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23-06-05

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env 파일 이름 변경
돌연변이 설정 수정
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jung-geun
2023-07-05 18:42:28 +09:00
parent 174d68d518
commit e49d99a12d
8 changed files with 104 additions and 140773 deletions
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5
pso/__init__.py
View File

@@ -1,5 +1,6 @@
from .optimizer import Optimizer
from .particle import Particle
__all__ = [
'Optimizer'
'Optimizer',
'Particle'
]

82
pso/optimizer.py
View File

@@ -6,11 +6,10 @@ from datetime import datetime
import numpy as np
import tensorflow as tf
from pso.particle import Particle
from tensorflow import keras
from tqdm import tqdm
# import cupy as cp
from .particle import Particle
gpus = tf.config.experimental.list_physical_devices("GPU")
if gpus:
@@ -21,6 +20,10 @@ if gpus:
print(e)
class Optimizer:
"""
particle swarm optimization
PSO 실행을 위한 클래스
"""
def __init__(
self,
@@ -47,11 +50,12 @@ class Optimizer:
c1 (float): global rate - 전역 최적값 관성 수치
w_min (float): 최소 관성 수치
w_max (float): 최대 관성 수치
nefative_swarm (float): 최적해와 반대로 이동할 파티클 비율 - 0 ~ 1 사이의 값
momentun_swarm (float): 관성을 추가로 사용할 파티클 비율 - 0 ~ 1 사이의 값
negative_swarm (float): 최적해와 반대로 이동할 파티클 비율 - 0 ~ 1 사이의 값
mutation_swarm (float): 돌연변이가 일어날 확률
"""
np.random.seed(np_seed)
tf.random.set_seed(tf_seed)
self.model = model # 모델 구조
self.loss = loss # 손실함수
self.n_particles = n_particles # 파티클 개수
@@ -66,22 +70,30 @@ class Optimizer:
self.g_best = None # 최고 점수를 받은 가중치
self.g_best_ = None # 최고 점수를 받은 가중치 - 값의 분산을 위한 변수
self.avg_score = 0 # 평균 점수
negative_count = 0
for i in tqdm(range(self.n_particles), desc="Initializing Particles"):
m = keras.models.model_from_json(model.to_json())
init_weights = m.get_weights()
w_, sh_, len_ = self._encode(init_weights)
w_ = np.random.uniform(-0.5, 0.5, len(w_))
w_ = np.random.uniform(-1, 2, len(w_))
m.set_weights(self._decode(w_, sh_, len_))
m.compile(loss=self.loss, optimizer="sgd", metrics=["accuracy"])
self.particles[i] = Particle(
m, loss,
m,
loss,
negative=True if i < negative_swarm * self.n_particles else False,
mutation=True if i > self.n_particles * (1 - self.mutation_swarm) else False
mutation=mutation_swarm,
)
if i < negative_swarm * self.n_particles:
negative_count += 1
print(f"negative swarm : {negative_count} / {self.n_particles}")
print(f"mutation swarm : {mutation_swarm*100/self.n_particles} / {self.n_particles}")
gc.collect()
def __del__(self):
del self.model
del self.loss
@@ -110,46 +122,44 @@ class Optimizer:
(list) : 가중치의 원본 shape
(list) : 가중치의 원본 shape의 길이
"""
# w_gpu = cp.array([])
w_gpu = np.array([])
lenght = []
length = []
shape = []
for layer in weights:
shape.append(layer.shape)
w_ = layer.reshape(-1)
lenght.append(len(w_))
# w_gpu = cp.append(w_gpu, w_)
length.append(len(w_))
w_gpu = np.append(w_gpu, w_)
del weights
return w_gpu, shape, lenght
return w_gpu, shape, length
def _decode(self, weight, shape, lenght):
def _decode(self, weight, shape, length):
"""
_encode 로 인코딩된 가중치를 원본 shape으로 복원
파라미터는 encode의 리턴값을 그대로 사용을 권장
Args:
