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da0e795f
编写于
3月 24, 2017
作者:
T
Travis CI
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Showing
6 changed file
with
102 addition
and
2 deletion
+102
-2
develop/doc/_sources/api/v2/config/layer.rst.txt
develop/doc/_sources/api/v2/config/layer.rst.txt
+6
-0
develop/doc/api/v2/config/layer.html
develop/doc/api/v2/config/layer.html
+44
-0
develop/doc/searchindex.js
develop/doc/searchindex.js
+1
-1
develop/doc_cn/_sources/api/v2/config/layer.rst.txt
develop/doc_cn/_sources/api/v2/config/layer.rst.txt
+6
-0
develop/doc_cn/api/v2/config/layer.html
develop/doc_cn/api/v2/config/layer.html
+44
-0
develop/doc_cn/searchindex.js
develop/doc_cn/searchindex.js
+1
-1
未找到文件。
develop/doc/_sources/api/v2/config/layer.rst.txt
浏览文件 @
da0e795f
...
...
@@ -109,6 +109,12 @@ sum_to_one_norm
:members: sum_to_one_norm
:noindex:
cross_channel_norm
------------------
.. automodule:: paddle.v2.layer
:members: cross_channel_norm
:noindex:
Recurrent Layers
================
...
...
develop/doc/api/v2/config/layer.html
浏览文件 @
da0e795f
...
...
@@ -203,6 +203,7 @@
<li><a
class=
"reference internal"
href=
"#img-cmrnorm"
>
img_cmrnorm
</a></li>
<li><a
class=
"reference internal"
href=
"#batch-norm"
>
batch_norm
</a></li>
<li><a
class=
"reference internal"
href=
"#sum-to-one-norm"
>
sum_to_one_norm
</a></li>
<li><a
class=
"reference internal"
href=
"#cross-channel-norm"
>
cross_channel_norm
</a></li>
</ul>
</li>
<li><a
class=
"reference internal"
href=
"#recurrent-layers"
>
Recurrent Layers
</a><ul>
...
...
@@ -1245,6 +1246,49 @@ and <span class="math">\(out\)</span> is a (batchSize x dataDim) output vector.<
</table>
</dd></dl>
</div>
<div
class=
"section"
id=
"cross-channel-norm"
>
<h3>
cross_channel_norm
<a
class=
"headerlink"
href=
"#cross-channel-norm"
title=
"Permalink to this headline"
>
¶
</a></h3>
<p><cite>
paddle.v2.layer
</cite>
is a part of model config packages in paddle.v2. In API v2,
we want to make Paddle a plain Python package. The model config package defined
the way how to configure a neural network topology in Paddle Python code.
</p>
<p>
The primary usage shows below.
</p>
<div
class=
"highlight-python"
><div
class=
"highlight"
><pre><span></span><span
class=
"kn"
>
import
</span>
<span
class=
"nn"
>
paddle.v2
</span>
<span
class=
"kn"
>
as
</span>
<span
class=
"nn"
>
paddle
</span>
<span
class=
"n"
>
img
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"n"
>
paddle
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
layer
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
data
</span><span
class=
"p"
>
(
</span><span
class=
"n"
>
name
</span><span
class=
"o"
>
=
</span><span
class=
"s1"
>
'
img
'
</span><span
class=
"p"
>
,
</span>
<span
class=
"nb"
>
type
</span><span
class=
"o"
>
=
</span><span
class=
"n"
>
paddle
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
data_type
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
dense_vector
</span><span
class=
"p"
>
(
</span><span
class=
"mi"
>
784
</span><span
class=
"p"
>
))
</span>
<span
class=
"n"
>
hidden
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"n"
>
paddle
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
layer
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
fc
</span><span
class=
"p"
>
(
</span><span
class=
"nb"
>
input
</span><span
class=
"o"
>
=
</span><span
class=
"n"
>
img
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
size
</span><span
class=
"o"
>
=
</span><span
class=
"mi"
>
200
</span><span
class=
"p"
>
)
</span>
<span
class=
"n"
>
prediction
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"n"
>
paddle
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
layer
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
fc
</span><span
class=
"p"
>
(
</span><span
class=
"nb"
>
input
</span><span
class=
"o"
>
=
</span><span
class=
"n"
>
hidden
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
size
</span><span
class=
"o"
>
=
</span><span
class=
"mi"
>
10
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
act
</span><span
class=
"o"
>
=
</span><span
class=
"n"
>
paddle
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
activation
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
Softmax
</span><span
class=
"p"
>
())
</span>
<span
class=
"c1"
>
# use prediction instance where needed.
</span>
<span
class=
"n"
>
parameters
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"n"
>
paddle
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
parameters
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
create
</span><span
class=
"p"
>
(
</span><span
class=
"n"
>
cost
</span><span
class=
"p"
>
)
</span>
</pre></div>
</div>
<dl
class=
"class"
>
<dt>
<em
class=
"property"
>
class
</em><code
class=
"descclassname"
>
paddle.v2.layer.
</code><code
class=
"descname"
>
cross_channel_norm
</code><span
class=
"sig-paren"
>
(
</span><em>
*args
</em>
,
<em>
**kwargs
</em><span
class=
"sig-paren"
>
)
</span></dt>
<dd><p>
Normalize a layer
’
s output. This layer is necessary for ssd.
This layer applys normalize across the channels of each sample to
a conv layer
’
s output and scale the output by a group of trainable
factors which dimensions equal to the channel
’
s number.
</p>
<table
class=
"docutils field-list"
frame=
"void"
rules=
"none"
>
<col
class=
"field-name"
/>
<col
class=
"field-body"
/>
<tbody
valign=
"top"
>
<tr
class=
"field-odd field"
><th
class=
"field-name"
>
Parameters:
</th><td
class=
"field-body"
><ul
class=
"first simple"
>
<li><strong>
name
</strong>
(
<em>
basestring
</em>
)
–
The Layer Name.
</li>
<li><strong>
input
</strong>
(
<em>
paddle.v2.config_base.Layer
</em>
)
–
The input layer.
</li>
<li><strong>
param_attr
</strong>
(
<em>
paddle.v2.attr.ParameterAttribute
</em>
)
–
The Parameter Attribute|list.
</li>
</ul>
</td>
</tr>
<tr
class=
"field-even field"
><th
class=
"field-name"
>
Returns:
</th><td
class=
"field-body"
><p
class=
"first last"
>
paddle.v2.config_base.Layer
</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
</div>
<div
class=
"section"
id=
"recurrent-layers"
>
...
...
