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636d46a1
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636d46a1
编写于
7年前
作者:
Y
Yu Yang
提交者:
GitHub
7年前
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差异文件
Merge pull request #3167 from reyoung/gradient_check_utils
Initialize Gradient Checker
上级
023fe1e3
1a34becf
6 合并请求
!11636
[IMPORTANT] MKLDNN layout: Support for sum operator
,
!8482
Release/0.11.0
,
!8190
Release/0.11.0
,
!8189
Release/0.11.0
,
!6633
给线性回归的get-started代码加上了预测的示例~~
,
!4615
Feature/tensor array add python binding
变更
3
显示空白变更内容
内联
并排
Showing
3 changed file
with
102 addition
and
2 deletion
+102
-2
paddle/framework/pybind.cc
paddle/framework/pybind.cc
+10
-1
python/paddle/v2/framework/tests/CMakeLists.txt
python/paddle/v2/framework/tests/CMakeLists.txt
+2
-1
python/paddle/v2/framework/tests/gradient_checker.py
python/paddle/v2/framework/tests/gradient_checker.py
+90
-0
未找到文件。
paddle/framework/pybind.cc
浏览文件 @
636d46a1
...
...
@@ -105,7 +105,16 @@ PYBIND11_PLUGIN(core) {
.
def
(
"set"
,
PyCUDATensorSetFromArray
<
float
>
)
.
def
(
"set"
,
PyCUDATensorSetFromArray
<
int
>
)
#endif
.
def
(
"shape"
,
[](
Tensor
&
self
)
{
return
vectorize
(
self
.
dims
());
});
.
def
(
"shape"
,
[](
Tensor
&
self
)
{
return
vectorize
(
self
.
dims
());
})
.
def
(
"set_float_element"
,
[](
Tensor
&
self
,
size_t
offset
,
float
f
)
{
// TODO(yuyang18): Only support GPU now.
self
.
data
<
float
>
()[
offset
]
=
f
;
})
.
def
(
"get_float_element"
,
[](
Tensor
&
self
,
size_t
offset
)
->
float
{
// TODO(yuyang18): Only support GPU now.
return
self
.
data
<
float
>
()[
offset
];
});
py
::
class_
<
Variable
>
(
m
,
"Variable"
,
R"DOC(Variable Class.
...
...
This diff is collapsed.
Click to expand it.
python/paddle/v2/framework/tests/CMakeLists.txt
浏览文件 @
636d46a1
...
...
@@ -13,4 +13,5 @@ add_python_test(test_framework
test_sigmoid_op.py
test_softmax_op.py
test_rowwise_add_op.py
test_network.py
)
test_network.py
gradient_checker.py
)
This diff is collapsed.
Click to expand it.
python/paddle/v2/framework/tests/gradient_checker.py
0 → 100644
浏览文件 @
636d46a1
import
paddle.v2.framework.core
as
core
from
paddle.v2.framework.create_op_creation_methods
import
op_creations
import
numpy
import
unittest
__all__
=
[
'get_numeric_gradient'
]
def
get_numeric_gradient
(
op
,
input_values
,
output_name
,
input_to_check
,
delta
=
1e-2
,
local_scope
=
None
):
"""
Get Numeric Gradient for an operator's input.
:param op: C++ operator instance, could be an network
:param input_values: The input variables. Should be an dictionary, key is
variable name. Value is numpy array.
:param output_name: The final output variable name.
:param input_to_check: The input variable need to get gradient.
:param delta: The perturbation value for numeric gradient method. The
smaller delta is, the more accurate result will get. But if that delta is
too small, it could occur numerical stability problem.
:param local_scope: The local scope used for get_numeric_gradient.
:return: The gradient array in numpy format.
"""
if
local_scope
is
None
:
local_scope
=
core
.
Scope
()
# Create all input variable in local_scope
for
var_name
in
input_values
:
var
=
local_scope
.
new_var
(
var_name
)
tensor
=
var
.
get_tensor
()
tensor
.
set_dims
(
input_values
[
var_name
].
shape
)
tensor
.
alloc_float
(
core
.
CPUPlace
())
tensor
.
set
(
input_values
[
var_name
],
core
.
CPUPlace
())
# Create all output variable in local_scope
for
output
in
op
.
outputs
():
if
local_scope
.
find_var
(
output
)
is
None
:
local_scope
.
new_var
(
output
).
get_tensor
()
op
.
infer_shape
(
local_scope
)
# allocate output memory
for
output
in
op
.
outputs
():
local_scope
.
find_var
(
output
).
get_tensor
().
alloc_float
(
core
.
CPUPlace
())
# TODO(yuyang18): Only CPU is support now.
cpu_ctx
=
core
.
DeviceContext
.
create
(
core
.
CPUPlace
())
def
get_output
():
op
.
run
(
local_scope
,
cpu_ctx
)
return
numpy
.
array
(
local_scope
.
find_var
(
output_name
).
get_tensor
()).
sum
()
def
product
(
dim
):
return
reduce
(
lambda
a
,
b
:
a
*
b
,
dim
,
1
)
tensor_to_check
=
local_scope
.
find_var
(
input_to_check
).
get_tensor
()
tensor_size
=
product
(
tensor_to_check
.
get_dims
())
gradient_flat
=
numpy
.
zeros
(
shape
=
(
tensor_size
,
),
dtype
=
'float32'
)
for
i
in
xrange
(
tensor_size
):
origin
=
tensor_to_check
.
get_float_element
(
i
)
x_pos
=
origin
+
delta
tensor_to_check
.
set_float_element
(
i
,
x_pos
)
y_pos
=
get_output
()
x_neg
=
origin
-
delta
tensor_to_check
.
set_float_element
(
i
,
x_neg
)
y_neg
=
get_output
()
tensor_to_check
.
set_float_element
(
i
,
origin
)
# restore old value
gradient_flat
[
i
]
=
(
y_pos
-
y_neg
)
/
delta
/
2
return
gradient_flat
.
reshape
(
tensor_to_check
.
get_dims
())
if
__name__
==
'__main__'
:
class
GetNumericGradientTest
(
unittest
.
TestCase
):
def
test_add_op
(
self
):
add_op
=
op_creations
.
add_two
(
X
=
"X"
,
Y
=
"Y"
,
Out
=
"Z"
)
x
=
numpy
.
random
.
random
((
10
,
1
)).
astype
(
"float32"
)
y
=
numpy
.
random
.
random
((
10
,
1
)).
astype
(
"float32"
)
arr
=
get_numeric_gradient
(
add_op
,
{
'X'
:
x
,
"Y"
:
y
},
'Z'
,
'X'
)
self
.
assertAlmostEqual
(
arr
.
mean
(),
1.0
,
delta
=
1e-2
)
unittest
.
main
()
This diff is collapsed.
Click to expand it.
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