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f8c6dada
编写于
10月 25, 2017
作者:
A
Abhinav Arora
提交者:
GitHub
10月 25, 2017
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差异文件
Implementing the python wrapper for Adamax optimizer (#5061)
上级
39a6f43b
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
157 addition
and
2 deletion
+157
-2
python/paddle/v2/framework/optimizer.py
python/paddle/v2/framework/optimizer.py
+108
-2
python/paddle/v2/framework/tests/test_optimizer.py
python/paddle/v2/framework/tests/test_optimizer.py
+49
-0
未找到文件。
python/paddle/v2/framework/optimizer.py
浏览文件 @
f8c6dada
...
@@ -4,7 +4,8 @@ import paddle.v2.framework.framework as framework
...
@@ -4,7 +4,8 @@ import paddle.v2.framework.framework as framework
from
paddle.v2.framework.backward
import
append_backward_ops
from
paddle.v2.framework.backward
import
append_backward_ops
__all__
=
[
__all__
=
[
'SGDOptimizer'
,
'MomentumOptimizer'
,
'AdagradOptimizer'
,
'AdamOptimizer'
'SGDOptimizer'
,
'MomentumOptimizer'
,
'AdagradOptimizer'
,
'AdamOptimizer'
,
'AdamaxOptimizer'
]
]
...
@@ -399,7 +400,7 @@ class AdamOptimizer(Optimizer):
...
@@ -399,7 +400,7 @@ class AdamOptimizer(Optimizer):
param_and_grad
[
0
])
param_and_grad
[
0
])
moment2
=
self
.
_get_accumulator
(
self
.
_moment2_acc_str
,
moment2
=
self
.
_get_accumulator
(
self
.
_moment2_acc_str
,
param_and_grad
[
0
])
param_and_grad
[
0
])
# create the
momentu
m optimize op
# create the
ada
m optimize op
adam_op
=
block
.
append_op
(
adam_op
=
block
.
append_op
(
type
=
self
.
type
,
type
=
self
.
type
,
inputs
=
{
inputs
=
{
...
@@ -442,3 +443,108 @@ class AdamOptimizer(Optimizer):
...
@@ -442,3 +443,108 @@ class AdamOptimizer(Optimizer):
attrs
=
{
"scale"
:
self
.
_beta2
})
attrs
=
{
"scale"
:
self
.
_beta2
})
return
[
scale_beta1
,
scale_beta2
]
return
[
scale_beta1
,
scale_beta2
]
class
AdamaxOptimizer
(
Optimizer
):
"""Implements the Adamax Optimizer
"""
_moment_acc_str
=
"moment"
_inf_norm_acc_str
=
"inf_norm"
def
__init__
(
self
,
learning_rate
=
0.001
,
beta1
=
0.9
,
beta2
=
0.999
,
epsilon
=
1e-8
):
assert
learning_rate
is
not
None
assert
beta1
is
not
None
assert
beta2
is
not
None
assert
epsilon
is
not
None
super
(
AdamaxOptimizer
,
self
).
__init__
()
self
.
type
=
"adamax"
self
.
_learning_rate
=
learning_rate
self
.
_beta1
=
beta1
self
.
_beta2
=
beta2
self
.
_epsilon
=
epsilon
def
_initialize_tensors
(
self
,
block
):
assert
isinstance
(
block
,
framework
.
Block
)
lr_shape
=
[
1
]
# create a variable for learning_rate
self
.
_lr
=
block
.
create_var
(
dtype
=
"float32"
,
shape
=
lr_shape
,
lod_level
=
0
)
# create an op to init the learning_rate
# FIXME: Fix when Initialization design has been implemented
# https://github.com/PaddlePaddle/Paddle/pull/4852
block
.
append_op
(
type
=
"fill_constant"
,
outputs
=
{
"Out"
:
self
.
_lr
},
attrs
=
{
"shape"
:
lr_shape
,
"value"
:
self
.
_learning_rate
})
def
_create_accumulators
(
self
,
block
,
parameters
):
assert
isinstance
(
block
,
framework
.
Block
)
global_block
=
block
.
program
.
global_block
()
# Create beta1 power accumulator tensor
beta_shape
=
[
1
]
self
.
_beta1_pow_acc
=
global_block
.
create_var
(
dtype
=
"float32"
,
shape
=
beta_shape
,
lod_level
=
0
)
# Initialize beta1 power accumulator
# FIXME: Fix when Initialization design has been implemented
# https://github.com/PaddlePaddle/Paddle/pull/4852
global_block
.
append_op
(
type
=
"fill_constant"
,
outputs
=
{
"Out"
:
self
.
_beta1_pow_acc
},
attrs
=
{
"shape"
:
beta_shape
,
"value"
:
self
.
_beta1
})
# Create accumulator tensors for first moment and infinity norm
for
p
in
parameters
:
self
.
_add_accumulator
(
block
,
self
.
_moment_acc_str
,
p
,
'float32'
)
self
.
_add_accumulator
(
block
,
self
.
_inf_norm_acc_str
,
p
,
'float32'
)
def
_append_optimize_op
(
self
,
block
,
param_and_grad
):
assert
isinstance
(
block
,
framework
.