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10014_7
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57
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"
100gb
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:
37
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"
100gi
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:
43
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"
10m
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:
37
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"
1150u
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:
37
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"
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44
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5
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"
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44
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37
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5
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"
1550u
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37
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"
15mb
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53
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"
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58
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"
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53
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"
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37
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"
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51
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37
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37
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1gb
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:[
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37
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37
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53
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53
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50
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37
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37
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37
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39u
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37
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3rd
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58
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37
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4090u
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:
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51
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:
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"
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:
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:
24
,
"
\
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\
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\
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\
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\
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\
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\
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\
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\
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"
:
24
,
"
\
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\
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\
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\
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"
:
24
,
"
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\
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\
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\
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"
:
24
,
"
\
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\
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\
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\
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"
:
24
,
"
\
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"
:
24
,
"
\
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\
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"
:
24
,
"
\
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\
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"
:
24
,
"
\
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\
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"
:
24
,
"
\
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"
:
24
,
"
\
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\
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"
:
24
,
"
\
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\
u4e14
\
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\
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"
:
24
,
"
\
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"
:
24
,
"
\
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\
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\
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\
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"
:
24
,
"
\
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\
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\
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"
:
24
,
"
\
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"
:
24
,
"
\
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\
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"
:
24
,
"
\
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\
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"
:
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,
"
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:
24
,
"
\
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:
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:
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"
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:
24
,
"
\
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:
24
,
"
\
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:
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,
"
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"
:
24
,
"
\
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\
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\
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"
:
24
,
"
\
u662f
\
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\
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\
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\
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\
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"
:
24
,
"
\
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\
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\
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\
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\
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"
:
24
,
"
\
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\
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\
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\
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\
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\
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\
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\
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"
:
24
,
"
\
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\
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\
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\
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"
:
24
,
"
\
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\
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\
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\
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"
:
24
,
"
\
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\
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\
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\
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"
:
24
,
"
\
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\
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\
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\
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\
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"
:
24
,
"
\
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\
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\
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\
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\
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\
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"
:
24
,
"
\
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\
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\
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\
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\
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"
:
24
,
"
\
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\
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\
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\
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\
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"
:
24
,
"
\
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\
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\
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\
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"
:
24
,
"
\
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\
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\
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\
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"
:
24
,
"
\
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"
:
24
,
"
\
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"
:
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"
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"
:
24
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"
\
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:
24
,
"
\
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\
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\
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\
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"
:
24
,
"
\
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\
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\
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"
:
24
,
"
\
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\
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\
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\
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\
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\
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\
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"
:
24
,
"
\
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\
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\
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\
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"
:
24
,
"
\
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\
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\
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"
:
24
,
"
\
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\
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\
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"
:
24
,
"
\
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\
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"
:
24
,
"
\
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\
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"
:
24
,
"
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\
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\
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"
:
24
,
"
\
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\
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"
:
24
,
"
\
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\
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"
:
48
,
"
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\
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"
:
24
,
"
\
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"
:
24
,
"
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"
:
24
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"
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\
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"
:
24
,
"
\
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\
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"
:
24
,
"
\
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\
