Skip to content

P
Paddle

BaiXuePrincess / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle
提交 5816d195 编写于 作者: T Travis CI
浏览文件
操作

Deploy to GitHub Pages: 29b2693a

上级 9248c85c
展开全部 显示空白变更内容
内联 并排
Showing with 282 addition and 42 deletion
develop/doc/_sources/api/v2/fluid/layers.rst.txt
浏览文件 @ 5816d195
...@@ -38,6 +38,16 @@ elementwise_add ...@@ -38,6 +38,16 @@ elementwise_add
.. autofunction:: paddle.v2.fluid.layers.elementwise_add .. autofunction:: paddle.v2.fluid.layers.elementwise_add
:noindex: :noindex:
elementwise_sub
---------------
.. autofunction:: paddle.v2.fluid.layers.elementwise_sub
:noindex:
elementwise_mul
---------------
.. autofunction:: paddle.v2.fluid.layers.elementwise_mul
:noindex:
elementwise_div elementwise_div
--------------- ---------------
.. autofunction:: paddle.v2.fluid.layers.elementwise_div .. autofunction:: paddle.v2.fluid.layers.elementwise_div
......
develop/doc/api/v2/fluid/layers.html
浏览文件 @ 5816d195
...@@ -475,7 +475,108 @@ flattened. See comments of <cite>x_num_col_dims</cite> for more details.</li> ...@@ -475,7 +475,108 @@ flattened. See comments of <cite>x_num_col_dims</cite> for more details.</li>
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">elementwise_add</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span></dt> <code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">elementwise_add</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Limited Elementwise Add Operator.</p> <dd><p>Limited Elementwise Add Operator.</p>
<p>The equation is:</p> <p>The equation is:</p>
<p>$Out = X + Y$</p> <div class="math">
\[Out = X + Y\]</div>
<p>X is a tensor of any dimension and the dimensions of tensor Y must be smaller than
or equal to the dimensions of X.</p>
<p>There are two cases for this operator:
1. The shape of Y is same with X;
2. The shape of Y is a subset of X.</p>
<p>For case 2:
Y will be broadcasted to match the shape of X and axis should be
the starting dimension index for broadcasting Y onto X.</p>
<dl class="docutils">
<dt>For example</dt>
<dd><div class="first last highlight-python"><div class="highlight"><pre><span></span><span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(,)</span>
<span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</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">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="k">with</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span>
<span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="k">with</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span>
</pre></div>
</div>
</dd>
</dl>
<p>Either of the inputs X and Y or none can carry the LoD (Level of Details) information. However, the output only shares the LoD information with input X.</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>x</strong> &#8211; (Tensor) The first input tensor of elementwise op
Duplicable: False Optional: False</li>
<li><strong>y</strong> &#8211; (Tensor) The second input tensor of elementwise op
Duplicable: False Optional: False</li>
<li><strong>axis</strong> (<em>INT</em>) &#8211; (int, default -1) The starting dimension index for broadcasting Y onto X</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">The output of elementwise op</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="elementwise-sub">
<h2>elementwise_sub<a class="headerlink" href="#elementwise-sub" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">elementwise_sub</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Limited Elementwise Sub Operator.</p>
<p>The equation is:</p>
<div class="math">
\[Out = X - Y\]</div>
<p>X is a tensor of any dimension and the dimensions of tensor Y must be smaller than
or equal to the dimensions of X.</p>
<p>There are two cases for this operator:
1. The shape of Y is same with X;
2. The shape of Y is a subset of X.</p>
<p>For case 2:
Y will be broadcasted to match the shape of X and axis should be
the starting dimension index for broadcasting Y onto X.</p>
<dl class="docutils">
<dt>For example</dt>
<dd><div class="first last highlight-python"><div class="highlight"><pre><span></span><span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(,)</span>
<span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</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">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="k">with</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span>
<span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="k">with</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span>
</pre></div>
</div>
</dd>
</dl>
<p>Either of the inputs X and Y or none can carry the LoD (Level of Details) information. However, the output only shares the LoD information with input X.</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>x</strong> &#8211; (Tensor) The first input tensor of elementwise op
Duplicable: False Optional: False</li>
<li><strong>y</strong> &#8211; (Tensor) The second input tensor of elementwise op
Duplicable: False Optional: False</li>
<li><strong>axis</strong> (<em>INT</em>) &#8211; (int, default -1) The starting dimension index for broadcasting Y onto X</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">The output of elementwise op</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="elementwise-mul">
