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0d1bc8ab
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0d1bc8ab
编写于
8月 14, 2017
作者:
S
superjom
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电子邮件补丁
差异文件
fix res
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b1b71eab
变更
2
隐藏空白更改
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并排
Showing
2 changed file
with
25 addition
and
63 deletion
+25
-63
python/paddle/v2/framework/tests/test_fc_op.py
python/paddle/v2/framework/tests/test_fc_op.py
+0
-44
python/paddle/v2/framework/tests/test_recurrent_op.py
python/paddle/v2/framework/tests/test_recurrent_op.py
+25
-19
未找到文件。
python/paddle/v2/framework/tests/test_fc_op.py
已删除
100644 → 0
浏览文件 @
b1b71eab
import
unittest
import
numpy
as
np
import
paddle.v2.framework.core
as
core
from
paddle.v2.framework.op
import
Operator
class
TestFc
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
x_np_data
=
np
.
random
.
random
((
1000
,
784
))
self
.
W_np_data
=
np
.
random
.
random
((
784
,
100
))
def
test_fc
(
self
):
scope
=
core
.
Scope
()
place
=
core
.
CPUPlace
()
x_tensor
=
scope
.
new_var
(
"X"
).
get_tensor
()
x_tensor
.
set_dims
(
self
.
x_np_data
.
shape
)
x_tensor
.
set
(
self
.
x_np_data
,
place
)
W_tensor
=
scope
.
new_var
(
"W"
).
get_tensor
()
W_tensor
.
set_dims
(
self
.
W_np_data
.
shape
)
W_tensor
.
set
(
self
.
W_np_data
,
place
)
op
=
Operator
(
"fc"
,
X
=
"X"
,
Y
=
"Y"
,
W
=
"W"
)
for
out
in
op
.
outputs
():
if
scope
.
find_var
(
out
)
is
None
:
scope
.
new_var
(
out
).
get_tensor
()
Y_tensor
=
scope
.
find_var
(
"Y"
).
get_tensor
()
op
.
infer_shape
(
scope
)
self
.
assertEqual
([
1000
,
100
],
Y_tensor
.
shape
())
ctx
=
core
.
DeviceContext
.
create
(
place
)
op
.
run
(
scope
,
ctx
)
py_data
=
np
.
matmul
(
self
.
x_np_data
,
self
.
W_np_data
)
op_data
=
np
.
array
(
Y_tensor
)
print
py_data
-
op_data
self
.
assertTrue
(
np
.
allclose
(
py_data
,
op_data
))
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/v2/framework/tests/test_recurrent_op.py
浏览文件 @
0d1bc8ab
...
...
@@ -8,22 +8,22 @@ from paddle.v2.framework.op import Operator
def
py_sigmoid
(
x
):
return
1.
/
(
1.
+
np
.
exp
(
-
x
))
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
):
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
)]
self
.
mems
=
[
np
.
zeros
(
shape
=
(
batch_size
,
input_dim
))
for
i
in
range
(
sent_len
)
]
def
forward
(
self
):
xs
=
self
.
segment_inputs
()
...
...
@@ -43,7 +43,7 @@ class PySimpleRNN(object):
'''
mem
=
self
.
mems
[
step_id
]
if
step_id
>
0
:
pre_mem
=
self
.
mems
[
step_id
-
1
]
pre_mem
=
self
.
mems
[
step_id
-
1
]
else
:
pre_mem
=
self
.
h_boot
xW
=
np
.
matmul
(
x
,
self
.
W
)
...
...
@@ -52,6 +52,7 @@ class PySimpleRNN(object):
sum
=
xW
+
hU
self
.
mems
[
step_id
]
=
py_sigmoid
(
sum
)
class
PySimpleRNNTest
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
rnn
=
PySimpleRNN
()
...
...
@@ -91,11 +92,8 @@ class TestRecurrentOp(unittest.TestCase):
sent_len
=
11
def
setUp
(
self
):
self
.
py_rnn
=
PySimpleRNN
(
self
.
input_dim
,
self
.
batch_size
,
self
.
weight_dim
,
self
.
sent_len
)
self
.
py_rnn
=
PySimpleRNN
(
self
.
input_dim
,
self
.
batch_size
,
self
.
weight_dim
,
self
.
sent_len
)
def
forward
(
self
):
self
.
scope
=
core
.
Scope
()
...
...
@@ -111,22 +109,27 @@ class TestRecurrentOp(unittest.TestCase):
# create inlink
x_np_data
=
self
.
py_rnn
.
x
create_tensor
(
self
.
scope
,
"x"
,
[
self
.
sent_len
,
self
.
batch_size
,
self
.
input_dim
],
x_np_data
)
[
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
)
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
)
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
)
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
=
Operator
(
"recurrent_op"
,
rnnop
=
Operator
(
"recurrent_op"
,
# inputs
inlinks
=
[
"x"
],
boot_memories
=
[
"h_boot"
],
...
...
@@ -145,8 +148,10 @@ class TestRecurrentOp(unittest.TestCase):
var
=
self
.
scope
.
new_var
(
"stepnet"
)
stepnet
=
var
.
get_net
()
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("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"
)
...
...
@@ -163,5 +168,6 @@ class TestRecurrentOp(unittest.TestCase):
print
'py_output'
,
py_output
self
.
assertEqual
(
pd_output
.
shape
,
py_output
.
shape
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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