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  <div class="section" id="nlp">
<h1>NLP<a class="headerlink" href="#nlp" title="Permalink to this headline"></a></h1>
<div class="section" id="sequence-conv-pool">
<h2>sequence_conv_pool<a class="headerlink" href="#sequence-conv-pool" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.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">Parameters:</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 or 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 or 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 or None</em>) &#8211; fc layer parameter attribute. None if user don&#8217;t care.</li>
<li><strong>fc_bias_attr</strong> (<em>ParameterAttribute or 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 or None.</em>) &#8211; pooling layer bias attr. None if don&#8217;t care.
False if no bias.</li>
<li><strong>fc_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; fc layer extra attribute.</li>
<li><strong>context_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; context projection layer extra attribute.</li>
<li><strong>pool_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; pooling layer extra attribute.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">output layer name.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</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">
<h2>text_conv_pool<a class="headerlink" href="#text-conv-pool" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.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">Parameters:</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 or 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 or 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 or None</em>) &#8211; fc layer parameter attribute. None if user don&#8217;t care.</li>
<li><strong>fc_bias_attr</strong> (<em>ParameterAttribute or 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 or None.</em>) &#8211; pooling layer bias attr. None if don&#8217;t care.
False if no bias.</li>
<li><strong>fc_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; fc layer extra attribute.</li>
<li><strong>context_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; context projection layer extra attribute.</li>
<li><strong>pool_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; pooling layer extra attribute.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">output layer name.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
</div>
<div class="section" id="images">
<h1>Images<a class="headerlink" href="#images" title="Permalink to this headline"></a></h1>
<div class="section" id="img-conv-bn-pool">
<h2>img_conv_bn_pool<a class="headerlink" href="#img-conv-bn-pool" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.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">Parameters:</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> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#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> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#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_start</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> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; see img_pool_layer&#8217;s document.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">Layer groups output</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</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">
<h2>img_conv_group<a class="headerlink" href="#img-conv-group" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.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>
<p>TODO(yuyang18): Complete docs</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>conv_batchnorm_drop_rate</strong> &#8211; </li>
<li><strong>input</strong> &#8211; </li>
<li><strong>conv_num_filter</strong> &#8211; </li>
<li><strong>pool_size</strong> &#8211; </li>
<li><strong>num_channels</strong> &#8211; </li>
<li><strong>conv_padding</strong> &#8211; </li>
<li><strong>conv_filter_size</strong> &#8211; </li>
<li><strong>conv_act</strong> &#8211; </li>
<li><strong>conv_with_batchnorm</strong> &#8211; </li>
<li><strong>pool_stride</strong> &#8211; </li>
<li><strong>pool_type</strong> &#8211; </li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"></p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="simple-img-conv-pool">
<h2>simple_img_conv_pool<a class="headerlink" href="#simple-img-conv-pool" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.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">Parameters:</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> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#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> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#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> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; see img_conv_layer for details</li>
<li><strong>pool_stride</strong> (<em>int</em>) &#8211; see img_conv_layer for details</li>
<li><strong>pool_start</strong> (<em>int</em>) &#8211; see img_conv_layer for details</li>
<li><strong>pool_padding</strong> (<em>int</em>) &#8211; see img_conv_layer for details</li>
<li><strong>pool_layer_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; see img_conv_layer for details</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">Layer&#8217;s output</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="vgg-16-network">
<h2>vgg_16_network<a class="headerlink" href="#vgg-16-network" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.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">Parameters:</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">Returns:</th><td class="field-body"><p class="first last"></p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
</div>
<div class="section" id="recurrent">
<h1>Recurrent<a class="headerlink" href="#recurrent" title="Permalink to this headline"></a></h1>
<div class="section" id="lstm">
<h2>LSTM<a class="headerlink" href="#lstm" title="Permalink to this headline"></a></h2>
<div class="section" id="lstmemory-unit">
<h3>lstmemory_unit<a class="headerlink" href="#lstmemory-unit" title="Permalink to this headline"></a></h3>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.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>TODO(yuyang18): complete docs</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; input layer name.</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> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; Parameter config, None if use default.</li>
<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; lstm final activate type</li>
<li><strong>gate_act</strong> (<em>BaseActivation</em>) &#8211; lstm gate activate type</li>
<li><strong>state_act</strong> (<em>BaseActivation</em>) &#8211; lstm state activate type.</li>
<li><strong>mixed_bias_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; bias parameter attribute of mixed layer.
