diff --git a/develop/doc/_sources/api/v2/fluid/layers.rst.txt b/develop/doc/_sources/api/v2/fluid/layers.rst.txt index a7c8670f66cc7f319e41155211ead2d89126117f..696a8012aa4aacb78250be3a2d7f49718c009839 100644 --- a/develop/doc/_sources/api/v2/fluid/layers.rst.txt +++ b/develop/doc/_sources/api/v2/fluid/layers.rst.txt @@ -38,6 +38,16 @@ elementwise_add .. autofunction:: paddle.v2.fluid.layers.elementwise_add :noindex: +elementwise_sub +--------------- +.. autofunction:: paddle.v2.fluid.layers.elementwise_sub + :noindex: + +elementwise_mul +--------------- +.. autofunction:: paddle.v2.fluid.layers.elementwise_mul + :noindex: + elementwise_div --------------- .. autofunction:: paddle.v2.fluid.layers.elementwise_div diff --git a/develop/doc/api/v2/fluid/layers.html b/develop/doc/api/v2/fluid/layers.html index 198398678664400498ab40baf896fb8fd402daac..85555dd0d73db8ebe40f1fb79cb7d4328b57d96c 100644 --- a/develop/doc/api/v2/fluid/layers.html +++ b/develop/doc/api/v2/fluid/layers.html @@ -475,7 +475,108 @@ flattened. See comments of x_num_col_dims for more details. paddle.v2.fluid.layers.elementwise_add(**kwargs)

Limited Elementwise Add Operator.

The equation is:

-

$Out = X + Y$

+
+\[Out = X + Y\]
+

X is a tensor of any dimension and the dimensions of tensor Y must be smaller than +or equal to the dimensions of X.

+

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.

+

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.

+
+
For example
+
shape(X) = (2, 3, 4, 5), shape(Y) = (,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
+shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+
+
+
+
+

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.

+ +++ + + + + + +
Parameters:
    +
  • x – (Tensor) The first input tensor of elementwise op +Duplicable: False Optional: False
    • +
  • y – (Tensor) The second input tensor of elementwise op +Duplicable: False Optional: False
    • +
  • axis (INT) – (int, default -1) The starting dimension index for broadcasting Y onto X
    • +
+
Returns:

The output of elementwise op

+
+
+ + +
+

elementwise_sub

+
+
+paddle.v2.fluid.layers.elementwise_sub(**kwargs)
+

Limited Elementwise Sub Operator.

+

The equation is:

+
+\[Out = X - Y\]
+

X is a tensor of any dimension and the dimensions of tensor Y must be smaller than +or equal to the dimensions of X.

+

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.

+

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.

+
+
For example
+
shape(X) = (2, 3, 4, 5), shape(Y) = (,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
+shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+
+
+
+
+

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.

+ +++ + + + + + +
Parameters:
    +
  • x – (Tensor) The first input tensor of elementwise op +Duplicable: False Optional: False
    • +
  • y – (Tensor) The second input tensor of elementwise op +Duplicable: False Optional: False
    • +
  • axis (INT) – (int, default -1) The starting dimension index for broadcasting Y onto X
    • +
+
Returns:

The output of elementwise op

+
+
+ +
+
+

elementwise_mul

+
+
+paddle.v2.fluid.layers.elementwise_mul(**kwargs)
+

Limited Elementwise Mul Operator.

+

The equation is:

+
+\[Out = X \odot\ Y\]

X is a tensor of any dimension and the dimensions of tensor Y must be smaller than or equal to the dimensions of X.

There are two cases for this operator: @@ -484,14 +585,18 @@ or equal to the dimensions of X.

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.

-

example

-

shape(X) = (2, 3, 4, 5), shape(Y) = (,) -shape(X) = (2, 3, 4, 5), shape(Y) = (5,) -shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5) -shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1 -shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0

-

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.

+
+
For example
+
shape(X) = (2, 3, 4, 5), shape(Y) = (,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
+shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+
+
+
+
+

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.

@@ -520,7 +625,8 @@ Duplicable: False Optional: Falsepaddle.v2.fluid.layers.elementwise_div(**kwargs)

Limited Elementwise Div Operator.