<h2>elementwise_mul<a class="headerlink" href="#elementwise-mul" title="Permalink to this headline"></a></h2>
<dl class="function">
<dt>
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">elementwise_mul</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Limited Elementwise Mul Operator.</p>
<p>The equation is:</p>
<div class="math">
\[Out = X \odot\ Y\]</div>
<p>X is a tensor of any dimension and the dimensions of tensor Y must be smaller than <p>X is a tensor of any dimension and the dimensions of tensor Y must be smaller than
or equal to the dimensions of X.</p> or equal to the dimensions of X.</p>
<p>There are two cases for this operator: <p>There are two cases for this operator:
...@@ -484,14 +585,18 @@ or equal to the dimensions of X.</p> ...@@ -484,14 +585,18 @@ or equal to the dimensions of X.</p>
<p>For case 2: <p>For case 2:
Y will be broadcasted to match the shape of X and axis should be Y will be broadcasted to match the shape of X and axis should be
the starting dimension index for broadcasting Y onto X.</p> the starting dimension index for broadcasting Y onto X.</p>
<p class="rubric">example</p> <dl class="docutils">
<p>shape(X) = (2, 3, 4, 5), shape(Y) = (,) <dt>For example</dt>
shape(X) = (2, 3, 4, 5), shape(Y) = (5,) <dd><div class="first last highlight-python"><div class="highlight"><pre><span></span><span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(,)</span>
shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5) <span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">5</span><span class="p">,)</span>
shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1 <span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0</p> <span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="k">with</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span>
<p>Both the input X and Y can carry the LoD (Level of Details) information, <span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="k">with</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span>
or not. But the output only shares the LoD information with input X.</p> </pre></div>
</div>
</dd>
</dl>
<p>Either of the inputs X and Y or none can carry the LoD (Level of Details) information. However, the output only shares the LoD information with input X.</p>
<table class="docutils field-list" frame="void" rules="none"> <table class="docutils field-list" frame="void" rules="none">
<col class="field-name" /> <col class="field-name" />
<col class="field-body" /> <col class="field-body" />
...@@ -520,7 +625,8 @@ Duplicable: False Optional: False</li> ...@@ -520,7 +625,8 @@ Duplicable: False Optional: False</li>
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">elementwise_div</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span></dt> <code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">elementwise_div</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Limited Elementwise Div Operator.</p> <dd><p>Limited Elementwise Div Operator.</p>
<p>The equation is:</p> <p>The equation is:</p>
<p>$Out = X / Y$</p> <div class="math">
\[Out = X / Y\]</div>
<p>X is a tensor of any dimension and the dimensions of tensor Y must be smaller than <p>X is a tensor of any dimension and the dimensions of tensor Y must be smaller than
or equal to the dimensions of X.</p> or equal to the dimensions of X.</p>
<p>There are two cases for this operator: <p>There are two cases for this operator:
...@@ -529,14 +635,18 @@ or equal to the dimensions of X.</p> ...@@ -529,14 +635,18 @@ or equal to the dimensions of X.</p>
<p>For case 2: <p>For case 2:
Y will be broadcasted to match the shape of X and axis should be Y will be broadcasted to match the shape of X and axis should be
the starting dimension index for broadcasting Y onto X.</p> the starting dimension index for broadcasting Y onto X.</p>
<p class="rubric">example</p> <dl class="docutils">
<p>shape(X) = (2, 3, 4, 5), shape(Y) = (,) <dt>For example</dt>
shape(X) = (2, 3, 4, 5), shape(Y) = (5,) <dd><div class="first last highlight-python"><div class="highlight"><pre><span></span><span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(,)</span>
shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5) <span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">5</span><span class="p">,)</span>
shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1 <span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0</p> <span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="k">with</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span>
<p>Both the input X and Y can carry the LoD (Level of Details) information, <span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="k">with</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span>
or not. But the output only shares the LoD information with input X.</p> </pre></div>
</div>
</dd>
</dl>
<p>Either of the inputs X and Y or none can carry the LoD (Level of Details) information. However, the output only shares the LoD information with input X.</p>
<table class="docutils field-list" frame="void" rules="none"> <table class="docutils field-list" frame="void" rules="none">
<col class="field-name" /> <col class="field-name" />
<col class="field-body" /> <col class="field-body" />
......
