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esde
machine-learning
MLAir
Commits
602ecea1
Commit
602ecea1
authored
5 years ago
by
lukas leufen
Browse files
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learning rate decay is implemented with docs and test /close
#5
parent
38849c46
No related branches found
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2 merge requests
!9
new version v0.2.0
,
!7
l_p_loss and lrdecay implementation
Pipeline
#25747
passed
5 years ago
Stage: test
Stage: pages
Stage: deploy
Changes
2
Pipelines
1
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2 changed files
src/helpers.py
+46
-23
46 additions, 23 deletions
src/helpers.py
test/test_helpers.py
+38
-2
38 additions, 2 deletions
test/test_helpers.py
with
84 additions
and
25 deletions
src/helpers.py
+
46
−
23
View file @
602ecea1
...
@@ -6,6 +6,7 @@ import logging
...
@@ -6,6 +6,7 @@ import logging
import
keras
import
keras
import
keras.backend
as
K
import
keras.backend
as
K
import
math
import
math
from
typing
import
Union
def
to_list
(
arg
):
def
to_list
(
arg
):
...
@@ -26,35 +27,57 @@ def l_p_loss(power: int):
...
@@ -26,35 +27,57 @@ def l_p_loss(power: int):
return
loss
return
loss
class
lrDecay
(
keras
.
callbacks
.
History
):
class
LearningRateDecay
(
keras
.
callbacks
.
History
):
"""
Decay learning rate during model training. Start with a base learning rate and lower this rate after every
n(=epochs_drop) epochs by drop value (0, 1], drop value = 1 means no decay in learning rate.
"""
def
__init__
(
self
,
base_lr
:
float
=
0.01
,
drop
:
float
=
0.96
,
epochs_drop
:
int
=
8
):
def
__init__
(
self
,
base_lr
:
float
=
0.01
,
drop
:
float
=
0.96
,
epochs_drop
:
int
=
8
):
super
(
lrDecay
,
self
).
__init__
()
super
().
__init__
()
self
.
lr
=
{
'
lr
'
:
[]}
self
.
lr
=
{
'
lr
'
:
[]}
self
.
base_lr
=
base_lr
self
.
base_lr
=
self
.
check_param
(
base_lr
,
'
base_lr
'
)
self
.
drop
=
drop
self
.
drop
=
self
.
check_param
(
drop
,
'
drop
'
)
self
.
epochs_drop
=
epochs_drop
self
.
epochs_drop
=
self
.
check_param
(
epochs_drop
,
'
epochs_drop
'
,
upper
=
None
)
def
on_epoch_begin
(
self
,
epoch
:
int
,
logs
=
None
):
@staticmethod
if
epoch
>
0
:
def
check_param
(
value
:
float
,
name
:
str
,
lower
:
Union
[
float
,
None
]
=
0
,
upper
:
Union
[
float
,
None
]
=
1
):
current_lr
=
self
.
base_lr
*
math
.
pow
(
self
.
drop
,
math
.
floor
(
1
+
epoch
)
/
self
.
epochs_drop
)
"""
Check if given value is in interval. The left (lower) endpoint is open, right (upper) endpoint is closed. To
only one side of the interval, set the other endpoint to None. If both ends are set to None, just return the
value without any check.
:param value: value to check
:param name: name of the variable to display in error message
:param lower: left (lower) endpoint of interval, opened
:param upper: right (upper) endpoint of interval, closed
:return: unchanged value or raise ValueError
"""
if
all
(
v
is
not
None
for
v
in
[
lower
,
upper
]):
if
lower
<
value
<=
upper
:
return
value
else
:
raise
ValueError
(
f
"
{
name
}
is out of allowed range (
{
lower
}
,
{
upper
}
]:
{
name
}
=
{
value
}
"
)
elif
lower
is
not
None
:
if
lower
<
value
:
return
value
else
:
else
:
current_lr
=
self
.
base_lr
raise
ValueError
(
f
"
{
name
}
is out of allowed range (
{
lower
}
, +inf):
{
name
}
=
{
value
}
"
)
elif
upper
is
not
None
:
if
value
<=
upper
:
return
value
else
:
raise
ValueError
(
f
"
{
name
}
is out of allowed range (-inf,
{
upper
}
]:
{
name
}
=
{
value
}
"
)
return
value
def
on_epoch_begin
(
self
,
epoch
:
int
,
logs
=
None
):
"""
Lower learning rate every epochs_drop epochs by factor drop.
:param epoch: current epoch
:param logs: ?
