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f189ad74
编写于
1月 17, 2018
作者:
F
fengjiayi
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电子邮件补丁
差异文件
refine the defination of class GradientClipByGlobalNorm
上级
adc26dff
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1
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1 changed file
with
27 addition
and
20 deletion
+27
-20
python/paddle/v2/fluid/clip.py
python/paddle/v2/fluid/clip.py
+27
-20
未找到文件。
python/paddle/v2/fluid/clip.py
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f189ad74
...
...
@@ -93,41 +93,48 @@ class GradientClipByNorm(BaseGradientClipAttr):
class
GradientClipByGlobalNorm
(
BaseGradientClipAttr
):
global_norm_var
=
None
clip_norm_var
=
None
ratio
_var
=
None
scale
_var
=
None
@
classmethod
def
init
(
cls
,
clip_norm
):
if
not
(
isinstance
(
clip_norm
,
int
)
or
isinstance
(
clip_norm
,
float
)):
raise
TypeError
(
"The 'clip_norm' must be a value of int or float"
)
cls
.
global_norm_var
=
layers
.
fill_constant
(
shape
=
[
1
],
dtype
=
"float32"
,
value
=
0.0
)
cls
.
clip_norm_var
=
layers
.
fill_constant
(
shape
=
[
1
],
dtype
=
"float32"
,
value
=
clip_norm
)
def
__init__
(
self
):
if
not
(
isinstance
(
self
.
__class__
.
global_norm_var
,
Variable
)
and
isinstance
(
self
.
__class__
.
clip_norm_var
,
Variable
)):
@
classmethod
def
check_init
(
cls
):
if
not
(
isinstance
(
cls
.
global_norm_var
,
Variable
)
and
isinstance
(
cls
.
clip_norm_var
,
Variable
)):
raise
ValueError
(
"Class 'GradientClipByGlobalNorm' has not been properly initialized. Please call GradientClipByGlobalNorm.init() first."
)
"Class 'GradientClipByGlobalNorm' has not been properly initialized.
\
Please call GradientClipByGlobalNorm.init() first."
)
@
classmethod
def
process_context
(
cls
,
context
,
param
,
grad
):
cls
.
check_init
()
def
process_context
(
self
,
context
,
param
,
grad
):
local_norm_var
=
layers
.
reduce_sum
(
x
=
layers
.
pow
(
x
=
grad
,
factor
=
2
),
reduce_all
=
True
)
layers
.
sums
(
input
=
[
local_norm_var
,
self
.
__class__
.
global_norm_var
],
out
=
[
self
.
__class__
.
global_norm_var
])
input
=
[
local_norm_var
,
cls
.
global_norm_var
],
out
=
[
cls
.
global_norm_var
])
def
create_operators
(
self
,
param
,
grad
):
if
self
.
__class__
.
ratio_var
is
None
:
self
.
__class__
.
global_norm_var
=
layers
.
sqrt
(
x
=
self
.
__class__
.
global_norm_var
)
self
.
__class__
.
ratio_var
=
layers
.
elementwise_div
(
x
=
self
.
__class__
.
clip_norm_var
,
@
classmethod
def
create_operators
(
cls
,
param
,
grad
):
cls
.
check_init
()
if
cls
.
scale_var
is
None
:
cls
.
global_norm_var
=
layers
.
sqrt
(
x
=
cls
.
global_norm_var
)
cls
.
scale_var
=
layers
.
elementwise_div
(
x
=
cls
.
clip_norm_var
,
y
=
layers
.
elementwise_max
(
x
=
self
.
__class__
.
clip_norm_var
,
y
=
self
.
__class__
.
global_norm_var
))
# 缺乏elementwise_max
# 没法将ratio_var送给scale_op。
# new_grad = layers.
x
=
cls
.
clip_norm_var
,
y
=
cls
.
global_norm_var
))
new_grad
=
layers
.
elementwise_mul
(
x
=
grad
,
y
=
cls
.
scale_var
)
return
param
,
new_grad
def
append_gradient_clip_ops
(
param_grad
):
...
...
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