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体验新版 GitCode,发现更多精彩内容 >>
提交
e41f28cb
编写于
10月 31, 2017
作者:
A
Abhinav Arora
提交者:
zchen0211
10月 31, 2017
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差异文件
Adding a framework for variable initializers (#5232)
上级
ddde829a
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
128 addition
and
55 deletion
+128
-55
python/paddle/v2/framework/framework.py
python/paddle/v2/framework/framework.py
+4
-15
python/paddle/v2/framework/initializer.py
python/paddle/v2/framework/initializer.py
+109
-0
python/paddle/v2/framework/layer_helper.py
python/paddle/v2/framework/layer_helper.py
+4
-15
python/paddle/v2/framework/layers.py
python/paddle/v2/framework/layers.py
+7
-19
python/paddle/v2/framework/tests/test_recognize_digits_mlp.py
...on/paddle/v2/framework/tests/test_recognize_digits_mlp.py
+4
-6
未找到文件。
python/paddle/v2/framework/framework.py
浏览文件 @
e41f28cb
...
...
@@ -354,8 +354,8 @@ class Block(object):
def
create_var
(
self
,
*
args
,
**
kwargs
):
var
=
Variable
(
self
,
*
args
,
**
kwargs
)
if
'init
_att
r'
in
kwargs
:
self
.
_prepend_initialize_ops_
(
var
,
kwargs
[
'init_attr'
]
)
if
'init
ialize
r'
in
kwargs
:
kwargs
[
'initializer'
](
var
,
self
)
return
var
def
has_var
(
self
,
name
):
...
...
@@ -364,8 +364,8 @@ class Block(object):
def
create_parameter
(
self
,
*
args
,
**
kwargs
):
global_block
=
self
.
program
.
global_block
()
param
=
Parameter
(
global_block
,
*
args
,
**
kwargs
)
if
'init
_att
r'
in
kwargs
:
self
.
_prepend_initialize_ops_
(
param
,
kwargs
[
'init_attr'
]
)
if
'init
ialize
r'
in
kwargs
:
kwargs
[
'initializer'
](
param
,
self
)
return
param
def
append_op
(
self
,
*
args
,
**
kwargs
):
...
...
@@ -424,17 +424,6 @@ class Block(object):
for
index
in
range
(
len
(
self
.
ops
)):
assert
self
.
ops
[
index
].
desc
==
ops_in_cpp
[
index
]
def
_prepend_initialize_ops_
(
self
,
param
,
init_attr
):
op_type
=
init_attr
[
'type'
]
init_attr
[
'shape'
]
=
param
.
shape
init_attr
[
'data_type'
]
=
int
(
param
.
data_type
)
op
=
self
.
prepend_op
(
type
=
op_type
,
inputs
=
None
,
outputs
=
{
'Out'
:
[
param
]},
attrs
=
init_attr
)
param
.
op
=
op
class
Program
(
object
):
def
__init__
(
self
):
...
...
python/paddle/v2/framework/initializer.py
0 → 100644
浏览文件 @
e41f28cb
import
paddle.v2.framework.framework
as
framework
__all__
=
[
'ConstantInitializer'
,
'UniformInitializer'
]
class
Initializer
(
object
):
"""Base class for variable initializers
Defines the common interface of variable initializers.
They add operations to the init program that are used
to initialize variables. Users should not use this class
directly, but need to use one of its implementations.
"""
def
__init_
(
self
):
pass
def
__call__
(
self
,
param
,
block
):
"""Add corresponding initialization operations to the network
"""
raise
NotImplementedError
()
class
ConstantInitializer
(
Initializer
):
"""Implements the constant initializer
"""
def
__init__
(
self
,
value
=
0.0
):
"""Constructor for ConstantInitializer
Args:
value: constant value to initialize the variable
"""
assert
value
is
not
None
super
(
ConstantInitializer
,
self
).
__init__
()
self
.
_value
=
value
def
__call__
(
self
,
var
,
block
):
"""Add constant 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
)
# Initialization Ops should be prepended and not appended
op
=
block
.
prepend_op
(
type
=
"fill_constant"
,
outputs
=
{
"Out"
:
var
},
attrs
=
{
"shape"
:
var
.
shape
,
"data_type"
:
int
(
var
.
data_type
),
"value"
:
self
.
_value
})
var
.
op
=
op
return
op
class
UniformInitializer
(
Initializer
):
"""Implements for random uniform distribution initializer
"""
def
__init__
(
self
,
low
=-
1.0
,
high
=
1.0
,
seed
=
0
):
"""Constructor for UniformInitializer
Args:
low: lower boundary of the uniform distribution
high: upper boundary of the uniform distribution
seed: random seed
"""
assert
low
is
not
None
assert
high
is
not
None
assert
seed
is
not
None
super
(
UniformInitializer
,
self
).
__init__
()
self
.
_low
=
low
self
.
_high
=
high
self
.
_seed
=
seed
def
__call__
(
self
,
var
,
block
):
"""Add uniform distribution 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
)
# Initialization Ops should be prepended and not appended
op
=
block
.
prepend_op
(
type
=
"uniform_random"
,
outputs
=
{
"Out"
:
var
},
attrs
=
{
"shape"
:
var
.
shape
,
"data_type"
:
int
(
var
.
data_type
),
"min"
:
self
.
_low
,
"max"
:
self
.
