Deploy to GitHub Pages: fc5c432a

develop/doc/_sources/api/v2/fluid/layers.rst.txt

@@ -170,6 +170,18 @@ sequence_pool
     :noindex:
 
 
+sequence_first_step
+-------------------
+..  autofunction:: paddle.v2.fluid.layers.sequence_first_step
+    :noindex:
+
+
+sequence_last_step
+------------------
+..  autofunction:: paddle.v2.fluid.layers.sequence_last_step
+    :noindex:
+
+
 pool2d
 ------
 ..  autofunction:: paddle.v2.fluid.layers.pool2d

develop/doc/api/v2/fluid/layers.html

@@ -961,8 +961,129 @@ conv-2d output, if mentioned in the input parameters.</p>
 <dt>
 <code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">sequence_pool</code><span class="sig-paren">(</span><em>input</em>, <em>pool_type</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>This function add the operator for sequence pooling.
-This is applied on top of the input using pool_type mentioned
-in the parameters.</p>
+It pools features of all time-steps of each instance, and is applied
+on top of the input using pool_type mentioned in the parameters.</p>
+<p>It supports four pool_type:</p>
+<ul class="simple">
+<li>average: <span class="math">\(Out[i] = \frac{\sum_i X_i}{N}\)</span></li>
+<li>sum:     <span class="math">\(Out[i] = \sum_jX_{ij}\)</span></li>
+<li>sqrt:    <span class="math">\(Out[i] = \frac{\sum_jX_{ij}}{\sqrt{len(X_i)}}\)</span></li>
+<li>max:     <span class="math">\(Out[i] = max(X_i)\)</span></li>
+</ul>
+<div class="highlight-text"><div class="highlight"><pre><span></span>x is a 1-level LoDTensor:
+  x.lod = [[0, 2, 5, 7]]
+  x.data = [1, 3, 2, 4, 6, 5, 1]
+  x.dims = [7, 1]
+
+then output is a Tensor:
+  out.dim = [3, 1]
+  with condition len(x.lod[-1]) - 1 == out.dims[0]
+
+for different pool_type:
+  average: out.data = [2, 4, 3], where 2=(1+3)/2, 4=(2+4+6)/3, 3=(5+1)/2
+  sum    : out.data = [4, 12, 6], where 4=1+3, 12=2+4+6, 6=5+1
+  sqrt   : out.data = [2.82, 6.93, 4.24], where 2.82=(1+3)/sqrt(2),
+             6.93=(2+4+6)/sqrt(3), 4.24=(5+1)/sqrt(2)
+  max    : out.data = [3, 6, 5], where 3=max(1,3), 6=max(2,4,6), 5=max(5,1)
+</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>input</strong> (<em>variable</em>) &#8211; The input variable which is a LoDTensor.</li>
+<li><strong>pool_type</strong> (<em>string</em>) &#8211; The pooling type of sequence_pool.
+It supports average, sum, sqrt and max.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">The sequence pooling variable which is a Tensor.</p>
+</td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Examples</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
+                 <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;float32&#39;</span><span class="p">,</span> <span class="n">lod_level</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">avg_x</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">sequence_pool</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">pool_type</span><span class="o">=</span><span class="s1">&#39;average&#39;</span><span class="p">)</span>
+<span class="n">sum_x</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">sequence_pool</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">pool_type</span><span class="o">=</span><span class="s1">&#39;sum&#39;</span><span class="p">)</span>
+<span class="n">sqrt_x</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">sequence_pool</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">pool_type</span><span class="o">=</span><span class="s1">&#39;sqrt&#39;</span><span class="p">)</span>
+<span class="n">max_x</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">sequence_pool</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">pool_type</span><span class="o">=</span><span class="s1">&#39;max&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</div>
+<div class="section" id="sequence-first-step">
+<h2>sequence_first_step<a class="headerlink" href="#sequence-first-step" title="Permalink to this headline">¶</a></h2>
+<dl class="function">
+<dt>
+<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">sequence_first_step</code><span class="sig-paren">(</span><em>input</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<dd><p>This funciton get the first step of sequence.</p>
+<div class="highlight-text"><div class="highlight"><pre><span></span>x is a 1-level LoDTensor:
+  x.lod = [[0, 2, 5, 7]]
+  x.data = [1, 3, 2, 4, 6, 5, 1]
+  x.dims = [7, 1]
+
+then output is a Tensor:
+  out.dim = [3, 1]
+  with condition len(x.lod[-1]) - 1 == out.dims[0]
+  out.data = [1, 2, 5], where 1=first(1,3), 2=first(2,4,6), 5=first(5,1)
+</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"><strong>input</strong> (<em>variable</em>) &#8211; The input variable which is a LoDTensor.</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">The sequence&#8217;s first step variable which is a Tensor.</td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Examples</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
+                 <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;float32&#39;</span><span class="p">,</span> <span class="n">lod_level</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">x_first_step</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">sequence_first_step</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x</span><span class="p">)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</div>
+<div class="section" id="sequence-last-step">
+<h2>sequence_last_step<a class="headerlink" href="#sequence-last-step" title="Permalink to this headline">¶</a></h2>
+<dl class="function">
+<dt>
+<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">sequence_last_step</code><span class="sig-paren">(</span><em>input</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<dd><p>This funciton get the last step of sequence.</p>
+<div class="highlight-text"><div class="highlight"><pre><span></span>x is a 1-level LoDTensor:
+  x.lod = [[0, 2, 5, 7]]
+  x.data = [1, 3, 2, 4, 6, 5, 1]
+  x.dims = [7, 1]
+
+then output is a Tensor:
+  out.dim = [3, 1]
+  with condition len(x.lod[-1]) - 1 == out.dims[0]
+  out.data = [3, 6, 1], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1)
+</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"><strong>input</strong> (<em>variable</em>) &#8211; The input variable which is a LoDTensor.</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">The sequence&#8217;s last step variable which is a Tensor.</td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Examples</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
+                 <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;float32&#39;</span><span class="p">,</span> <span class="n">lod_level</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">x_last_step</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">sequence_last_step</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x</span><span class="p">)</span>
+</pre></div>
+</div>
 </dd></dl>
 
