Deploy to GitHub Pages: 27fea24f

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

@@ -850,7 +850,44 @@ constructor.</p>
 <dl class="function">
 <dt>
 <code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">split_lod_tensor</code><span class="sig-paren">(</span><em>input</em>, <em>mask</em>, <em>level=0</em><span class="sig-paren">)</span></dt>
-<dd></dd></dl>
+<dd><p><strong>split_lod_tensor</strong></p>
+<p>This function takes in an input that contains the complete lod information,
+and takes in a mask which is used to mask certain parts of the input.
+The output is the true branch and the false branch with the mask applied to
+the input at a certain level in the tensor.</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>tuple|list|None</em>) &#8211; The input tensor that contains complete
+lod information needed to construct the output.</li>
+<li><strong>mask</strong> (<em>list</em>) &#8211; A bool column vector which masks the input.</li>
+<li><strong>level</strong> (<em>int</em>) &#8211; The specific lod level to rank.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The true branch of tensor as per the mask applied to input.
+Variable: The false branch of tensor as per the mask applied to input.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">Variable</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">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">1</span><span class="p">])</span>
+<span class="n">x</span><span class="o">.</span><span class="n">persistable</span> <span class="o">=</span> <span class="bp">True</span>
+
+<span class="n">y</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;y&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+<span class="n">y</span><span class="o">.</span><span class="n">persistable</span> <span class="o">=</span> <span class="bp">True</span>
+
+<span class="n">out_true</span><span class="p">,</span> <span class="n">out_false</span> <span class="o">=</span> <span class="n">layers</span><span class="o">.</span><span class="n">split_lod_tensor</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">mask</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="n">level</span><span class="p">)</span>
+</pre></div>
+</div>
+</dd></dl>
 
 </div>
 <div class="section" id="merge-lod-tensor">
@@ -858,7 +895,48 @@ constructor.</p>
 <dl class="function">
 <dt>
 <code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">merge_lod_tensor</code><span class="sig-paren">(</span><em>in_true</em>, <em>in_false</em>, <em>x</em>, <em>mask</em>, <em>level=0</em><span class="sig-paren">)</span></dt>
-<dd></dd></dl>
+<dd><p><strong>merge_lod_tensor</strong></p>
+<p>This function takes in an input <span class="math">\(x\)</span>, the True branch, the False
+branch and a binary <span class="math">\(mask\)</span>. Using this information, this function
+merges the True and False branches of the tensor into a single Output
+at a certain lod level indiacted by <span class="math">\(level\)</span>.</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>in_true</strong> (<em>tuple|list|None</em>) &#8211; The True branch to be merged.</li>
+<li><strong>in_false</strong> (<em>tuple|list|None</em>) &#8211; The False branch to be merged.</li>
+<li><strong>x</strong> (<em>tuple|list|None</em>) &#8211; The input tensor that contains complete
+lod information needed to construct the output.</li>
+<li><strong>mask</strong> (<em>list</em>) &#8211; A bool column vector which masks the input.</li>
+<li><strong>level</strong> (<em>int</em>) &#8211; The specific lod level to rank.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The merged output tensor.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">Variable</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">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">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">stop_gradient</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
+<span class="n">y</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;y&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</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;bool&#39;</span><span class="p">,</span> <span class="n">stop_gradient</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
+
+<span class="n">level</span> <span class="o">=</span> <span class="mi">0</span>
+
+<span class="n">out_true</span><span class="p">,</span> <span class="n">out_false</span> <span class="o">=</span> <span class="n">layers</span><span class="o">.</span><span class="n">split_lod_tensor</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">mask</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="n">level</span><span class="p">)</span>
+<span class="n">out</span> <span class="o">=</span> <span class="n">layers</span><span class="o">.</span><span class="n">merge_lod_tensor</span><span class="p">(</span>
+      <span class="n">in_true</span><span class="o">=</span><span class="n">out_true</span><span class="p">,</span> <span class="n">in_false</span><span class="o">=</span><span class="n">out_false</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="n">level</span><span class="p">)</span>
+</pre></div>
+</div>
+</dd></dl>
 