_high
,
"seed"
:
self
.
_seed
})
var
.
op
=
op
return
op
python/paddle/v2/framework/layer_helper.py
浏览文件 @
e41f28cb
...
...
@@ -5,6 +5,8 @@ import paddle.v2.framework.core as core
from
paddle.v2.framework.framework
import
Variable
,
g_program
,
\
g_init_program
from
paddle.v2.framework.initializer
import
ConstantInitializer
,
\
UniformInitializer
def
unique_name
(
prefix
):
...
...
@@ -66,14 +68,7 @@ class LayerHelper(object):
@
property
def
param_attr
(
self
):
default
=
{
'name'
:
None
,
'init_attr'
:
{
'type'
:
'uniform_random'
,
'min'
:
-
1.0
,
'max'
:
1.0
}
}
default
=
{
'name'
:
None
,
'initializer'
:
UniformInitializer
()}
actual
=
self
.
kwargs
.
get
(
'param_attr'
,
None
)
if
actual
is
None
:
actual
=
default
...
...
@@ -83,13 +78,7 @@ class LayerHelper(object):
return
actual
def
bias_attr
(
self
):
default
=
{
'name'
:
None
,
'init_attr'
:
{
'type'
:
'fill_constant'
,
'value'
:
0.0
}
}
default
=
{
'name'
:
None
,
'initializer'
:
ConstantInitializer
()}
bias_attr
=
self
.
kwargs
.
get
(
'bias_attr'
,
None
)
if
bias_attr
is
True
:
bias_attr
=
default
...
...
python/paddle/v2/framework/layers.py
浏览文件 @
e41f28cb
from
paddle.v2.framework.layer_helper
import
LayerHelper
,
unique_name
import
paddle.v2.framework.core
as
core
from
paddle.v2.framework.framework
import
OpProtoHolder
,
Variable
,
Program
from
paddle.v2.framework.initializer
import
ConstantInitializer
import
re
__all__
=
[
...
...
@@ -440,26 +441,12 @@ def batch_norm(input,
else
:
raise
ValueError
(
"unsupported data layout:"
+
data_layout
)
def
get_init_attr
(
value
):
if
not
isinstance
(
value
,
float
):
raise
ValueError
(
"attr value should be a float"
)
return
{
'type'
:
'fill_constant'
,
'value'
:
value
}
def
prepend_init_op
(
var
,
init_attr
):
assert
isinstance
(
var
,
Variable
)
op_type
=
init_attr
[
'type'
]
init_attr
[
'shape'
]
=
var
.
shape
init_attr
[
'data_type'
]
=
int
(
var
.
data_type
)
op
=
var
.
block
.
prepend_op
(
type
=
op_type
,
inputs
=
None
,
outputs
=
{
'Out'
:
[
var
]},
attrs
=
init_attr
)
return
op
def
create_persistable_var
(
dtype
,
shape
,
init_attr
=
None
):
def
create_persistable_var
(
dtype
,
shape
,
initializer
=
None
):
name
=
unique_name
(
"."
.
join
([
helper
.
name
,
"xxxx"
]))
var
=
init_program
.
global_block
().
create_var
(
dtype
=
dtype
,
shape
=
shape
,
name
=
name
,
persistable
=
True
)
if
'init_attr'
is
not
None
:
prepend_init_op
(
var
,
init_attr
)
if
initializer
is
not
None
:
initializer
(
var
,
var
.
block
)
return
program
.
global_block
().
create_var
(
name
=
name
,
dtype
=
dtype
,
shape
=
shape
,
persistable
=
True
)
...
...
@@ -472,8 +459,9 @@ def batch_norm(input,
attr
=
helper
.
param_attr
,
shape
=
param_shape
,
dtype
=
dtype
)
# create input
mean
=
create_persistable_var
(
dtype
,
param_shape
,
get_init_attr
(
0.0
))
variance
=
create_persistable_var
(
dtype
,
param_shape
,
get_init_attr
(
1.0
))
mean
=
create_persistable_var
(
dtype
,
param_shape
,
ConstantInitializer
(
0.0
))
variance
=
create_persistable_var
(
dtype
,
param_shape
,
ConstantInitializer
(
1.0
))
# create output
# mean and mean_out share the same memory
...
...
python/paddle/v2/framework/tests/test_recognize_digits_mlp.py
浏览文件 @
e41f28cb
...
...
@@ -3,9 +3,10 @@ import paddle.v2.framework.layers as layers
import
paddle.v2.framework.core
as
core
import
paddle.v2.framework.optimizer
as
optimizer
from
paddle.v2.framework.framework
import
Program
,
g_program
from
paddle.v2.framework.framework
import
Program
from
paddle.v2.framework.executor
import
Executor
from
paddle.v2.framework.regularizer
import
L2DecayRegularizer
from
paddle.v2.framework.initializer
import
UniformInitializer
import
numpy
as
np
...
...
@@ -21,11 +22,8 @@ image = layers.data(
param_attr
=
{
'name'
:
None
,
'init_attr'
:
{
'type'
:
'uniform_random'
,
'min'
:
-
1.0
,
'max'
:
1.0
},
'initializer'
:
UniformInitializer
(
low
=-
1.0
,
high
=
1.0
),
'regularization'
:
L2DecayRegularizer
(
0.0005
*
BATCH_SIZE
)
}
...
...
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