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\
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\
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\
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\
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\
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\
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\
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"
:
24
,
"
\
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\
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"
:
24
,
"
\
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\
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\
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"
:
24
,
"
\
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\
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\
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\
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\
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"
:
24
,
"
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\
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\
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\
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"
:
24
,
"
\
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\
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\
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"
:
24
,
"
\
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\
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"
:
24
,
"
\
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\
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\
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\
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\
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\
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\
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\
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"
:
24
,
"
\
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\
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"
:
24
,
"
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\
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"
:
24
,
"
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"
:
24
,
"
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\
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"
:
24
,
"
\
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\
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\
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\
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\
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"
:
24
,
"
\
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"
:
24
,
"
\
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\
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\
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"
:
24
,
"
\
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\
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\
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\
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"
:
24
,
"
\
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\
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"
:
24
,
"
\
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"
:
24
,
"
\
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\
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"
:
24
,
"
\
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\
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\
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"
:
24
,
"
\
ufb01xed
"
:
58
,
"
abstract
"
:[
35
,
40
],
"
api
\
u4e2d
\
u4f7f
\
u7528
"
:
24
,
"
boolean
"
:[
10
,
16
,
24
],
"
break
"
:
53
,
"
c99
\
u662f
\
u76ee
\
u524dc
\
u6700
\
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\
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\
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\
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\
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\
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\
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"
:
24
,
"
c
\
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\
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\
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\
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"
:
24
,
"
c
\
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\
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\
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\
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\
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\
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\
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\
u6807
\
u51c6
\
u7684
"
:
24
,
"
case
"
:[
10
,
16
,
25
,
26
,
33
,
34
,
35
,
37
,
41
,
43
,
49
,
53
],
"
char
"
:
55
,
"
class
"
:[
5
,
7
,
10
,
12
,
14
,
15
,
16
,
17
,
19
,
20
,
23
,
24
,
39
,
50
,
57
],
"
const
"
:
35
,
"
default
"
:[
3
,
7
,
9
,
10
,
11
,
12
,
15
,
16
,
17
,
19
,
20
,
22
,
23
,
28
,
38
,
40
,
42
,
43
,
44
,
53
,
55
,
57
,
58
],
"
export
"
:[
27
,
28
,
50
],
"
final
"
:[
11
,
17
,
26
,
27
,
35
,
55
,
57
],
"
float
"
:[
3
,
7
,
9
,
10
,
12
,
15
,
16
,
20
,
26
,
35
,
37
,
42
,
48
,
51
,
55
],
"
function
"
:[
3
,
5
,
8
,
10
,
11
,
12
,
16
,
17
,
20
,
23
,
25
,
26
,
33
,
35
,
37
,
38
,
40
,
49
,
50
,
53
,
56
,
57
,
58
],
"
golang
\
u53ef
\
u4ee5
\
u4f7f
\
u7528
"
:
24
,
"
golang
\
u7684
"
:
24
,
"
h
\
u5e76
\
u4e0d
\
u56f0
\
u96be
"
:
24
,
"
import
"
:[
3
,
5
,
9
,
10
,
16
,
23
,
26
,
28
,
33
,
37
,
43
,
48
,
49
,
50
,
51
,
53
,
55
,
57
,
58
],
"
int
"
:[
3
,
7
,
9
,
10
,
11
,
12
,
15
,
16
,
17
,
20
,
24
,
25
,
35
,
42
,
53
,
55
,
56
],
"
interface
\
u6587
\
u4ef6
\
u7684
\
u5199
\
u6cd5
\
u975e
\
u5e38
"
:
24
,
"
long
"
:[
2
,
10
,
11
,
16
,
17
,
20
,
28
,
37
,
56
,
57
],
"
new
"
:[
3
,
10
,
16
,
20
,
25
,
34
,
36
,
43
,
44
,
49
,
53
,
56
,
57
],
"
null
"
:[
10
,
35
,
40
,
55
],
"
paddle
\
u4e00
\
u4e2a
\
u52a8
\
u6001
\
u5e93
\
u53ef
\
u4ee5
\
u5728
\
u4efb
\
u4f55linux
\
u7cfb
\
u7edf
\
u4e0a
\
u8fd0
\
u884c
"
:
24
,
"
paddle
\
u5185
\
u5d4c
\
u7684python
\
u89e3
\
u91ca
\
u5668
\
u548c
\
u5916
\
u90e8
\
u4f7f
\
u7528
\
u7684python
\
u5982
\
u679c
\
u7248
\
u672c
\
u4e0d
\
u540c
"
:
24
,
"
paddle
\
u5185
\
u90e8
\
u7684
\
u7c7b
\
u4e3ac
"
:
24
,
"
paddle
\
u7684
\
u591a
\
u8bed
\
u8a00
\
u63a5
\
u53e3
\
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\
u73b0
\
u5305
\
u62ec
\
u4e00
\
u4e0b
\
u51e0
\
u4e2a
\
u65b9
\
u9762
"
:
24
,
"
paddle
\
u7684
\
u94fe
\
u63a5
\
u65b9
\
u5f0f
\
u6bd4
\
u8f83
\
u590d
\
u6742
"
:
24
,
"
paddle
\
u9700
\
u8981
\
u4e00
\
u4e2a
\
u591a
\
u8bed
\
u8a00
\
u63a5
\
u53e3
"
:
24
,
"
paddle
\
u9759
\
u6001
\
u5e93
\
u94fe
\
u63a5
\
u590d
\
u6742
"
:
24
,
"
public
"
:[
35
,
38
,
43
,
44
,
57
],
"
return
"
:[
3
,
8
,
9
,
10
,
11
,
16
,
17
,
19
,
20
,
22
,
23
,
26
,
33
,
35
,
43
,
49
,
51
,
53
,
54
,
55
,
58
],
"
short
"
:[
10
,
11
,
16
,
17
,
26
,
55
,
56
,
57
],
"
static
"
:[
10
,
43
],
"
super
"
:
35
,
"
swig
\
u652f
\
u6301
\
u7684
\
u8bed
\
u8a00
\
u6216
\
u8005
\
u89e3
\
u91ca
\
u5668
\
u6709
\
u5c40
\
u9650
"
:
24
,
"
swig
\
u66b4
\
u9732
\
u7684
\
u63a5
\
u53e3
\
u4fdd
\
u7559
\
u4e86c
"
:
24
,
"
swig
\
u751f
\
u6210
\
u7684
\
u4ee3
\
u7801
\
u4e0d
\
u80fd
\
u4fdd
\
u8bc1
\
u591a
\
u8bed
\
u8a00
\
u4ee3
\
u7801
\
u98ce
\
u683c
\
u7684
\
u4e00
\
u81f4
\
u6027
"
:
24
,
"
swig
\
u76f4
\
u63a5
\
u8bfb
\
u53d6c
"
:
24
,
"
swig
\
u9700
\
u8981
\
u5199
\
u4e00
\
u4e2ainterface
\
u6587
\
u4ef6
"
:
24
,
"
switch
"
:[
43
,
57
],
"
throw
"
:
43
,
"
true
"
:[
3
,
7
,
9
,
10
,
11
,
12
,
15
,
16
,
17
,
19
,
20
,
23
,
25
,
26
,
33
,
35
,
40
,
42
,
43
,
51
,
55
,
56
,
57
,
58
],
"
try
"
:[
12
,
25
,
37
,
49
,
55
],
"
void
"
:[
24
,
35
],
"
while
"
:[
2
,
3
,
7
,
9
,
15
,
20
,
25
,
33
,
40
,
49
,
53
,
57
,
58
],
AGE
:[
43
,
44
],
AND
:
55
,
ARE
:
55
,
AWS
:[
36
,
45
,
46
],
Abs
:
6
,
Age
:
54
,
And
:[
3
,
9
,
10
,
12
,
16
,
25
,
28
,
30
,
34
,
42
,
43
,
44
,
48
,
51
,
55
,
57
,
58
],
But
:[
3
,
10
,
11
,
16
,
17
],
EOS
:[
10
,
16
],
For
:[
2
,
3
,
8
,
9
,
10
,
12
,
16
,
20
,
23
,
25
,
26
,
27
,
28
,
33
,
35
,
37
,
38
,
39
,
40
,
42
,
48
,
50
,
51
,
53
,
57
,
58
],
Going
:
57
,
Has
:
3
,
IDs
:
53
,
Ids
:
53
,
Into
:
43
,
Its
:[
3
,
33
,
43
,
55
],
Not
:[
23
,
38
],
ONE
:
3
,
One
:[
9
,
10
,
11
,
17
,
33
,
35
,
40
,
49
,
53
,
57
,
58
],
QoS
:
44
,
THE
:
3
,
TLS
:[
23
,
43
],
That
:[
10
,
16
,
20
,
25
,
28
,
40
,
42
],
The
:[
2
,
3
,
5
,
7
,
8
,
9
,
10
,
11
,
12
,
14
,
15
,
16
,
17
,
20
,
22
,
23
,
25
,
26
,
27
,
28
,
29
,
30
,
33
,
34
,
35
,
37
,
38
,
40
,
42
,
43
,
44
,
48
,
49
,
50
,
51
,
53
,
54
,
55
,
56
,
57
,
58
],
Their
:[
3
,
10
,
16
],
Then
:[
5
,
10
,
27
,
28
,
33
,
34
,
35
,
37
,
43
,
44
,
48
,
50
,
55
,
56
,
57
],
There
:[
9
,
10
,
16
,
22
,
23
,
26
,
30
,
37
,
43
,
49
,
50
,
51
,
52
,
53
,
55
,
58
],
These
:[
38
,
42
,
50
,
56
],
USE
:
55
,
USING
:
55
,
Use
:[
3
,
23
,
25
,
35
,
37
,
40
,
41
,
43
,
55
],
Used
:[
11
,
17
],
Useful
:
3
,
Using
:[
44
,
57
],
VPS
:
43
,
WITH
:
34
,
Will
:
20
,
With
:[
3
,
10
,
11
,
16
,
17
,
26
,
49
,
56
],
Yes
:
28
,
___fc_layer_0__
:
43
,
__init__
:
35
,
__list_to_map__
:
55
,
__main__
:
51
,
__meta__
:
55
,
__name__
:
51
,
__rnn_step__
:
33
,
_error
:
49
,
_link
:[
11
,
17
],
_proj
:[
10
,
16
],
_res2_1_branch1_bn
:
51
,
_source_language_embed
:[
33
,
48
],
_target_language_embed
:[
33
,
48
],
aaaaaaaaaaaaa
:
43
,
abc
:[
10
,
16
],
abl
:[
10
,
16
,
23
,
49
,
57
],
about
:[
5
,
10
,
11
,
16
,
17
,
26
,
28
,
37
,
39
,
40
,
43
,
47
,
56
,
57
,
58
],
abov
:[
3
,
5
,
10
,
16
,
23
,
26
,
28
,
37
,
43
,
44
,
49
,
51
,
53
,
56
],
abs
:[
11
,
17
,
49
],
absolut
:[
2
,
38
],
academ
:
54
,
acceler
:
42
,
accept
:[
3
,
5
,
20
,
23
,
25
,
53
,
56
],
acceptor
:
56
,
access
:[
2
,
10
,
11
,
17
,
23
,
33
,
58
],
accessmod
:
43
,
accident
:
54
,
accord
:[
2
,
3
,
9
,
10
,
16
,
33
,
34
,
38
,
39
,
40
,
42
],
accordingli
:[
5
,
35
],
accordingto
:
56
,
accrod
:[
11
,
17
],
accuraci
:[
9
,
35
,
53
,
54
,
57
],
achiev
:[
37
,
50
],
ack
:
40
,
acl
:
57
,
aclimdb
:
57
,
aclimdb_v1
:
20
,
across
:[
10
,
16
],
act
:[
10
,
11
,
16
,
17
,
26
,
33
,
53
],
act_typ
:
53
,
action
:[
43
,
54
],
activ
:[
0
,
4
,
5
,
10
,
11
,
16
,
17
,
21
,
26
,
27
,
35
,
40
,
53
,
57
],
activi
:[
11
,
17
],
actual
:[
3
,
10
,
16
,
26
,
28
],
adadelta
:[
12
,
53
],
adagrad
:[
12
,
53
],
adam
:[
12
,
23
,
53
,
57
,
58
],
adamax
:[
12
,
53
],
adamoptim
:[
48
,
53
,
57
,
58
],
adapt
:[
9
,
12
,
26
,
57
,
58
],
add
:[
3
,
10
,
11
,
16
,
17
,
20
,
26
,
27
,
34
,
35
,
37
,
42
,
53
,
55
],
add_input
:
35
,
add_test
:
35
,
add_to
:[
10
,
16
],
add_unittest_without_exec
:
35
,
addbia
:
35
,
added
:[
3
,
9
,
35
],
adding
:
51
,
addit
:[
10
,
11
,
16
,
17
,
28
,
53
],
address
:[
28
,
37
,
40
],
addrow
:
35
,
addtion
:
38
,
addto
:
10
,
addtolay
:[
10
,
16
],
adject
:
57
,
adjust
:
26
,
admin
:
54
,
adopt
:
56
,
advanc
:[
33
,
37
,
40
],
advantag
:[
28
,
57
],
adventur
:
54
,
adverb
:
57
,
adversari
:
25
,
advic
:
37
,
affect
:[
10
,
16
],
afi
:
3
,
aforement
:
38
,
after
:[
10
,
16
,
27
,
30
,
33
,
35
,
38
,
40
,
42
,
43
,
44
,
49
,
50
,
51
,
53
,
55
,
56
,
57
,
58
],
again
:[
23
,
37
],
against
:
43
,
age
:
55
,
agg_level
:[
10
,
16
],
aggreg
:
43
,
aggregatelevel
:[
10
,
16
],
aid
:
37
,
aim
:[
57
,
58
],
aircraft
:
58
,
airplan
:
50
,
aistat
:[
10
,
16
],
alex
:[
10
,
16
,
57
],
alexnet_pass1
:
42
,
alexnet_pass2
:
42
,
algorithm
:[
10
,
12
,
16
,
26
,
33
,
48
,
50
,
57
,
58
],
alia
:[
6
,
7
,
13
,
14
,
15
],
align
:[
10
,
11
,
16
,
17
,
20
,
58
],
all
:[
0
,
3
,
7
,
9
,
10
,
12
,
15
,
16
,
22
,
23
,
26
,
28
,
33
,
34
,
35
,
37
,
38
,
39
,
40
,
42
,
43
,
44
,
48
,
49
,
51
,
53
,
54
,
55
,
56
,
57
,
58
],
alloc
:[
7
,
15
,
35
,
42
],
allow
:[
23
,
28
,
34
,
35
,
37
,
40
,
43
,
53
],
allow_only_one_model_on_one_gpu
:[
39
,
40
,
42
],
almost
:[
11
,
17
,
26
,
38
,
48
],
along
:
57
,
alreadi
:[
28
,
37
,
38
,
40
,
43
,
44
,
57
],
alreali
:[
39
,
58
],
also
:[
2
,
3
,
9
,
10
,
11
,
16
,
17
,
23
,
25
,
27
,
28
,
33
,
35
,
37
,
38
,
44
,
49
,
50
,
51
,
53
,
56
,
57
],
although
:
26
,
alwai
:[
5
,
10
,
11
,
16
,
17
,
25
,
26
,
40
,
43
,
58
],
amaz
:
50
,
amazon
:[
43
,
44
,
53
,
57
],
amazonaw
:
43
,
amazonec2fullaccess
:
43
,
amazonelasticfilesystemfullaccess
:
43
,
amazonroute53domainsfullaccess
:
43
,
amazonroute53fullaccess
:
43
,
amazons3fullaccess
:
43
,
amazonvpcfullaccess
:
43
,
ambigu
:[
25
,
56
],
amd64
:
43
,
amend
:
34
,
american
:
50
,
among
:[
43
,
57
],
amount
:[
37
,
57
],
analysi
:[
26
,
37
,
52
,
56
],
analyz
:[
53
,
57
],
andd
:
43
,
ani
:[
2
,
3
,
10
,
11
,
16
,
17
,
20
,
23
,
25
,
33
,
34
,
37
,
43
,
53
,
55
,
58
],
anim
:
54
,
annot
:
56
,
annual
:
56
,
anoth
:[
3
,
10
,
16
,
23
,
28
,
40
,
43
,
56
,
57
],
ans
:
43
,
answer
:[
26
,
43
,
56
],
anyth
:[
20
,
25
,
34
