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abda6d16
编写于
10月 18, 2018
作者:
G
guosheng
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Refine the doc of dynamic_gru and gru_unit. test=develop
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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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@@ -684,8 +684,18 @@ def dynamic_gru(input,
The first part are weights of the update gate and reset gate with
shape :math:`(D
\\
times 2D)`, and the second part are weights for
candidate hidden state with shape :math:`(D
\\
times D)`.
bias_attr(ParamAttr): The parameter attribute for learnable the
hidden-hidden bias.
If it is set to None or one attribute of ParamAttr, dynamic_gru will
create ParamAttr as param_attr. If the Initializer of the param_attr
is not set, the parameter is initialized with Xavier. Default: None.
bias_attr (ParamAttr|bool|None): The parameter attribute for the bias
of GRU. Note that the bias with :math:`(1
\\
times 3D)` concatenates
the bias in the update gate, reset gate and candidate calculations.
If it is set to False, no bias will be applied to the update gate,
reset gate and candidate calculations. If it is set to None or one
attribute of ParamAttr, dynamic_gru will create ParamAttr as
bias_attr. If the Initializer of the bias_attr is not set, the bias
is initialized zero. Default: None.
is_reverse(bool): Whether to compute reversed GRU, default
:attr:`False`.
gate_activation(str): The activation for update gate and reset gate.
...
...
@@ -784,10 +794,29 @@ def gru_unit(input,
Args:
input (Variable): The fc transformed input value of current step.
hidden (Variable): The hidden value of
lstm
unit from previous step.
hidden (Variable): The hidden value of
gru
unit from previous step.
size (integer): The input dimension value.
param_attr (ParamAttr): The weight parameters for gru unit. Default: None
bias_attr (ParamAttr): The bias parameters for gru unit. Default: None
param_attr(ParamAttr|None): The parameter attribute for the learnable
hidden-hidden weight matrix. Note:
- The shape of the weight matrix is :math:`(T
\\
times 3D)`, where
:math:`D` is the hidden size.
- All elements in the weight matrix can be divided into two parts.
The first part are weights of the update gate and reset gate with
shape :math:`(D
\\
times 2D)`, and the second part are weights for
candidate hidden state with shape :math:`(D
\\
times D)`.
If it is set to None or one attribute of ParamAttr, gru_unit will
create ParamAttr as param_attr. If the Initializer of the param_attr
is not set, the parameter is initialized with Xavier. Default: None.
bias_attr (ParamAttr|bool|None): The parameter attribute for the bias
of GRU. Note that the bias with :math:`(1
\\
times 3D)` concatenates
the bias in the update gate, reset gate and candidate calculations.
If it is set to False, no bias will be applied to the update gate,
reset gate and candidate calculations. If it is set to None or one
attribute of ParamAttr, gru_unit will create ParamAttr as
bias_attr. If the Initializer of the bias_attr is not set, the bias
is initialized zero. Default: None.
activation (string): The activation type for cell (actNode).
Default: 'tanh'
gate_activation (string): The activation type for gates (actGate).
...
...
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