Block
)
moment
=
self
.
_get_accumulator
(
self
.
_moment_acc_str
,
param_and_grad
[
0
])
inf_norm
=
self
.
_get_accumulator
(
self
.
_inf_norm_acc_str
,
param_and_grad
[
0
])
# create the adamax optimize op
adamax_op
=
block
.
append_op
(
type
=
self
.
type
,
inputs
=
{
"Param"
:
param_and_grad
[
0
],
"Grad"
:
param_and_grad
[
1
],
"LearningRate"
:
self
.
_lr
,
"Moment"
:
moment
,
"InfNorm"
:
inf_norm
,
"Beta1Pow"
:
self
.
_beta1_pow_acc
},
outputs
=
{
"ParamOut"
:
param_and_grad
[
0
],
"MomentOut"
:
moment
,
"InfNormOut"
:
inf_norm
},
attrs
=
{
"beta1"
:
self
.
_beta1
,
"beta2"
:
self
.
_beta2
,
"epsilon"
:
self
.
_epsilon
})
return
adamax_op
def
_finish_update
(
self
,
block
):
"""Update Beta1 Power accumulator
"""
assert
isinstance
(
block
,
framework
.
Block
)
global_block
=
block
.
program
.
global_block
()
scale_beta1
=
global_block
.
append_op
(
type
=
"scale"
,
inputs
=
{
"X"
:
self
.
_beta1_pow_acc
},
outputs
=
{
"Out"
:
self
.
_beta1_pow_acc
},
attrs
=
{
"scale"
:
self
.
_beta1
})
return
[
scale_beta1
]
python/paddle/v2/framework/tests/test_optimizer.py
浏览文件 @
f8c6dada
...
@@ -196,5 +196,54 @@ class TestAdamOptimizer(unittest.TestCase):
...
@@ -196,5 +196,54 @@ class TestAdamOptimizer(unittest.TestCase):
self
.
assertTrue
(
mul_x
.
name
in
moment2_acc
)
self
.
assertTrue
(
mul_x
.
name
in
moment2_acc
)
class
TestAdamaxOptimizer
(
unittest
.
TestCase
):
class
MockAdamax
(
optimizer
.
AdamaxOptimizer
):
def
get_accumulators
(
self
):
return
self
.
_accumulators
def
get_moment_str
(
self
):
return
self
.
_moment_acc_str
def
get_inf_norm_str
(
self
):
return
self
.
_inf_norm_acc_str
def
test_adamax_optimizer
(
self
):
program
=
framework
.
Program
()
block
=
program
.
global_block
()
mul_x
=
block
.
create_parameter
(
dtype
=
"float32"
,
shape
=
[
5
,
10
],
lod_level
=
0
,
name
=
"mul.x"
)
mul_y
=
block
.
create_var
(
dtype
=
"float32"
,
shape
=
[
10
,
8
],
lod_level
=
0
,
name
=
"mul.y"
)
mul_out
=
block
.
create_var
(
dtype
=
"float32"
,
shape
=
[
5
,
8
],
lod_level
=
0
,
name
=
"mul.out"
)
block
.
append_op
(
type
=
"mul"
,
inputs
=
{
"X"
:
mul_x
,
"Y"
:
mul_y
},
outputs
=
{
"Out"
:
mul_out
},
attrs
=
{
"x_num_col_dims"
:
1
})
adamax_optimizer
=
self
.
MockAdamax
(
learning_rate
=
0.01
,
beta1
=
0.9
,
beta2
=
0.999
)
params_grads
=
append_backward_ops
(
mul_out
)
self
.
assertEqual
(
len
(
params_grads
),
1
)
self
.
assertEqual
(
len
(
adamax_optimizer
.
get_accumulators
()),
0
)
opts
=
adamax_optimizer
.
create_optimization_pass
(
params_grads
,
mul_out
)
self
.
assertEqual
(
len
(
opts
),
2
)
adam_op
=
opts
[
0
]
self
.
assertEqual
(
adam_op
.
type
,
"adamax"
)
# Check accumulators
accumulators
=
adamax_optimizer
.
get_accumulators
()
self
.
assertEqual
(
len
(
accumulators
),
2
)
self
.
assertTrue
(
adamax_optimizer
.
get_moment_str
()
in
accumulators
)
self
.
assertTrue
(
adamax_optimizer
.
get_inf_norm_str
()
in
accumulators
)
moment_acc
=
accumulators
[
adamax_optimizer
.
get_moment_str
()]
inf_norm_acc
=
accumulators
[
adamax_optimizer
.
get_inf_norm_str
()]
self
.
assertEqual
(
len
(
moment_acc
),
1
)
self
.
assertEqual
(
len
(
inf_norm_acc
),
1
)
self
.
assertTrue
(
mul_x
.
name
in
moment_acc
)
self
.
assertTrue
(
mul_x
.
name
in
inf_norm_acc
)
if
__name__
==
'__main__'
:
if
__name__
==
'__main__'
:
unittest
.
main
()
unittest
.
main
()
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