,
43
,
56
],
api
:[
16
,
20
,
23
,
27
,
35
,
37
,
43
,
47
,
49
,
53
,
55
,
57
],
apiserv
:
43
,
apivers
:[
43
,
44
],
apo
:
58
,
appar
:
58
,
appear
:
56
,
append
:[
3
,
25
,
33
,
35
,
38
,
55
],
appleclang
:
27
,
appleyard
:
37
,
appli
:[
0
,
10
,
11
,
16
,
17
,
33
,
35
,
50
,
53
],
applic
:[
28
,
37
,
43
,
44
,
57
],
appreci
:[
34
,
57
],
approach
:[
10
,
16
],
apt
:[
27
,
30
,
50
],
arbitrari
:
10
,
architectur
:[
48
,
56
,
57
,
58
],
architecur
:
57
,
archiv
:
24
,
arg
:[
3
,
8
,
9
,
10
,
11
,
12
,
16
,
17
,
20
,
26
,
28
,
39
,
49
,
50
,
51
,
53
,
55
,
56
,
57
],
arg_nam
:[
10
,
16
],
argu
:
56
,
argument
:[
3
,
5
,
8
,
10
,
16
,
20
,
33
,
35
,
40
,
41
,
48
,
49
,
50
,
51
,
55
,
56
,
57
,
58
],
argv
:
51
,
arn
:
43
,
around
:[
3
,
10
,
16
,
43
],
arrai
:[
5
,
10
,
16
,
20
,
22
,
25
,
26
,
51
],
art
:[
26
,
56
],
articl
:[
38
,
44
],
artifact
:
43
,
artifici
:
49
,
artist
:
54
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present
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price
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primari
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primarili
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principl
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print
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printer
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printstatu
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prite
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privileg
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probability_of_label_0
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probability_of_label_1
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problem
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12
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proc
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proc_from_raw_data
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proce
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proceed
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process
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process_test
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process_train
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processor
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produc
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productgraph
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profil
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proflier
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programm
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progress
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proj
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project
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prompt
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prone
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propos
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protect
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proto
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provis
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provod
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prune
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pserver_num_thread
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pserverstart_pserv
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pull
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punctuat
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purpos
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pvc
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pwd
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py_paddl
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pydataprovid
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pydataprovider2
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pydataproviderwrapp
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pyramid_height
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python
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qualifi
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qualiti
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quick_start_data
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quickli
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quickstart
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quit
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quot
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ramnath
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rather
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read
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read_from_realistic_imag
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read_from_rng
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read_mnist_imag
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read_ranking_model_data
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reader
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reader_creator_bool
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reader_creator_random_imag
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reader_creator_random_image_and_label
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reader_dict
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readi
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readm
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readonesamplefromfil
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readwritemani
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realist
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rebas
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recal
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receiv
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recent
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reciev
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recommand
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recommend
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recommonmark
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recompil
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record
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recordio
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recov
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rectangular
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recurr
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recurrent_group
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recurrent_lay
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recurrentgroup
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recurrentlay
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recurs
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recv
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referenc
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regard
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regardless
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regex
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regist
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register_gpu_profil
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register_lay
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register_timer_info
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regress
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regular
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relationship
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releas
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relev
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reli
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reluactiv
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remain
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rememb
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remot
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remoteparameterupdat
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remov
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renam
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reorgan
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repeat
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replac
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repo
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report
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repositori
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repres
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represent
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reproduc
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request
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requir
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requrest
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res5_3_branch2c_bn
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res5_3_branch2c_conv
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res
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research
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resembl
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reserv
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reserveoutput
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reshap
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resnet
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resnet_101
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resnet_152
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resnet_50
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resolv
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resourc
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respect
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restart
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restartpolici
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restrict
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resu
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result
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result_fil
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retran
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retriev
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return_seq
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reus
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reveal
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revers
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review
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reviews_electronics_5
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revis
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rewrit
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rgb
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rgen
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rmsprop
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rmspropoptim
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rnn
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rnn_bias_attr
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rnn_layer_attr
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rnn_out
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rnn_use_batch
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rnnlm
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roman
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root_dir
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rotat
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routin
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routledg
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row_id
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scalingproject
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screen
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seaplane_s_000978
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sed
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selector
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with_gpucompil
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with_profil
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with_profilercompil
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with_pythoncompil
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with_rdmacompil
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with_style_checkcompil
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with_swig_pycompil
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with_testingcompil
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with_tim
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with_timercompil
