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691b5cac
编写于
1月 07, 2018
作者:
S
Siddharth Goyal
提交者:
Yi Wang
1月 07, 2018
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Fix equation for gru op (#7274)
上级
758fe473
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1
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with
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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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691b5cac
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@@ -243,18 +243,21 @@ def gru_unit(input,
r_t & = actGate(xr_{t} + W_r h_{t-1} + b_r)
ch_t & = actNode(xc_t + W_c dot(r_t, h_{t-1}) + b_c
)
m_t & = actNode(xm_t + W_c dot(r_t, h_{t-1}) + b_m
)
h_t & = dot((1-u_t),
ch_{t-1}) + dot(u_t, h_t
)
h_t & = dot((1-u_t),
m_t) + dot(u_t, h_{t-1}
)
The inputs of gru unit includes :math:`z_t`, :math:`h_{t-1}`. In terms
of the equation above, the :math:`z_t` is split into 3 parts -
:math:`xu_t`, :math:`xr_t` and :math:`x
c
_t`. This means that in order to
:math:`xu_t`, :math:`xr_t` and :math:`x
m
_t`. This means that in order to
implement a full GRU unit operator for an input, a fully
connected layer has to be applied, such that :math:`z_t = W_{fc}x_t`.
This layer has three outputs :math:`h_t`, :math:`dot(r_t, h_{t - 1})`
and concatenation of :math:`u_t`, :math:`r_t` and :math:`ch_t`.
The terms :math:`u_t` and :math:`r_t` represent the update and reset gates
of the GRU cell. Unlike LSTM, GRU has one lesser gate. However, there is
an intermediate candidate hidden output, which is denoted by :math:`m_t`.
This layer has three outputs :math:`h_t`, :math:`dot(r_t, h_{t-1})`
and concatenation of :math:`u_t`, :math:`r_t` and :math:`m_t`.
Args:
input (Variable): The fc transformed input value of current step.
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