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4a2b0ae4
编写于
11月 21, 2017
作者:
A
Abhinav Arora
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差异文件
Implementing the MSRA initializer for rectifier units
上级
55ecd6d2
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
187 addition
and
0 deletion
+187
-0
python/paddle/v2/fluid/initializer.py
python/paddle/v2/fluid/initializer.py
+83
-0
python/paddle/v2/fluid/tests/test_initializer.py
python/paddle/v2/fluid/tests/test_initializer.py
+104
-0
未找到文件。
python/paddle/v2/fluid/initializer.py
浏览文件 @
4a2b0ae4
...
@@ -285,3 +285,86 @@ class XavierInitializer(Initializer):
...
@@ -285,3 +285,86 @@ class XavierInitializer(Initializer):
})
})
var
.
op
=
op
var
.
op
=
op
return
op
return
op
class
MSRAInitializer
(
Initializer
):
"""Implements the MSRA initializer a.k.a. Kaiming Initializer
This class implements the weight initialization from the paper
Delving Deep into Rectifiers: Surpassing Human-Level Performance on
ImageNet Classification[1] by Kaiming He, Xiangyu Zhang, Shaoqing Ren
and Jian Sun. This is a robust initialization method that particularly
considers the rectifier nonlinearities. In case of Uniform distribution,
the range is [-x, x], where x = sqrt(6 / fan_in). In case of Normal
distribution, the mean is 0 and the standard deviation
is sqrt(2/ fan_in).
References:
[1] Delving Deep into Rectifiers: Surpassing Human-Level Performance
on ImageNet Classification
(https://arxiv.org/abs/1502.01852)
"""
def
__init__
(
self
,
uniform
=
True
,
fan_in
=
None
,
seed
=
0
):
"""Constructor for MSRAInitializer
Args:
uniform: whether to use uniform or normal distribution
fan_in: fan_in for MSRAInitializer. If None, it is
inferred from the variable.
seed: random seed
Note: It is recommended to set fan_in to None for most cases.
"""
assert
uniform
is
not
None
assert
seed
is
not
None
super
(
MSRAInitializer
,
self
).
__init__
()
self
.
_uniform
=
uniform
self
.
_fan_in
=
fan_in
self
.
_seed
=
seed
def
__call__
(
self
,
var
,
block
):
"""Add MSRA initialization ops for a variable
Args:
var: Variable that needs to be initialized
block: The block in which initialization ops
should be added
Returns:
the initialization op
"""
assert
isinstance
(
var
,
framework
.
Variable
)
assert
isinstance
(
block
,
framework
.
Block
)
f_in
,
f_out
=
self
.
_compute_fans
(
var
)
# If fan_in is passed, use it
fan_in
=
f_in
if
self
.
_fan_in
is
None
else
self
.
_fan_in
if
self
.
_uniform
:
limit
=
np
.
sqrt
(
6.0
/
float
(
fan_in
))
op
=
block
.
prepend_op
(
type
=
"uniform_random"
,
outputs
=
{
"Out"
:
var
},
attrs
=
{
"shape"
:
var
.
shape
,
"data_type"
:
int
(
var
.
data_type
),
"min"
:
-
limit
,
"max"
:
limit
,
"seed"
:
self
.
_seed
})
else
:
std
=
np
.
sqrt
(
2.0
/
float
(
fan_in
))
op
=
block
.
prepend_op
(
type
=
"gaussian_random"
,
outputs
=
{
"Out"
:
var
},
attrs
=
{
"shape"
:
var
.
shape
,
"data_type"
:
int
(
var
.
data_type
),
"mean"
:
0.0
,
"std"
:
std
,
"seed"
:
self
.
_seed
})
var
.
op
=
op
return
op
python/paddle/v2/fluid/tests/test_initializer.py
浏览文件 @
4a2b0ae4
...
@@ -223,5 +223,109 @@ class TestXavierInitializer(unittest.TestCase):
...
@@ -223,5 +223,109 @@ class TestXavierInitializer(unittest.TestCase):
self
.
assertEqual
(
init_op
.
attr
(
'seed'
),
134
)
self
.
assertEqual
(
init_op
.
attr
(
'seed'
),
134
)
class
TestMSRAInitializer
(
unittest
.
TestCase
):
def
test_uniform_msra_initializer
(
self
):
"""Test MSRA initializer with uniform distribution on
for matrix multiply.
"""
program
=
framework
.
Program
()
block
=
program
.
global_block
()
param
=
block
.
create_parameter
(
dtype
=
"float32"
,
shape
=
[
5
,
10
],
lod_level
=
0
,
name
=
"param"
,
initializer
=
initializer
.
