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Wang, Mia
MetaRL
Commits
e1828d43
Commit
e1828d43
authored
2 years ago
by
Sun Jin Kim
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Merge branch 'master' of gitlab.doc.ic.ac.uk:yw21218/metarl
parents
7bffb088
c361532f
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Pipeline
#272640
passed
2 years ago
Stage: test
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2
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1
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2 changed files
autoaug/autoaugment_learners/GenLearner.py
+10
-12
10 additions, 12 deletions
autoaug/autoaugment_learners/GenLearner.py
ttest_gen.py
+6
-5
6 additions, 5 deletions
ttest_gen.py
with
16 additions
and
17 deletions
autoaug/autoaugment_learners/
g
en
_l
earner.py
→
autoaug/autoaugment_learners/
G
en
L
earner.py
+
10
−
12
View file @
e1828d43
...
...
@@ -171,38 +171,36 @@ class Genetic_learner(AaLearner):
def
generate_children
(
self
):
parent_acc
=
sorted
(
self
.
history
,
key
=
lambda
x
:
x
[
1
],
reverse
=
True
)
[:
self
.
sp_num
]
parent_acc
=
sorted
(
self
.
history
,
key
=
lambda
x
:
x
[
1
],
reverse
=
True
)
parents
=
[
x
[
0
]
for
x
in
parent_acc
]
parents_weights
=
[
x
[
1
]
for
x
in
parent_acc
]
new_pols
=
[]
for
_
in
range
(
self
.
num_offspring
):
parent1
,
parent2
=
self
.
choose_parents
(
parents
,
parents_weights
)
cross_over
=
random
.
randrange
(
1
,
len
(
parent2
),
1
)
cross_over
=
random
.
randrange
(
1
,
int
(
len
(
parent2
)
/
2
),
1
)
cross_over2
=
random
.
randrange
(
int
(
len
(
parent2
)
/
2
),
int
(
len
(
parent2
)),
1
)
child
=
parent1
[:
cross_over
]
child
+=
parent2
[
cross_over
:]
child
+=
parent2
[
cross_over
:
int
(
len
(
parent2
)
/
2
)]
child
+=
parent1
[
int
(
len
(
parent2
)
/
2
):
int
(
len
(
parent2
)
/
2
)
+
cross_over2
]
child
+=
parent2
[
int
(
len
(
parent2
)
/
2
)
+
cross_over2
:]
new_pols
.
append
(
child
)
return
new_pols
def
learn
(
self
,
train_dataset
,
test_dataset
,
child_network_architecture
,
iterations
=
10
):
def
learn
(
self
,
train_dataset
,
test_dataset
,
child_network_architecture
,
iterations
=
10
0
):
for
idx
in
range
(
iterations
):
print
(
"
iteration
:
"
,
idx
)
if
len
(
self
.
history
)
<
self
.
sp_
num
:
print
(
"
ITERATION
:
"
,
idx
)
if
len
(
self
.
history
)
<
self
.
num
_offspring
:
policy
=
[
self
.
gen_random_subpol
()]
else
:
policy
=
self
.
bin_to_subpol
(
random
.
choice
(
self
.
generate_children
()))
print
(
"
policyu:
"
,
policy
)
reward
=
self
.
_test_autoaugment_policy
(
policy
,
child_network_architecture
,
train_dataset
,
test_dataset
)
print
(
"
reward:
"
,
reward
)
print
(
"
new len hsitory:
"
,
len
(
self
.
history
))
print
(
"
hsitory:
"
,
self
.
history
)
print
(
"
reward:
"
,
reward
)
...
...
This diff is collapsed.
Click to expand it.
ttest_gen.py
+
6
−
5
View file @
e1828d43
...
...
@@ -6,7 +6,7 @@ import torchvision
import
autoaug.child_networks
as
cn
from
autoaug.autoaugment_learners.AaLearner
import
AaLearner
from
autoaug.autoaugment_learners.
g
en
_l
earner
import
Genetic_learner
from
autoaug.autoaugment_learners.
G
en
L
earner
import
Genetic_learner
import
random
...
...
@@ -29,14 +29,15 @@ agent = Genetic_learner(
learning_rate
=
0.05
,
max_epochs
=
float
(
'
inf
'
),
early_stop_num
=
10
,
num_offspring
=
10
)
agent
.
learn
(
train_dataset
,
test_dataset
,
child_network_architecture
=
child_network_architecture
,
iterations
=
10
)
iterations
=
10
0
)
# with open('randomsearch_logs.pkl', 'wb') as file:
# pickle.dump(self.history, file)
print
(
agent
.
history
)
\ No newline at end of file
# with open('genetic_logs.pkl', 'wb') as file:
# pickle.dump(agent.history, file)
print
(
sorted
(
agent
.
history
,
key
=
lambda
x
:
x
[
1
]))
\ No newline at end of file
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