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8 changed file
--- a/develop/doc/_sources/api/v2/config/layer.rst.txt
+++ b/develop/doc/_sources/api/v2/config/layer.rst.txt
@@ -257,6 +257,11 @@ seq_concat
 ..  autoclass:: paddle.v2.layer.seq_concat
    :noindex:
+kmax_sequence_score
+-------------------
+..  autoclass:: paddle.v2.layer.kmax_sequence_score
+    :noindex:
 sub_nested_seq
 --------------
 ..  autoclass:: paddle.v2.layer.sub_nested_seq

--- a/develop/doc/api/v2/config/layer.html
+++ b/develop/doc/api/v2/config/layer.html
@@ -2143,6 +2143,39 @@ default Bias.</li>
 </table>
 </dd></dl>
+</div>
+<div class="section" id="kmax-sequence-score">
+<h3>kmax_sequence_score<a class="headerlink" href="#kmax-sequence-score" title="Permalink to this headline">¶</a></h3>
+<dl class="class">
+<dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">kmax_sequence_score</code></dt>
+<dd><p>This layer accepts one input which are scores over a sequence or a nested
+sequence, and returns indices of beam_size sequences with highest scores.</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">kmax_indices</span> <span class="o">=</span> <span class="n">kmax_sequence_score</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">beam_size</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; The Layer Name.</li>
+<li><strong>input</strong> (<em>paddle.v2.config_base.Layer.</em>) &#8211; The input layer. It stores scores over a sequence or a nested
+sequence and its size must be 1.</li>
+<li><strong>beam_size</strong> (<em>double</em>) &#8211; squence indices with top beam_size scores are returned.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">paddle.v2.config_base.Layer object.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">paddle.v2.config_base.Layer</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
 </div>
 <div class="section" id="sub-nested-seq">
 <h3>sub_nested_seq<a class="headerlink" href="#sub-nested-seq" title="Permalink to this headline">¶</a></h3>
@@ -2731,7 +2764,9 @@ ight)</p>
 </tr>
 <tr class="field-even field"><th class="field-name">type max:</th><td class="field-body">double</td>
 </tr>
-<tr class="field-odd field"><th class="field-name">return:</th><td class="field-body">paddle.v2.config_base.Layer</td>
+<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>

--- a/develop/doc/api/v2/data.html
+++ b/develop/doc/api/v2/data.html
@@ -477,84 +477,6 @@ nested sequence.</li>
 </div>
 <div class="section" id="datafeeder">
 <h2>DataFeeder<a class="headerlink" href="#datafeeder" title="Permalink to this headline">¶</a></h2>
-<dl class="class">
-<dt>
-<em class="property">class </em><code class="descclassname">paddle.v2.data_feeder.</code><code class="descname">DataFeeder</code><span class="sig-paren">(</span><em>data_types</em>, <em>feeding=None</em><span class="sig-paren">)</span></dt>
-<dd><p>DataFeeder converts the data returned by paddle.reader into a data structure
-of Arguments which is defined in the API. The paddle.reader usually returns
-a list of mini-batch data entries. Each data entry in the list is one sample.
-Each sample is a list or a tuple with one feature or multiple features.
