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体验新版 GitCode,发现更多精彩内容 >>
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634faab1
编写于
1月 28, 2018
作者:
Y
Yibing Liu
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
Format doc & add unit test for dynamic_lstmp api
上级
cc82ff0d
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
48 addition
and
27 deletion
+48
-27
doc/api/v2/fluid/layers.rst
doc/api/v2/fluid/layers.rst
+2
-2
paddle/operators/CMakeLists.txt
paddle/operators/CMakeLists.txt
+1
-0
python/paddle/v2/fluid/layers/nn.py
python/paddle/v2/fluid/layers/nn.py
+33
-25
python/paddle/v2/fluid/tests/test_layers.py
python/paddle/v2/fluid/tests/test_layers.py
+12
-0
未找到文件。
doc/api/v2/fluid/layers.rst
浏览文件 @
634faab1
...
...
@@ -19,11 +19,11 @@ dynamic_lstm
:noindex:
dynamic_lstmp
------------
------------
-
.. autofunction:: paddle.v2.fluid.layers.dynamic_lstmp
:noindex:
dynamic_gru
dynamic_gru
-----------
.. autofunction:: paddle.v2.fluid.layers.dynamic_gru
:noindex:
...
...
paddle/operators/CMakeLists.txt
浏览文件 @
634faab1
...
...
@@ -147,6 +147,7 @@ op_library(max_sequence_len_op DEPS lod_rank_table)
op_library
(
sequence_conv_op DEPS context_project
)
op_library
(
sequence_pool_op DEPS sequence_pooling
)
op_library
(
lstm_op DEPS sequence2batch lstm_compute
)
op_library
(
lstmp_op DEPS sequence2batch lstm_compute
)
op_library
(
gru_op DEPS sequence2batch gru_compute
)
op_library
(
recurrent_op DEPS executor
)
op_library
(
warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale math_function
)
...
...
python/paddle/v2/fluid/layers/nn.py
浏览文件 @
634faab1
...
...
@@ -257,7 +257,8 @@ def dynamic_lstm(input,
gate_activation
=
'sigmoid'
,
cell_activation
=
'tanh'
,
candidate_activation
=
'tanh'
,
dtype
=
'float32'
):
dtype
=
'float32'
,
name
=
None
):
"""
**Dynamic LSTM Layer**
...
...
@@ -309,25 +310,25 @@ def dynamic_lstm(input,
(T X 4D), where T is the total time steps in this
mini-batch, D is the hidden size.
size(int): 4 * hidden size.
param_attr(ParamAttr): The parameter attribute for the learnable
param_attr(ParamAttr
|None
): The parameter attribute for the learnable
hidden-hidden weights.
- The shape is (D x 4D), where D is the hidden
size.
- Weights = {:math:`W_{ch}, W_{ih},
\
W_{fh}, W_{oh}`}
bias_attr(ParamAttr): The bias attribute for the learnable bias
- The shape is (D x 4D), where D is the hidden
size.
bias_attr(ParamAttr|None): The bias attribute for the learnable bias
weights, which contains two parts, input-hidden
bias weights and peephole connections weights if
setting `use_peepholes` to `True`.
1. `use_peepholes = False`
- The shape is (1 x 4D).
- Biases = {:math:`b_c, b_i, b_f, b_o`}.
- The shape is (1 x 4D).
2. `use_peepholes = True`
- The shape is (1 x 7D).
- Biases = { :math:`b_c, b_i, b_f, b_o, W_{ic},
\
W_{fc}, W_{oc}`}.
- The shape is (1 x 7D).
use_peepholes(bool): Whether to enable diagonal/peephole connections,
default `True`.
is_reverse(bool): Whether to compute reversed LSTM, default `False`.
...
...
@@ -340,6 +341,8 @@ def dynamic_lstm(input,
Choices = ["sigmoid", "tanh", "relu", "identity"],
default "tanh".
dtype(str): Data type. Choices = ["float32", "float64"], default "float32".
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
tuple: The hidden state, and cell state of LSTM. The shape of both
\
...
...
@@ -354,6 +357,7 @@ def dynamic_lstm(input,
forward, _ = fluid.layers.dynamic_lstm(
input=forward_proj, size=hidden_dim * 4, use_peepholes=False)
"""
helper
=
LayerHelper
(
'lstm'
,
**
locals
())
size
=
size
/
4
weight
=
helper
.
create_parameter
(
...
...
@@ -401,7 +405,8 @@ def dynamic_lstmp(input,
cell_activation
=
'tanh'
,
candidate_activation
=
'tanh'
,
proj_activation
=
'tanh'
,
dtype
=
'float32'
):
dtype
=
'float32'
,
name
=
None
):
"""
**Dynamic LSTMP Layer**
...
...