develop/doc/operators.json
浏览文件 @ 5816d195
...@@ -1780,7 +1780,7 @@ ...@@ -1780,7 +1780,7 @@
"attrs" : [ ] "attrs" : [ ]
},{ },{
"type" : "elementwise_sub", "type" : "elementwise_sub",
"comment" : "\nLimited Elementwise Sub Operator.\n\nThe equation is:\n\n$Out = X - Y$\n\nX is a tensor of any dimension and the dimensions of tensor Y must be smaller than\nor equal to the dimensions of X. \n\nThere are two cases for this operator:\n1. The shape of Y is same with X;\n2. The shape of Y is a subset of X.\n\nFor case 2:\nY will be broadcasted to match the shape of X and axis should be \nthe starting dimension index for broadcasting Y onto X.\n\nexample:\n shape(X) = (2, 3, 4, 5), shape(Y) = (,)\n shape(X) = (2, 3, 4, 5), shape(Y) = (5,)\n shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)\n shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1\n shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0\n\nBoth the input X and Y can carry the LoD (Level of Details) information,\nor not. But the output only shares the LoD information with input X.\n\n", "comment" : "\nLimited Elementwise Sub Operator.\n\nThe equation is:\n\n.. math::\n Out = X - Y\n\nX is a tensor of any dimension and the dimensions of tensor Y must be smaller than\nor equal to the dimensions of X. \n\nThere are two cases for this operator:\n1. The shape of Y is same with X;\n2. The shape of Y is a subset of X.\n\nFor case 2:\nY will be broadcasted to match the shape of X and axis should be \nthe starting dimension index for broadcasting Y onto X.\n\nFor example\n .. code-block:: python\n\n shape(X) = (2, 3, 4, 5), shape(Y) = (,)\n shape(X) = (2, 3, 4, 5), shape(Y) = (5,)\n shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)\n shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1\n shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0\n\nEither of the inputs X and Y or none can carry the LoD (Level of Details) information. However, the output only shares the LoD information with input X.\n\n",
"inputs" : [ "inputs" : [
{ {
"name" : "X", "name" : "X",
...@@ -3600,7 +3600,7 @@ ...@@ -3600,7 +3600,7 @@
} ] } ]
},{ },{
"type" : "elementwise_mul", "type" : "elementwise_mul",
"comment" : "\nLimited Elementwise Mul Operator.\n\nThe equation is:\n\n$Out = X \\odot\\ Y$\n\nX is a tensor of any dimension and the dimensions of tensor Y must be smaller than\nor equal to the dimensions of X. \n\nThere are two cases for this operator:\n1. The shape of Y is same with X;\n2. The shape of Y is a subset of X.\n\nFor case 2:\nY will be broadcasted to match the shape of X and axis should be \nthe starting dimension index for broadcasting Y onto X.\n\nexample:\n shape(X) = (2, 3, 4, 5), shape(Y) = (,)\n shape(X) = (2, 3, 4, 5), shape(Y) = (5,)\n shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)\n shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1\n shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0\n\nBoth the input X and Y can carry the LoD (Level of Details) information,\nor not. But the output only shares the LoD information with input X.\n\n", "comment" : "\nLimited Elementwise Mul Operator.\n\nThe equation is:\n\n.. math::\n Out = X \\odot\\ Y\n\nX is a tensor of any dimension and the dimensions of tensor Y must be smaller than\nor equal to the dimensions of X. \n\nThere are two cases for this operator:\n1. The shape of Y is same with X;\n2. The shape of Y is a subset of X.\n\nFor case 2:\nY will be broadcasted to match the shape of X and axis should be \nthe starting dimension index for broadcasting Y onto X.\n\nFor example\n .. code-block:: python\n\n shape(X) = (2, 3, 4, 5), shape(Y) = (,)\n shape(X) = (2, 3, 4, 5), shape(Y) = (5,)\n shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)\n shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1\n shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0\n\nEither of the inputs X and Y or none can carry the LoD (Level of Details) information. However, the output only shares the LoD information with input X.\n\n",
"inputs" : [ "inputs" : [