-DataFeeder converts this mini-batch data entries into Arguments in order
-to feed it to C++ interface.</p>
-<p>The simple usage shows below</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">feeding</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;image&#39;</span><span class="p">,</span> <span class="s1">&#39;label&#39;</span><span class="p">]</span>
-<span class="n">data_types</span> <span class="o">=</span> <span class="n">enumerate_data_types_of_data_layers</span><span class="p">(</span><span class="n">topology</span><span class="p">)</span>
-<span class="n">feeder</span> <span class="o">=</span> <span class="n">DataFeeder</span><span class="p">(</span><span class="n">data_types</span><span class="o">=</span><span class="n">data_types</span><span class="p">,</span> <span class="n">feeding</span><span class="o">=</span><span class="n">feeding</span><span class="p">)</span>
-<span class="n">minibatch_data</span> <span class="o">=</span> <span class="p">[([</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="mi">5</span><span class="p">)]</span>
-<span class="n">arg</span> <span class="o">=</span> <span class="n">feeder</span><span class="p">(</span><span class="n">minibatch_data</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>If mini-batch data and data layers are not one to one mapping, we
-could pass a dictionary to feeding parameter to represent the mapping
-relationship.</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">data_types</span> <span class="o">=</span> <span class="p">[(</span><span class="s1">&#39;image&#39;</span><span class="p">,</span> <span class="n">paddle</span><span class="o">.</span><span class="n">data_type</span><span class="o">.</span><span class="n">dense_vector</span><span class="p">(</span><span class="mi">784</span><span class="p">)),</span>
-              <span class="p">(</span><span class="s1">&#39;label&#39;</span><span class="p">,</span> <span class="n">paddle</span><span class="o">.</span><span class="n">data_type</span><span class="o">.</span><span class="n">integer_value</span><span class="p">(</span><span class="mi">10</span><span class="p">))]</span>
-<span class="n">feeding</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;image&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span> <span class="s1">&#39;label&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">}</span>
-<span class="n">feeder</span> <span class="o">=</span> <span class="n">DataFeeder</span><span class="p">(</span><span class="n">data_types</span><span class="o">=</span><span class="n">data_types</span><span class="p">,</span> <span class="n">feeding</span><span class="o">=</span><span class="n">feeding</span><span class="p">)</span>
-<span class="n">minibatch_data</span> <span class="o">=</span> <span class="p">[</span>
-                   <span class="p">(</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span><span class="mf">2.0</span><span class="p">,</span><span class="mf">3.0</span><span class="p">,</span><span class="mf">4.0</span><span class="p">],</span> <span class="mi">5</span><span class="p">,</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="mi">8</span><span class="p">]</span> <span class="p">),</span>  <span class="c1"># first sample</span>
-                   <span class="p">(</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span><span class="mf">2.0</span><span class="p">,</span><span class="mf">3.0</span><span class="p">,</span><span class="mf">4.0</span><span class="p">],</span> <span class="mi">5</span><span class="p">,</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="mi">8</span><span class="p">]</span> <span class="p">)</span>   <span class="c1"># second sample</span>
-                 <span class="p">]</span>
-<span class="c1"># or minibatch_data = [</span>
-<span class="c1">#                       [ [1.0,2.0,3.0,4.0], 5, [6,7,8] ],  # first sample</span>
-<span class="c1">#                       [ [1.0,2.0,3.0,4.0], 5, [6,7,8] ]   # second sample</span>
-<span class="c1">#                     ]</span>
-<span class="n">arg</span> <span class="o">=</span> <span class="n">feeder</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="n">minibatch_data</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="admonition note">
