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68399ab9
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体验新版 GitCode,发现更多精彩内容 >>
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68399ab9
编写于
9月 20, 2017
作者:
S
superjom
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差异文件
Merge remote-tracking branch 'origin/rnn-backward-python' into feature/recurrent_op_backward_fix
上级
075e0e3c
1543eeb4
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
48 addition
and
2 deletion
+48
-2
paddle/operators/recurrent_op.cc
paddle/operators/recurrent_op.cc
+0
-1
paddle/pybind/pybind.cc
paddle/pybind/pybind.cc
+9
-0
python/paddle/v2/framework/tests/test_recurrent_op.py
python/paddle/v2/framework/tests/test_recurrent_op.py
+39
-1
未找到文件。
paddle/operators/recurrent_op.cc
浏览文件 @
68399ab9
...
...
@@ -80,7 +80,6 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
// Now all variables in scope must be created outside of op.
PADDLE_ENFORCE_NOT_NULL
(
stepnet_
);
PADDLE_ENFORCE
(
!
(
*
stepnet_
)
->
Outputs
().
empty
(),
"stepnet_ op has no outputs"
);
PADDLE_ENFORCE
(
!
(
*
stepnet_
)
->
Outputs
().
empty
(),
"net_op has no outputs"
);
if
(
seq_len_
>
step_scopes
->
size
())
{
for
(
size_t
i
=
step_scopes
->
size
();
i
<
seq_len_
;
++
i
)
{
...
...
paddle/pybind/pybind.cc
浏览文件 @
68399ab9
...
...
@@ -311,6 +311,15 @@ All parameter, weight, gradient are variables in Paddle.
self
.
set_falsenet
(
net
.
Clone
());
});
rnn
.
def
(
"backward"
,
[](
const
operators
::
RecurrentOp
&
forwardOp
,
const
std
::
unordered_set
<
std
::
string
>
&
no_grad_vars
)
{
const
auto
&
op
=
*
static_cast
<
const
OperatorBase
*>
(
&
forwardOp
);
return
Backward
(
op
,
no_grad_vars
);
});
ExposeOperator
(
rnn
);
m
.
def
(
"unique_integer"
,
UniqueIntegerGenerator
);
m
.
def
(
"is_compile_gpu"
,
IsCompileGPU
);
...
...
python/paddle/v2/framework/tests/test_recurrent_op.py
浏览文件 @
68399ab9
...
...
@@ -3,6 +3,7 @@ import paddle.v2.framework.core as core
import
unittest
import
numpy
as
np
from
paddle.v2.framework.op
import
Operator
,
RecurrentOp
from
gradient_checker
import
GradientChecker
def
py_sigmoid
(
x
):
...
...
@@ -68,7 +69,7 @@ def create_tensor(scope, name, shape, np_data):
return
tensor
class
TestRecurrentOp
(
unittest
.
TestCase
):
class
RecurrentOpTest
(
unittest
.
TestCase
):
'''
Test RNNOp
...
...
@@ -160,5 +161,42 @@ class TestRecurrentOp(unittest.TestCase):
self
.
assertEqual
(
pd_output
.
shape
,
py_output
.
shape
)
class
RecurrentGradientOpTest
(
unittest
.
TestCase
):
def
create_forward_op
(
self
):
self
.
forward_op
=
RecurrentOp
(
# inputs
inlinks
=
[
"x"
],
boot_memories
=
[
"h_boot"
],
step_net
=
"stepnet"
,
# outputs
outlinks
=
[
"h"
],
step_scopes
=
"step_scopes"
,
# attributes
inlink_alias
=
[
"x@alias"
],
outlink_alias
=
[
"h@alias"
],
pre_memories
=
[
"h@pre"
],
memories
=
[
"h@alias"
])
# create a stepnet for RNN
stepnet
=
core
.
Net
.
create
()
x_fc_op
=
Operator
(
"mul"
,
X
=
"x@alias"
,
Y
=
"W"
,
Out
=
"Wx"
)
h_fc_op
=
Operator
(
"mul"
,
X
=
"h@pre"
,
Y
=
"U"
,
Out
=
"Uh"
)
sum_op
=
Operator
(
"add_two"
,
X
=
"Wx"
,
Y
=
"Uh"
,
Out
=
"sum"
)
sig_op
=
Operator
(
"sigmoid"
,
X
=
"sum"
,
Y
=
"h@alias"
)
for
op
in
[
x_fc_op
,
h_fc_op
,
sum_op
,
sig_op
]:
stepnet
.
add_op
(
op
)
stepnet
.
complete_add_op
(
True
)
self
.
forward_op
.
set_stepnet
(
stepnet
)
def
create_gradient_op
(
self
):
a
=
set
()
backward_op
=
core
.
RecurrentOp
.
backward
(
self
.
forward_op
,
a
)
def
test_grad
(
self
):
self
.
create_forward_op
()
self
.
create_gradient_op
()
if
__name__
==
'__main__'
:
unittest
.
main
()
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