgg" title="永久链接至标题">¶</a></h3>
</div>
<div class="section" id="vgg-16-network">
<h3>vgg_16_network<a class="headerlink" href="#vgg-16-network" title="永久链接至标题">¶</a></h3>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.networks.</code><code class="descname">vgg_16_network</code><span class="sig-paren">(</span><em>input_image</em>, <em>num_channels</em>, <em>num_classes=1000</em><span class="sig-paren">)</span></dt>
<dd><p>Same model from <a class="reference external" href="https://gist.github.com/ksimonyan/211839e770f7b538e2d8">https://gist.github.com/ksimonyan/211839e770f7b538e2d8</a></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>num_classes</strong> &#8211; </li>
<li><strong>input_image</strong> (<em>LayerOutput</em>) &#8211; </li>
<li><strong>num_channels</strong> (<em>int</em>) &#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>
</dd></dl>

</div>
</div>
<div class="section" id="recurrent">
<h2>Recurrent<a class="headerlink" href="#recurrent" title="永久链接至标题">¶</a></h2>
<div class="section" id="lstm">
<h3>LSTM<a class="headerlink" href="#lstm" title="永久链接至标题">¶</a></h3>
<div class="section" id="lstmemory-unit">
<h4>lstmemory_unit<a class="headerlink" href="#lstmemory-unit" title="永久链接至标题">¶</a></h4>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.networks.</code><code class="descname">lstmemory_unit</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Define calculations that a LSTM unit performs during a single time step.
This function itself is not a recurrent layer, so it can not be
directly used to process sequence inputs. This function is always used in
recurrent_group (see layers.py for more details) to implement attention
mechanism.</p>
<p>Please refer to  <strong>Generating Sequences With Recurrent Neural Networks</strong>
for more details about LSTM. The link goes as follows:
.. _Link: <a class="reference external" href="https://arxiv.org/abs/1308.0850">https://arxiv.org/abs/1308.0850</a></p>
<div class="math">
\[ \begin{align}\begin{aligned}i_t &amp; = \sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + W_{c_i}c_{t-1} + b_i)\\f_t &amp; = \sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + W_{c_f}c_{t-1} + b_f)\\c_t &amp; = f_tc_{t-1} + i_t tanh (W_{x_c}x_t+W_{h_c}h_{t-1} + b_c)\\o_t &amp; = \sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + W_{c_o}c_t + b_o)\\h_t &amp; = o_t tanh(c_t)\end{aligned}\end{align} \]</div>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">lstm_step</span> <span class="o">=</span> <span class="n">lstmemory_unit</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">size</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span>
                           <span class="n">act</span><span class="o">=</span><span class="n">TanhActivation</span><span class="p">(),</span>
                           <span class="n">gate_act</span><span class="o">=</span><span class="n">SigmoidActivation</span><span class="p">(),</span>
                           <span class="n">state_act</span><span class="o">=</span><span class="n">TanhActivation</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>LayerOutput</em>) &#8211; input layer name.</li>
<li><strong>out_memory</strong> (<em>LayerOutput | None</em>) &#8211; output of previous time step</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; lstmemory unit name.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; lstmemory unit size.</li>
<li><strong>param_attr</strong> (<em>ParameterAttribute</em>) &#8211; Parameter config, None if use default.</li>
<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; lstm final activiation type</li>
<li><strong>gate_act</strong> (<em>BaseActivation</em>) &#8211; lstm gate activiation type</li>
<li><strong>state_act</strong> (<em>BaseActivation</em>) &#8211; lstm state activiation type.</li>
<li><strong>input_proj_bias_attr</strong> (<em>ParameterAttribute|False|None</em>) &#8211; bias attribute for input-to-hidden projection.
False means no bias, None means default bias.</li>
<li><strong>input_proj_layer_attr</strong> (<em>ExtraLayerAttribute</em>) &#8211; extra layer attribute for input to hidden
projection of the LSTM unit, such as dropout, error clipping.</li>
<li><strong>lstm_bias_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; bias parameter attribute of lstm layer.
