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2ec13062
编写于
12月 23, 2017
作者:
C
Cao Ying
提交者:
GitHub
12月 23, 2017
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Merge pull request #6907 from lcy-seso/fix_doc
fix doc.
上级
ed08a483
8a463939
变更
2
隐藏空白更改
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Showing
2 changed file
with
9 addition
and
8 deletion
+9
-8
paddle/operators/mul_op.cc
paddle/operators/mul_op.cc
+1
-1
paddle/operators/positive_negative_pair_op.cc
paddle/operators/positive_negative_pair_op.cc
+8
-7
未找到文件。
paddle/operators/mul_op.cc
浏览文件 @
2ec13062
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@@ -113,7 +113,7 @@ This operator is used to perform matrix multiplication for input $X$ and $Y$.
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@@ -113,7 +113,7 @@ This operator is used to perform matrix multiplication for input $X$ and $Y$.
The equation is:
The equation is:
$$Out = X * Y$$
$$Out = X * Y$$
Both the input $X$ and $Y$ can carry the LoD (Level of Details) information,
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 $X$.
or not. But the output only shares the LoD information with input $X$.
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...
paddle/operators/positive_negative_pair_op.cc
浏览文件 @
2ec13062
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@@ -154,13 +154,14 @@ class PositiveNegativePairOpMaker : public framework::OpProtoAndCheckerMaker {
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@@ -154,13 +154,14 @@ class PositiveNegativePairOpMaker : public framework::OpProtoAndCheckerMaker {
"Noting that reducing on the first dim will make the LoD info lost."
)
"Noting that reducing on the first dim will make the LoD info lost."
)
.
SetDefault
(
0
);
.
SetDefault
(
0
);
AddComment
(
R"DOC(
AddComment
(
R"DOC(
PositiveNegativePairOp can be used to evaluate Learning To Rank(LTR)
PositiveNegativePairOp can be used to evaluate Learning To Rank(LTR) model's
model performance.
performance.
Within some context, e.g. the "query", a LTR model generates scores
for a list of items, which gives a partial order of the items.
Within some context, e.g. the "query", a LTR model generates scores for a list
PositiveNegativePairOp takes a list of reference rank order
of items, which gives a partial order of the items. PositiveNegativePairOp
(Input("Label")) and the model generated scores (Input(Score)) as
takes a list of reference rank order (Input("Label")) and the model generated
inputs and counts the pairs that ranked correctly and incorrectly.
scores (Input(Score)) as inputs and counts the pairs that ranked correctly
and incorrectly.
)DOC"
);
)DOC"
);
}
}
};
};
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