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83ce2dce
编写于
8月 05, 2017
作者:
C
caoying03
浏览文件
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电子邮件补丁
差异文件
split sorting into another layer. fix config helper.
上级
00b6d266
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
51 addition
and
67 deletion
+51
-67
proto/ModelConfig.proto
proto/ModelConfig.proto
+0
-2
python/paddle/trainer/config_parser.py
python/paddle/trainer/config_parser.py
+17
-11
python/paddle/trainer_config_helpers/layers.py
python/paddle/trainer_config_helpers/layers.py
+19
-15
python/paddle/trainer_config_helpers/tests/configs/protostr/test_seq_select_layers.protostr
...rs/tests/configs/protostr/test_seq_select_layers.protostr
+10
-36
python/paddle/trainer_config_helpers/tests/configs/test_seq_select_layers.py
...er_config_helpers/tests/configs/test_seq_select_layers.py
+5
-3
未找到文件。
proto/ModelConfig.proto
浏览文件 @
83ce2dce
...
...
@@ -497,8 +497,6 @@ message LayerConfig {
repeated
uint32
offset
=
55
;
repeated
uint32
shape
=
56
;
// for sub_nest_seq layer to select top k sequence with highest scores
optional
uint32
top_k
=
57
[
default
=
1
];
}
message
EvaluatorConfig
{
...
...
python/paddle/trainer/config_parser.py
浏览文件 @
83ce2dce
...
...
@@ -2659,22 +2659,28 @@ class SubSequenceLayer(LayerBase):
@
config_layer
(
'sub_nested_seq'
)
class
SubNestedSequenceLayer
(
LayerBase
):
def
__init__
(
self
,
name
,
inputs
,
top_k
=
1
,
bias
=
False
,
**
xargs
):
def
__init__
(
self
,
name
,
inputs
,
selected_indices
,
bias
=
False
,
**
xargs
):
if
isinstance
(
inputs
,
list
):
assert
len
(
inputs
)
==
1
,
(
'the first input of sub_nested_seq '
'layer is a single nested sequence.'
)
inputs
=
inputs
[
0
]
if
isinstance
(
selected_indices
,
list
):
assert
len
(
selected_indices
)
==
1
,
(
'the second input of '
'sub_nested_seq layer is a single layer which is a '
'set of selected indices.'
)
selected_indices
=
selected_indices
[
0
]
super
(
SubNestedSequenceLayer
,
self
).
__init__
(
name
,
'sub_nested_seq'
,
0
,
inputs
=
inputs
,
**
xargs
)
config_assert
(
len
(
inputs
)
==
2
,
(
'SubNestSequenceLayer must have 2 inputs: '
'input1 is a nested sequence; input2 is a learnable distribution '
'or scores over each sentence in the nested sequence. '
))
name
,
'sub_nested_seq'
,
0
,
inputs
=
[
inputs
,
selected_indices
],
**
xargs
)
input_layer0
=
self
.
get_input_layer
(
0
)
size
=
input_layer0
.
size
self
.
set_layer_size
(
size
)
self
.
config
.
top_k
=
top_k
input_layer1
=
self
.
get_input_layer
(
1
)
assert
(
input_layer1
.
size
==
1
)
@
config_layer
(
'out_prod'
)
class
OuterProdLayer
(
LayerBase
):
...
...
python/paddle/trainer_config_helpers/layers.py
浏览文件 @
83ce2dce
...
...
@@ -6092,37 +6092,41 @@ def crop_layer(input, offset, axis=2, shape=None, name=None, layer_attr=None):
@
wrap_name_default
()
@
layer_support
()
def
sub_nested_seq_layer
(
input
,
name
=
None
,
top_k
=
1
):
def
sub_nested_seq_layer
(
input
,
selected_indices
,
name
=
None
):
"""
The sub_nested_seq_layer accepts two inputs: the first one is a nested
sequence in PaddlePaddle; the second one is a learnable score or
distribution over each sequence in the nested sequence.
sequence; the second one is a set of selceted indices in the nested sequence.
Then sub_nest_seq_layer selects top k sentences with highest scores or
probabilites according to the second input.
Then sub_nest_seq_layer selects trims the first input according to the
selected indices to give a new output. This layer is used in beam training.