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within
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without
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wmt14
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wmt14_data
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wmt14_model
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wmt
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woboq
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won
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wonder
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word
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word_dict
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53
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word_dim
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word_id
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word_slot
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word_vector
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word_vector_dim
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words_freq_sort
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work
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worker
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workercount
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workspac
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worri
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wors
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would
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,
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wouldn
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wrap
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wrapper
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writ
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write
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writelin
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writer
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written
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wrong
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wsize
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wsj
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www
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28
,
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,
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x64
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27
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xarg
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xgbe0
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40
,
xgbe1
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40
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xiaojun
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57
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xrang
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25
,
26
,
35
],
xxbow
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57
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xxx
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23
,
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,
51
,
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],
xxxxxxxxx
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43
,
xxxxxxxxxx
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43
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xxxxxxxxxxxxx
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43
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xxxxxxxxxxxxxxxxxxx
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43
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xzf
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27
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y_i
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y_predict
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26
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yaml
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yann
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20
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year
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yeild
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50
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yield
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,
23
,
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,
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,
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,
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,
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,
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,
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you
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,
3
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5
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7
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,
11
,
12
,
15
,
16
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17
,
26
,
27
,
28
,
30
,
33
,
34
,
35
,
37
,
38
,
40
,
42
,
43
,
48
,
49
,
50
,
51
,
53
,
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,
56
,
57
,
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your
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3
,
10
,
16
,
23
,
27
,
28
,
35
,
37
,
38
,
42
,
43
,
53
,
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your_access_key_id
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43
,
your_secrete_access_kei
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43
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yum
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27
,
yuyang18
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,
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,
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,
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],
yyi
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28
,
zachari
:
57
,
zeng
:
57
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zero
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3
,
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,
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,
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,
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,
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,
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,
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,
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,
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,
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zhidao
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,
zhou
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zip
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zxvf
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zzz
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},
titles
:[
"
ABOUT
"
,
"
API
"
,
"
Introduction
"
,
"
PyDataProvider2
"
,
"
API
"
,
"
Python Prediction
"
,
"
Activations
"
,
"
Parameter Attributes
"
,
"
DataSources
"
,
"
Evaluators
"
,
"
Layers
"
,
"
Networks
"
,
"
Optimizers
"
,
"
Poolings
"
,
"
Activation
"
,
"
Parameter Attribute
"
,
"
Layers
"
,
"
Networks
"
,
"
Optimizer
"
,
"
Pooling
"
,
"
Datasets
"
,
"
Model Configuration
"
,
"
Training and Inference
"
,
"
PaddlePaddle Design Doc
"
,
"
Paddle
\
u591a
\
u8bed
\
u8a00
\
u63a5
\
u53e3
\
u5b9e
\
u73b0
"
,
"
Python Data Reader Design Doc
"
,
"
Simple Linear Regression
"
,
"
Installing from Sources
"
,
"
PaddlePaddle in Docker Containers
"
,
"
Install and Build
"
,
"
Debian Package installation guide
"
,
"
GET STARTED
"
,
"
RNN Models
"
,
"
RNN Configuration
"
,
"
Contribute Code
"
,
"
Write New Layers
"
,
"
HOW TO
"
,
"
Tune GPU Performance
"
,
"
Run Distributed Training
"
,
"
Argument Outline
"
,
"
Detail Description
"
,
"
Set Command-line Parameters
"
,
"
Use Case
"
,
"
Distributed PaddlePaddle Training on AWS with Kubernetes
"
,
"
Paddle On Kubernetes
"
,
"
<no title>
"
,
"
<no title>
"
,
"
PaddlePaddle Documentation
"
,
"
Chinese Word Embedding Model Tutorial
"
,
"
Generative Adversarial Networks (GAN)
"
,
"
Image Classification Tutorial
"
,
"
Model Zoo - ImageNet
"
,
"
TUTORIALS
"
,
"
Quick Start
"
,
"
MovieLens Dataset
"
,
"
Regression MovieLens Ratting
"
,
"
Semantic Role labeling Tutorial
"
,
"
Sentiment Analysis Tutorial
"
,
"
Text generation Tutorial
"
],
titleterms
:{
"
\
u4e0d
\
u4f7f
\
u7528
"
:
24
,
"
\
u4e0d
\
u4f7f
\
u7528swig
\
u8fd9
\
u79cd
\
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u7801
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\
u6210
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"
:
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,
"
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"
:
24
,
"
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"
:
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,
"
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"
:
24
,
"
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"
:
24
,
"
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:
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:
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"
:
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"
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"
:
24
,
"
\
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"
:
24
,
"
\
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"
:
24
,
"
\
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\
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\
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"
:
24
,
"
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"
:
24
,
"
\
u800c
\
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\
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\
u5199
\
u591a
\
u8bed
\
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\
u7ed1
\
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"
:
24
,
"
\
u80cc
\
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"
:
24
,
"
\
u8fd9
\
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\
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\
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\
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\
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\
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\
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\
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\
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\
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\
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\
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\
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\
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\
u51fd
\
u6570
"
:
24
,
"
case
"
:
42
,
"
class
"
:
35
,
"
function
"
:
48
,
"
new
"
:
35
,
"
paddle
\
u52a8
\
u6001
\
u5e93
\
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"
:
24
,
"
paddle
\
u591a
\
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\
u8a00
\
u63a5
\
u53e3
\
u5b9e
\
u73b0
"
:
24
,
"
return
"
:
25
,
AWS
:
43
,
Abs
:
14
,
DNS
:
43
,
EFS
:
43
,
For
:
44
,
KMS
:
43
,
Use
:[
42
,
44
],
Using
:[
28
,
34
],
about
:
0
,
absactiv
:
6
,
access
:
43
,
account
:
43
,
activ
:[
6
,
14
],
adadelta
:
18
,
adadeltaoptim
:
12
,
adagrad
:
18
,
adagradoptim
:
12
,
adam
:
18
,
adamax
:
18
,
adamaxoptim
:
12
,
adamoptim
:
12
,
add
:
43
,
address
:
43
,
addto
:
16
,
addto_lay
:
10
,
adversari
:
49
,
aggreg
:[
10
,
16
],
algorithm
:
53
,
analysi
:
57
,
api
:[
1
,
4
],
appendix
:
53
,
applic
:
4
,
approach
:
37
,
architectur
:[
33
,
53
],
argument
:[
25
,
39
,
42
,
53
],
asset
:
43
,
associ
:
43
,
async
:
40
,
attent
:
33
,
attribut
:[
7
,
15
],
auc_evalu
:
9
,
avg
:
19
,
avgpool
:
13
,
avx
:
28
,
aws
:
43
,
background
:
26
,
base
:[
9
,
10
],
baseactiv
:
6
,
basepool
:
19
,
basepoolingtyp
:
13
,
basesgdoptim
:
12
,
batch
:
25
,
batch_norm
:
16
,
batch_norm_lay
:
10
,
batch_siz
:
25
,
beam_search
:[
10
,
16
],
between
:
23
,
bidirect
:
57
,
bidirectional_lstm
:[
11
,
17
],
bilinear_interp
:
16
,
bilinear_interp_lay
:
10
,
bleu
:
58
,
block_expand
:
16
,
block_expand_lay
:
10
,
book
:
28
,
brelu
:
14
,
breluactiv
:
6
,
bucket
:
43
,
build
:[
27
,
29
,
44
],
built
:
37
,
cach
:
3
,
cento
:
27
,
check
:[
10
,
16
,
35
,
38
],
chines
:
48
,
choos
:
43
,
chunk_evalu
:
9
,
cifar
:
20
,
classif
:[
9
,
50
],
classification_error_evalu
:
9
,
classification_error_printer_evalu
:
9
,
clone
:
34
,
cloudform
:
43
,
cluster
:[
38
,
42
,
43
],
code
:
34
,
column_sum_evalu
:
9
,
command
:[
41
,
42
,
53
,
58
],
commit
:[
34
,
44
],
common
:
40
,
commun
:
40
,
compos
:
25
,
concat
:
16
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\ No newline at end of file
Search
.