-<p class="first admonition-title">Note</p>
-<p class="last">This module is for internal use only. Users should use the <cite>reader</cite>
-interface.</p>
-</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 last simple">
-<li><strong>data_types</strong> (<em>list</em>) &#8211; A list to specify data name and type. Each item is
-a tuple of (data_name, data_type).</li>
-<li><strong>feeding</strong> (<em>dict|collections.Sequence|None</em>) &#8211; A dictionary or a sequence to specify the position of each
-data in the input data.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-<dl class="method">
-<dt>
-<code class="descname">convert</code><span class="sig-paren">(</span><em>dat</em>, <em>argument=None</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 last simple">
-<li><strong>dat</strong> (<em>list</em>) &#8211; A list of mini-batch data. Each sample is a list or tuple
-one feature or multiple features.</li>
-<li><strong>argument</strong> (<em>py_paddle.swig_paddle.Arguments</em>) &#8211; An Arguments object contains this mini-batch data with
-one or multiple features. The Arguments definition is
-in the API.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-</dd></dl>
 </div>
 <div class="section" id="reader">
 <h2>Reader<a class="headerlink" href="#reader" title="Permalink to this headline">¶</a></h2>

--- a/develop/doc/searchindex.js
+++ b/develop/doc/searchindex.js
--- a/develop/doc_cn/_sources/api/v2/config/layer.rst.txt
+++ b/develop/doc_cn/_sources/api/v2/config/layer.rst.txt
@@ -257,6 +257,11 @@ seq_concat
 ..  autoclass:: paddle.v2.layer.seq_concat
    :noindex:
+kmax_sequence_score
+-------------------
+..  autoclass:: paddle.v2.layer.kmax_sequence_score
+    :noindex:
 sub_nested_seq
 --------------
 ..  autoclass:: paddle.v2.layer.sub_nested_seq

--- a/develop/doc_cn/api/v2/config/layer.html
+++ b/develop/doc_cn/api/v2/config/layer.html
@@ -2148,6 +2148,39 @@ default Bias.</li>
 </table>
 </dd></dl>
+</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>
+<dl class="class">
+<dt>
+<em class="property">class </em><code class="descclassname">paddle.v2.layer.</code><code class="descname">kmax_sequence_score</code></dt>
+<dd><p>This layer accepts one input which are scores over a sequence or a nested
+sequence, and returns indices of beam_size sequences with highest scores.</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">kmax_indices</span> <span class="o">=</span> <span class="n">kmax_sequence_score</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">beam_size</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. It stores scores over a sequence or a nested
+sequence and its size must be 1.</li>
+<li><strong>beam_size</strong> (<em>double</em>) &#8211; squence indices with top beam_size scores are returned.</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="sub-nested-seq">
 <h3>sub_nested_seq<a class="headerlink" href="#sub-nested-seq" title="永久链接至标题">¶</a></h3>
@@ -2736,7 +2769,9 @@ ight)</p>
 </tr>
 <tr class="field-even field"><th class="field-name">type max:</th><td class="field-body">double</td>
 </tr>
-<tr class="field-odd field"><th class="field-name">return:</th><td class="field-body">paddle.v2.config_base.Layer</td>
+<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>

--- a/develop/doc_cn/api/v2/data.html
+++ b/develop/doc_cn/api/v2/data.html
@@ -482,84 +482,6 @@ nested sequence.</li>
 </div>
 <div class="section" id="datafeeder">
 <h2>DataFeeder<a class="headerlink" href="#datafeeder" title="永久链接至标题">¶</a></h2>
-<dl class="class">
-<dt>
-<em class="property">class </em><code class="descclassname">paddle.v2.data_feeder.</code><code class="descname">DataFeeder</code><span class="sig-paren">(</span><em>data_types</em>, <em>feeding=None</em><span class="sig-paren">)</span></dt>
-<dd><p>DataFeeder converts the data returned by paddle.reader into a data structure
-of Arguments which is defined in the API. The paddle.reader usually returns
-a list of mini-batch data entries. Each data entry in the list is one sample.
-Each sample is a list or a tuple with one feature or multiple features.