False means no bias, None means default bias.</li>
<li><strong>lstm_layer_attr</strong> (<em>ExtraLayerAttribute</em>) &#8211; lstm 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">lstmemory unit 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">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="lstmemory-group">
<h4>lstmemory_group<a class="headerlink" href="#lstmemory-group" title="永久链接至标题">¶</a></h4>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.networks.</code><code class="descname">lstmemory_group</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>lstm_group is a recurrent_group version of Long Short Term Memory. It
does exactly the same calculation as the lstmemory layer (see lstmemory in
layers.py for the maths) does. A promising benefit is that LSTM memory
cell states, or hidden states in every time step are accessible to the
user. This is especially useful in attention model. If you do not need to
access the internal states of the lstm, but merely use its outputs,
it is recommended to use the lstmemory, which is relatively faster than
lstmemory_group.</p>
<p>NOTE: In PaddlePaddle&#8217;s implementation, the following input-to-hidden
multiplications:
<span class="math">\(W_{x_i}x_{t}\)</span> , <span class="math">\(W_{x_f}x_{t}\)</span>,
<span class="math">\(W_{x_c}x_t\)</span>, <span class="math">\(W_{x_o}x_{t}\)</span> are not done in lstmemory_unit to
speed up the calculations. Consequently, an additional mixed_layer with
full_matrix_projection must be included before lstmemory_unit is called.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">lstm_step</span> <span class="o">=</span> <span class="n">lstmemory_group</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">size</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span>
                            <span class="n">act</span><span class="o">=</span><span class="n">TanhActivation</span><span class="p">(),</span>
                            <span class="n">gate_act</span><span class="o">=</span><span class="n">SigmoidActivation</span><span class="p">(),</span>
                            <span class="n">state_act</span><span class="o">=</span><span class="n">TanhActivation</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>LayerOutput</em>) &#8211; input layer name.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; lstmemory group size.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; name of the lstmemory group.</li>
<li><strong>out_memory</strong> (<em>LayerOutput | None</em>) &#8211; output of previous time step</li>
<li><strong>reverse</strong> (<em>bool</em>) &#8211; is lstm reversed</li>
<li><strong>param_attr</strong> (<em>ParameterAttribute</em>) &#8211; Parameter config, None if use default.</li>
<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; lstm final activiation type</li>
<li><strong>gate_act</strong> (<em>BaseActivation</em>) &#8211; lstm gate activiation type</li>
<li><strong>state_act</strong> (<em>BaseActivation</em>) &#8211; lstm state activiation type.</li>
<li><strong>lstm_bias_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; bias parameter attribute of lstm layer.
False means no bias, None means default bias.</li>
<li><strong>input_proj_bias_attr</strong> (<em>ParameterAttribute|False|None</em>) &#8211; bias attribute for input-to-hidden projection.
False means no bias, None means default bias.</li>
<li><strong>input_proj_layer_attr</strong> (<em>ExtraLayerAttribute</em>) &#8211; extra layer attribute for input to hidden
projection of the LSTM unit, such as dropout, error clipping.</li>
<li><strong>lstm_layer_attr</strong> (<em>ExtraLayerAttribute</em>) &#8211; lstm 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">the lstmemory group.</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">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="simple-lstm">
<h4>simple_lstm<a class="headerlink" href="#simple-lstm" title="永久链接至标题">¶</a></h4>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.networks.</code><code class="descname">simple_lstm</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Simple LSTM Cell.</p>
<p>It just combine a mixed layer with fully_matrix_projection and a lstmemory
layer. The simple lstm 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>Please refer <strong>Generating Sequences With Recurrent Neural Networks</strong> if you
want to know what lstm is. <a class="reference external" href="http://arxiv.org/abs/1308.0850">Link</a> is here.</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; lstm layer name.</li>
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; input layer name.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; lstm layer size.</li>
<li><strong>reverse</strong> (<em>bool</em>) &#8211; whether to process the input data in a reverse order</li>
<li><strong>mat_param_attr</strong> (<em>ParameterAttribute</em>) &#8211; mixed layer&#8217;s matrix projection parameter attribute.</li>
<li><strong>bias_param_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; bias parameter attribute. False means no bias, None