The equation is:

-

$Out = X / Y$

+
+\[Out = X / Y\]

X is a tensor of any dimension and the dimensions of tensor Y must be smaller than or equal to the dimensions of X.

There are two cases for this operator: @@ -529,14 +635,18 @@ or equal to the dimensions of X.

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.

-

example

-

shape(X) = (2, 3, 4, 5), shape(Y) = (,) -shape(X) = (2, 3, 4, 5), shape(Y) = (5,) -shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5) -shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1 -shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0

-

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.

+
+
For example
+
shape(X) = (2, 3, 4, 5), shape(Y) = (,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
+shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+
+
+
+
+

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.

diff --git a/develop/doc/operators.json b/develop/doc/operators.json index ffb1c0c82feda7bddfddb86335192ac1db6b0be0..618d1743947c29856170bf44150faec4360832de 100644 --- a/develop/doc/operators.json +++ b/develop/doc/operators.json @@ -1780,7 +1780,7 @@ "attrs" : [ ] },{ "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" : [ { "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. 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Parameters","Introduction","Design Doc: PaddlePaddle Programs","Prune","Design Doc: Python API","Python Data Reader Design Doc","Design Doc: Refactorization Overview","Design Doc: Gradient Operators Registration","Regularization in PaddlePaddle","PaddlePaddle\u53d1\u884c\u89c4\u8303","Design of Scope in Paddle","Design Doc: Selected Rows","Interaction between C++ and Python","Design Doc: Supporting new Device/Library","Background","Design for TensorArray","Background","Build from Sources","Run in Docker Containers","Install and Build","Install Using pip","GET STARTED","RNN Models","RNN Configuration","Build using Docker","Contribute Code","Write New Layers","How to write a new operator","How to use Eigen in Paddle","Contribute Documentation","HOW TO","Profiling the Python Code","Tune GPU Performance","PaddlePaddle Fluid Source Code Overview","Distributed Training","Cluster Training Using Fabric","Distributed PaddlePaddle Training on AWS with Kubernetes","PaddlePaddle On 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Parameters","Introduction","Design Doc: PaddlePaddle Programs","Prune","Design Doc: Python API","Python Data Reader Design Doc","Design Doc: Refactorization Overview","Design Doc: Gradient Operators Registration","Regularization in PaddlePaddle","PaddlePaddle\u53d1\u884c\u89c4\u8303","Design of Scope in Paddle","Design Doc: Selected Rows","Interaction between C++ and Python","Design Doc: Supporting new Device/Library","Background","Design for TensorArray","Background","Build from Sources","Run in Docker Containers","Install and Build","Install Using pip","GET STARTED","RNN Models","RNN Configuration","Build using Docker","Contribute Code","Write New Layers","How to write a new operator","How to use Eigen in Paddle","Contribute Documentation","HOW TO","Profiling the Python Code","Tune GPU Performance","PaddlePaddle Fluid Source Code Overview","Distributed Training","Cluster Training Using Fabric","Distributed PaddlePaddle Training on AWS with Kubernetes","PaddlePaddle On 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90,variabl:[31,61,78,80,84,90],vector:116,verifi:110,version:[43,52,94],vgg_16_network:9,visual:128,volum:110,vpc:110,warp_ctc:8,weightdecayregular:26,what:[37,41,98,106],when:[41,84],whl:94,why:[52,53,73,79,80,89,106],wmt14:14,word:[126,129],worker:43,workflow:99,wrapper:100,write:[99,100,101,103,129],www:103,xavieriniti:20,yaml:111,your:92,zero:21,zoo:128}}) \ No newline at end of file diff --git a/develop/doc_cn/_sources/api/v2/fluid/layers.rst.txt b/develop/doc_cn/_sources/api/v2/fluid/layers.rst.txt index a7c8670f66cc7f319e41155211ead2d89126117f..696a8012aa4aacb78250be3a2d7f49718c009839 100644 --- a/develop/doc_cn/_sources/api/v2/fluid/layers.rst.txt +++ b/develop/doc_cn/_sources/api/v2/fluid/layers.rst.txt @@ -38,6 +38,16 @@ elementwise_add .. autofunction:: paddle.v2.fluid.layers.elementwise_add :noindex: +elementwise_sub +--------------- +.. autofunction:: paddle.v2.fluid.layers.elementwise_sub + :noindex: + +elementwise_mul +--------------- +.. autofunction:: paddle.v2.fluid.layers.elementwise_mul + :noindex: + elementwise_div --------------- .. autofunction:: paddle.v2.fluid.layers.elementwise_div diff --git a/develop/doc_cn/api/v2/fluid/layers.html b/develop/doc_cn/api/v2/fluid/layers.html index b77782fadb85ec8dacb89a147f947d49d0e6d26e..b3a1fbf990bcc8abae00996fd3a35de1d9a614f5 100644 --- a/develop/doc_cn/api/v2/fluid/layers.html +++ b/develop/doc_cn/api/v2/fluid/layers.html @@ -488,7 +488,108 @@ flattened. See comments of x_num_col_dims for more details.paddle.v2.fluid.layers.elementwise_add(**kwargs)