 </div>

develop/doc_cn/_sources/api/v2/fluid/layers.rst.txt

@@ -170,6 +170,18 @@ sequence_pool
     :noindex:
 
 
+sequence_first_step
+-------------------
+..  autofunction:: paddle.v2.fluid.layers.sequence_first_step
+    :noindex:
+
+
+sequence_last_step
+------------------
+..  autofunction:: paddle.v2.fluid.layers.sequence_last_step
+    :noindex:
+
+
 pool2d
 ------
 ..  autofunction:: paddle.v2.fluid.layers.pool2d

develop/doc_cn/api/v2/fluid/layers.html

@@ -974,8 +974,129 @@ conv-2d output, if mentioned in the input parameters.</p>
 <dt>
 <code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">sequence_pool</code><span class="sig-paren">(</span><em>input</em>, <em>pool_type</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
 <dd><p>This function add the operator for sequence pooling.
-This is applied on top of the input using pool_type mentioned
-in the parameters.</p>
+It pools features of all time-steps of each instance, and is applied
+on top of the input using pool_type mentioned in the parameters.</p>
+<p>It supports four pool_type:</p>
+<ul class="simple">
+<li>average: <span class="math">\(Out[i] = \frac{\sum_i X_i}{N}\)</span></li>
+<li>sum:     <span class="math">\(Out[i] = \sum_jX_{ij}\)</span></li>
+<li>sqrt:    <span class="math">\(Out[i] = \frac{\sum_jX_{ij}}{\sqrt{len(X_i)}}\)</span></li>
+<li>max:     <span class="math">\(Out[i] = max(X_i)\)</span></li>
+</ul>
+<div class="highlight-text"><div class="highlight"><pre><span></span>x is a 1-level LoDTensor:
+  x.lod = [[0, 2, 5, 7]]
+  x.data = [1, 3, 2, 4, 6, 5, 1]
+  x.dims = [7, 1]
+
+then output is a Tensor:
+  out.dim = [3, 1]
+  with condition len(x.lod[-1]) - 1 == out.dims[0]
+
+for different pool_type:
+  average: out.data = [2, 4, 3], where 2=(1+3)/2, 4=(2+4+6)/3, 3=(5+1)/2
+  sum    : out.data = [4, 12, 6], where 4=1+3, 12=2+4+6, 6=5+1
+  sqrt   : out.data = [2.82, 6.93, 4.24], where 2.82=(1+3)/sqrt(2),
+             6.93=(2+4+6)/sqrt(3), 4.24=(5+1)/sqrt(2)
+  max    : out.data = [3, 6, 5], where 3=max(1,3), 6=max(2,4,6), 5=max(5,1)
+</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>variable</em>) &#8211; The input variable which is a LoDTensor.</li>
+<li><strong>pool_type</strong> (<em>string</em>) &#8211; The pooling type of sequence_pool.
+It supports average, sum, sqrt and max.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first last">The sequence pooling variable which is a Tensor.</p>
+</td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Examples</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
+                 <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;float32&#39;</span><span class="p">,</span> <span class="n">lod_level</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">avg_x</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">sequence_pool</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">pool_type</span><span class="o">=</span><span class="s1">&#39;average&#39;</span><span class="p">)</span>
+<span class="n">sum_x</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">sequence_pool</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">pool_type</span><span class="o">=</span><span class="s1">&#39;sum&#39;</span><span class="p">)</span>
+<span class="n">sqrt_x</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">sequence_pool</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">pool_type</span><span class="o">=</span><span class="s1">&#39;sqrt&#39;</span><span class="p">)</span>
