mirror of
https://github.com/jung-geun/PSO.git
synced 2025-12-19 20:44:39 +09:00
361 lines
13 KiB
Python
361 lines
13 KiB
Python
import os
|
|
import sys
|
|
|
|
import tensorflow as tf
|
|
from tensorflow import keras
|
|
|
|
import numpy as np
|
|
|
|
# import cupy as cp
|
|
|
|
from tqdm import tqdm
|
|
from datetime import datetime
|
|
import json
|
|
import gc
|
|
from copy import copy, deepcopy
|
|
|
|
from pso.particle import Particle
|
|
|
|
|
|
class Optimizer:
|
|
"""
|
|
Args:
|
|
model (keras.models): 모델 구조
|
|
loss (str): 손실함수
|
|
n_particles (int): 파티클 개수
|
|
c0 (float): local rate - 지역 최적값 관성 수치
|
|
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
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
model: keras.models,
|
|
loss="mse",
|
|
n_particles: int = 10,
|
|
c0=0.5,
|
|
c1=1.5,
|
|
w_min=0.5,
|
|
w_max=1.5,
|
|
negative_swarm: float = 0,
|
|
):
|
|
self.model = model # 모델 구조
|
|
self.loss = loss # 손실함수
|
|
self.n_particles = n_particles # 파티클 개수
|
|
self.particles = [None] * n_particles # 파티클 리스트
|
|
self.c0 = c0 # local rate - 지역 최적값 관성 수치
|
|
self.c1 = c1 # global rate - 전역 최적값 관성 수치
|
|
self.w_min = w_min # 최소 관성 수치
|
|
self.w_max = w_max # 최대 관성 수치
|
|
|
|
self.g_best_score = 0 # 최고 점수 - 시작은 0으로 초기화
|
|
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
|
|
|
|
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(-1.5, 1.5, len(w_))
|
|
m.set_weights(self._decode(w_, sh_, len_))
|
|
m.compile(loss=self.loss, optimizer="sgd", metrics=["accuracy"])
|
|
if i < negative_swarm * self.n_particles:
|
|
self.particles[i] = Particle(m, loss, negative=True)
|
|
else:
|
|
self.particles[i] = Particle(m, loss, negative=False)
|
|
gc.collect()
|
|
|
|
"""
|
|
Args:
|
|
weights (list) : keras model의 가중치
|
|
Returns:
|
|
(numpy array) : 가중치 - 1차원으로 풀어서 반환
|
|
(list) : 가중치의 원본 shape
|
|
(list) : 가중치의 원본 shape의 길이
|
|
"""
|
|
|
|
def _encode(self, weights):
|
|
# w_gpu = cp.array([])
|
|
w_gpu = np.array([])
|
|
lenght = []
|
|
shape = []
|
|
for layer in weights:
|
|
shape.append(layer.shape)
|
|
w_ = layer.reshape(-1)
|
|
lenght.append(len(w_))
|
|
# w_gpu = cp.append(w_gpu, w_)
|
|
w_gpu = np.append(w_gpu, w_)
|
|
|
|
del weights
|
|
gc.collect()
|
|
return w_gpu, shape, lenght
|
|
|
|
"""
|
|
Args:
|
|
weight (numpy array) : 가중치 - 1차원으로 풀어진 상태
|
|
shape (list) : 가중치의 원본 shape
|
|
lenght (list) : 가중치의 원본 shape의 길이
|
|
Returns:
|
|
(list) : 가중치 원본 shape으로 복원
|
|
"""
|
|
|
|
def _decode(self, weight, shape, lenght):
|
|
weights = []
|
|
start = 0
|
|
for i in range(len(shape)):
|
|
end = start + lenght[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
|
|
gc.collect()
|
|
|
|
return weights
|
|
|
|
def f(self, x, y, weights):
|
|
self.model.set_weights(weights)
|
|
self.model.compile(loss=self.loss, optimizer="sgd", metrics=["accuracy"])
|
|
score = self.model.evaluate(x, y, verbose=0)[1]
|
|
|
|
gc.collect()
|
|
if score > 0:
|
|
return 1 / (1 + score)
|
|
else:
|
|
return 1 + np.abs(score)
|
|
|
|
"""
|
|
Args:
|
|
x_test : numpy.ndarray,
