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493e1c04
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493e1c04
编写于
8月 14, 2017
作者:
Q
qingqing01
提交者:
GitHub
8月 14, 2017
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差异文件
Merge pull request #3335 from Superjom/rnn_forward_result_test
Rnn forward result test
上级
e54ce779
a0b49a6c
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
97 addition
and
21 deletion
+97
-21
python/paddle/v2/framework/tests/CMakeLists.txt
python/paddle/v2/framework/tests/CMakeLists.txt
+1
-0
python/paddle/v2/framework/tests/test_recurrent_op.py
python/paddle/v2/framework/tests/test_recurrent_op.py
+96
-21
未找到文件。
python/paddle/v2/framework/tests/CMakeLists.txt
浏览文件 @
493e1c04
...
...
@@ -24,3 +24,4 @@ py_test(test_default_scope_funcs SRCS test_default_scope_funcs.py)
py_test
(
test_operator SRCS test_operator.py
)
# py_test(test_gaussian_random_op SRCS test_gaussian_random_op.py)
py_test
(
test_uniform_random_op SRCS test_uniform_random_op.py
)
py_test
(
test_recurrent_op SRCS test_recurrent_op.py
)
python/paddle/v2/framework/tests/test_recurrent_op.py
浏览文件 @
493e1c04
...
...
@@ -2,19 +2,74 @@ import logging
import
paddle.v2.framework.core
as
core
import
unittest
import
numpy
as
np
import
paddle.v2.framework.create_op_creation_methods
as
creation
from
paddle.v2.framework.op
import
Operator
ops
=
creation
.
op_creations
def
py_sigmoid
(
x
):
return
1.
/
(
1.
+
np
.
exp
(
-
x
))
def
create_tensor
(
scope
,
name
,
shape
):
class
PySimpleRNN
(
object
):
'''
A simple implementation of RNN based on numpy, to futhur test RecurrentOp's alogorithm
'''
def
__init__
(
self
,
input_dim
=
30
,
batch_size
=
50
,
weight_dim
=
15
,
sent_len
=
11
):
self
.
x
=
np
.
random
.
normal
(
size
=
(
sent_len
,
batch_size
,
input_dim
))
self
.
W
=
np
.
random
.
normal
(
size
=
(
input_dim
,
input_dim
))
self
.
U
=
np
.
random
.
normal
(
size
=
(
input_dim
,
input_dim
))
self
.
h_boot
=
np
.
random
.
normal
(
size
=
(
batch_size
,
input_dim
))
# memories
self
.
mems
=
[
np
.
zeros
(
shape
=
(
batch_size
,
input_dim
))
for
i
in
range
(
sent_len
)
]
def
forward
(
self
):
xs
=
self
.
segment_inputs
()
for
step_id
in
range
(
self
.
x
.
shape
[
0
]):
self
.
step
(
step_id
,
xs
[
step_id
])
return
self
.
concat_outputs
()
def
segment_inputs
(
self
):
return
[
self
.
x
[
i
]
for
i
in
range
(
self
.
x
.
shape
[
0
])]
def
concat_outputs
(
self
):
return
np
.
array
(
self
.
mems
)
def
step
(
self
,
step_id
,
x
):
'''
run a step
'''
mem
=
self
.
mems
[
step_id
]
if
step_id
>
0
:
pre_mem
=
self
.
mems
[
step_id
-
1
]
else
:
pre_mem
=
self
.
h_boot
xW
=
np
.
matmul
(
x
,
self
.
W
)
hU
=
np
.
matmul
(
mem
,
self
.
U
)
sum
=
xW
+
hU
self
.
mems
[
step_id
]
=
py_sigmoid
(
sum
)
class
PySimpleRNNTest
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
rnn
=
PySimpleRNN
()
def
test_forward
(
self
):
output
=
self
.
rnn
.
forward
()
print
'output'
,
output
def
create_tensor
(
scope
,
name
,
shape
,
np_data
):
tensor
=
scope
.
new_var
(
name
).
get_tensor
()
tensor
.
set_dims
(
shape
)
tensor
.
set
(
np
.
random
.
random
(
shape
)
,
core
.
CPUPlace
())
tensor
.
set
(
np
_data
,
core
.
CPUPlace
())
return
tensor
class
TestR
NN
(
unittest
.