@@ -416,19 +421,19 @@ def dynamic_lstmp(input,
.. math::
i_t
= \sigma(W_{ix}x_{t} + W_{ir}r_{t-1} + W_{ic}c_{t-1} + b_i)
\\
i_t
& = \sigma(W_{ix}x_{t} + W_{ir}r_{t-1} + W_{ic}c_{t-1} + b_i)
f_t
= \sigma(W_{fx}x_{t} + W_{fr}r_{t-1} + W_{fc}c_{t-1} + b_f)
\\
f_t
& = \sigma(W_{fx}x_{t} + W_{fr}r_{t-1} + W_{fc}c_{t-1} + b_f)
\
t
ilde{c_t} = act_g(W_{cx}x_t + W_{cr}r_{t-1} + b_c)
\\
\
\
tilde{c_t} & = act_g(W_{cx}x_t + W_{cr}r_{t-1} + b_c)
o_t
= \sigma(W_{ox}x_{t} + W_{or}r_{t-1} + W_{oc}c_t + b_o)
\\
o_t
& = \sigma(W_{ox}x_{t} + W_{or}r_{t-1} + W_{oc}c_t + b_o)
c_t
= f_t \odot c_{t-1} + i_t \odot
\t
ilde{c_t}
\\
c_t
& = f_t \odot c_{t-1} + i_t \odot
\\
tilde{c_t}
h_t
= o_t \odot act_h(c_t)
\\
h_t
& = o_t \odot act_h(c_t)
r_t = \overline{act_h}(W_{rh}h_t)
r_t
&
= \overline{act_h}(W_{rh}h_t)
where the :math:`W` terms denote weight matrices (e.g. :math:`W_{xi}` is
the matrix of weights from the input gate to the input), :math:`W_{ic}`,
...
...
@@ -441,7 +446,7 @@ def dynamic_lstmp(input,
vectors, respectively, all of which have the same size as the cell output
activation vector :math:`h`. Here :math:`h` is usually called the hidden
state and :math:`r` denotes its recurrent projection. And
:math:`
\t
ilde{c_t}` is also called the candidate hidden state, whose
:math:`
\
\
tilde{c_t}` is also called the candidate hidden state, whose
computation is based on the current input and previous hidden state.
The :math:`\odot` is the element-wise product of the vectors. :math:`act_g`
...
...
@@ -466,28 +471,28 @@ def dynamic_lstmp(input,
mini-batch, D is the hidden size.
size(int): 4 * hidden size.
proj_size(int): The size of projection output.
param_attr(ParamAttr): The parameter attribute for the learnable
param_attr(ParamAttr
|None
): The parameter attribute for the learnable
hidden-hidden weight and projection weight.
- Hidden-hidden weight = {:math:`W_{ch}, W_{ih},
\
W_{fh}, W_{oh}`}.
- The shape of hidden-hidden weight is (P x 4D),
where P is the projection size and D the hidden
size.
- The shape of projection weight is (D x P).
- Hidden-hidden weight = {:math:`W_{ch}, W_{ih},
\
W_{fh}, W_{oh}`}.
- Projection weight = {:math:`W_{rh}`}.
bias_attr(ParamAttr): The bias attribute for the learnable bias
- The shape of projection weight is (D x P).
bias_attr(ParamAttr|None): The bias attribute for the learnable bias
weights, which contains two parts, input-hidden
bias weights and peephole connections weights if
setting `use_peepholes` to `True`.
1. `use_peepholes = False`
- The shape is (1 x 4D).
- Biases = {:math:`b_c, b_i, b_f, b_o`}.
- The shape is (1 x 4D).
2. `use_peepholes = True`
- The shape is (1 x 7D).
- Biases = { :math:`b_c, b_i, b_f, b_o, W_{ic},
\
W_{fc}, W_{oc}`}.
- The shape is (1 x 7D).
use_peepholes(bool): Whether to enable diagonal/peephole connections,
default `True`.
is_reverse(bool): Whether to compute reversed LSTM, default `False`.
...
...
@@ -503,10 +508,12 @@ def dynamic_lstmp(input,
Choices = ["sigmoid", "tanh", "relu", "identity"],
default "tanh".
dtype(str): Data type. Choices = ["float32", "float64"], default "float32".
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
tuple: The projection of hidden state, and cell state of LSTMP. The
shape of projection is (T x P), for the cell state which is
tuple: The projection of hidden state, and cell state of LSTMP. The
\
shape of projection is (T x P), for the cell state which is
\
(T x D), and both LoD is the same with the `input`.
Examples:
...
...
@@ -519,6 +526,7 @@ def dynamic_lstmp(input,
proj_out, _ = fluid.layers.dynamic_lstmp(input=fc_out,
size=hidden_dim * 4, proj_size=proj_dim, use_peepholes=False)
"""
helper
=
LayerHelper
(
'lstmp'
,
**
locals
())
size
=
size
/
4
weight
=
helper
.
create_parameter
(
...
...
python/paddle/v2/fluid/tests/test_layers.py
浏览文件 @
634faab1
...
...
@@ -202,6 +202,18 @@ class TestBook(unittest.TestCase):
x_t
=
x_t
,
hidden_t_prev
=
prev_hidden
,
cell_t_prev
=
prev_cell
))
print
(
str
(
program
))
def
test_dynamic_lstmp
(
self
):
program
=
Program
()
with
program_guard
(
program
):
hidden_dim
,
proj_dim
=
16
,
8
seq_data
=
layers
.
data
(
name
=
'seq_data'
,
shape
=
[
10
,
10
],
dtype
=
'float32'
,
lod_level
=
1
)
fc_out
=
layers
.
fc
(
input
=
seq_data
,
size
=
4
*
hidden_dim
)
self
.
assertIsNotNone
(
layers
.
dynamic_lstmp
(
input
=
fc_out
,
size
=
4
*
hidden_dim
,
proj_size
=
proj_dim
))
print
(
str
(
program
))
def
test_sequence_softmax
(
self
):
program
=
Program
()
with
program_guard
(
program
):
...
...
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