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23-11-05

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Update PSO and neural network parameters
best score 초기화 를 무작위 값에서 계산 후 설정으로 변경
This commit is contained in:
jung-geun
2023-11-05 17:14:07 +09:00
parent 80695f304d
commit c45ee5873e
7 changed files with 191 additions and 46 deletions
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6
digits.py
View File

@@ -19,7 +19,7 @@ os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
def make_model():
model = Sequential()
model.add(Dense(12, input_dim=64, activation="relu"))
model.add(Dense(8, activation="relu"))
model.add(Dense(10, activation="relu"))
model.add(Dense(10, activation="softmax"))
return model
@@ -55,7 +55,7 @@ digits_pso = optimizer(
mutation_swarm=0.1,
convergence_reset=True,
convergence_reset_patience=10,
convergence_reset_monitor="acc",
convergence_reset_monitor="loss",
convergence_reset_min_delta=0.001,
)
@@ -66,7 +66,7 @@ digits_pso.fit(
validate_data=(x_test, y_test),
log=2,
save_info=True,
renewal="acc",
renewal="loss",
log_name="digits",
)

81
digits_tf.py Normal file
View File

@@ -0,0 +1,81 @@
import os
import sys
import pandas as pd
import tensorflow as tf
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
from tensorflow.keras.utils import to_categorical
gpus = tf.config.experimental.list_physical_devices("GPU")
if gpus:
try:
tf.config.experimental.set_memory_growth(gpus[0], True)
except RuntimeError as r:
print(r)
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
def make_model():
model = Sequential()
model.add(Dense(12, input_dim=64, activation="relu"))
model.add(Dense(12, activation="relu"))
model.add(Dense(10, activation="softmax"))
return model
def get_data():
digits = load_digits()
X = digits.data
y = digits.target
x = X.astype("float32")
y_class = to_categorical(y)
x_train, x_test, y_train, y_test = train_test_split(
x, y_class, test_size=0.2, random_state=42, shuffle=True
)
return x_train, x_test, y_train, y_test
if __name__ == "__main__":
model = make_model()
x_train, x_test, y_train, y_test = get_data()
callbacks = [
tf.keras.callbacks.EarlyStopping(
monitor="val_loss", patience=10, restore_best_weights=True
)
]
print(x_train.shape, y_train.shape)
model.compile(
optimizer="adam",
loss="categorical_crossentropy",
metrics=["accuracy", "mse"],
)
print(model.summary())
history = model.fit(
x_train,
y_train,
epochs=500,
batch_size=32,
verbose=1,
validation_data=(x_test, y_test),
callbacks=callbacks,
)
print("Done!")
sys.exit(0)

6
iris.py
View File

@@ -44,13 +44,13 @@ pso_iris = optimizer(
n_particles=100,
c0=0.5,
c1=0.3,
w_min=0.2,
w_min=0.1,
w_max=0.9,
negative_swarm=0,
mutation_swarm=0.1,
convergence_reset=True,
convergence_reset_patience=10,
convergence_reset_monitor="mse",
convergence_reset_monitor="loss",
convergence_reset_min_delta=0.001,
)
@@ -61,7 +61,7 @@ best_score = pso_iris.fit(
save_info=True,
log=2,
log_name="iris",
renewal="mse",
renewal="loss",
check_point=25,
validate_data=(x_test, y_test),
)

7
mnist.py
View File

@@ -65,6 +65,7 @@ loss = [
"huber_loss",
"mean_absolute_error",
"mean_absolute_percentage_error",
]
# rs = random_state()
@@ -75,13 +76,13 @@ pso_mnist = optimizer(
n_particles=500,
c0=0.5,
c1=0.3,
w_min=0.2,
w_min=0.1,
w_max=0.9,
negative_swarm=0.0,
mutation_swarm=0.1,
convergence_reset=True,
convergence_reset_patience=10,
convergence_reset_monitor="mse",
convergence_reset_monitor="loss",
convergence_reset_min_delta=0.005,
)
@@ -92,7 +93,7 @@ best_score = pso_mnist.fit(
save_info=True,
log=2,
log_name="mnist",
renewal="mse",
renewal="loss",
check_point=25,
empirical_balance=False,
dispersion=False,