setIndex
({
docnames
:[
"
about/index_en
"
,
"
api/index_en
"
,
"
api/v1/data_provider/dataprovider_en
"
,
"
api/v1/data_provider/pydataprovider2_en
"
,
"
api/v1/index_en
"
,
"
api/v1/predict/swig_py_paddle_en
"
,
"
api/v1/trainer_config_helpers/activations
"
,
"
api/v1/trainer_config_helpers/attrs
"
,
"
api/v1/trainer_config_helpers/data_sources
"
,
"
api/v1/trainer_config_helpers/evaluators
"
,
"
api/v1/trainer_config_helpers/layers
"
,
"
api/v1/trainer_config_helpers/networks
"
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:
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:
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:
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:
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abstract
"
:[
35
,
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api
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:
24
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boolean
"
:[
10
,
16
,
24
],
"
break
"
:
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"
c99
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:
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case
"
:[
10
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16
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25
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26
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33
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34
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35
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37
,
41
,
43
,
49
,
53
],
"
char
"
:
55
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"
class
"
:[
5
,
7
,
10
,
12
,
14
,
15
,
16
,
17
,
19
,
20
,
23
,
24
,
39
,
50
,
57
],
"
const
"
:
35
,
"
default
"
:[
3
,
7
,
9
,
10
,
11
,
12
,
15
,
16
,
17
,
19
,
20
,
22
,
23
,
28
,
38
,
40
,
42
,
43
,
44
,
53
,
55
,
57
,
58
],
"
export
"
:[
27
,
28
,
50
],
"
final
"
:[
11
,
17
,
26
,
27
,
35
,
55
,
57
],
"
float
"
:[
3
,
7
,
9
,
10
,
12
,
15
,
16
,
20
,
26
,
35
,
37
,
42
,
48
,
51
,
55
],
"
function
"
:[
3
,
5
,
8
,
10
,
11
,
12
,
16
,
17
,
20
,
23
,
25
,
26
,
33
,
35
,
37
,
38
,
40
,
49
,
50
,
53
,
56
,
57
,
58
],
"
golang
\
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"
:
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golang
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:
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:
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import
"
:[
3
,
5
,
9
,
10
,
16
,
23
,
26
,
28
,
33
,
37
,
43
,
48
,
49
,
50
,
51
,
53
,
55
,
57
,
58
],
"
int
"
:[
3
,
7
,
9
,
10
,
11
,
12
,
15
,
16
,
17
,
20
,
24
,
25
,
35
,
42
,
53
,
55
,
56
],
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interface
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\
u6cd5
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u5e38
"
:
24
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"
long
"
:[
2
,
10
,
11
,
16
,
17
,
20
,
28
,
37
,
56
,
57
],
"
new
"
:[
3
,
10
,
16
,
20
,
25
,
34
,
36
,
43
,
44
,
49
,
53
,
56
,
57
],
"
null
"
:[
10
,
35
,
40
,
55
],
"
paddle
\
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\
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paddle
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paddle
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:
24
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"
public
"
:[
35
,
38
,
43
,
44
,
57
],
"
return
"
:[
3
,
8
,
9
,
10
,
11
,
16
,
17
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19
,
20
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22
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23
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26
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33
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35
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43
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49
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51
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53
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54
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55
,
58
],
"
short
"
:[
10
,
11
,
16
,
17
,
26
,
55
,
56
,
57
],
"
static
"
:[
10
,
43
],
"
super
"
:
35
,
"
swig
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:
24
,
"
swig
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:
24
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swig
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:
24
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swig
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:
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swig
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"
:
24
,
"
switch
"
:[
43
,
57
],
"
throw
"
:
43
,
"
true
"
:[
3
,
7
,
9
,
10
,
11
,
12
,
15
,
16
,
17
,
19
,
20
,
23
,
25
,
26
,
33
,
35
,
40
,
42
,
43
,
51
,
55
,
56
,
57
,
58
],
"
try
"
:[
12
,
25
,
37
,
49
,
55
],
"
void
"
:[
24
,
35
],
"
while
"
:[
2
,
3
,
7
,
9
,
15
,
20
,
25
,
33
,
40
,
49
,
53
,
57
,
58
],
AGE
:[
43
,
44
],
AND
:
55
,
ARE
:
55
,
AWS
:[
36
,
45
,
46
],
Abs
:
6
,
Age
:
54
,
And
:[
3
,
9
,
10
,
12
,
16
,
25
,
28
,
30
,
34
,
42
,
43
,
44
,
48
,
51
,
55
,
57
,
58
],
But
:[
3
,
10
,
11
,
16
,
17
],
EOS
:[
10
,
16
],
For
:[
2
,
3
,
8
,
9
,
10
,
12
,
16
,
20
,
23
,
25
,
26
,
27
,
28
,
33
,
35
,
37
,
38
,
39
,
40
,
42
,
48
,
50
,
51
,
53
,
57
,
58
],
Going
:
57
,
Has
:
3
,
IDs
:
53
,
Ids
:
53
,
Into
:
43
,
Its
:[
3
,
33
,
43
,
55
],
Not
:[
23
,
38
],
ONE
:
3
,
One
:[
9
,
10
,
11
,
17
,
33
,
35
,
40
,
49
,
53
,
57
,
58
],
QoS
:
44
,
THE
:
3
,
TLS
:[
23
,
43
],
That
:[
10
,
16
,
20
,
25
,
28
,
40
,
42
],
The
:[
2
,
3
,
5
,
7
,
8
,
9
,
10
,
11
,
12
,
14
,
15
,
16
,
17
,
20
,
22
,
23
,
25
,
26
,
27
,
28
,
29
,
30
,
33
,
34
,
35
,
37
,
38
,
40
,
42
,
43
,
44
,
48
,
49
,
50
,
51
,
53
,
54
,
55
,
56
,
57
,
58
],
Their
:[
3
,
10
,
16
],
Then
:[
5
,
10
,
27
,
28
,
33
,
34
,
35
,
37
,
43
,
44
,
48
,
50
,
55
,
56
,
57
],
There
:[
9
,
10
,
16
,
22
,
23
,
26
,
30
,
37
,
43
,
49
,
50
,
51
,
52
,
53
,
55
,
58
],
These
:[
38
,
42
,
50
,
56
],
USE
:
55
,
USING
:
55
,
Use
:[
3
,
23
,
25
,
35
,
37
,
40
,
41
,
43
,
55
],
Used
:[
11
,
17
],
Useful
:
3
,
Using
:[
44
,
57
],
VPS
:
43
,
WITH
:
34
,
Will
:
20
,
With
:[
3
,
10
,
11
,
16
,
17
,
26
,
49
,
56
],
Yes
:
28
,
___fc_layer_0__
:
43
,
__init__
:
35
,
__list_to_map__
:
55
,
__main__
:
51
,
__meta__
:
55
,
__name__
:
51
,
__rnn_step__
:
33
,
_error
:
49
,
_link
:[
11
,
17
],
_proj
:[
10
,
16
],
_res2_1_branch1_bn
:
51
,
_source_language_embed
:[
33
,
48
],
_target_language_embed
:[
33
,
48
],
aaaaaaaaaaaaa
:
43
,
abc
:[
10
,
16
],
abl
:[
10
,
16
,
23
,
49
,
57
],
about
:[
5
,
10
,
11
,
16
,
17
,
26
,
28
,
37
,
39
,
40
,
43
,
47
,
56
,
57
,
58
],
abov
:[
3
,
5
,
10
,
16
,
23
,
26
,
28
,
37
,
43
,
44
,
49
,
51
,
53
,
56
],
abs
:[
11
,
17
,
49
],
absolut
:[
2
,
38
],
academ
:
54
,
acceler
:
42
,
accept
:[
3
,
5
,
20
,
23
,
25
,
53
,
56
],
acceptor
:
56
,
access
:[
2
,
10
,
11
,
17
,
23
,
33
,
58
],
accessmod
:
43
,
accident
:
54
,
accord
:[
2
,
3
,
9
,
10
,
16
,
33
,
34
,
38
,
39
,
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novel
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nproc
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omit
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openbla
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opinion
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opt
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optim
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out_dir
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out_mem
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outlin
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outperform
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output
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overcom
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overhead
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paddle_n
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paddle_output
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paddle_port
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paddle_ports_num
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paddle_ports_num_for_spars
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paddle_pserver2
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paddle_root
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paddle_source_root
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paddle_train
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paddledev
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paddlepaddl
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paddlepadl
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paddlpaddl
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paddpepaddl
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page
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pair
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palmer
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paper
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paraconvert
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predicate_dict
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predicate_dict_fil
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predict
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primarili
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python
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rac
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randomnumberse
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read_from_rng
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read_mnist_imag
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read_ranking_model_data
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reader
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reader_creator_random_imag
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reader_creator_random_image_and_label
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recal
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receiv
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recent
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reciev
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recommand
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recommend
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recommonmark
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recompil
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record
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recordio
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rectangular
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recurr
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recurrent_lay
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recurrentgroup
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recurrentlay
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recurs
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recv
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referenc
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regard
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regardless
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regex
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register_gpu_profil
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register_lay
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register_timer_info
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releas
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reli
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reluactiv
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remain
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rememb
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titles
:[
"
ABOUT
"
,
"
API
"
,
"
Introduction
"
,
"
PyDataProvider2
"
,
"
API
"
,
"
Python Prediction
"
,
"
Activations
"
,
"
Parameter Attributes
"
,
"
DataSources
"
,
"
Evaluators
"
,
"
Layers
"
,
"
Networks
"
,
"
Optimizers
"
,
"
Poolings
"
,
"
Activation
"
,
"
Parameter Attribute
"
,
"
Layers
"
,
"
Networks
"
,
"
Optimizer
"
,
"
Pooling
"
,
"
Datasets
"
,
"
Model Configuration
"
,
"
Training and Inference
"
,
"
PaddlePaddle Design Doc
"
,
"
Paddle
\
u591a
\
u8bed
\
u8a00
\
u63a5
\
u53e3
\
u5b9e
\
u73b0
"
,
"
Python Data Reader Design Doc
"
,
"
Simple Linear Regression
"
,
"
Installing from Sources
"
,
"
PaddlePaddle in Docker Containers
"
,
"
Install and Build
"
,
"
Debian Package installation guide
"
,
"
GET STARTED
"
,
"
RNN Models
"
,
"
RNN Configuration
"
,
"
Contribute Code
"
,
"
Write New Layers
"
,
"
HOW TO
"
,
"
Tune GPU Performance
"
,
"
Run Distributed Training
"
,
"
Argument Outline
"
,
"
Detail Description
"
,
"
Set Command-line Parameters
"
,
"
Use Case
"
,
"
Distributed PaddlePaddle Training on AWS with Kubernetes
"
,
"
Paddle On Kubernetes
"
,
"
<no title>
"
,
"
<no title>
"
,
"
PaddlePaddle Documentation
"
,
"
Chinese Word Embedding Model Tutorial
"
,
"
Generative Adversarial Networks (GAN)
"
,
"
Image Classification Tutorial
"
,
"
Model Zoo - ImageNet
"
,
"
TUTORIALS
"
,
"
Quick Start
"
,
"
MovieLens Dataset
"
,
"
Regression MovieLens Ratting
"
,
"
Semantic Role labeling Tutorial
"
,
"
Sentiment Analysis Tutorial
"
,
"
Text generation Tutorial
"
],
titleterms
:{
"
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:
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"
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"
:
42
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"
class
"
:
35
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"
function
"
:
48
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"
new
"
:
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paddle
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:
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"
:
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,
"
return
"
:
25
,
AWS
:
43
,
Abs
:
14
,
DNS
:
43
,
EFS
:
43
,
For
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44
,
KMS
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43
,
Use
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Using
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about
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absactiv
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6
,
access
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43
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account
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43
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activ
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adadelta
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18
,
adadeltaoptim
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12
,
adagrad
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18
,
adagradoptim
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12
,
adam
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18
,
adamax
:
18
,
adamaxoptim
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12
,
adamoptim
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12
,
add
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43
,
address
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43
,
addto
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16
,
addto_lay
