From 1c564a7ae963ba343bcc4fb223b6d25ca8d31623 Mon Sep 17 00:00:00 2001 From: Travis CI Date: Tue, 29 Aug 2017 09:07:57 +0000 Subject: [PATCH] Deploy to GitHub Pages: 47eb86919712d92425aecdf000bc3d1e071d8d29 --- .../doc/_sources/api/v2/config/layer.rst.txt | 11 +- develop/doc/api/v2/config/layer.html | 121 +++++++++++++++--- develop/doc/searchindex.js | 2 +- .../_sources/api/v2/config/layer.rst.txt | 11 +- develop/doc_cn/api/v2/config/layer.html | 121 +++++++++++++++--- develop/doc_cn/searchindex.js | 2 +- 6 files changed, 220 insertions(+), 48 deletions(-) diff --git a/develop/doc/_sources/api/v2/config/layer.rst.txt b/develop/doc/_sources/api/v2/config/layer.rst.txt index 2273c8e8698..1329b77bb44 100644 --- a/develop/doc/_sources/api/v2/config/layer.rst.txt +++ b/develop/doc/_sources/api/v2/config/layer.rst.txt @@ -419,9 +419,14 @@ multi_binary_label_cross_entropy_cost .. autoclass:: paddle.v2.layer.multi_binary_label_cross_entropy_cost :noindex: -huber_cost ----------- -.. autoclass:: paddle.v2.layer.huber_cost +huber_regression_cost +------------------------- +.. autoclass:: paddle.v2.layer.huber_regression_cost + :noindex: + +huber_classification_cost +------------------------- +.. autoclass:: paddle.v2.layer.huber_classification_cost :noindex: lambda_cost diff --git a/develop/doc/api/v2/config/layer.html b/develop/doc/api/v2/config/layer.html index 0b36325152a..ed58971f6b1 100644 --- a/develop/doc/api/v2/config/layer.html +++ b/develop/doc/api/v2/config/layer.html @@ -3326,38 +3326,119 @@ Input should be a vector of positive numbers, without normalization.

-
-

huber_cost

+
+

huber_regression_cost

-class paddle.v2.layer.huber_cost
-

A loss layer for huber loss.

-

The example usage is:

-
cost = huber_cost(input=input,
-                  label=label)
+class paddle.v2.layer.huber_regression_cost
+

+
In statistics, the Huber loss is a loss function used in robust regression,
+that is less sensitive to outliers in data than the squared error loss.
+Given a prediction f(x), a label y and \(\delta\), the loss function
+is defined as:

+
ight )^2, left | y-f(x)
+ight |leq delta

+

+
loss = delta left | y-f(x)

+
ight |-0.5delta ^2, otherwise

+

+
The example usage is:

+
cost = huber_regression_cost(input=input, label=label)
 

 

 
-
+
-
+
-
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

 
 
 
Parameters:
    
-
  • input (paddle.v2.config_base.Layer.) – The first input layer.
    • 
-
  • label – The input label.
    • 
-
  • name (None|basestring.) – The name of this layers. It is not necessary.
    • 
-
  • coeff (float.) – The coefficient affects the gradient in the backward.
    • 
-
  • layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer Attribute.
    • 
-

-
param input:The first input layer.
 
Returns:
paddle.v2.config_base.Layer object.

-
type input:paddle.v2.config_base.Layer.
 
Return type:
paddle.v2.config_base.Layer.

-
param label:The input label.
type input:paddle.v2.config_base.Layer.
param name:The name of this layers. It is not necessary.
type name:None|basestring.
param delta:The difference between the observed and predicted values.
type delta:float.
param coeff:The coefficient affects the gradient in the backward.
type coeff:float.
param layer_attr:
 Extra Layer Attribute.
type layer_attr:
 paddle.v2.attr.ExtraAttribute
return:paddle.v2.config_base.Layer object.
rtype:paddle.v2.config_base.Layer.
 

 
 

+

+
+ +
+
+

huber_classification_cost

+
+
+class paddle.v2.layer.huber_classification_cost
+
+
For classification purposes, a variant of the Huber loss called modified Huber +is sometimes used. Given a prediction f(x) (a real-valued classifier score) and +a true binary class label :math:`yin left {-1, 1
+
+
ight }`, the modified Huber
+
loss is defined as:
+
ight )^2, yf(x)geq 1
+
+
loss = -4yf(x), ext{otherwise}
+

The example usage is:

+
cost = huber_classification_cost(input=input, label=label)
+
+
+ +++ + + + + + + + + + + + + + + + + + + + + + + + + + + + +
param input:The first input layer.
type input:paddle.v2.config_base.Layer.
param label:The input label.
type input:paddle.v2.config_base.Layer.
param name:The name of this layers. It is not necessary.
type name:None|basestring.
param coeff:The coefficient affects the gradient in the backward.
type coeff:float.
param layer_attr:
 Extra Layer Attribute.
type layer_attr:
 paddle.v2.attr.ExtraAttribute
return:paddle.v2.config_base.Layer object.
rtype:paddle.v2.config_base.Layer.
+
+
@@ -4017,7 +4098,7 @@ a_i * z_i &\quad \mathrm{otherwise}\end{split}\]

The gated unit layer implements a simple gating mechanism over the input. The input \(X\) is first projected into a new space \(X'\), and it is also used to produce a gate weight \(\sigma\). Element-wise -prodict between :match:`X’` and \(\sigma\) is finally returned.