2
pso/__init__.py
View File

@@ -1,7 +1,7 @@
from .optimizer import Optimizer as optimizer
from .particle import Particle as particle
__version__ = "1.0.4"
__version__ = "1.0.5"
print("pso2keras version : " + __version__)

13
pso/optimizer.py
View File

@@ -152,7 +152,6 @@ class Optimizer:
tf.keras.backend.reset_uids()
tf.keras.backend.clear_session()
self.particles[0].update_global_best()
print(f"negative swarm : {negative_count} / {n_particles}")
print(f"mutation swarm : {mutation_swarm * 100}%")
@@ -449,6 +448,15 @@ class Optimizer:
dataset = self.batch_generator(x, y, batch_size=batch_size)
for i in tqdm(
range(len(self.particles)),
desc="best score init",
ascii=True,
leave=True,
):
score = self.particles[i].get_score(x, y, self.renewal)
self.particles[i].check_global_best(self.renewal)
try:
epoch_sum = 0
epochs_pbar = tqdm(
@@ -477,7 +485,8 @@ class Optimizer:
position=1,
)
w = self.w_max - (self.w_max - self.w_min) * epoch / epochs
# w = self.w_max - (self.w_max - self.w_min) * epoch / epochs
w = self.w_max - (self.w_max - self.w_min) * (epoch % 100) / 100
for i in part_pbar:
part_pbar.set_description(
f"loss: {min_loss:.4f} acc: {max_acc:.4f} mse: {min_mse:.4f}"

