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体验新版 GitCode,发现更多精彩内容 >>
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8266fcc3
编写于
1月 16, 2018
作者:
Y
yangyaming
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电子邮件补丁
差异文件
Add pyton wrapper for row conv operator.
上级
29e71d29
变更
2
隐藏空白更改
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2 changed file
with
64 addition
and
6 deletion
+64
-6
doc/api/v2/fluid/layers.rst
doc/api/v2/fluid/layers.rst
+5
-0
python/paddle/v2/fluid/layers/nn.py
python/paddle/v2/fluid/layers/nn.py
+59
-6
未找到文件。
doc/api/v2/fluid/layers.rst
浏览文件 @
8266fcc3
...
...
@@ -493,3 +493,8 @@ swish
------
.. autofunction:: paddle.v2.fluid.layers.swish
:noindex:
row_conv
--------
.. autofunction:: paddle.v2.fluid.layers.row_conv
:noindex:
python/paddle/v2/fluid/layers/nn.py
浏览文件 @
8266fcc3
...
...
@@ -50,6 +50,7 @@ __all__ = [
'sequence_last_step'
,
'dropout'
,
'split'
,
'row_conv'
,
]
...
...
@@ -1547,13 +1548,13 @@ def split(input, num_or_sections, dim=-1):
Args:
input (Variable): The input variable which is a Tensor or LoDTensor.
num_or_sections (int|list): If :attr:`num_or_sections` is an integer,
then the integer indicates the number of equal sized sub-tensors
that the tensor will be divided into. If :attr:`num_or_sections`
is a list of integers, the length of list indicates the number of
sub-tensors and the integers indicate the sizes of sub-tensors'
num_or_sections (int|list): If :attr:`num_or_sections` is an integer,
then the integer indicates the number of equal sized sub-tensors
that the tensor will be divided into. If :attr:`num_or_sections`
is a list of integers, the length of list indicates the number of
sub-tensors and the integers indicate the sizes of sub-tensors'
:attr:`dim` dimension orderly.
dim (int): The dimension along which to split. If :math:`dim < 0`, the
dim (int): The dimension along which to split. If :math:`dim < 0`, the
dimension to split along is :math:`rank(input) + dim`.
Returns:
...
...
@@ -1597,3 +1598,55 @@ def split(input, num_or_sections, dim=-1):
'axis'
:
dim
})
return
outs
def
row_conv
(
input
,
future_context_size
,
param_attr
=
None
,
act
=
None
):
"""Row Conv Operator. This layer will apply lookahead convolution to
**input**. The input variable should be a 2D LoDTensor with shape [T, D].
Parameters with shape [future_context_size + 1, D] will be created. The math
equation of row convolution is as following:
.. math::
Out_{i} = \sum_{j = i} ^ {i +
\\
tau} X_{j} \odot W_{i - j}
In the above equation:
* :math:`Out_{i}`: The i-th row of output variable with shape [1, D].
* :math:`
\\
tau`: Future context size.
* :math:`X_{j}`: The j-th row of input variable with shape [1, D].
* :math:`W_{i-j}`: The (i-j)-th row of parameters with shape [1, D].
More details about row_conv please refer to the paper
\
(http://www.cs.cmu.edu/~dyogatam/papers/wang+etal.iclrworkshop2016.pdf) and
the design document
\
(https://github.com/PaddlePaddle/Paddle/issues/2228#issuecomment-303903645).
Args:
input (Variable): Input variable, a 2D LoDTensor with shape [T, D].
future_context_size (int): Future context size.
param_attr (ParamAttr): Attributes of parameters, including
name, initializer etc.
act (str): Non-linear activation to be applied to output variable.
Returns:
Variable: The output tensor with same shape as input tensor.
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[16],
dtype='float32', lod_level=1)
out = fluid.layers.row_conv(input=x, future_context_size=2)
"""
helper
=
LayerHelper
(
'row_conv'
,
**
locals
())
dtype
=
helper
.
input_dtype
()
filter_shape
=
[
future_context_size
+
1
,
input
.
shape
[
1
]]
filter_param
=
helper
.
create_parameter
(
attr
=
helper
.
param_attr
,
shape
=
filter_shape
,
dtype
=
dtype
)
out
=
helper
.
create_tmp_variable
(
dtype
)
helper
.
append_op
(
type
=
'row_conv'
,
inputs
=
{
'X'
:
[
input
],
'Filter'
:
[
filter_param
]},
outputs
=
{
'Out'
:
[
out
]})
return
out
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