|
|
y_test : numpy.ndarray,
|
|
epochs : int,
|
|
save : bool - True : save, False : not save
|
|
save_path : str ex) "./result",
|
|
renewal : str ex) "acc" or "loss",
|
|
empirical_balance : bool - True :
|
|
Dispersion : bool - True : g_best 의 값을 분산시켜 전역해를 찾음, False : g_best 의 값만 사용
|
|
check_point : int - 저장할 위치 - None : 저장 안함
|
|
"""
|
|
|
|
def fit(
|
|
self,
|
|
x,
|
|
y,
|
|
epochs: int = 100,
|
|
save: bool = False,
|
|
save_path: str = "./result",
|
|
renewal: str = "acc",
|
|
empirical_balance: bool = False,
|
|
Dispersion: bool = False,
|
|
check_point: int = None,
|
|
):
|
|
self.save_path = save_path
|
|
|
|
self.renewal = renewal
|
|
if renewal == "acc":
|
|
self.g_best_score = 0
|
|
elif renewal == "loss":
|
|
self.g_best_score = np.inf
|
|
try:
|
|
if save:
|
|
if save_path is None:
|
|
raise ValueError("save_path is None")
|
|
else:
|
|
self.save_path = save_path
|
|
if not os.path.exists(save_path):
|
|
os.makedirs(save_path, exist_ok=True)
|
|
self.day = datetime.now().strftime("%m-%d-%H-%M")
|
|
except ValueError as e:
|
|
print(e)
|
|
sys.exit(1)
|
|
# for i, p in enumerate(self.particles):
|
|
for i in tqdm(range(self.n_particles), desc="Initializing Particles"):
|
|
p = copy(self.particles[i])
|
|
local_score = p.get_score(x, y, renewal=renewal)
|
|
|
|
if renewal == "acc":
|
|
if local_score[1] > self.g_best_score:
|
|
self.g_best_score = local_score[1]
|
|
self.g_best = p.get_best_weights()
|
|
self.g_best_ = p.get_best_weights()
|
|
elif renewal == "loss":
|
|
if local_score[0] < self.g_best_score:
|
|
self.g_best_score = local_score[0]
|
|
self.g_best = p.get_best_weights()
|
|
self.g_best_ = p.get_best_weights()
|
|
del local_score
|
|
del p
|
|
gc.collect()
|
|
|
|
print(f"initial g_best_score : {self.g_best_score}")
|
|
|
|
try:
|
|
for _ in range(epochs):
|
|
print(f"epoch {_ + 1}/{epochs}")
|
|
acc = 0
|
|
loss = 0
|
|
min_score = np.inf
|
|
max_score = 0
|
|
min_loss = np.inf
|
|
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)
|
|
|
|
# for i in tqdm(range(len(self.particles)), desc=f"epoch {_ + 1}/{epochs}", ascii=True):
|
|
for i in range(len(self.particles)):
|
|
w = self.w_max - (self.w_max - self.w_min) * _ / epochs
|
|
|
|
if Dispersion:
|
|
g_best = self.g_best_
|
|
else:
|
|
g_best = self.g_best
|
|
|
|
if empirical_balance:
|
|
if np.random.rand() < np.exp(-(_) / 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_
|
|
|
|
else:
|
|
p_b = self.particles[i].get_best_score()
|
|
g_a = self.avg_score
|
|
l_b = p_b - g_a
|
|
l_b = np.sqrt(np.power(l_b, 2))
|
|
p_ = (
|
|
1
|
|
/ (self.n_particles * np.linalg.norm(self.c1 - self.c0))
|
|
* l_b
|
|
)
|
|
p_ = np.exp(-1 * p_)
|
|
w_p = p_
|
|
w_g = 1 - p_
|
|
del p_b
|
|
del g_a
|
|
del l_b
|
|
del p_
|
|
|
|
score = self.particles[i].step_w(
|
|
x, y, self.c0, self.c1, w, g_best, w_p, w_g, renewal=renewal
|
|
)
|
|
|
|
else:
|
|
score = self.particles[i].step(
|
|
x, y, self.c0, self.c1, w, g_best, renewal=renewal
|
|
)
|
|
|
|
if renewal == "acc":
|
|