+prodict between :match:`X’` and \(\sigma\) is finally returned.

Reference:
Language Modeling with Gated Convolutional Networks diff --git a/develop/doc/searchindex.js b/develop/doc/searchindex.js index 1b643e9b4c6..c5d338247b9 100644 --- a/develop/doc/searchindex.js +++ b/develop/doc/searchindex.js @@ -1 +1 @@ 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\ No newline at end of file diff --git a/develop/doc_cn/_sources/api/v2/config/layer.rst.txt b/develop/doc_cn/_sources/api/v2/config/layer.rst.txt index 2273c8e8698..1329b77bb44 100644 --- a/develop/doc_cn/_sources/api/v2/config/layer.rst.txt +++ b/develop/doc_cn/_sources/api/v2/config/layer.rst.txt @@ -419,9 +419,14 @@ multi_binary_label_cross_entropy_cost .. autoclass:: paddle.v2.layer.multi_binary_label_cross_entropy_cost :noindex: -huber_cost ----------- -.. autoclass:: paddle.v2.layer.huber_cost +huber_regression_cost +------------------------- +.. autoclass:: paddle.v2.layer.huber_regression_cost + :noindex: + +huber_classification_cost +------------------------- +.. autoclass:: paddle.v2.layer.huber_classification_cost :noindex: lambda_cost diff --git a/develop/doc_cn/api/v2/config/layer.html b/develop/doc_cn/api/v2/config/layer.html index 59d50f6b051..c68dc8a600e 100644 --- a/develop/doc_cn/api/v2/config/layer.html +++ b/develop/doc_cn/api/v2/config/layer.html @@ -3333,38 +3333,119 @@ Input should be a vector of positive numbers, without normalization.

-
-

huber_cost

+
+

huber_regression_cost

-class paddle.v2.layer.huber_cost
-

A loss layer for huber loss.

-

The example usage is:

-
cost = huber_cost(input=input,
-                  label=label)
+class paddle.v2.layer.huber_regression_cost
+

+
In statistics, the Huber loss is a loss function used in robust regression,
+that is less sensitive to outliers in data than the squared error loss.
+Given a prediction f(x), a label y and \(\delta\), the loss function
+is defined as:

+
ight )^2, left | y-f(x)
+ight |leq delta

+

+
loss = delta left | y-f(x)

+
ight |-0.5delta ^2, otherwise

+

+
The example usage is:

+
cost = huber_regression_cost(input=input, label=label)
 

 

 
-
+
-
+
-
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

 
 
 
参数:
    
-
  • input (paddle.v2.config_base.Layer.) – The first input layer.
    • 
-
  • label – The input label.
    • 
-
  • name (None|basestring.) – The name of this layers. It is not necessary.
    • 
-
  • coeff (float.) – The coefficient affects the gradient in the backward.
    • 
-
  • layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer Attribute.
    • 
-

-
param input:The first input layer.
 
返回:
paddle.v2.config_base.Layer object.

-
type input:paddle.v2.config_base.Layer.
 
返回类型:
paddle.v2.config_base.Layer.

-
param label:The input label.
type input:paddle.v2.config_base.Layer.
param name:The name of this layers. It is not necessary.
type name:None|basestring.
param delta:The difference between the observed and predicted values.
type delta:float.
param coeff:The coefficient affects the gradient in the backward.
type coeff:float.
param layer_attr:
 Extra Layer Attribute.
type layer_attr:
 paddle.v2.attr.ExtraAttribute
return:paddle.v2.config_base.Layer object.
rtype:paddle.v2.config_base.Layer.
 

 
 

+

+
+ +
+
+

huber_classification_cost

+
+
+class paddle.v2.layer.huber_classification_cost
+
+
For classification purposes, a variant of the Huber loss called modified Huber +is sometimes used. Given a prediction f(x) (a real-valued classifier score) and +a true binary class label :math:`yin left {-1, 1
+
+
ight }`, the modified Huber
+
loss is defined as:
+
ight )^2, yf(x)geq 1
+
+
loss = -4yf(x), ext{otherwise}
+

The example usage is:

+
cost = huber_classification_cost(input=input, label=label)
+
+
+ +++ + + + + + + + + + + + + + + + + + + + + + + + + + + + +
param input:The first input layer.
type input:paddle.v2.config_base.Layer.
param label:The input label.
type input:paddle.v2.config_base.Layer.
param name:The name of this layers. It is not necessary.
type name:None|basestring.
param coeff:The coefficient affects the gradient in the backward.
type coeff:float.
param layer_attr:
 Extra Layer Attribute.
type layer_attr:
 paddle.v2.attr.ExtraAttribute
return:paddle.v2.config_base.Layer object.
rtype:paddle.v2.config_base.Layer.
+
+
@@ -4024,7 +4105,7 @@ a_i * z_i &\quad \mathrm{otherwise}\end{split}\]

The gated unit layer implements a simple gating mechanism over the input. The input \(X\) is first projected into a new space \(X'\), and it is also used to produce a gate weight \(\sigma\). Element-wise -prodict between :match:`X’` and \(\sigma\) is finally returned.

+prodict between :match:`X’` and \(\sigma\) is finally returned.

Reference:
Language Modeling with Gated Convolutional Networks diff --git a/develop/doc_cn/searchindex.js b/develop/doc_cn/searchindex.js index 4ed2c76785e..b355dbacb62 100644 --- a/develop/doc_cn/searchindex.js +++ b/develop/doc_cn/searchindex.js @@ -1 +1 @@ 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