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1a1b5047
编写于
9月 20, 2016
作者:
T
Travis CI
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doc/ui/api/trainer_config_helpers/evaluators.html
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...
@@ -61,7 +61,7 @@
...
@@ -61,7 +61,7 @@
<h1>
Base
<a
class=
"headerlink"
href=
"#base"
title=
"Permalink to this headline"
>
¶
</a></h1>
<h1>
Base
<a
class=
"headerlink"
href=
"#base"
title=
"Permalink to this headline"
>
¶
</a></h1>
<dl
class=
"function"
>
<dl
class=
"function"
>
<dt>
<dt>
<code
class=
"descclassname"
>
paddle.trainer_config_helpers.evaluators.
</code><code
class=
"descname"
>
evaluator_base
</code><span
class=
"sig-paren"
>
(
</span><em>
input
</em>
,
<em>
type
</em>
,
<em>
label=None
</em>
,
<em>
weight=None
</em>
,
<em>
name=None
</em>
,
<em>
chunk_scheme=None
</em>
,
<em>
num_chunk_types=None
</em>
,
<em>
classification_threshold=
0.5
</em>
,
<em>
positive_label=-1
</em>
,
<em>
dict_file=''
</em>
,
<em>
result_file=''
</em>
,
<em>
num_results=1
</em>
,
<em>
delimited=Tru
e
</em><span
class=
"sig-paren"
>
)
</span></dt>
<code
class=
"descclassname"
>
paddle.trainer_config_helpers.evaluators.
</code><code
class=
"descname"
>
evaluator_base
</code><span
class=
"sig-paren"
>
(
</span><em>
input
</em>
,
<em>
type
</em>
,
<em>
label=None
</em>
,
<em>
weight=None
</em>
,
<em>
name=None
</em>
,
<em>
chunk_scheme=None
</em>
,
<em>
num_chunk_types=None
</em>
,
<em>
classification_threshold=
None
</em>
,
<em>
positive_label=None
</em>
,
<em>
dict_file=None
</em>
,
<em>
result_file=None
</em>
,
<em>
num_results=None
</em>
,
<em>
delimited=Non
e
</em><span
class=
"sig-paren"
>
)
</span></dt>
<dd><p>
Evaluator will evaluate the network status while training/testing.
</p>
<dd><p>
Evaluator will evaluate the network status while training/testing.
</p>
<p>
User can use evaluator by classify/regression job. For example.
</p>
<p>
User can use evaluator by classify/regression job. For example.
</p>
<div
class=
"highlight-python"
><div
class=
"highlight"
><pre><span></span><span
class=
"n"
>
classify
</span><span
class=
"p"
>
(
</span><span
class=
"n"
>
prediction
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
output
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
evaluator
</span><span
class=
"o"
>
=
</span><span
class=
"n"
>
classification_error_evaluator
</span><span
class=
"p"
>
)
</span>
<div
class=
"highlight-python"
><div
class=
"highlight"
><pre><span></span><span
class=
"n"
>
classify
</span><span
class=
"p"
>
(
</span><span
class=
"n"
>
prediction
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
output
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
evaluator
</span><span
class=
"o"
>
=
</span><span
class=
"n"
>
classification_error_evaluator
</span><span
class=
"p"
>
)
</span>
...
...
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