<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>
<dlclass="docutils">
<dt>For example</dt>
<dd><divclass="first last highlight-python"><divclass="highlight"><pre><span></span><spanclass="n">shape</span><spanclass="p">(</span><spanclass="n">X</span><spanclass="p">)</span><spanclass="o">=</span><spanclass="p">(</span><spanclass="mi">2</span><spanclass="p">,</span><spanclass="mi">3</span><spanclass="p">,</span><spanclass="mi">4</span><spanclass="p">,</span><spanclass="mi">5</span><spanclass="p">),</span><spanclass="n">shape</span><spanclass="p">(</span><spanclass="n">Y</span><spanclass="p">)</span><spanclass="o">=</span><spanclass="p">(,)</span>
<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>
<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>
<dlclass="docutils">
<dt>For example</dt>
<dd><divclass="first last highlight-python"><divclass="highlight"><pre><span></span><spanclass="n">shape</span><spanclass="p">(</span><spanclass="n">X</span><spanclass="p">)</span><spanclass="o">=</span><spanclass="p">(</span><spanclass="mi">2</span><spanclass="p">,</span><spanclass="mi">3</span><spanclass="p">,</span><spanclass="mi">4</span><spanclass="p">,</span><spanclass="mi">5</span><spanclass="p">),</span><spanclass="n">shape</span><spanclass="p">(</span><spanclass="n">Y</span><spanclass="p">)</span><spanclass="o">=</span><spanclass="p">(,)</span>
<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>
<p>Both the input X and Y can carry the LoD (Level of Details) information,
or not. But the output only shares the LoD information with input X.</p>
<dlclass="docutils">
<dt>For example</dt>
<dd><divclass="first last highlight-python"><divclass="highlight"><pre><span></span><spanclass="n">shape</span><spanclass="p">(</span><spanclass="n">X</span><spanclass="p">)</span><spanclass="o">=</span><spanclass="p">(</span><spanclass="mi">2</span><spanclass="p">,</span><spanclass="mi">3</span><spanclass="p">,</span><spanclass="mi">4</span><spanclass="p">,</span><spanclass="mi">5</span><spanclass="p">),</span><spanclass="n">shape</span><spanclass="p">(</span><spanclass="n">Y</span><spanclass="p">)</span><spanclass="o">=</span><spanclass="p">(,)</span>
<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>
<p>Both the input X and Y can carry the LoD (Level of Details) information,
or not. But the output only shares the LoD information with input X.</p>
<dlclass="docutils">
<dt>For example</dt>
<dd><divclass="first last highlight-python"><divclass="highlight"><pre><span></span><spanclass="n">shape</span><spanclass="p">(</span><spanclass="n">X</span><spanclass="p">)</span><spanclass="o">=</span><spanclass="p">(</span><spanclass="mi">2</span><spanclass="p">,</span><spanclass="mi">3</span><spanclass="p">,</span><spanclass="mi">4</span><spanclass="p">,</span><spanclass="mi">5</span><spanclass="p">),</span><spanclass="n">shape</span><spanclass="p">(</span><spanclass="n">Y</span><spanclass="p">)</span><spanclass="o">=</span><spanclass="p">(,)</span>
<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>
"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":[
{
"name":"X",
...
...
@@ -3600,7 +3600,7 @@
}]
},{
"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":[
{
"name":"X",
...
...
@@ -3972,7 +3972,7 @@
}]
},{
"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":[
{
"name":"X",
...
...
@@ -4001,7 +4001,7 @@
}]
},{
"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",
<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>
<dlclass="docutils">
<dt>For example</dt>
<dd><divclass="first last highlight-python"><divclass="highlight"><pre><span></span><spanclass="n">shape</span><spanclass="p">(</span><spanclass="n">X</span><spanclass="p">)</span><spanclass="o">=</span><spanclass="p">(</span><spanclass="mi">2</span><spanclass="p">,</span><spanclass="mi">3</span><spanclass="p">,</span><spanclass="mi">4</span><spanclass="p">,</span><spanclass="mi">5</span><spanclass="p">),</span><spanclass="n">shape</span><spanclass="p">(</span><spanclass="n">Y</span><spanclass="p">)</span><spanclass="o">=</span><spanclass="p">(,)</span>
<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>
<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>
<dlclass="docutils">
<dt>For example</dt>
<dd><divclass="first last highlight-python"><divclass="highlight"><pre><span></span><spanclass="n">shape</span><spanclass="p">(</span><spanclass="n">X</span><spanclass="p">)</span><spanclass="o">=</span><spanclass="p">(</span><spanclass="mi">2</span><spanclass="p">,</span><spanclass="mi">3</span><spanclass="p">,</span><spanclass="mi">4</span><spanclass="p">,</span><spanclass="mi">5</span><spanclass="p">),</span><spanclass="n">shape</span><spanclass="p">(</span><spanclass="n">Y</span><spanclass="p">)</span><spanclass="o">=</span><spanclass="p">(,)</span>
<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>
<p>Both the input X and Y can carry the LoD (Level of Details) information,
or not. But the output only shares the LoD information with input X.</p>
<dlclass="docutils">
<dt>For example</dt>
<dd><divclass="first last highlight-python"><divclass="highlight"><pre><span></span><spanclass="n">shape</span><spanclass="p">(</span><spanclass="n">X</span><spanclass="p">)</span><spanclass="o">=</span><spanclass="p">(</span><spanclass="mi">2</span><spanclass="p">,</span><spanclass="mi">3</span><spanclass="p">,</span><spanclass="mi">4</span><spanclass="p">,</span><spanclass="mi">5</span><spanclass="p">),</span><spanclass="n">shape</span><spanclass="p">(</span><spanclass="n">Y</span><spanclass="p">)</span><spanclass="o">=</span><spanclass="p">(,)</span>
<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>
<p>Both the input X and Y can carry the LoD (Level of Details) information,
or not. But the output only shares the LoD information with input X.</p>
<dlclass="docutils">
<dt>For example</dt>
<dd><divclass="first last highlight-python"><divclass="highlight"><pre><span></span><spanclass="n">shape</span><spanclass="p">(</span><spanclass="n">X</span><spanclass="p">)</span><spanclass="o">=</span><spanclass="p">(</span><spanclass="mi">2</span><spanclass="p">,</span><spanclass="mi">3</span><spanclass="p">,</span><spanclass="mi">4</span><spanclass="p">,</span><spanclass="mi">5</span><spanclass="p">),</span><spanclass="n">shape</span><spanclass="p">(</span><spanclass="n">Y</span><spanclass="p">)</span><spanclass="o">=</span><spanclass="p">(,)</span>
<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>