means default bias.</li>
<li><strong>inner_param_attr</strong> (<em>ParameterAttribute</em>) &#8211; lstm cell parameter attribute.</li>
<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; lstm final activiation type</li>
<li><strong>gate_act</strong> (<em>BaseActivation</em>) &#8211; lstm gate activiation type</li>
<li><strong>state_act</strong> (<em>BaseActivation</em>) &#8211; lstm state activiation type.</li>
<li><strong>mixed_layer_attr</strong> (<em>ExtraLayerAttribute</em>) &#8211; mixed layer&#8217;s extra attribute.</li>
<li><strong>lstm_cell_attr</strong> (<em>ExtraLayerAttribute</em>) &#8211; lstm 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">lstm 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">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="bidirectional-lstm">
<h4>bidirectional_lstm<a class="headerlink" href="#bidirectional-lstm" title="永久链接至标题">¶</a></h4>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.networks.</code><code class="descname">bidirectional_lstm</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>A bidirectional_lstm is a recurrent unit that iterates over the input
sequence both in forward and bardward orders, and then concatenate two
outputs form a final output. However, concatenation of two outputs
is not the only way to form the final output, you can also, for example,
just add them together.</p>
<p>Please refer to  <strong>Neural Machine Translation by Jointly Learning to Align
and Translate</strong> for more details about the bidirectional lstm.
The link goes as follows:
.. _Link: <a class="reference external" href="https://arxiv.org/pdf/1409.0473v3.pdf">https://arxiv.org/pdf/1409.0473v3.pdf</a></p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">bi_lstm</span> <span class="o">=</span> <span class="n">bidirectional_lstm</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">input1</span><span class="p">],</span> <span class="n">size</span><span class="o">=</span><span class="mi">512</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; bidirectional lstm layer name.</li>
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; input layer.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; lstm layer size.</li>
<li><strong>return_seq</strong> (<em>bool</em>) &#8211; If set False, outputs of the last time step are
concatenated and returned.
If set True, the entire output sequences that are
processed in forward and backward directions are
concatenated and returned.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">LayerOutput object accroding to the return_seq.</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">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
</div>
<div class="section" id="gru">
<h3>GRU<a class="headerlink" href="#gru" title="永久链接至标题">¶</a></h3>
<div class="section" id="gru-unit">
<h4>gru_unit<a class="headerlink" href="#gru-unit" title="永久链接至标题">¶</a></h4>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.networks.</code><code class="descname">gru_unit</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Define calculations that a gated recurrent unit performs in a single time
step. This function itself is not a recurrent layer, so it can not be
directly used to process sequence inputs. This function is always used in
the recurrent_group (see layers.py for more details) to implement attention
mechanism.</p>
<p>Please see grumemory in layers.py for the details about the maths.</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>LayerOutput</em>) &#8211; input layer name.</li>
<li><strong>memory_boot</strong> (<em>LayerOutput | None</em>) &#8211; the initialization state of the LSTM cell.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; name of the gru group.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; hidden size of the gru.</li>
<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; type of the activation</li>
<li><strong>gate_act</strong> (<em>BaseActivation</em>) &#8211; type of the gate activation</li>
<li><strong>gru_layer_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; Extra parameter attribute of the gru layer.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">the gru output layer.</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">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="gru-group">
<h4>gru_group<a class="headerlink" href="#gru-group" title="永久链接至标题">¶</a></h4>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.networks.</code><code class="descname">gru_group</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>gru_group is a recurrent_group version of Gated Recurrent Unit. It
does exactly the same calculation as the grumemory layer does. A promising
benefit is that gru hidden states are accessible to the user. This is
especially useful in attention model. If you do not need to access
any internal state, but merely use the outputs of a GRU, it is recommended
to use the grumemory, which is relatively faster.</p>
