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22022017
编写于
12月 20, 2017
作者:
L
Luo Tao
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add python wrapper for sequence_pool
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python/paddle/v2/fluid/layers/nn.py
python/paddle/v2/fluid/layers/nn.py
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python/paddle/v2/fluid/layers/nn.py
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22022017
...
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@@ -520,9 +520,53 @@ def conv2d(input,
def
sequence_pool
(
input
,
pool_type
,
**
kwargs
):
"""
This function add the operator for sequence pooling.
This is applied on top of the input using pool_type mentioned
in the parameters.
This function add the operator for sequence pooling.
It pools features of all time-steps of each instance, and is applied
on top of the input using pool_type mentioned in the parameters.
It supports four pool_type:
- average: :math:`Out[i] =
\\
frac{\sum_i X_i}{N}`
- sum: :math:`Out[i] = \sum_jX_{ij}`
- sqrt: :math:`Out[i] =
\\
frac{\sum_jX_{ij}}{\sqrt{len(X_i)}}`
- max: :math:`Out[i] = max(X_i)`
.. code-block:: text
x is a 1-level LoDTensor:
x.lod = [[0, 2, 5, 7]]
x.data = [1, 3, 2, 4, 6, 5, 1]
x.dims = [7, 1]
then output is a Tensor:
out.dim = [3, 1]
with condition len(x.lod[-1]) - 1 == out.dims[0]
for different pool_type:
average: out.data = [2, 4, 3], where 2=(1+3)/2, 4=(2+4+6)/3, 3=(5+1)/2
sum : out.data = [4, 12, 6], where 4=1+3, 12=2+4+6, 6=5+1
sqrt : out.data = [2.82, 6.93, 4.24], where 2.82=(1+3)/sqrt(2),
6.93=(2+4+6)/sqrt(3), 4.24=(5+1)/sqrt(2)
max : out.data = [3, 6, 5], where 3=max(1,3), 6=max(2,4,6), 5=max(5,1)
Args:
input(variable): The input variable which is a LoDTensor.
pool_type (string): The pooling type of sequence_pool.
It supports average, sum, sqrt and max.
Returns:
The sequence pooling variable which is a Tensor.
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[7, 1],
dtype='float32', lod_level=1)
avg_x = fluid.layers.sequence_pool(input=x, pool_type='average')
sum_x = fluid.layers.sequence_pool(input=x, pool_type='sum')
sqrt_x = fluid.layers.sequence_pool(input=x, pool_type='sqrt')
max_x = fluid.layers.sequence_pool(input=x, pool_type='max')
"""
helper
=
LayerHelper
(
'sequence_pool'
,
input
=
input
,
**
kwargs
)
dtype
=
helper
.
input_dtype
()
...
...
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