 </div>
 <div class="section" id="cos-sim">
@@ -1526,8 +1604,32 @@ obtained from the <cite>input</cite> tensor.</p>
 <dl class="function">
 <dt>
 <code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">ones</code><span class="sig-paren">(</span><em>shape</em>, <em>dtype</em><span class="sig-paren">)</span></dt>
-<dd><p>This function performs the same function as fill_constant() declared above
-with the constant value being 1.0.</p>
+<dd><p><strong>ones</strong></p>
+<p>This function creates a tensor of specified <em>shape</em> and
+<em>dtype</em>, and initializes this with 1.</p>
+<p>It also sets <em>stop_gradient</em> to True.</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>shape</strong> (<em>tuple|list|None</em>) &#8211; Shape of output tensor</li>
+<li><strong>dtype</strong> (<em>np.dtype|core.DataType|str</em>) &#8211; Data type of output tensor</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The tensor variable storing the output</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">Variable</p>
+</td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Examples</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">data</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">ones</span><span class="p">(</span><span class="n">shape</span><span class="o">=</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;int64&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
 </dd></dl>
 
 </div>
@@ -1536,8 +1638,32 @@ with the constant value being 1.0.</p>
 <dl class="function">
 <dt>
 <code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">zeros</code><span class="sig-paren">(</span><em>shape</em>, <em>dtype</em><span class="sig-paren">)</span></dt>
-<dd><p>This function performs the same function as fill_constant() declared above
-with the constant value being 0.0.</p>
+<dd><p><strong>zeros</strong></p>
+<p>This function creates a tensor of specified <em>shape</em> and
+<em>dtype</em>, and initializes this with 0.</p>
+<p>It also sets <em>stop_gradient</em> to True.</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>shape</strong> (<em>tuple|list|None</em>) &#8211; Shape of output tensor</li>
+<li><strong>dtype</strong> (<em>np.dtype|core.DataType|str</em>) &#8211; Data type of output tensor</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The tensor variable storing the output</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">Variable</p>
+</td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Examples</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">data</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">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</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;int64&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
 </dd></dl>
 
 </div>

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

@@ -863,7 +863,44 @@ constructor.</p>
 <dl class="function">
 <dt>
 <code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">split_lod_tensor</code><span class="sig-paren">(</span><em>input</em>, <em>mask</em>, <em>level=0</em><span class="sig-paren">)</span></dt>
-<dd></dd></dl>
+<dd><p><strong>split_lod_tensor</strong></p>
+<p>This function takes in an input that contains the complete lod information,
+and takes in a mask which is used to mask certain parts of the input.
+The output is the true branch and the false branch with the mask applied to
+the input at a certain level in the tensor.</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>tuple|list|None</em>) &#8211; The input tensor that contains complete
+lod information needed to construct the output.</li>
+<li><strong>mask</strong> (<em>list</em>) &#8211; A bool column vector which masks the input.</li>
+<li><strong>level</strong> (<em>int</em>) &#8211; The specific lod level to rank.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">The true branch of tensor as per the mask applied to input.
+Variable: The false branch of tensor as per the mask applied to input.</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">Variable</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">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">1</span><span class="p">])</span>
+<span class="n">x</span><span class="o">.</span><span class="n">persistable</span> <span class="o">=</span> <span class="bp">True</span>
+
+<span class="n">y</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;y&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+<span class="n">y</span><span class="o">.</span><span class="n">persistable</span> <span class="o">=</span> <span class="bp">True</span>
+
+<span class="n">out_true</span><span class="p">,</span> <span class="n">out_false</span> <span class="o">=</span> <span class="n">layers</span><span class="o">.</span><span class="n">split_lod_tensor</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">mask</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="n">level</span><span class="p">)</span>
+</pre></div>
+</div>
+</dd></dl>
 
