提交 8266fcc3 编写于 作者: Y yangyaming

Add pyton wrapper for row conv operator.

上级 29e71d29
......@@ -493,3 +493,8 @@ swish
------
.. autofunction:: paddle.v2.fluid.layers.swish
:noindex:
row_conv
--------
.. autofunction:: paddle.v2.fluid.layers.row_conv
:noindex:
......@@ -50,6 +50,7 @@ __all__ = [
'sequence_last_step',
'dropout',
'split',
'row_conv',
]
......@@ -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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