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10
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adversari
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49
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aggreg
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algorithm
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53
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analysi
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57
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api
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4
],
appendix
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53
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applic
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4
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approach
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37
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architectur
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33
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53
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argument
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25
,
39
,
42
,
53
],
asset
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43
,
associ
:
43
,
async
:
40
,
attent
:
33
,
attribut
:[
7
,
15
],
auc_evalu
:
9
,
avg
:
19
,
avgpool
:
13
,
avx
:
28
,
aws
:
43
,
background
:
26
,
base
:[
9
,
10
],
baseactiv
:
6
,
basepool
:
19
,
basepoolingtyp
:
13
,
basesgdoptim
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12
,
batch
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25
,
batch_norm
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16
,
batch_norm_lay
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10
,
batch_siz
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25
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beam_search
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between
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23
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bidirect
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57
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bidirectional_lstm
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,
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],
bilinear_interp
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16
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bilinear_interp_lay
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10
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bleu
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58
,
block_expand
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16
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block_expand_lay
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10
,
book
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28
,
brelu
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14
,
breluactiv
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6
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bucket
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43
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build
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27
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29
,
44
],
built
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37
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cach
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3
,
cento
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27
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check
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10
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,
35
,
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],
chines
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48
,
choos
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43
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chunk_evalu
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9
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cifar
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20
,
classif
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],
classification_error_evalu
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9
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classification_error_printer_evalu
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9
,
clone
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34
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cloudform
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43
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cluster
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38
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42
,
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code
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34
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column_sum_evalu
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command
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42
,
53
,
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commit
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34
,
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common
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40
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commun
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40
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compos
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concat
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16
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concat_lay
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concept
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config
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configur
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33
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36
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38
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43
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,
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conll05
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connect
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contain
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content
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37
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contribut
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conv
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conv_project
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conv_shift
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conv_shift_lay
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10
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convolut
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50
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core
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cos_sim
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cost
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16
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cpu
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creat
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25
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34
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43
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creator
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25
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credenti
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43
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credit
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0
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16
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crf_decod
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crf_decoding_lay
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10
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crf_layer
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10
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cross_channel_norm
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cross_entropi
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10
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cross_entropy_cost
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,
cross_entropy_with_selfnorm
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cross_entropy_with_selfnorm_cost
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16
,
ctc
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16
,
ctc_error_evalu
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9
,
ctc_layer
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10
,
cudnnavg
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19
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cudnnmax
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custom
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dat
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data
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data_lay
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datafeed
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dataprovid
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dataset
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datasourc
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datatyp
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date
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debian
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decayedadagrad
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delet
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dictionari
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differ
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directori
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doc
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docker
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document
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down
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download
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dropout_lay
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ec2
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elast
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embed
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embedding_lay
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entri
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10
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equat
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35
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evalu
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26
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evalutaion
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event
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exampl
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exercis
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exp
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expactiv
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expand
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expand_lay
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extern
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extract
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fc_layer
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featur
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field
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file
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find
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first_seq
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fork
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format
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from
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gan
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gate
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gener
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get
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github
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gpu
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group
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gru
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hand
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handler
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hook
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how
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36
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37
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huber_cost
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iam
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ident
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identity_project
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identityactiv
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imag
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16
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17
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28
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44
,
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imagenet
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imdb
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img_cmrnorm_lay
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img_conv_bn_pool
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img_pool
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img_pool_lay
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imikolov
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implement
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,
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infer
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info
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ingredi
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init_hook
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initi
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30
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,
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integr
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kei
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kill
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modifi
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momentum
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network
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neuralnetwork
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nlp
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non
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norm
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nvprof
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nvvp
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object
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onli
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\ No newline at end of file
develop/doc_cn/_sources/api/v2/config/layer.rst.txt
浏览文件 @
da0e795f
...