The example usage is:
.. code-block:: python
prob = fc_layer(input=data, size=1, act=SequenceSoftmaxActivation()
)
sub_nest_seq = sub_nested_seq_layer(input=[data, prob], top_k=3)
sub_nest_seq = sub_nested_seq_layer(input=[data, selected_indices]
)
:param input: The two input layers. The first input must be a nested
sequence. The second input is a learnable scores, whose size must be 1.
:param input: A nested sequence.
:type input: LayerOutput
:param selected_indices: a set of sequence indices in the nested sequence.
:type input: LayerOutput
:param name: name of this layer.
:type name: basestring
:param top_k: number of sequences with highest probabilies to select.
:type top_k: int
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert
isinstance
(
input
,
collections
.
Sequence
)
and
len
(
input
)
==
2
,
(
'sub_nest_seq_layer has exactly two inputs.'
)
assert
isinstance
(
input
,
LayerOutput
),
(
'The first input of '
'sub_nested_seq_layer must be a Paddle layer.'
)
assert
isinstance
(
selected_indices
,
LayerOutput
),
(
'The second input of '
'sub_nested_seq_layer must be a Paddle layer.'
)
l
=
Layer
(
inputs
=
[
x
.
name
for
x
in
input
],
inputs
=
input
.
name
,
selected_indices
=
selected_indices
.
name
,
name
=
name
,
top_k
=
top_k
,
type
=
LayerType
.
SUB_NESTED_SEQ
)
return
LayerOutput
(
name
=
name
,
...
...
python/paddle/trainer_config_helpers/tests/configs/protostr/test_seq_select_layers.protostr
浏览文件 @
83ce2dce
type: "nn"
layers {
name: "input"
name: "input
_seq
"
type: "data"
size: 300
active_type: ""
}
layers {
name: "__fc_layer_0__"
type: "fc"
size: 1
active_type: "sequence_softmax"
inputs {
input_layer_name: "input"
input_parameter_name: "___fc_layer_0__.w0"
}
bias_parameter_name: "___fc_layer_0__.wbias"
name: "input"
type: "data"
size: 5
active_type: ""
}
layers {
name: "__sub_nested_seq_layer_0__"
...
...
@@ -22,41 +17,20 @@ layers {
size: 300
active_type: ""
inputs {
input_layer_name: "input"
input_layer_name: "input
_seq
"
}
inputs {
input_layer_name: "
__fc_layer_0__
"
input_layer_name: "
input
"
}
top_k: 1
}
parameters {
name: "___fc_layer_0__.w0"
size: 300
initial_mean: 0.0
initial_std: 0.057735026919
dims: 300
dims: 1
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___fc_layer_0__.wbias"
size: 1
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 1
initial_strategy: 0
initial_smart: false
}
input_layer_names: "input"
input_layer_names: "input
_seq
"
output_layer_names: "__sub_nested_seq_layer_0__"
sub_models {
name: "root"
layer_names: "input_seq"
layer_names: "input"
layer_names: "__fc_layer_0__"
layer_names: "__sub_nested_seq_layer_0__"
input_layer_names: "input"
input_layer_names: "input
_seq
"
output_layer_names: "__sub_nested_seq_layer_0__"
is_recurrent_layer_group: false
}
...
...
python/paddle/trainer_config_helpers/tests/configs/test_seq_select_layers.py
浏览文件 @
83ce2dce
...
...
@@ -2,8 +2,10 @@
#coding=utf-8
from
paddle.trainer_config_helpers
import
*
data
=
data_layer
(
name
=
'input'
,
size
=
300
)
prob
=
fc_layer
(
input
=
data
,
size
=
1
,
act
=
SequenceSoftmaxActivation
())
sub_nest_seq
=
sub_nested_seq_layer
(
input
=
[
data
,
prob
],
top_k
=
1
)
beam_size
=
5
data
=
data_layer
(
name
=
'input_seq'
,
size
=
300
)
selected_ids
=
data_layer
(
name
=
'input'
,
size
=
beam_size
)
sub_nest_seq
=
sub_nested_seq_layer
(
input
=
data
,
selected_indices
=
selected_ids
)
outputs
(
sub_nest_seq
)
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