if score[1] >= self.g_best_score:
|
|
self.g_best_score = score[1]
|
|
self.g_best = self.particles[i].get_best_weights()
|
|
elif renewal == "loss":
|
|
if score[0] <= self.g_best_score:
|
|
self.g_best_score = score[0]
|
|
self.g_best = self.particles[i].get_best_weights()
|
|
|
|
loss = loss + score[0]
|
|
acc = acc + score[1]
|
|
if score[0] < min_loss:
|
|
min_loss = score[0]
|
|
if score[0] > max_loss:
|
|
max_loss = score[0]
|
|
|
|
if score[1] < min_score:
|
|
min_score = score[1]
|
|
if score[1] > max_score:
|
|
max_score = score[1]
|
|
|
|
if save:
|
|
with open(
|
|
f"./{save_path}/{self.day}_{self.n_particles}_{epochs}_{self.c0}_{self.c1}_{self.w_min}_{renewal}.csv",
|
|
"a",
|
|
) as f:
|
|
f.write(f"{score[0]}, {score[1]}")
|
|
if i != self.n_particles - 1:
|
|
f.write(", ")
|
|
else:
|
|
f.write("\n")
|
|
|
|
# print(f"loss min : {min_loss} | loss max : {max_loss} | acc min : {min_score} | acc max : {max_score}")
|
|
# print(f"loss avg : {loss/self.n_particles} | acc avg : {acc/self.n_particles} | Best {renewal} : {self.g_best_score}")
|
|
print(
|
|
f"loss min : {round(min_loss, 4)} | acc max : {round(max_score, 4)} | Best {renewal} : {self.g_best_score}"
|
|
)
|
|
|
|
gc.collect()
|
|
|
|
if check_point is not None:
|
|
if _ % check_point == 0:
|
|
os.makedirs(f"./{save_path}/{self.day}", exist_ok=True)
|
|
self._check_point_save(f"./{save_path}/{self.day}/ckpt-{_}")
|
|
self.avg_score = acc / self.n_particles
|
|
|
|
except KeyboardInterrupt:
|
|
print("Ctrl + C : Stop Training")
|
|
except MemoryError:
|
|
print("Memory Error : Stop Training")
|
|
except Exception as e:
|
|
print(e)
|
|
finally:
|
|
self.model_save(save_path)
|
|
print("model save")
|
|
self.save_info(save_path)
|
|
print("save info")
|
|
|
|
return self.g_best, self.g_best_score
|
|
|
|
def get_best_model(self):
|
|
model = keras.models.model_from_json(self.model.to_json())
|
|
model.set_weights(self.g_best)
|
|
model.compile(loss=self.loss, optimizer="sgd", metrics=["accuracy"])
|
|
return model
|
|
|
|
def get_best_score(self):
|
|
return self.g_best_score
|
|
|
|
def get_best_weights(self):
|
|
return self.g_best
|
|
|
|
def save_info(self, path: str = "./result"):
|
|
json_save = {
|
|
"name": f"{self.day}_{self.n_particles}_{self.c0}_{self.c1}_{self.w_min}.h5",
|
|
"n_particles": self.n_particles,
|
|
"score": self.g_best_score,
|
|
"c0": self.c0,
|
|
"c1": self.c1,
|
|
"w_min": self.w_min,
|
|
"w_max": self.w_max,
|
|
"loss_method": self.loss,
|
|
"renewal": self.renewal,
|
|
}
|
|
|
|
with open(
|
|
f"./{path}/{self.day}/{self.loss}_{self.n_particles}.json",
|
|
"a",
|
|
) as f:
|
|
json.dump(json_save, f, indent=4)
|
|
|
|
def _check_point_save(self, save_path: str = f"./result/check_point"):
|
|
model = self.get_best_model()
|
|
model.save_weights(save_path)
|
|
|
|
def model_save(self, save_path: str = "./result"):
|
|
model = self.get_best_model()
|
|
model.save(
|
|
f"./{save_path}/{self.day}/{self.n_particles}_{self.c0}_{self.c1}_{self.w_min}.h5"
|
|
)
|
|
return model
|