False means no bias, None means default bias.</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>mixed_layer_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; mixed layer&#8217;s extra attribute.</li>
<li><strong>lstm_layer_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; lstm layer&#8217;s extra attribute.</li>
<li><strong>get_output_layer_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; get output layer&#8217;s extra attribute.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">lstmemory unit name.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="lstmemory-group">
<h3>lstmemory_group<a class="headerlink" href="#lstmemory-group" title="Permalink to this headline"></a></h3>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.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>TODO(yuyang18): complete docs</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; input layer name.</li>
<li><strong>name</strong> (<em>basestring</em>) &#8211; lstmemory group name.</li>
<li><strong>size</strong> (<em>int</em>) &#8211; lstmemory group size.</li>
<li><strong>reverse</strong> (<em>bool</em>) &#8211; is lstm reversed</li>
<li><strong>param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; Parameter config, None if use default.</li>
<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; lstm final activate type</li>
<li><strong>gate_act</strong> (<em>BaseActivation</em>) &#8211; lstm gate activate type</li>
<li><strong>state_act</strong> (<em>BaseActivation</em>) &#8211; lstm state activate type.</li>
<li><strong>mixed_bias_attr</strong> (<em>ParameterAttribute|False</em>) &#8211; bias parameter attribute of mixed layer.
False means no bias, None means default bias.</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>mixed_layer_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; mixed layer&#8217;s extra attribute.</li>
<li><strong>lstm_layer_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; lstm layer&#8217;s extra attribute.</li>
<li><strong>get_output_layer_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; get output layer&#8217;s extra attribute.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">lstmemory group name.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="simple-lstm">
<h3>simple_lstm<a class="headerlink" href="#simple-lstm" title="Permalink to this headline"></a></h3>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.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">
\[\begin{split}i_t &amp; = \sigma(W_{xi}x_{t} + W_{hi}h_{t-1} + W_{ci}c_{t-1} + b_i)\end{split}\]\[\begin{split}f_t &amp; = \sigma(W_{xf}x_{t} + W_{hf}h_{t-1} + W_{cf}c_{t-1} + b_f)\end{split}\]\[\begin{split}c_t &amp; = f_tc_{t-1} + i_t tanh (W_{xc}x_t+W_{hc}h_{t-1} + b_c)\end{split}\]\[\begin{split}o_t &amp; = \sigma(W_{xo}x_{t} + W_{ho}h_{t-1} + W_{co}c_t + b_o)\end{split}\]\[\begin{split}h_t &amp; = o_t tanh(c_t)\end{split}\]</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">Parameters:</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; is lstm reversed</li>
<li><strong>mat_param_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#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> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#8211; lstm cell parameter attribute.</li>
<li><strong>act</strong> (<em>BaseActivation</em>) &#8211; lstm final activate type</li>
<li><strong>gate_act</strong> (<em>BaseActivation</em>) &#8211; lstm gate activate type</li>
<li><strong>state_act</strong> (<em>BaseActivation</em>) &#8211; lstm state activate type.</li>
<li><strong>mixed_layer_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; mixed layer&#8217;s extra attribute.</li>
<li><strong>lstm_cell_attr</strong> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ExtraLayerAttribute" title="paddle.trainer_config_helpers.attrs.ExtraLayerAttribute"><em>ExtraLayerAttribute</em></a>) &#8211; lstm layer&#8217;s extra attribute.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">lstm layer name.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="bidirectional-lstm">
<h3>bidirectional_lstm<a class="headerlink" href="#bidirectional-lstm" title="Permalink to this headline"></a></h3>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.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>TODO(yuyang18): Complete docs</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>name</strong> (<em>basestring</em>) &#8211; 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 False, concat word in last time step and return.
