jung-geun/PSO
1
0
Fork 0
mirror of https://github.com/jung-geun/PSO.git synced 2025-12-20 04:50:45 +09:00
Code Issues Packages Projects Releases Wiki Activity

23-06-23

Browse Source 
readme 파일 수정 - env 파일 및 설명 추가 , 참고 자료 수정
iris_tf.py 모델의 성능 교차 검증을 위해 추가
This commit is contained in:
jung-geun
2023-06-23 06:37:01 +00:00
parent 953cd44396
commit 2a28b7fa04
7 changed files with 98 additions and 57 deletions
Download Patch File Download Diff File Expand all files Collapse all files

41
pso/optimizer.py
View File

@@ -5,7 +5,6 @@ import tensorflow as tf
from tensorflow import keras
import numpy as np
# import cupy as cp
from tqdm import tqdm
@@ -57,7 +56,7 @@ class Optimizer:
self.w_min = w_min # 최소 관성 수치
self.w_max = w_max # 최대 관성 수치
self.negative_swarm = negative_swarm # 최적해와 반대로 이동할 파티클 비율 - 0 ~ 1 사이의 값
self.g_best_score = 0 # 최고 점수 - 시작은 0으로 초기화
self.g_best_score = [0 , np.inf] # 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
@@ -196,10 +195,6 @@ class Optimizer:
self.Dispersion = Dispersion
self.renewal = renewal
if renewal == "acc":
self.g_best_score = 0
elif renewal == "loss":
self.g_best_score = np.inf
try:
if save:
@@ -219,13 +214,13 @@ class Optimizer:
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]
if local_score[1] > self.g_best_score[0]:
self.g_best_score[0] = 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]
if local_score[0] < self.g_best_score[1]:
self.g_best_score[1] = local_score[0]
self.g_best = p.get_best_weights()
self.g_best_ = p.get_best_weights()
@@ -248,13 +243,11 @@ class Optimizer:
del local_score
gc.collect()
print(f"initial g_best_score : {self.g_best_score}")
print(f"initial g_best_score : {self.g_best_score[0] if self.renewal == 'acc' else self.g_best_score[1]}")
try:
epochs_pbar = tqdm(range(epochs), desc=f"best {self.renewal} : {self.g_best_score:.4f}", ascii=True, leave=True)
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:
epochs_pbar.set_description(f"best {self.renewal} : {self.g_best_score:.4f}")
acc = 0
loss = 0
min_score = np.inf
@@ -315,13 +308,19 @@ class Optimizer:
)
if renewal == "acc":
if score[1] >= self.g_best_score:
self.g_best_score = score[1]
if score[1] >= self.g_best_score[0]:
self.g_best_score[0] = score[1]
if score[0] <= self.g_best_score[1]:
self.g_best_score[1] = score[0]
self.g_best = self.particles[i].get_best_weights()
epochs_pbar.set_description(f"best {self.g_best_score[0]:.4f} | {self.g_best_score[1]:.4f}")
elif renewal == "loss":
if score[0] <= self.g_best_score:
self.g_best_score = score[0]
if score[0] <= self.g_best_score[1]:
self.g_best_score[1] = score[0]
if score[1] >= self.g_best_score[0]:
self.g_best_score[0] = score[1]
self.g_best = self.particles[i].get_best_weights()
epochs_pbar.set_description(f"best {self.g_best_score[0]:.4f} | {self.g_best_score[1]:.4f}")
if score[0] == None:
score[0] = np.inf
@@ -350,12 +349,6 @@ class Optimizer:
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}"
# )
if check_point is not None:
if _ % check_point == 0:
os.makedirs(f"./{save_path}/{self.day}", exist_ok=True)