<p>Please see grumemory in layers.py for more detail about the maths.</p>
<p>The example 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">gur_group</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">size</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span>
                <span class="n">act</span><span class="o">=</span><span class="n">TanhActivation</span><span class="p">(),</span>
                <span class="n">gate_act</span><span class="o">=</span><span class="n">SigmoidActivation</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>LayerOutput</em>) &#8211; input layer name.</li>
<li><strong>memory_boot</strong> (<em>LayerOutput | None</em>) &#8211; the initialization state of the LSTM cell.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; name of the gru group.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; hidden size of the gru.</li>
<li><strong>reverse</strong> (<em>bool</em>) &#8211; whether to process the input data in a reverse order</li>
<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; type of the activiation</li>
<li><strong>gate_act</strong> (<em>BaseActivation</em>) &#8211; type of the gate activiation</li>
<li><strong>gru_bias_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; bias. False means no bias, None means default bias.</li>
<li><strong>gru_layer_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; Extra parameter attribute of the gru layer.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">the gru group.</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">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="simple-gru">
<h4>simple_gru<a class="headerlink" href="#simple-gru" title="永久链接至标题">¶</a></h4>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.networks.</code><code class="descname">simple_gru</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>You maybe see gru_step_layer, grumemory in layers.py, gru_unit, gru_group,
simple_gru in network.py. The reason why there are so many interfaces is
that we have two ways to implement recurrent neural network. One way is to
use one complete layer to implement rnn (including simple rnn, gru and lstm)
with multiple time steps, such as recurrent_layer, lstmemory, grumemory. But,
the multiplication operation <span class="math">\(W x_t\)</span> is not computed in these layers.
See details in their interfaces in layers.py.
The other implementation is to use an recurrent group which can ensemble a
series of layers to compute rnn step by step. This way is flexible for
attenion mechanism or other complex connections.</p>
<ul class="simple">
<li>gru_step_layer: only compute rnn by one step. It needs an memory as input
and can be used in recurrent group.</li>
<li>gru_unit: a wrapper of gru_step_layer with memory.</li>
<li>gru_group: a GRU cell implemented by a combination of multiple layers in
recurrent group.
But <span class="math">\(W x_t\)</span> is not done in group.</li>
<li>gru_memory: a GRU cell implemented by one layer, which does same calculation
with gru_group and is faster than gru_group.</li>
<li>simple_gru: a complete GRU implementation inlcuding <span class="math">\(W x_t\)</span> and
gru_group. <span class="math">\(W\)</span> contains <span class="math">\(W_r\)</span>, <span class="math">\(W_z\)</span> and <span class="math">\(W\)</span>, see
formula in grumemory.</li>
</ul>
<p>The computational speed is that, grumemory is relatively better than
gru_group, and gru_group is relatively better than simple_gru.</p>
<p>The example 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">simple_gru</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">size</span><span class="o">=</span><span class="mi">256</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>LayerOutput</em>) &#8211; input layer name.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; name of the gru group.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; hidden size of the gru.</li>
<li><strong>reverse</strong> (<em>bool</em>) &#8211; whether to process the input data in a reverse order</li>
<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; type of the activiation</li>
<li><strong>gate_act</strong> (<em>BaseActivation</em>) &#8211; type of the gate activiation</li>
<li><strong>gru_bias_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; bias. False means no bias, None means default bias.</li>
<li><strong>gru_layer_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; Extra parameter attribute of the gru layer.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">the gru group.</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">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="simple-gru2">
<h4>simple_gru2<a class="headerlink" href="#simple-gru2" title="永久链接至标题">¶</a></h4>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.networks.</code><code class="descname">simple_gru2</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>simple_gru2 is the same with simple_gru, but using grumemory instead
Please see grumemory in layers.py for more detail about the maths.