{ {
"name" : "X", "name" : "X",
...@@ -3972,7 +3972,7 @@ ...@@ -3972,7 +3972,7 @@
} ] } ]
},{ },{
"type" : "elementwise_div", "type" : "elementwise_div",
"comment" : "\nLimited Elementwise Div Operator.\n\nThe equation is:\n\n$Out = X / Y$\n\nX is a tensor of any dimension and the dimensions of tensor Y must be smaller than\nor equal to the dimensions of X. \n\nThere are two cases for this operator:\n1. The shape of Y is same with X;\n2. The shape of Y is a subset of X.\n\nFor case 2:\nY will be broadcasted to match the shape of X and axis should be \nthe starting dimension index for broadcasting Y onto X.\n\nexample:\n shape(X) = (2, 3, 4, 5), shape(Y) = (,)\n shape(X) = (2, 3, 4, 5), shape(Y) = (5,)\n shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)\n shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1\n shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0\n\nBoth the input X and Y can carry the LoD (Level of Details) information,\nor not. But the output only shares the LoD information with input X.\n\n", "comment" : "\nLimited Elementwise Div Operator.\n\nThe equation is:\n\n.. math::\n Out = X / Y\n\nX is a tensor of any dimension and the dimensions of tensor Y must be smaller than\nor equal to the dimensions of X. \n\nThere are two cases for this operator:\n1. The shape of Y is same with X;\n2. The shape of Y is a subset of X.\n\nFor case 2:\nY will be broadcasted to match the shape of X and axis should be \nthe starting dimension index for broadcasting Y onto X.\n\nFor example\n .. code-block:: python\n\n shape(X) = (2, 3, 4, 5), shape(Y) = (,)\n shape(X) = (2, 3, 4, 5), shape(Y) = (5,)\n shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)\n shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1\n shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0\n\nEither of the inputs X and Y or none can carry the LoD (Level of Details) information. However, the output only shares the LoD information with input X.\n\n",
"inputs" : [ "inputs" : [
{ {
"name" : "X", "name" : "X",
...@@ -4001,7 +4001,7 @@ ...@@ -4001,7 +4001,7 @@
} ] } ]
},{ },{
"type" : "elementwise_add", "type" : "elementwise_add",
"comment" : "\nLimited Elementwise Add Operator.\n\nThe equation is:\n\n$Out = X + Y$\n\nX is a tensor of any dimension and the dimensions of tensor Y must be smaller than\nor equal to the dimensions of X. \n\nThere are two cases for this operator:\n1. The shape of Y is same with X;\n2. The shape of Y is a subset of X.\n\nFor case 2:\nY will be broadcasted to match the shape of X and axis should be \nthe starting dimension index for broadcasting Y onto X.\n\nexample:\n shape(X) = (2, 3, 4, 5), shape(Y) = (,)\n shape(X) = (2, 3, 4, 5), shape(Y) = (5,)\n shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)\n shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1\n shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0\n\nBoth the input X and Y can carry the LoD (Level of Details) information,\nor not. But the output only shares the LoD information with input X.\n\n", "comment" : "\nLimited Elementwise Add Operator.\n\nThe equation is:\n\n.. math::\n Out = X + Y\n\nX is a tensor of any dimension and the dimensions of tensor Y must be smaller than\nor equal to the dimensions of X. \n\nThere are two cases for this operator:\n1. The shape of Y is same with X;\n2. The shape of Y is a subset of X.\n\nFor case 2:\nY will be broadcasted to match the shape of X and axis should be \nthe starting dimension index for broadcasting Y onto X.\n\nFor example\n .. code-block:: python\n\n shape(X) = (2, 3, 4, 5), shape(Y) = (,)\n shape(X) = (2, 3, 4, 5), shape(Y) = (5,)\n shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)\n shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1\n shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0\n\nEither of the inputs X and Y or none can carry the LoD (Level of Details) information. However, the output only shares the LoD information with input X.\n\n",
"inputs" : [ "inputs" : [
{ {
"name" : "X", "name" : "X",
......
