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体验新版 GitCode,发现更多精彩内容 >>
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63ce906b
编写于
12月 12, 2017
作者:
G
guosheng
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电子邮件补丁
差异文件
Refine ChunkEvalutor by following comments
上级
1eaeacb2
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
20 addition
and
19 deletion
+20
-19
paddle/operators/chunk_eval_op.cc
paddle/operators/chunk_eval_op.cc
+9
-7
python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py
...n/paddle/v2/fluid/tests/book/test_label_semantic_roles.py
+11
-12
未找到文件。
paddle/operators/chunk_eval_op.cc
浏览文件 @
63ce906b
...
...
@@ -80,14 +80,16 @@ class ChunkEvalOpMaker : public framework::OpProtoAndCheckerMaker {
"sensitivity) of chunks on the given mini-batch."
);
AddOutput
(
"F1-Score"
,
"(float). The evaluated F1-Score on the given mini-batch."
);
AddOutput
(
"NumInferChunks"
,
"(int64_t). The number of chunks in Inference on the given "
"mini-batch."
);
AddOutput
(
"NumInferChunks"
,
"(int). The number of chunks in Inference on the given mini-batch."
);
AddOutput
(
"NumLabelChunks"
,
"(int). The number of chunks in Label on the given mini-batch."
);
AddOutput
(
"NumCorrectChunks"
,
"(int). The number of chunks both in Inference and Label on the "
"given mini-batch."
);
"NumLabelChunks"
,
"(int64_t). The number of chunks in Label on the given mini-batch."
);
AddOutput
(
"NumCorrectChunks"
,
"(int64_t). The number of chunks both in Inference and Label on the "
"given mini-batch."
);
AddAttr
<
int
>
(
"num_chunk_types"
,
"(int). The number of chunk type. See below for details."
);
AddAttr
<
std
::
string
>
(
...
...
python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py
浏览文件 @
63ce906b
...
...
@@ -178,20 +178,19 @@ def main():
for
pass_id
in
xrange
(
PASS_NUM
):
chunk_evaluator
.
reset
(
exe
)
for
data
in
train_data
():
outs
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
avg_cost
]
+
chunk_evaluator
.
metrics
)
precision
,
recall
,
f1_score
=
chunk_evaluator
.
eval
(
exe
)
avg_cost_val
=
np
.
array
(
outs
[
0
])
precision_val
=
np
.
array
(
precision
)
recall_val
=
np
.
array
(
recall
)
f1_score_val
=
np
.
array
(
f1_score
)
cost
,
precision
,
recall
,
f1_score
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
avg_cost
]
+
chunk_evaluator
.
metrics
)
pass_precision
,
pass_recall
,
pass_f1_score
=
chunk_evaluator
.
eval
(
exe
)
if
batch_id
%
10
==
0
:
print
(
"avg_cost="
+
str
(
avg_cost_val
))
print
(
"precision_val="
+
str
(
precision_val
))
print
(
"recall_val:"
+
str
(
recall_val
))
print
(
"f1_score_val:"
+
str
(
f1_score_val
))
print
(
"avg_cost:"
+
str
(
cost
)
+
" precision:"
+
str
(
precision
)
+
" recall:"
+
str
(
recall
)
+
" f1_score:"
+
str
(
f1_score
)
+
" pass_precision:"
+
str
(
pass_precision
)
+
" pass_recall:"
+
str
(
pass_recall
)
+
" pass_f1_score:"
+
str
(
pass_f1_score
))
# exit early for CI
exit
(
0
)
...
...
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