simple_gru2 is faster than simple_gru.</p>
<p>The example 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">simple_gru2</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">size</span><span class="o">=</span><span class="mi">256</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>LayerOutput</em>) &#8211; input layer name.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; name of the gru group.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; hidden size of the gru.</li>
<li><strong>reverse</strong> (<em>bool</em>) &#8211; whether to process the input data in a reverse order</li>
<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; type of the activiation</li>
<li><strong>gate_act</strong> (<em>BaseActivation</em>) &#8211; type of the gate activiation</li>
<li><strong>gru_bias_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; bias. False means no bias, None means default bias.</li>
<li><strong>gru_layer_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; Extra parameter attribute of the gru layer.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">the gru group.</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">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="bidirectional-gru">
<h4>bidirectional_gru<a class="headerlink" href="#bidirectional-gru" title="永久链接至标题">¶</a></h4>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.networks.</code><code class="descname">bidirectional_gru</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>A bidirectional_gru is a recurrent unit that iterates over the input
sequence both in forward and bardward orders, and then concatenate two
outputs to form a final output. However, concatenation of two outputs
is not the only way to form the final output, you can also, for example,
just add them together.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">bi_gru</span> <span class="o">=</span> <span class="n">bidirectional_gru</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="p">[</span><span class="n">input1</span><span class="p">],</span> <span class="n">size</span><span class="o">=</span><span class="mi">512</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; bidirectional gru layer name.</li>
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; input layer.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; gru layer size.</li>
<li><strong>return_seq</strong> (<em>bool</em>) &#8211; If set False, outputs of the last time step are
concatenated and returned.
If set True, the entire output sequences that are
processed in forward and backward directions are
concatenated and returned.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">LayerOutput 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">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
</div>
<div class="section" id="simple-attention">
<h3>simple_attention<a class="headerlink" href="#simple-attention" title="永久链接至标题">¶</a></h3>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.networks.</code><code class="descname">simple_attention</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Calculate and then return a context vector by attention machanism.
Size of the context vector equals to size of the encoded_sequence.</p>
<div class="math">
\[ \begin{align}\begin{aligned}a(s_{i-1},h_{j}) &amp; = v_{a}f(W_{a}s_{t-1} + U_{a}h_{j})\\e_{i,j} &amp; = a(s_{i-1}, h_{j})\\a_{i,j} &amp; = \frac{exp(e_{i,j})}{\sum_{k=1}^{T_x}{exp(e_{i,k})}}\\c_{i} &amp; = \sum_{j=1}^{T_{x}}a_{i,j}h_{j}\end{aligned}\end{align} \]</div>
<p>where <span class="math">\(h_{j}\)</span> is the jth element of encoded_sequence,
<span class="math">\(U_{a}h_{j}\)</span> is the jth element of encoded_proj
<span class="math">\(s_{i-1}\)</span> is decoder_state
<span class="math">\(f\)</span> is weight_act, and is set to tanh by default.</p>
<p>Please refer to <strong>Neural Machine Translation by Jointly Learning to
Align and Translate</strong> for more details. The link is as follows:
<a class="reference external" href="https://arxiv.org/abs/1409.0473">https://arxiv.org/abs/1409.0473</a>.</p>
<p>The example usage is:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">context</span> <span class="o">=</span> <span class="n">simple_attention</span><span class="p">(</span><span class="n">encoded_sequence</span><span class="o">=</span><span class="n">enc_seq</span><span class="p">,</span>
                           <span class="n">encoded_proj</span><span class="o">=</span><span class="n">enc_proj</span><span class="p">,</span>
                           <span class="n">decoder_state</span><span class="o">=</span><span class="n">decoder_prev</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 the attention model.</li>
<li><strong>softmax_param_attr</strong> (<em>ParameterAttribute</em>) &#8211; parameter attribute of sequence softmax
that is used to produce attention weight</li>
<li><strong>weight_act</strong> (<em>Activation</em>) &#8211; activation of the attention model</li>
<li><strong>encoded_sequence</strong> (<em>LayerOutput</em>) &#8211; output of the encoder</li>
<li><strong>encoded_proj</strong> (<em>LayerOutput</em>) &#8211; attention weight is computed by a feed forward neural
network which has two inputs : decoder&#8217;s hidden state
of previous time step and encoder&#8217;s output.
encoded_proj is output of the feed-forward network for
encoder&#8217;s output. Here we pre-compute it outside
simple_attention for speed consideration.</li>
<li><strong>decoder_state</strong> (<em>LayerOutput</em>) &#8211; hidden state of decoder in previous time step</li>
<li><strong>transform_param_attr</strong> (<em>ParameterAttribute</em>) &#8211; parameter attribute of the feed-forward
network that takes decoder_state as inputs to
compute attention weight.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first last">a context vector</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
</div>
</div>


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