MSRAInitializer
())
self
.
assertEqual
(
len
(
block
.
ops
),
1
)
init_op
=
block
.
ops
[
0
]
self
.
assertEqual
(
init_op
.
type
,
'uniform_random'
)
limit
=
np
.
sqrt
(
6.0
/
param
.
shape
[
0
])
self
.
assertAlmostEqual
(
init_op
.
attr
(
'min'
),
-
limit
,
delta
=
DELTA
)
self
.
assertAlmostEqual
(
init_op
.
attr
(
'max'
),
limit
,
delta
=
DELTA
)
self
.
assertEqual
(
init_op
.
attr
(
'seed'
),
0
)
def
test_uniform_msra_initializer_conv
(
self
):
"""Test MSRA initializer with uniform distribution on
for convolutions.
"""
program
=
framework
.
Program
()
block
=
program
.
global_block
()
param
=
block
.
create_parameter
(
dtype
=
"float32"
,
shape
=
[
5
,
10
,
15
,
20
],
lod_level
=
0
,
name
=
"param"
,
initializer
=
initializer
.
MSRAInitializer
())
self
.
assertEqual
(
len
(
block
.
ops
),
1
)
init_op
=
block
.
ops
[
0
]
self
.
assertEqual
(
init_op
.
type
,
'uniform_random'
)
receptive_field_size
=
float
(
15
*
20
)
limit
=
np
.
sqrt
(
6.0
/
(
param
.
shape
[
1
]
*
receptive_field_size
))
self
.
assertAlmostEqual
(
init_op
.
attr
(
'min'
),
-
limit
,
delta
=
DELTA
)
self
.
assertAlmostEqual
(
init_op
.
attr
(
'max'
),
limit
,
delta
=
DELTA
)
self
.
assertEqual
(
init_op
.
attr
(
'seed'
),
0
)
def
test_normal_msra_initializer
(
self
):
"""Test MSRA initializer with normal distribution on
for matrix multiply.
"""
program
=
framework
.
Program
()
block
=
program
.
global_block
()
param
=
block
.
create_parameter
(
dtype
=
"float32"
,
shape
=
[
5
,
10
],
lod_level
=
0
,
name
=
"param"
,
initializer
=
initializer
.
MSRAInitializer
(
uniform
=
False
))
self
.
assertEqual
(
len
(
block
.
ops
),
1
)
init_op
=
block
.
ops
[
0
]
self
.
assertEqual
(
init_op
.
type
,
'gaussian_random'
)
std
=
np
.
sqrt
(
2.0
/
param
.
shape
[
0
])
self
.
assertAlmostEqual
(
init_op
.
attr
(
'mean'
),
0.0
,
delta
=
DELTA
)
self
.
assertAlmostEqual
(
init_op
.
attr
(
'std'
),
std
,
delta
=
DELTA
)
self
.
assertEqual
(
init_op
.
attr
(
'seed'
),
0
)
def
test_normal_msra_initializer_conv
(
self
):
"""Test MSRA initializer with normal distribution on
for convolutions.
"""
program
=
framework
.
Program
()
block
=
program
.
global_block
()
param
=
block
.
create_parameter
(
dtype
=
"float32"
,
shape
=
[
5
,
10
,
15
,
20
],
lod_level
=
0
,
name
=
"param"
,
initializer
=
initializer
.
MSRAInitializer
(
uniform
=
False
))
self
.
assertEqual
(
len
(
block
.
ops
),
1
)
init_op
=
block
.
ops
[
0
]
self
.
assertEqual
(
init_op
.
type
,
'gaussian_random'
)
receptive_field_size
=
float
(
15
*
20
)
std
=
np
.
sqrt
(
2.0
/
(
param
.
shape
[
1
]
*
receptive_field_size
))
self
.
assertAlmostEqual
(
init_op
.
attr
(
'mean'
),
0.0
,
delta
=
DELTA
)
self
.
assertAlmostEqual
(
init_op
.
attr
(
'std'
),
std
,
delta
=
DELTA
)
self
.
assertEqual
(
init_op
.
attr
(
'seed'
),
0
)
def
test_msra_initializer_supplied_arguments
(
self
):
"""Test the MSRA initializer with supplied arguments
"""
program
=
framework
.
Program
()
block
=
program
.
global_block
()
block
.
create_parameter
(
dtype
=
"float32"
,
shape
=
[
5
,
10
],
lod_level
=
0
,
name
=
"param"
,
initializer
=
initializer
.
MSRAInitializer
(
fan_in
=
12
,
seed
=
134
))
self
.
assertEqual
(
len
(
block
.
ops
),
1
)
init_op
=
block
.
ops
[
0
]
self
.
assertEqual
(
init_op
.
type
,
'uniform_random'
)
limit
=
np
.
sqrt
(
6.0
/
12
)
self
.
assertAlmostEqual
(
init_op
.
attr
(
'min'
),
-
limit
,
delta
=
DELTA
)
self
.
assertAlmostEqual
(
init_op
.
attr
(
'max'
),
limit
,
delta
=
DELTA
)
self
.
assertEqual
(
init_op
.
attr
(
'seed'
),
134
)
if
__name__
==
'__main__'
:
if
__name__
==
'__main__'
:
unittest
.
main
()
unittest
.
main
()
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