weight (numpy|cupy array): 가중치 - 1차원으로 풀어서 반환
weight (numpy array): 가중치 - 1차원으로 풀어서 반환
shape (list): 가중치의 원본 shape
lenght (list): 가중치의 원본 shape의 길이
length (list): 가중치의 원본 shape의 길이
Returns:
(list) : 가중치 원본 shape으로 복원
"""
weights = []
start = 0
for i in range(len(shape)):
end = start + lenght[i]
end = start + length[i]
w_ = weight[start:end]
# w_ = weight[start:end].get()
w_ = np.reshape(w_, shape[i])
# w_ = w_.reshape(shape[i])
weights.append(w_)
start = end
del weight
del shape
del lenght
del length
return weights
@@ -188,8 +198,8 @@ class Optimizer:
):
"""
Args:
x_test : numpy.ndarray,
y_test : numpy.ndarray,
x_test : numpy array,
y_test : numpy array,
epochs : int,
save : bool - True : save, False : not save
save_path : str ex) "./result",
@@ -248,6 +258,8 @@ class Optimizer:
f.write(", ")
else:
f.write("\n")
f.close()
del local_score
gc.collect()
@@ -255,7 +267,7 @@ class Optimizer:
try:
epochs_pbar = tqdm(range(epochs), desc=f"best {self.g_best_score[0]:.4f}|{self.g_best_score[1]:.4f}", ascii=True, leave=True)
for _ in epochs_pbar:
for epoch in epochs_pbar:
acc = 0
loss = 0
min_score = np.inf
@@ -264,15 +276,16 @@ class Optimizer:
max_loss = 0
ts = self.c0 + np.random.rand() * (self.c1 - self.c0)
g_, g_sh, g_len = self._encode(self.g_best)
decrement = (epochs - (_) + 1) / epochs
g_ = (1 - decrement) * g_ + decrement * ts
self.g_best_ = self._decode(g_, g_sh, g_len)
part_pbar = tqdm(range(len(self.particles)), desc=f"acc : {max_score:.4f} loss : {min_loss:.4f}", ascii=True, leave=False)
for i in part_pbar:
part_pbar.set_description(f"acc : {max_score:.4f} loss : {min_loss:.4f}")
w = self.w_max - (self.w_max - self.w_min) * _ / epochs
w = self.w_max - (self.w_max - self.w_min) * epoch / epochs
g_, g_sh, g_len = self._encode(self.g_best)
decrement = (epochs - (epoch) + 1) / epochs
g_ = (1 - decrement) * g_ + decrement * ts
self.g_best_ = self._decode(g_, g_sh, g_len)
if Dispersion:
g_best = self.g_best_
@@ -280,11 +293,12 @@ class Optimizer:
g_best = self.g_best
if empirical_balance:
if np.random.rand() < np.exp(-(_) / epochs):
if np.random.rand() < np.exp(-(epoch) / epochs):
w_p_ = self.f(x, y, self.particles[i].get_best_weights())
w_g_ = self.f(x, y, self.g_best)
w_p = w_p_ / (w_p_ + w_g_)
w_g = w_p_ / (w_p_ + w_g_)
del w_p_
del w_g_
@@ -301,6 +315,7 @@ class Optimizer:
p_ = np.exp(-1 * p_)
w_p = p_
w_g = 1 - p_
del p_b
del g_a
del l_b
@@ -362,11 +377,12 @@ class Optimizer:
f.write(", ")
else:
f.write("\n")
f.close()
if check_point is not None:
if _ % check_point == 0:
if epoch % check_point == 0:
os.makedirs(f"./{save_path}/{self.day}", exist_ok=True)
self._check_point_save(f"./{save_path}/{self.day}/ckpt-{_}")
self._check_point_save(f"./{save_path}/{self.day}/ckpt-{epoch}")
self.avg_score = acc / self.n_particles
gc.collect()
@@ -439,10 +455,12 @@ class Optimizer:
}
with open(
f"./{path}/{self.day}/{self.loss}_{self.n_particles}.json",
f"./{path}/{self.day}/{self.loss}_{self.g_best_score}.json",
"a",
) as f:
json.dump(json_save, f, indent=4)
f.close()
def _check_point_save(self, save_path: str = f"./result/check_point"):
"""