:return: update keras learning rate
"""
current_lr
=
self
.
base_lr
*
math
.
pow
(
self
.
drop
,
math
.
floor
(
epoch
/
self
.
epochs_drop
))
K
.
set_value
(
self
.
model
.
optimizer
.
lr
,
current_lr
)
K
.
set_value
(
self
.
model
.
optimizer
.
lr
,
current_lr
)
self
.
lr
[
'
lr
'
].
append
(
current_lr
)
self
.
lr
[
'
lr
'
].
append
(
current_lr
)
logging
.
info
(
f
"
Set learning rate to
{
current_lr
}
"
)
logging
.
info
(
f
"
Set learning rate to
{
current_lr
}
"
)
return
K
.
get_value
(
self
.
model
.
optimizer
.
lr
)
return
K
.
get_value
(
self
.
model
.
optimizer
.
lr
)
class
lrCallback
(
keras
.
callbacks
.
History
):
def
__init__
(
self
):
super
(
lrCallback
,
self
).
__init__
()
self
.
lr
=
None
def
on_train_begin
(
self
,
logs
=
None
):
self
.
lr
=
{}
def
on_epoch_end
(
self
,
epoch
,
logs
=
None
):
self
.
lr
.
append
(
self
.
model
.
optimizer
.
lr
)
\ No newline at end of file
This diff is collapsed.
Click to expand it.
test/test_helpers.py
+
38
−
2
View file @
602ecea1
import
pytest
import
pytest
from
src.helpers
import
l_p_loss
from
src.helpers
import
l_p_loss
,
LearningRateDecay
import
logging
import
logging
import
os
import
os
import
keras
import
keras
import
keras.backend
as
K
import
numpy
as
np
import
numpy
as
np
...
@@ -19,3 +18,40 @@ class TestLoss:
...
@@ -19,3 +18,40 @@ class TestLoss:
hist
=
model
.
fit
(
np
.
array
([
1
,
0
,
-
2
,
0.5
]),
np
.
array
([
1
,
1
,
0
,
0.5
]),
epochs
=
1
)
hist
=
model
.
fit
(
np
.
array
([
1
,
0
,
-
2
,
0.5
]),
np
.
array
([
1
,
1
,
0
,
0.5
]),
epochs
=
1
)
assert
hist
.
history
[
'
loss
'
][
0
]
==
2.25
assert
hist
.
history
[
'
loss
'
][
0
]
==
2.25
class
TestLearningRateDecay
:
def
test_init
(
self
):
lr_decay
=
LearningRateDecay
()
assert
lr_decay
.
lr
==
{
'
lr
'
:
[]}
assert
lr_decay
.
base_lr
==
0.01
assert
lr_decay
.
drop
==
0.96
assert
lr_decay
.
epochs_drop
==
8
def
test_check_param
(
self
):
lr_decay
=
object
.
__new__
(
LearningRateDecay
)
assert
lr_decay
.
check_param
(
1
,
"
tester
"
)
==
1
assert
lr_decay
.
check_param
(
0.5
,
"
tester
"
)
==
0.5
with
pytest
.
raises
(
ValueError
)
as
e
:
lr_decay
.
check_param
(
0
,
"
tester
"
)
assert
"
tester is out of allowed range (0, 1]: tester=0
"
in
e
.
value
.
args
[
0
]
with
pytest
.
raises
(
ValueError
)
as
e
:
lr_decay
.
check_param
(
1.5
,
"
tester
"
)
assert
"
tester is out of allowed range (0, 1]: tester=1.5
"
in
e
.
value
.
args
[
0
]
assert
lr_decay
.
check_param
(
1.5
,
"
tester
"
,
upper
=
None
)
==
1.5
with
pytest
.
raises
(
ValueError
)
as
e
:
lr_decay
.
check_param
(
0
,
"
tester
"
,
upper
=
None
)
assert
"
tester is out of allowed range (0, +inf): tester=0
"
in
e
.
value
.
args
[
0
]
assert
lr_decay
.
check_param
(
0.5
,
"
tester
"
,
lower
=
None
)
==
0.5
with
pytest
.
raises
(
ValueError
)
as
e
:
lr_decay
.
check_param
(
0.5
,
"
tester
"
,
lower
=
None
,
upper
=
0.2
)
assert
"
tester is out of allowed range (-inf, 0.2]: tester=0.5
"
in
e
.
value
.
args
[
0
]
assert
lr_decay
.
check_param
(
10
,
"
tester
"
,
upper
=
None
,
lower
=
None
)
def
test_on_epoch_begin
(
self
):
lr_decay
=
LearningRateDecay
(
base_lr
=
0.02
,
drop
=
0.95
,
epochs_drop
=
2
)
model
=
keras
.
Sequential
()
model
.
add
(
keras
.
layers
.
Dense
(
1
,
input_dim
=
1
))
model
.
compile
(
optimizer
=
keras
.
optimizers
.
Adam
(),
loss
=
l_p_loss
(
2
))
model
.
fit
(
np
.
array
([
1
,
0
,
2
,
0.5
]),
np
.
array
([
1
,
1
,
0
,
0.5
]),
epochs
=
5
,
callbacks
=
[
lr_decay
])
assert
lr_decay
.
lr
[
'
lr
'
]
==
[
0.02
,
0.02
,
0.02
*
0.95
,
0.02
*
0.95
,
0.02
*
0.95
*
0.95
]
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