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8690deb0
编写于
11月 03, 2018
作者:
Q
Qiyang Min
提交者:
GitHub
11月 03, 2018
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Merge pull request #14101 from velconia/polish_op_comment
Polish hash op comment
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67b86d4b
4d06d1d7
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-11
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
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python/paddle/fluid/layers/nn.py
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8690deb0
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@@ -7735,19 +7735,59 @@ def affine_channel(x, scale=None, bias=None, data_layout='NCHW', name=None):
def
hash
(
input
,
hash_size
,
num_hash
=
1
,
name
=
None
):
"""
hash the input
Args:
input (Variable): The input variable which is a one-hot word.
hash_size (int): The space size for hash algorithm.
Hash the input to an integer whose value is less than the given hash size.
The hash algorithm we used was xxHash - Extremely fast hash algorithm
(https://github.com/Cyan4973/xxHash/tree/v0.6.5)
A simple example as below:
.. code-block:: text
Given:
# shape [2, 2]
input.data = [
[[1], [2]],
[[3], [4]],
]
input.lod = [[0, 2]]
hash_size = 10000
num_hash = 4
Then:
Hash op will take all number in input's 2nd dimension as hash algorithm's
input for each time. Each input will be hashed for 4 times, and get an
array whose length is 4. Each value in the array ranges from 0 to 9999.
# shape [2, 4]
output.data = [
[[9662], [9217], [1129], [8487]],
[[8310], [1327], [1654], [4567]],
]
output.lod = [[0, 2]]
Args:
input (Variable): The input variable which is a one-hot word. The
dimensions of the input variable must be 2.
hash_size (int): The space size for hash algorithm. The output value
will keep in the range:math:`[0, hash_size - 1]`.
num_hash (int): The times of hash, default 1.
name (str, default None): The name of this layer.
Returns:
Variable: The hash result variable which is a LoDTensor.
Examples:
.. code-block:: python
word_dict = paddle.dataset.imdb.word_dict()
x = fluid.layers.data(shape[1], dtype='int32', lod_level=1)
out = fluid.layers.hash(input=x, len(word_dict))
Returns:
Variable: The hash result variable which is a LoDTensor.
Examples:
.. code-block:: python
word_dict = paddle.dataset.imdb.word_dict()
x = fluid.layers.data(shape[1], dtype='int32', lod_level=1)
out = fluid.layers.hash(input=x, num_hash=4, hash_size=1000)
"""
helper
=
LayerHelper
(
'hash'
,
**
locals
())
out
=
helper
.
create_variable_for_type_inference
(
...
...
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