100
pso/particle.py
View File

@@ -12,6 +12,7 @@ class Particle:
4. 가중치 업데이트
5. 2번으로 돌아가서 반복
"""
g_best_score = [np.inf, 0, np.inf]
g_best_weights = None
count = 0
@@ -40,7 +41,12 @@ class Particle:
self.loss = loss
try:
if converge_reset and converge_reset_monitor not in ["acc", "accuracy", "loss", "mse"]:
if converge_reset and converge_reset_monitor not in [
"acc",
"accuracy",
"loss",
"mse",
]:
raise ValueError(
"converge_reset_monitor must be 'acc' or 'accuracy' or 'loss'"
)
@@ -154,7 +160,13 @@ class Particle:
return score
def __check_converge_reset(self, score, monitor: str = None, patience: int = 10, min_delta: float = 0.0001):
def __check_converge_reset(
self,
score,
monitor: str = None,
patience: int = 10,
min_delta: float = 0.0001,
):
"""
early stop을 구현한 함수
@@ -173,8 +185,7 @@ class Particle:
elif monitor in ["mse"]:
self.score_history.append(score[2])
else:
raise ValueError(
"monitor must be 'acc' or 'accuracy' or 'loss' or 'mse'")
raise ValueError("monitor must be 'acc' or 'accuracy' or 'loss' or 'mse'")
if len(self.score_history) > patience:
last_scores = self.score_history[-patience:]
@@ -187,10 +198,10 @@ class Particle:
self.model.compile(
optimizer="adam",
loss=self.loss,
metrics=["accuracy", "mse"]
metrics=["accuracy", "mse"],
)
i_w_, i_s, i_l = self._encode(self.model.get_weights())
i_w_ = np.random.uniform(-0.05, 0.1, len(i_w_))
i_w_ = np.random.uniform(-0.1, 0.1, len(i_w_))
self.velocities = self._decode(i_w_, i_s, i_l)
del i_w_, i_s, i_l
@@ -228,10 +239,13 @@ class Particle:
# 지역 최적해와 전역 최적해를 음수로 사용하여 전역 탐색을 유도
new_v = (
w * encode_v
- local_rate * r_0 * (encode_p - encode_w)
+ local_rate * r_0 * (encode_p - encode_w)
- global_rate * r_1 * (encode_g - encode_w)
)
if len(self.score_history) > 10 and max(self.score_history[-10:]) - min(self.score_history[-10:]) < 0.01:
if (
len(self.score_history) > 10
and max(self.score_history[-10:]) - min(self.score_history[-10:]) < 0.01
):
self.__reset_particle()
else:
@@ -285,7 +299,7 @@ class Particle:
if self.negative:
new_v = (
w * encode_v
- local_rate * r_0 * (w_p * encode_p - encode_w)
+ local_rate * r_0 * (w_p * encode_p - encode_w)
- global_rate * r_1 * (w_g * encode_g - encode_w)
)
else:
@@ -296,7 +310,7 @@ class Particle:
)
if np.random.rand() < self.mutation:
m_v = np.random.uniform(-0.05, 0.05, len(encode_v))
m_v = np.random.uniform(-0.1, 0.1, len(encode_v))
new_v = m_v
self.velocities = self._decode(new_v, w_sh, w_len)
@@ -341,11 +355,28 @@ class Particle:
score = self.get_score(x, y, renewal)
if self.converge_reset and self.__check_converge_reset(
score, self.converge_reset_monitor, self.converge_reset_patience, self.converge_reset_min_delta):
score,
self.converge_reset_monitor,
self.converge_reset_patience,
self.converge_reset_min_delta,
):
self.__reset_particle()
score = self.get_score(x, y, renewal)
while np.isnan(score[0]) or np.isnan(score[1]) or np.isnan(score[2]) or score[0] == 0 or score[1] == 0 or score[2] == 0 or np.isinf(score[0]) or np.isinf(score[1]) or np.isinf(score[2]) or score[0] > 1000 or score[1] > 1 or score[2] > 1000:
while (
np.isnan(score[0])
or np.isnan(score[1])
or np.isnan(score[2])
or score[0] == 0
or score[1] == 0
or score[2] == 0
or np.isinf(score[0])
or np.isinf(score[1])
or np.isinf(score[2])
or score[0] > 1000
or score[1] > 1
or score[2] > 1000
):
self.__reset_particle()
score = self.get_score(x, y, renewal)
@@ -362,9 +393,7 @@ class Particle:
return score
def step_w(
self, x, y, local_rate, global_rate, w, w_p, w_g, renewal: str = "acc"
):
def step_w(self, x, y, local_rate, global_rate, w, w_p, w_g, renewal: str = "acc"):
"""
파티클의 한 스텝을 진행합니다.
기본 스텝의 변형으로, 지역최적해와 전역최적해의 분산 정도를 조정할 수 있습니다
@@ -389,11 +418,28 @@ class Particle:
score = self.get_score(x, y, renewal)
if self.converge_reset and self.__check_converge_reset(
score, self.converge_reset_monitor, self.converge_reset_patience, self.converge_reset_min_delta):
score,
self.converge_reset_monitor,
self.converge_reset_patience,
self.converge_reset_min_delta,
):
self.__reset_particle()
score = self.get_score(x, y, renewal)
while np.isnan(score[0]) or np.isnan(score[1]) or np.isnan(score[2]) or score[0] == 0 or score[1] == 0 or score[2] == 0 or np.isinf(score[0]) or np.isinf(score[1]) or np.isinf(score[2]) or score[0] > 1000 or score[1] > 1 or score[2] > 1000:
while (
np.isnan(score[0])
or np.isnan(score[1])
or np.isnan(score[2])
or score[0] == 0
or score[1] == 0
or score[2] == 0
or np.isinf(score[0])
or np.isinf(score[1])
or np.isinf(score[2])
or score[0] > 1000
or score[1] > 1
or score[2] > 1000
):
self.__reset_particle()
score = self.get_score(x, y, renewal)
@@ -429,17 +475,25 @@ class Particle:
return self.best_weights
def set_global_score(self):
"""전역 최고점수를 현재 파티클의 최고점수로 설정합니다
"""
"""전역 최고점수를 현재 파티클의 최고점수로 설정합니다"""
Particle.g_best_score = self.best_score
def set_global_weights(self):
"""전역 최고점수를 받은 가중치를 현재 파티클의 최고점수를 받은 가중치로 설정합니다
"""
"""전역 최고점수를 받은 가중치를 현재 파티클의 최고점수를 받은 가중치로 설정합니다"""
Particle.g_best_weights = self.best_weights
def update_global_best(self):
"""현재 파티클의 점수와 가중치를 전역 최고점수와 가중치로 설정합니다
"""
"""현재 파티클의 점수와 가중치를 전역 최고점수와 가중치로 설정합니다"""
self.set_global_score()
self.set_global_weights()
def check_global_best(self, renewal: str = "loss"):
if renewal == "loss":
if self.best_score[0] < Particle.g_best_score[0]:
self.update_global_best()
elif renewal == "acc":
if self.best_score[1] > Particle.g_best_score[1]:
self.update_global_best()
elif renewal == "mse":
if self.best_score[2] < Particle.g_best_score[2]:
self.update_global_best()