If True, concat sequnce in all time step and return.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">lstm layer name.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">LayerOutput</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
</div>
<div class="section" id="gru">
<h2>GRU<a class="headerlink" href="#gru" title="Permalink to this headline"></a></h2>
<div class="section" id="gru-unit">
<h3>gru_unit<a class="headerlink" href="#gru-unit" title="Permalink to this headline"></a></h3>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.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><table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>input</strong> (<em>LayerOutput</em>) &#8211; </li>
<li><strong>name</strong> &#8211; </li>
<li><strong>size</strong> &#8211; </li>
<li><strong>gru_bias_attr</strong> &#8211; </li>
<li><strong>act</strong> &#8211; </li>
<li><strong>gate_act</strong> &#8211; </li>
<li><strong>gru_layer_attr</strong> &#8211; </li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"></p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="gru-group">
<h3>gru_group<a class="headerlink" href="#gru-group" title="Permalink to this headline"></a></h3>
</div>
<div class="section" id="simple-gru">
<h3>simple_gru<a class="headerlink" href="#simple-gru" title="Permalink to this headline"></a></h3>
</div>
</div>
<div class="section" id="simple-attention">
<h2>simple_attention<a class="headerlink" href="#simple-attention" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.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">
\[\begin{split}a(s_{i-1},h_{j}) &amp; = v_{a}f(W_{a}s_{t-1} + U_{a}h_{j})\end{split}\]\[\begin{split}e_{i,j} &amp; = a(s_{i-1}, h_{j})\end{split}\]\[\begin{split}a_{i,j} &amp; = \frac{exp(e_{i,j})}{\sum_{k=1}^{T_x}{exp(e_{i,k})}}\end{split}\]\[\begin{split}c_{i} &amp; = \sum_{j=1}^{T_{x}}a_{i,j}h_{j}\end{split}\]</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">Parameters:</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> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#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> (<a class="reference internal" href="attrs.html#paddle.trainer_config_helpers.attrs.ParameterAttribute" title="paddle.trainer_config_helpers.attrs.ParameterAttribute"><em>ParameterAttribute</em></a>) &#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">Returns:</th><td class="field-body"><p class="first last">a context vector</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
</div>
<div class="section" id="miscs">
<h1>Miscs<a class="headerlink" href="#miscs" title="Permalink to this headline"></a></h1>
<div class="section" id="dropout-layer">
<h2>dropout_layer<a class="headerlink" href="#dropout-layer" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">dropout_layer</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span></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">Parameters:</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">Returns:</th><td class="field-body"><p class="first last"></p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="outputs">
<h2>outputs<a class="headerlink" href="#outputs" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.trainer_config_helpers.networks.</code><code class="descname">outputs</code><span class="sig-paren">(</span><em>layers</em><span class="sig-paren">)</span></dt>
<dd><p>Declare the end of network. Currently it will only calculate the
input/output order of network. It will calculate the predict network or
train network&#8217;s output automatically.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>layers</strong> (<em>list|tuple|LayerOutput</em>) &#8211; </td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"></td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
</div>


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  <h3><a href="../../../index.html">Table Of Contents</a></h3>
  <ul>
<li><a class="reference internal" href="#">NLP</a><ul>
<li><a class="reference internal" href="#sequence-conv-pool">sequence_conv_pool</a></li>
<li><a class="reference internal" href="#text-conv-pool">text_conv_pool</a></li>
</ul>
</li>
<li><a class="reference internal" href="#images">Images</a><ul>
<li><a class="reference internal" href="#img-conv-bn-pool">img_conv_bn_pool</a></li>
<li><a class="reference internal" href="#img-conv-group">img_conv_group</a></li>
<li><a class="reference internal" href="#simple-img-conv-pool">simple_img_conv_pool</a></li>
<li><a class="reference internal" href="#vgg-16-network">vgg_16_network</a></li>
</ul>
</li>
<li><a class="reference internal" href="#recurrent">Recurrent</a><ul>
<li><a class="reference internal" href="#lstm">LSTM</a><ul>
<li><a class="reference internal" href="#lstmemory-unit">lstmemory_unit</a></li>
<li><a class="reference internal" href="#lstmemory-group">lstmemory_group</a></li>
<li><a class="reference internal" href="#simple-lstm">simple_lstm</a></li>
<li><a class="reference internal" href="#bidirectional-lstm">bidirectional_lstm</a></li>
</ul>
</li>
<li><a class="reference internal" href="#gru">GRU</a><ul>
<li><a class="reference internal" href="#gru-unit">gru_unit</a></li>
<li><a class="reference internal" href="#gru-group">gru_group</a></li>
<li><a class="reference internal" href="#simple-gru">simple_gru</a></li>
</ul>
</li>
<li><a class="reference internal" href="#simple-attention">simple_attention</a></li>
</ul>
</li>
<li><a class="reference internal" href="#miscs">Miscs</a><ul>
<li><a class="reference internal" href="#dropout-layer">dropout_layer</a></li>
<li><a class="reference internal" href="#outputs">outputs</a></li>
</ul>
</li>
</ul>

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