TestCase
):
class
TestR
ecurrentOp
(
unittest
.
TestCase
):
'''
Test RNNOp
...
...
@@ -28,7 +83,7 @@ class TestRNN(unittest.TestCase):
memories:
- h
outputs:
- h
- h
'''
input_dim
=
30
...
...
@@ -36,33 +91,45 @@ class TestRNN(unittest.TestCase):
weight_dim
=
15
sent_len
=
11
def
init
(
self
):
def
setUp
(
self
):
self
.
py_rnn
=
PySimpleRNN
(
self
.
input_dim
,
self
.
batch_size
,
self
.
weight_dim
,
self
.
sent_len
)
def
forward
(
self
):
self
.
scope
=
core
.
Scope
()
self
.
create_global_variables
()
self
.
create_step_net
()
rnn_op
=
self
.
create_rnn_op
()
ctx
=
core
.
DeviceContext
.
create
(
core
.
CPUPlace
())
print
'infer_shape'
rnn_op
.
infer_shape
(
self
.
scope
)
rnn_op
.
run
(
self
.
scope
,
ctx
)
return
np
.
array
(
self
.
scope
.
find_var
(
"h"
).
get_tensor
())
def
create_global_variables
(
self
):
# create inlink
x_np_data
=
self
.
py_rnn
.
x
create_tensor
(
self
.
scope
,
"x"
,
[
self
.
sent_len
,
self
.
batch_size
,
self
.
input_dim
])
create_tensor
(
self
.
scope
,
"W"
,
[
self
.
input_dim
,
self
.
input_dim
])
create_tensor
(
self
.
scope
,
"U"
,
[
self
.
input_dim
,
self
.
input_dim
])
create_tensor
(
self
.
scope
,
"h_boot"
,
[
self
.
batch_size
,
self
.
input_dim
])
[
self
.
sent_len
,
self
.
batch_size
,
self
.
input_dim
],
x_np_data
)
W_np_data
=
self
.
py_rnn
.
W
create_tensor
(
self
.
scope
,
"W"
,
[
self
.
input_dim
,
self
.
input_dim
],
W_np_data
)
U_np_data
=
self
.
py_rnn
.
U
create_tensor
(
self
.
scope
,
"U"
,
[
self
.
input_dim
,
self
.
input_dim
],
U_np_data
)
h_boot_np_data
=
self
.
py_rnn
.
h_boot
create_tensor
(
self
.
scope
,
"h_boot"
,
[
self
.
batch_size
,
self
.
input_dim
],
h_boot_np_data
)
self
.
scope
.
new_var
(
"step_scopes"
)
self
.
scope
.
new_var
(
"h@alias"
)
self
.
scope
.
new_var
(
"h"
)
def
create_rnn_op
(
self
):
# create RNNOp
rnnop
=
ops
.
recurrent_op
(
rnnop
=
Operator
(
"recurrent_op"
,
# inputs
inlinks
=
[
"x"
],
boot_memories
=
[
"h_boot"
],
...
...
@@ -81,17 +148,25 @@ class TestRNN(unittest.TestCase):
var
=
self
.
scope
.
new_var
(
"stepnet"
)
stepnet
=
var
.
get_net
()
x_fc_op
=
ops
.
fc
(
X
=
"x@alias"
,
W
=
"W"
,
Y
=
"Wx"
)
h_fc_op
=
ops
.
fc
(
X
=
"h@pre"
,
W
=
"U"
,
Y
=
"Uh"
)
sum_op
=
ops
.
add_two
(
X
=
"Wx"
,
Y
=
"Uh"
,
Out
=
"sum"
)
sig_op
=
ops
.
sigmoid
(
X
=
"sum"
,
Y
=
"h@alias"
)
# x_fc_op = Operator("fc", X="x@alias", W="W", Y="Wx")
# h_fc_op = Operator("fc", X="h@pre", W="U", Y="Uh")
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
)
def
test_recurrent
(
self
):
self
.
init
()
def
test_forward
(
self
):
print
'test recurrent op forward'
pd_output
=
self
.
forward
()
py_output
=
self
.
py_rnn
.
forward
()
print
'pd_output'
,
pd_output
print
print
'py_output'
,
py_output
self
.
assertEqual
(
pd_output
.
shape
,
py_output
.
shape
)
if
__name__
==
'__main__'
:
...
...
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