 </div>
 <div class="section" id="merge-lod-tensor">
@@ -871,7 +908,48 @@ constructor.</p>
 <dl class="function">
 <dt>
 <code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">merge_lod_tensor</code><span class="sig-paren">(</span><em>in_true</em>, <em>in_false</em>, <em>x</em>, <em>mask</em>, <em>level=0</em><span class="sig-paren">)</span></dt>
-<dd></dd></dl>
+<dd><p><strong>merge_lod_tensor</strong></p>
+<p>This function takes in an input <span class="math">\(x\)</span>, the True branch, the False
+branch and a binary <span class="math">\(mask\)</span>. Using this information, this function
+merges the True and False branches of the tensor into a single Output
+at a certain lod level indiacted by <span class="math">\(level\)</span>.</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>in_true</strong> (<em>tuple|list|None</em>) &#8211; The True branch to be merged.</li>
+<li><strong>in_false</strong> (<em>tuple|list|None</em>) &#8211; The False branch to be merged.</li>
+<li><strong>x</strong> (<em>tuple|list|None</em>) &#8211; The input tensor that contains complete
+lod information needed to construct the output.</li>
+<li><strong>mask</strong> (<em>list</em>) &#8211; A bool column vector which masks the input.</li>
+<li><strong>level</strong> (<em>int</em>) &#8211; The specific lod level to rank.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">The merged output tensor.</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">Variable</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">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">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">stop_gradient</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
+<span class="n">y</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;y&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</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;bool&#39;</span><span class="p">,</span> <span class="n">stop_gradient</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
+
+<span class="n">level</span> <span class="o">=</span> <span class="mi">0</span>
+
+<span class="n">out_true</span><span class="p">,</span> <span class="n">out_false</span> <span class="o">=</span> <span class="n">layers</span><span class="o">.</span><span class="n">split_lod_tensor</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">mask</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="n">level</span><span class="p">)</span>
+<span class="n">out</span> <span class="o">=</span> <span class="n">layers</span><span class="o">.</span><span class="n">merge_lod_tensor</span><span class="p">(</span>
+      <span class="n">in_true</span><span class="o">=</span><span class="n">out_true</span><span class="p">,</span> <span class="n">in_false</span><span class="o">=</span><span class="n">out_false</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="n">level</span><span class="p">)</span>
+</pre></div>
+</div>
+</dd></dl>
 
 </div>
 <div class="section" id="cos-sim">
@@ -1539,8 +1617,32 @@ obtained from the <cite>input</cite> tensor.</p>
 <dl class="function">
 <dt>
 <code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">ones</code><span class="sig-paren">(</span><em>shape</em>, <em>dtype</em><span class="sig-paren">)</span></dt>
-<dd><p>This function performs the same function as fill_constant() declared above
-with the constant value being 1.0.</p>
+<dd><p><strong>ones</strong></p>
+<p>This function creates a tensor of specified <em>shape</em> and
+<em>dtype</em>, and initializes this with 1.</p>
+<p>It also sets <em>stop_gradient</em> to True.</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>shape</strong> (<em>tuple|list|None</em>) &#8211; Shape of output tensor</li>
+<li><strong>dtype</strong> (<em>np.dtype|core.DataType|str</em>) &#8211; Data type of output tensor</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">The tensor variable storing the 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">Variable</p>
+</td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Examples</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">data</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">ones</span><span class="p">(</span><span class="n">shape</span><span class="o">=</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;int64&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
 </dd></dl>
 
 </div>
@@ -1549,8 +1651,32 @@ with the constant value being 1.0.</p>
 <dl class="function">
 <dt>
 <code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">zeros</code><span class="sig-paren">(</span><em>shape</em>, <em>dtype</em><span class="sig-paren">)</span></dt>
-<dd><p>This function performs the same function as fill_constant() declared above
-with the constant value being 0.0.</p>
+<dd><p><strong>zeros</strong></p>
+<p>This function creates a tensor of specified <em>shape</em> and
+<em>dtype</em>, and initializes this with 0.</p>
+<p>It also sets <em>stop_gradient</em> to True.</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>shape</strong> (<em>tuple|list|None</em>) &#8211; Shape of output tensor</li>
+<li><strong>dtype</strong> (<em>np.dtype|core.DataType|str</em>) &#8211; Data type of output tensor</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">The tensor variable storing the 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">Variable</p>
+</td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Examples</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">data</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">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</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;int64&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
 </dd></dl>
 
 </div>