-DataFeeder converts this mini-batch data entries into Arguments in order
-to feed it to C++ interface.</p>
-<p>The simple usage shows below</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">feeding</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;image&#39;</span><span class="p">,</span> <span class="s1">&#39;label&#39;</span><span class="p">]</span>
-<span class="n">data_types</span> <span class="o">=</span> <span class="n">enumerate_data_types_of_data_layers</span><span class="p">(</span><span class="n">topology</span><span class="p">)</span>
-<span class="n">feeder</span> <span class="o">=</span> <span class="n">DataFeeder</span><span class="p">(</span><span class="n">data_types</span><span class="o">=</span><span class="n">data_types</span><span class="p">,</span> <span class="n">feeding</span><span class="o">=</span><span class="n">feeding</span><span class="p">)</span>
-<span class="n">minibatch_data</span> <span class="o">=</span> <span class="p">[([</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="mi">5</span><span class="p">)]</span>
-<span class="n">arg</span> <span class="o">=</span> <span class="n">feeder</span><span class="p">(</span><span class="n">minibatch_data</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>If mini-batch data and data layers are not one to one mapping, we
-could pass a dictionary to feeding parameter to represent the mapping
-relationship.</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">data_types</span> <span class="o">=</span> <span class="p">[(</span><span class="s1">&#39;image&#39;</span><span class="p">,</span> <span class="n">paddle</span><span class="o">.</span><span class="n">data_type</span><span class="o">.</span><span class="n">dense_vector</span><span class="p">(</span><span class="mi">784</span><span class="p">)),</span>
-              <span class="p">(</span><span class="s1">&#39;label&#39;</span><span class="p">,</span> <span class="n">paddle</span><span class="o">.</span><span class="n">data_type</span><span class="o">.</span><span class="n">integer_value</span><span class="p">(</span><span class="mi">10</span><span class="p">))]</span>
-<span class="n">feeding</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;image&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span> <span class="s1">&#39;label&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">}</span>
-<span class="n">feeder</span> <span class="o">=</span> <span class="n">DataFeeder</span><span class="p">(</span><span class="n">data_types</span><span class="o">=</span><span class="n">data_types</span><span class="p">,</span> <span class="n">feeding</span><span class="o">=</span><span class="n">feeding</span><span class="p">)</span>
-<span class="n">minibatch_data</span> <span class="o">=</span> <span class="p">[</span>
-                   <span class="p">(</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span><span class="mf">2.0</span><span class="p">,</span><span class="mf">3.0</span><span class="p">,</span><span class="mf">4.0</span><span class="p">],</span> <span class="mi">5</span><span class="p">,</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="mi">8</span><span class="p">]</span> <span class="p">),</span>  <span class="c1"># first sample</span>
-                   <span class="p">(</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span><span class="mf">2.0</span><span class="p">,</span><span class="mf">3.0</span><span class="p">,</span><span class="mf">4.0</span><span class="p">],</span> <span class="mi">5</span><span class="p">,</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="mi">8</span><span class="p">]</span> <span class="p">)</span>   <span class="c1"># second sample</span>
-                 <span class="p">]</span>
-<span class="c1"># or minibatch_data = [</span>
-<span class="c1">#                       [ [1.0,2.0,3.0,4.0], 5, [6,7,8] ],  # first sample</span>
-<span class="c1">#                       [ [1.0,2.0,3.0,4.0], 5, [6,7,8] ]   # second sample</span>
-<span class="c1">#                     ]</span>
-<span class="n">arg</span> <span class="o">=</span> <span class="n">feeder</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="n">minibatch_data</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="admonition note">
-<p class="first admonition-title">注解</p>
-<p class="last">This module is for internal use only. Users should use the <cite>reader</cite>
-interface.</p>
-</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 last simple">
-<li><strong>data_types</strong> (<em>list</em>) &#8211; A list to specify data name and type. Each item is
-a tuple of (data_name, data_type).</li>
-<li><strong>feeding</strong> (<em>dict|collections.Sequence|None</em>) &#8211; A dictionary or a sequence to specify the position of each
-data in the input data.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-<dl class="method">
-<dt>
-<code class="descname">convert</code><span class="sig-paren">(</span><em>dat</em>, <em>argument=None</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">参数:</th><td class="field-body"><ul class="first last simple">
-<li><strong>dat</strong> (<em>list</em>) &#8211; A list of mini-batch data. Each sample is a list or tuple
-one feature or multiple features.</li>
-<li><strong>argument</strong> (<em>py_paddle.swig_paddle.Arguments</em>) &#8211; An Arguments object contains this mini-batch data with
-one or multiple features. The Arguments definition is
-in the API.</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-</dd></dl>
 </div>
 <div class="section" id="reader">
 <h2>Reader<a class="headerlink" href="#reader" title="永久链接至标题">¶</a></h2>

--- a/develop/doc_cn/searchindex.js
+++ b/develop/doc_cn/searchindex.js