+<span class="n">max_x</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">sequence_pool</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">pool_type</span><span class="o">=</span><span class="s1">&#39;max&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</div>
+<div class="section" id="sequence-first-step">
+<h2>sequence_first_step<a class="headerlink" href="#sequence-first-step" title="永久链接至标题">¶</a></h2>
+<dl class="function">
+<dt>
+<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">sequence_first_step</code><span class="sig-paren">(</span><em>input</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<dd><p>This funciton get the first step of sequence.</p>
+<div class="highlight-text"><div class="highlight"><pre><span></span>x is a 1-level LoDTensor:
+  x.lod = [[0, 2, 5, 7]]
+  x.data = [1, 3, 2, 4, 6, 5, 1]
+  x.dims = [7, 1]
+
+then output is a Tensor:
+  out.dim = [3, 1]
+  with condition len(x.lod[-1]) - 1 == out.dims[0]
+  out.data = [1, 2, 5], where 1=first(1,3), 2=first(2,4,6), 5=first(5,1)
+</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"><strong>input</strong> (<em>variable</em>) &#8211; The input variable which is a LoDTensor.</td>
+</tr>
+<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body">The sequence&#8217;s first step variable which is a Tensor.</td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Examples</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
+                 <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;float32&#39;</span><span class="p">,</span> <span class="n">lod_level</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">x_first_step</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">sequence_first_step</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x</span><span class="p">)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</div>
+<div class="section" id="sequence-last-step">
+<h2>sequence_last_step<a class="headerlink" href="#sequence-last-step" title="永久链接至标题">¶</a></h2>
+<dl class="function">
+<dt>
+<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">sequence_last_step</code><span class="sig-paren">(</span><em>input</em>, <em>**kwargs</em><span class="sig-paren">)</span></dt>
+<dd><p>This funciton get the last step of sequence.</p>
+<div class="highlight-text"><div class="highlight"><pre><span></span>x is a 1-level LoDTensor:
+  x.lod = [[0, 2, 5, 7]]
+  x.data = [1, 3, 2, 4, 6, 5, 1]
+  x.dims = [7, 1]
+
+then output is a Tensor:
+  out.dim = [3, 1]
+  with condition len(x.lod[-1]) - 1 == out.dims[0]
+  out.data = [3, 6, 1], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1)
+</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"><strong>input</strong> (<em>variable</em>) &#8211; The input variable which is a LoDTensor.</td>
+</tr>
+<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body">The sequence&#8217;s last step variable which is a Tensor.</td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Examples</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
+                 <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;float32&#39;</span><span class="p">,</span> <span class="n">lod_level</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">x_last_step</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">sequence_last_step</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x</span><span class="p">)</span>
+</pre></div>
+</div>
 </dd></dl>
 
 </div>