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f0e797e5
编写于
1月 03, 2018
作者:
Y
yangyaming
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电子邮件补丁
差异文件
Doc fix and enhancement for lstm_unit python wrapper.
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39502e6e
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66 addition
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60 deletion
+66
-60
python/paddle/v2/fluid/layers/nn.py
python/paddle/v2/fluid/layers/nn.py
+66
-60
未找到文件。
python/paddle/v2/fluid/layers/nn.py
浏览文件 @
f0e797e5
...
...
@@ -1168,25 +1168,26 @@ def lstm_unit(x_t,
.. math::
i_t & = \sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} +
W_{c_i}c_{t-1} +
b_i)
i_t & = \sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + b_i)
f_t & = \sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} +
W_{c_f}c_{t-1} +
b_f)
f_t & = \sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + b_f)
c_t & = f_tc_{t-1} + i_t tanh (W_{x_c}x_t
+
W_{h_c}h_{t-1} + b_c)
c_t & = f_tc_{t-1} + i_t tanh (W_{x_c}x_t
+
W_{h_c}h_{t-1} + b_c)
o_t & = \sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} +
W_{c_o}c_t +
b_o)
o_t & = \sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + b_o)
h_t & = o_t tanh(c_t)
The inputs of lstm unit includes :math:`x_t`, :math:`h_{t-1}` and
:math:`c_{t-1}`. The implementation separates the linear transformation
and non-linear transformation apart. Here, we take :math:`i_t` as an
example. The linear transformation is applied by calling a `fc` layer and
the equation is:
The inputs of lstm unit include :math:`x_t`, :math:`h_{t-1}` and
:math:`c_{t-1}`. The 2nd dimensions of :math:`h_{t-1}` and :math:`c_{t-1}`
should be same. The implementation separates the linear transformation and
non-linear transformation apart. Here, we take :math:`i_t` as an example.
The linear transformation is applied by calling a `fc` layer and the
equation is:
.. math::
L_{i_t} = W_{x_i}x_{t} + W_{h_i}h_{t-1} +
W_{c_i}c_{t-1} +
b_i
L_{i_t} = W_{x_i}x_{t} + W_{h_i}h_{t-1} + b_i
The non-linear transformation is applied by calling `lstm_unit_op` and the
equation is:
...
...
@@ -1213,14 +1214,15 @@ def lstm_unit(x_t,
Raises:
ValueError: The ranks of **x_t**, **hidden_t_prev** and **cell_t_prev**
\
not be 2 or the 1st dimensions of **x_t**, **hidden_t_prev**
\
and **cell_t_prev** not be the same.
and **cell_t_prev** not be the same or the 2nd dimensions of
\
**hidden_t_prev** and **cell_t_prev** not be the same.
Examples:
.. code-block:: python
x_t = fluid.layers.fc(input=x_t_data, size=10)
prev_hidden = fluid.layers.fc(input=prev_hidden_data, size=
2
0)
prev_hidden = fluid.layers.fc(input=prev_hidden_data, size=
3
0)
prev_cell = fluid.layers.fc(input=prev_cell_data, size=30)
hidden_value, cell_value = fluid.layers.lstm_unit(x_t=x_t,
hidden_t_prev=prev_hidden,
...
...
@@ -1239,7 +1241,11 @@ def lstm_unit(x_t,
if
x_t
.
shape
[
0
]
!=
hidden_t_prev
.
shape
[
0
]
or
x_t
.
shape
[
0
]
!=
cell_t_prev
.
shape
[
0
]:
raise
ValueError
(
"The 1s dimension of x_t, hidden_t_prev and "
raise
ValueError
(
"The 1s dimensions of x_t, hidden_t_prev and "
"cell_t_prev must be the same."
)
if
hidden_t_prev
.
shape
[
1
]
!=
cell_t_prev
.
shape
[
1
]:
raise
ValueError
(
"The 2nd dimensions of hidden_t_prev and "
"cell_t_prev must be the same."
)
if
bias_attr
is
None
:
...
...
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