develop/doc/searchindex.js
浏览文件 @ 5816d195
因为 它太大了无法显示 source diff 。你可以改为 查看blob。
develop/doc_cn/_sources/api/v2/fluid/layers.rst.txt
浏览文件 @ 5816d195
...@@ -38,6 +38,16 @@ elementwise_add ...@@ -38,6 +38,16 @@ elementwise_add
.. autofunction:: paddle.v2.fluid.layers.elementwise_add .. autofunction:: paddle.v2.fluid.layers.elementwise_add
:noindex: :noindex:
elementwise_sub
---------------
.. autofunction:: paddle.v2.fluid.layers.elementwise_sub
:noindex:
elementwise_mul
---------------
.. autofunction:: paddle.v2.fluid.layers.elementwise_mul
:noindex:
elementwise_div elementwise_div
--------------- ---------------
.. autofunction:: paddle.v2.fluid.layers.elementwise_div .. autofunction:: paddle.v2.fluid.layers.elementwise_div
......
develop/doc_cn/api/v2/fluid/layers.html
浏览文件 @ 5816d195
...@@ -488,7 +488,108 @@ flattened. See comments of <cite>x_num_col_dims</cite> for more details.</li> ...@@ -488,7 +488,108 @@ flattened. See comments of <cite>x_num_col_dims</cite> for more details.</li>
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">elementwise_add</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span></dt> <code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">elementwise_add</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Limited Elementwise Add Operator.</p> <dd><p>Limited Elementwise Add Operator.</p>
<p>The equation is:</p> <p>The equation is:</p>
<p>$Out = X + Y$</p> <div class="math">
\[Out = X + Y\]</div>
<p>X is a tensor of any dimension and the dimensions of tensor Y must be smaller than
or equal to the dimensions of X.</p>
<p>There are two cases for this operator:
1. The shape of Y is same with X;
2. The shape of Y is a subset of X.</p>
<p>For case 2:
Y will be broadcasted to match the shape of X and axis should be
the starting dimension index for broadcasting Y onto X.</p>
<dl class="docutils">
<dt>For example</dt>
<dd><div class="first last highlight-python"><div class="highlight"><pre><span></span><span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(,)</span>
<span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</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">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="k">with</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span>
<span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="k">with</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span>
</pre></div>
</div>
</dd>
</dl>
<p>Either of the inputs X and Y or none can carry the LoD (Level of Details) information. However, the output only shares the LoD information with input X.</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>x</strong> &#8211; (Tensor) The first input tensor of elementwise op
Duplicable: False Optional: False</li>
<li><strong>y</strong> &#8211; (Tensor) The second input tensor of elementwise op
Duplicable: False Optional: False</li>
<li><strong>axis</strong> (<em>INT</em>) &#8211; (int, default -1) The starting dimension index for broadcasting Y onto X</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 output of elementwise op</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="elementwise-sub">
<h2>elementwise_sub<a class="headerlink" href="#elementwise-sub" 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">elementwise_sub</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Limited Elementwise Sub Operator.</p>
<p>The equation is:</p>
<div class="math">
\[Out = X - Y\]</div>
<p>X is a tensor of any dimension and the dimensions of tensor Y must be smaller than
or equal to the dimensions of X.</p>
<p>There are two cases for this operator:
1. The shape of Y is same with X;
2. The shape of Y is a subset of X.</p>
<p>For case 2:
Y will be broadcasted to match the shape of X and axis should be
the starting dimension index for broadcasting Y onto X.</p>
<dl class="docutils">
<dt>For example</dt>
<dd><div class="first last highlight-python"><div class="highlight"><pre><span></span><span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(,)</span>
<span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</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">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="k">with</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span>
<span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="k">with</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span>
</pre></div>
</div>
</dd>
</dl>
<p>Either of the inputs X and Y or none can carry the LoD (Level of Details) information. However, the output only shares the LoD information with input X.</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>x</strong> &#8211; (Tensor) The first input tensor of elementwise op
Duplicable: False Optional: False</li>
<li><strong>y</strong> &#8211; (Tensor) The second input tensor of elementwise op
Duplicable: False Optional: False</li>
<li><strong>axis</strong> (<em>INT</em>) &#8211; (int, default -1) The starting dimension index for broadcasting Y onto X</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 output of elementwise op</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="elementwise-mul">
<h2>elementwise_mul<a class="headerlink" href="#elementwise-mul" 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">elementwise_mul</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Limited Elementwise Mul Operator.</p>