...
@@ -109,6 +109,12 @@ sum_to_one_norm
:members: sum_to_one_norm
:noindex:
cross_channel_norm
------------------
.. automodule:: paddle.v2.layer
:members: cross_channel_norm
:noindex:
Recurrent Layers
================
...
...
develop/doc_cn/api/v2/config/layer.html
浏览文件 @
da0e795f
...
...
@@ -210,6 +210,7 @@
<li><a
class=
"reference internal"
href=
"#img-cmrnorm"
>
img_cmrnorm
</a></li>
<li><a
class=
"reference internal"
href=
"#batch-norm"
>
batch_norm
</a></li>
<li><a
class=
"reference internal"
href=
"#sum-to-one-norm"
>
sum_to_one_norm
</a></li>
<li><a
class=
"reference internal"
href=
"#cross-channel-norm"
>
cross_channel_norm
</a></li>
</ul>
</li>
<li><a
class=
"reference internal"
href=
"#recurrent-layers"
>
Recurrent Layers
</a><ul>
...
...
@@ -1252,6 +1253,49 @@ and <span class="math">\(out\)</span> is a (batchSize x dataDim) output vector.<
</table>
</dd></dl>
</div>
<div
class=
"section"
id=
"cross-channel-norm"
>
<h3>
cross_channel_norm
<a
class=
"headerlink"
href=
"#cross-channel-norm"
title=
"永久链接至标题"
>
¶
</a></h3>
<p><cite>
paddle.v2.layer
</cite>
is a part of model config packages in paddle.v2. In API v2,
we want to make Paddle a plain Python package. The model config package defined
the way how to configure a neural network topology in Paddle Python code.
</p>
<p>
The primary usage shows below.
</p>
<div
class=
"highlight-python"
><div
class=
"highlight"
><pre><span></span><span
class=
"kn"
>
import
</span>
<span
class=
"nn"
>
paddle.v2
</span>
<span
class=
"kn"
>
as
</span>
<span
class=
"nn"
>
paddle
</span>
<span
class=
"n"
>
img
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"n"
>
paddle
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
layer
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
data
</span><span
class=
"p"
>
(
</span><span
class=
"n"
>
name
</span><span
class=
"o"
>
=
</span><span
class=
"s1"
>
'
img
'
</span><span
class=
"p"
>
,
</span>
<span
class=
"nb"
>
type
</span><span
class=
"o"
>
=
</span><span
class=
"n"
>
paddle
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
data_type
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
dense_vector
</span><span
class=
"p"
>
(
</span><span
class=
"mi"
>
784
</span><span
class=
"p"
>
))
</span>
<span
class=
"n"
>
hidden
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"n"
>
paddle
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
layer
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
fc
</span><span
class=
"p"
>
(
</span><span
class=
"nb"
>
input
</span><span
class=
"o"
>
=
</span><span
class=
"n"
>
img
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
size
</span><span
class=
"o"
>
=
</span><span
class=
"mi"
>
200
</span><span
class=
"p"
>
)
</span>
<span
class=
"n"
>
prediction
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"n"
>
paddle
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
layer
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
fc
</span><span
class=
"p"
>
(
</span><span
class=
"nb"
>
input
</span><span
class=
"o"
>
=
</span><span
class=
"n"
>
hidden
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
size
</span><span
class=
"o"
>
=
</span><span
class=
"mi"
>
10
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
act
</span><span
class=
"o"
>
=
</span><span
class=
"n"
>
paddle
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
activation
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
Softmax
</span><span
class=
"p"
>
())
</span>
<span
class=
"c1"
>
# use prediction instance where needed.
</span>
<span
class=
"n"
>
parameters
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"n"
>
paddle
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
parameters
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
create
</span><span
class=
"p"
>
(
</span><span
class=
"n"
>
cost
</span><span
class=
"p"
>
)
</span>
</pre></div>
</div>
<dl
class=
"class"
>
<dt>
<em
class=
"property"
>
class
</em><code
class=
"descclassname"
>
paddle.v2.layer.
</code><code
class=
"descname"
>
cross_channel_norm
</code><span
class=
"sig-paren"
>
(
</span><em>
*args
</em>
,
<em>
**kwargs
</em><span
class=
"sig-paren"
>
)
</span></dt>
<dd><p>
Normalize a layer
’
s output. This layer is necessary for ssd.
This layer applys normalize across the channels of each sample to
a conv layer
’
s output and scale the output by a group of trainable
factors which dimensions equal to the channel
’
s number.
</p>
<table
class=
"docutils field-list"
frame=
"void"
rules=
"none"
>
<col
class=
"field-name"
/>
<col
class=
"field-body"
/>
<tbody
valign=
"top"
>
<tr
class=
"field-odd field"
><th
class=
"field-name"
>
参数:
</th><td
class=
"field-body"
><ul
class=
"first simple"
>
<li><strong>
name
</strong>
(
<em>
basestring
</em>
)
–
The Layer Name.
</li>
<li><strong>
input
</strong>
(
<em>
paddle.v2.config_base.Layer
</em>
)
–
The input layer.
</li>
<li><strong>
param_attr
</strong>
(
<em>
paddle.v2.attr.ParameterAttribute
</em>
)
–
The Parameter Attribute|list.
</li>
</ul>
</td>
</tr>
<tr
class=
"field-even field"
><th
class=
"field-name"
>
返回:
</th><td
class=
"field-body"
><p
class=
"first last"
>
paddle.v2.config_base.Layer
</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
</div>
<div
class=
"section"
id=
"recurrent-layers"
>
...
...
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