48
pso/particle.py
View File

@@ -1,6 +1,5 @@
import gc
# import cupy as cp
import numpy as np
import tensorflow as tf
from tensorflow import keras
@@ -9,8 +8,14 @@ from tensorflow import keras
class Particle:
"""
Particle Swarm Optimization의 Particle을 구현한 클래스
한 파티클의 life cycle은 다음과 같다.
1. 초기화
2. 손실 함수 계산
3. 속도 업데이트
4. 가중치 업데이트
5. 2번으로 돌아가서 반복
"""
def __init__(self, model: keras.models, loss, negative: bool = False, mutation: bool = False):
def __init__(self, model: keras.models, loss, negative: bool = False, mutation: float = 0):
"""
Args:
model (keras.models): 학습 및 검증을 위한 모델
@@ -21,7 +26,7 @@ class Particle:
self.loss = loss
init_weights = self.model.get_weights()
i_w_, s_, l_ = self._encode(init_weights)
i_w_ = np.random.rand(len(i_w_)) / 2 - 0.25
i_w_ = np.random.uniform(-0.5, 0.5, len(i_w_))
self.velocities = self._decode(i_w_, s_, l_)
self.negative = negative
self.mutation = mutation
@@ -52,44 +57,40 @@ class Particle:
(list) : 가중치의 원본 shape
(list) : 가중치의 원본 shape의 길이
"""
# w_gpu = cp.array([])
w_gpu = np.array([])
lenght = []
length = []
shape = []
for layer in weights:
shape.append(layer.shape)
w_ = layer.reshape(-1)
lenght.append(len(w_))
# w_gpu = cp.append(w_gpu, w_)
length.append(len(w_))
w_gpu = np.append(w_gpu, w_)
return w_gpu, shape, lenght
return w_gpu, shape, length
def _decode(self, weight: list, shape, lenght):
def _decode(self, weight: list, shape, length):
"""
_encode 로 인코딩된 가중치를 원본 shape으로 복원
파라미터는 encode의 리턴값을 그대로 사용을 권장
Args:
weight (numpy|cupy array): 가중치 - 1차원으로 풀어서 반환
weight (numpy array): 가중치 - 1차원으로 풀어서 반환
shape (list): 가중치의 원본 shape
lenght (list): 가중치의 원본 shape의 길이
length (list): 가중치의 원본 shape의 길이
Returns:
(list) : 가중치 원본 shape으로 복원
"""
weights = []
start = 0
for i in range(len(shape)):
end = start + lenght[i]
end = start + length[i]
w_ = weight[start:end]
# w_ = weight[start:end].get()
w_ = np.reshape(w_, shape[i])
# w_ = w_.reshape(shape[i])
weights.append(w_)
start = end
del start, end, w_
del shape, lenght
del shape, length
del weight
return weights
@@ -104,11 +105,10 @@ class Particle:
renewal (str, optional): 점수 갱신 방식. Defaults to "acc" | "acc" or "loss".
Returns:
_type_: _description_
(float): 점수
"""
self.model.compile(loss=self.loss, optimizer="sgd", metrics=["accuracy"])
score = self.model.evaluate(x, y, verbose=0)
# print(score)
if renewal == "acc":
if score[1] > self.best_score:
self.best_score = score[1]
@@ -127,7 +127,7 @@ class Particle:
현재 속도 업데이트
Args:
local_rate (flost): 지역 최적해의 영향력
local_rate (float): 지역 최적해의 영향력
global_rate (float): 전역 최적해의 영향력
w (float): 현재 속도의 영향력 - 관성 | 0.9 ~ 0.4 이 적당
g_best (list): 전역 최적해
@@ -150,9 +150,11 @@ class Particle:
+ local_rate * r0 * (encode_p - encode_w)
+ global_rate * r1 * (encode_g - encode_w)
)
if self.mutation:
if np.random.rand() < self.mutation:
new_v += 0.5 * encode_v
self.velocities = self._decode(new_v, w_sh, w_len)
del encode_w, w_sh, w_len
del encode_v, v_sh, v_len
del encode_p, p_sh, p_len
@@ -178,6 +180,7 @@ class Particle:
encode_g, g_sh, g_len = self._encode(weights=g_best)
r0 = np.random.rand()
r1 = np.random.rand()
if self.negative:
new_v = (
w * encode_v
@@ -193,6 +196,7 @@ class Particle:
if self.mutation:
new_v += 0.5 * encode_v
self.velocities = self._decode(new_v, w_sh, w_len)
del encode_w, w_sh, w_len
del encode_v, v_sh, v_len
del encode_p, p_sh, p_len
@@ -207,6 +211,7 @@ class Particle:
encode_v, v_sh, v_len = self._encode(weights=self.velocities)
new_w = encode_w + encode_v
self.model.set_weights(self._decode(new_w, w_sh, w_len))
del encode_w, w_sh, w_len
del encode_v, v_sh, v_len
@@ -220,10 +225,11 @@ class Particle:
weights (list): 가중치
Returns:
flost: 목적함수 값
float: 목적함수 값
"""
self.model.set_weights(weights)
score = self.model.evaluate(x, y, verbose=0)[1]
if score > 0:
return 1 / (1 + score)
else:
@@ -247,6 +253,7 @@ class Particle:
"""
self._update_velocity(local_rate, global_rate, w, g_best)
self._update_weights()
return self.get_score(x, y, renewal)
def step_w(
@@ -272,6 +279,7 @@ class Particle:
"""
self._update_velocity_w(local_rate, global_rate, w, w_p, w_g, g_best)
self._update_weights()
return self.get_score(x, y, renewal)
def get_best_score(self):