<p>The equation is:</p>
<div class="math">
\[Out = X \odot\ Y\]</div>
<p>X is a tensor of any dimension and the dimensions of tensor Y must be smaller than <p>X is a tensor of any dimension and the dimensions of tensor Y must be smaller than
or equal to the dimensions of X.</p> or equal to the dimensions of X.</p>
<p>There are two cases for this operator: <p>There are two cases for this operator:
...@@ -497,14 +598,18 @@ or equal to the dimensions of X.</p> ...@@ -497,14 +598,18 @@ or equal to the dimensions of X.</p>
<p>For case 2: <p>For case 2:
Y will be broadcasted to match the shape of X and axis should be Y will be broadcasted to match the shape of X and axis should be
the starting dimension index for broadcasting Y onto X.</p> the starting dimension index for broadcasting Y onto X.</p>
<p class="rubric">example</p> <dl class="docutils">
<p>shape(X) = (2, 3, 4, 5), shape(Y) = (,) <dt>For example</dt>
shape(X) = (2, 3, 4, 5), shape(Y) = (5,) <dd><div class="first last highlight-python"><div class="highlight"><pre><span></span><span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(,)</span>
shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5) <span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">5</span><span class="p">,)</span>
shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1 <span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0</p> <span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="k">with</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span>
<p>Both the input X and Y can carry the LoD (Level of Details) information, <span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="k">with</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span>
or not. But the output only shares the LoD information with input X.</p> </pre></div>
</div>
</dd>
</dl>
<p>Either of the inputs X and Y or none can carry the LoD (Level of Details) information. However, the output only shares the LoD information with input X.</p>
<table class="docutils field-list" frame="void" rules="none"> <table class="docutils field-list" frame="void" rules="none">
<col class="field-name" /> <col class="field-name" />
<col class="field-body" /> <col class="field-body" />
...@@ -533,7 +638,8 @@ Duplicable: False Optional: False</li> ...@@ -533,7 +638,8 @@ Duplicable: False Optional: False</li>
<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">elementwise_div</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span></dt> <code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">elementwise_div</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span></dt>
<dd><p>Limited Elementwise Div Operator.</p> <dd><p>Limited Elementwise Div Operator.</p>
<p>The equation is:</p> <p>The equation is:</p>
<p>$Out = X / Y$</p> <div class="math">
\[Out = X / Y\]</div>
<p>X is a tensor of any dimension and the dimensions of tensor Y must be smaller than <p>X is a tensor of any dimension and the dimensions of tensor Y must be smaller than
or equal to the dimensions of X.</p> or equal to the dimensions of X.</p>
<p>There are two cases for this operator: <p>There are two cases for this operator:
...@@ -542,14 +648,18 @@ or equal to the dimensions of X.</p> ...@@ -542,14 +648,18 @@ or equal to the dimensions of X.</p>
<p>For case 2: <p>For case 2:
Y will be broadcasted to match the shape of X and axis should be Y will be broadcasted to match the shape of X and axis should be
the starting dimension index for broadcasting Y onto X.</p> the starting dimension index for broadcasting Y onto X.</p>
<p class="rubric">example</p> <dl class="docutils">
<p>shape(X) = (2, 3, 4, 5), shape(Y) = (,) <dt>For example</dt>
shape(X) = (2, 3, 4, 5), shape(Y) = (5,) <dd><div class="first last highlight-python"><div class="highlight"><pre><span></span><span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(,)</span>
shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5) <span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">5</span><span class="p">,)</span>
shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1 <span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0</p> <span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="k">with</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span>
<p>Both the input X and Y can carry the LoD (Level of Details) information, <span class="n">shape</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">shape</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="k">with</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span>
or not. But the output only shares the LoD information with input X.</p> </pre></div>
</div>
</dd>
</dl>
<p>Either of the inputs X and Y or none can carry the LoD (Level of Details) information. However, the output only shares the LoD information with input X.</p>
<table class="docutils field-list" frame="void" rules="none"> <table class="docutils field-list" frame="void" rules="none">
<col class="field-name" /> <col class="field-name" />
<col class="field-body" /> <col class="field-body" />
......
develop/doc_cn/searchindex.js
浏览文件 @ 5816d195
此差异已折叠。
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或