Limited Elementwise Add Operator.

The equation is:

-

$Out = X + Y$

+
+\[Out = X + Y\]
+

X is a tensor of any dimension and the dimensions of tensor Y must be smaller than +or equal to the dimensions of X.

+

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.

+

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.

+
+
For example
+
shape(X) = (2, 3, 4, 5), shape(Y) = (,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
+shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+
+
+
+
+

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.

+
+++ + + + + + +
参数:
    +
  • x – (Tensor) The first input tensor of elementwise op +Duplicable: False Optional: False
    • +
  • y – (Tensor) The second input tensor of elementwise op +Duplicable: False Optional: False
    • +
  • axis (INT) – (int, default -1) The starting dimension index for broadcasting Y onto X
    • +
+
返回:

The output of elementwise op

+
+
+ +
+
+

elementwise_sub

+
+
+paddle.v2.fluid.layers.elementwise_sub(**kwargs)
+

Limited Elementwise Sub Operator.

+

The equation is:

+
+\[Out = X - Y\]
+

X is a tensor of any dimension and the dimensions of tensor Y must be smaller than +or equal to the dimensions of X.

+

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.

+

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.

+
+
For example
+
shape(X) = (2, 3, 4, 5), shape(Y) = (,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
+shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+
+
+
+
+

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.

+ +++ + + + + + +
参数:
    +
  • x – (Tensor) The first input tensor of elementwise op +Duplicable: False Optional: False
    • +
  • y – (Tensor) The second input tensor of elementwise op +Duplicable: False Optional: False
    • +
  • axis (INT) – (int, default -1) The starting dimension index for broadcasting Y onto X
    • +
+
返回:

The output of elementwise op

+
+
+ +
+
+

elementwise_mul

+
+
+paddle.v2.fluid.layers.elementwise_mul(**kwargs)
+

Limited Elementwise Mul Operator.

+

The equation is:

+
+\[Out = X \odot\ Y\]

X is a tensor of any dimension and the dimensions of tensor Y must be smaller than or equal to the dimensions of X.

There are two cases for this operator: @@ -497,14 +598,18 @@ or equal to the dimensions of X.

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.

-

example

-

shape(X) = (2, 3, 4, 5), shape(Y) = (,) -shape(X) = (2, 3, 4, 5), shape(Y) = (5,) -shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5) -shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1 -shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0

-

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.

+
+
For example
+
shape(X) = (2, 3, 4, 5), shape(Y) = (,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
+shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+
+
+
+
+

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.

@@ -533,7 +638,8 @@ Duplicable: False Optional: Falsepaddle.v2.fluid.layers.elementwise_div(**kwargs)

Limited Elementwise Div Operator.

The equation is:

-

$Out = X / Y$

+
+\[Out = X / Y\]

X is a tensor of any dimension and the dimensions of tensor Y must be smaller than or equal to the dimensions of X.

There are two cases for this operator: @@ -542,14 +648,18 @@ or equal to the dimensions of X.

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.

-

example

-

shape(X) = (2, 3, 4, 5), shape(Y) = (,) -shape(X) = (2, 3, 4, 5), shape(Y) = (5,) -shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5) -shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1 -shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0

-

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.

+
+
For example
+
shape(X) = (2, 3, 4, 5), shape(Y) = (,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
+shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
+shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+
+
+
+
+

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.

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