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体验新版 GitCode,发现更多精彩内容 >>
提交
d2ff3e49
编写于
11月 15, 2016
作者:
L
LCY-Seso
提交者:
GitHub
11月 15, 2016
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Merge pull request #458 from luotao1/group
add layer check for recurrent_group
上级
c2aa8980
8c594376
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
22 addition
and
4 deletion
+22
-4
python/paddle/trainer_config_helpers/layers.py
python/paddle/trainer_config_helpers/layers.py
+22
-4
未找到文件。
python/paddle/trainer_config_helpers/layers.py
浏览文件 @
d2ff3e49
...
...
@@ -494,8 +494,7 @@ def scaling_projection(input, param_attr=None):
:return: A ScalingProjection object
:rtype: ScalingProjection
"""
proj
=
ScalingProjection
(
input_layer_name
=
input
.
name
,
**
param_attr
.
attr
)
proj
=
ScalingProjection
(
input_layer_name
=
input
.
name
,
**
param_attr
.
attr
)
proj
.
origin
=
input
return
proj
...
...
@@ -2783,7 +2782,12 @@ class SubsequenceInput(object):
@
wrap_name_default
(
"recurrent_group"
)
def
recurrent_group
(
step
,
input
,
reverse
=
False
,
name
=
None
,
targetInlink
=
None
):
def
recurrent_group
(
step
,
input
,
reverse
=
False
,
name
=
None
,
targetInlink
=
None
,
is_generating
=
False
):
"""
Recurrent layer group is an extremely flexible recurrent unit in
PaddlePaddle. As long as the user defines the calculation done within a
...
...
@@ -2848,6 +2852,12 @@ def recurrent_group(step, input, reverse=False, name=None, targetInlink=None):
:type targetInlink: LayerOutput|SubsequenceInput
:param is_generating: If is generating, none of input type should be LayerOutput;
else, for training or testing, one of the input type must
be LayerOutput.
: type is_generating: bool
:return: LayerOutput object.
:rtype: LayerOutput
"""
...
...
@@ -2895,6 +2905,7 @@ def recurrent_group(step, input, reverse=False, name=None, targetInlink=None):
seq_reversed
=
reverse
,
target_inlinkname
=
targetInlinkName
)
in_args
=
[]
has_LayerOutput
=
True
for
each_input
in
input
:
assert
is_single_input
(
each_input
)
if
isinstance
(
each_input
,
LayerOutput
):
...
...
@@ -2902,6 +2913,7 @@ def recurrent_group(step, input, reverse=False, name=None, targetInlink=None):
elif
isinstance
(
each_input
,
SubsequenceInput
):
in_args
.
append
(
each_input
.
input
)
else
:
has_LayerOutput
=
False
mem_name
=
"__%s_memory__"
%
each_input
.
input
.
name
mem
=
memory
(
name
=
mem_name
,
...
...
@@ -2915,6 +2927,8 @@ def recurrent_group(step, input, reverse=False, name=None, targetInlink=None):
mix
+=
identity_projection
(
mem
)
in_args
.
append
(
mem
)
assert
(
is_generating
!=
has_LayerOutput
)
layer_outs
=
step
(
*
in_args
)
if
isinstance
(
layer_outs
,
LayerOutput
):
...
...
@@ -3206,7 +3220,11 @@ def beam_search(step,
return
predict
tmp
=
recurrent_group
(
step
=
__real_step__
,
input
=
real_input
,
reverse
=
False
,
name
=
name
)
step
=
__real_step__
,
input
=
real_input
,
reverse
=
False
,
name
=
name
,
is_generating
=
True
)
return
tmp
...
...
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