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23-11-01

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tensorboard 선택 시 자동으로 프로세스 실행
비어있는 포트를 자동으로 탐색하여 오픈
이전 최적해와 비교하여 관성치를 높게 주는 방법을 일시 폐기
digits 테스트 추가
tensorboard 자동 설치 추가
This commit is contained in:
jung-geun
2023-11-01 23:40:31 +09:00
parent 389027409d
commit 80695f304d
9 changed files with 218 additions and 113 deletions
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115
pso/optimizer.py
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@@ -1,13 +1,17 @@
import gc
import json
import os
import socket
import subprocess
import sys
from datetime import datetime
import numpy as np
import tensorflow as tf
from tensorboard.plugins.hparams import api as hp
from tensorflow import keras
from tqdm.auto import tqdm
import atexit
from .particle import Particle
@@ -21,6 +25,14 @@ if gpus:
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
def find_free_port():
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.bind(("localhost", 0))
port = sock.getsockname()[1]
sock.close()
return port
class Optimizer:
"""
particle swarm optimization
@@ -33,9 +45,9 @@ class Optimizer:
loss: any = None,
n_particles: int = None,
c0: float = 0.5,
c1: float = 1.5,
w_min: float = 0.5,
w_max: float = 1.5,
c1: float = 0.3,
w_min: float = 0.2,
w_max: float = 0.9,
negative_swarm: float = 0,
mutation_swarm: float = 0,
np_seed: int = None,
@@ -94,11 +106,7 @@ class Optimizer:
if random_state is not None:
np.random.set_state(random_state)
model.compile(
loss=loss,
optimizer="adam",
metrics=["accuracy", "mse"]
)
model.compile(loss=loss, optimizer="adam", metrics=["accuracy", "mse"])
self.model = model # 모델 구조
self.loss = loss # 손실함수
self.n_particles = n_particles # 파티클 개수
@@ -124,11 +132,12 @@ class Optimizer:
print(f"start running time : {self.day}")
for i in tqdm(range(self.n_particles), desc="Initializing Particles"):
self.particles[i] = Particle(
model,
self.loss,
negative=True if i < self.negative_swarm * self.n_particles else False,
negative=True
if i < self.negative_swarm * self.n_particles
else False,
mutation=self.mutation_swarm,
converge_reset=convergence_reset,
converge_reset_patience=convergence_reset_patience,
@@ -308,8 +317,9 @@ class Optimizer:
def __getBatchSlice(self, batch_size):
return list(
tf.data.Dataset.from_tensor_slices(
(self.x, self.y)).shuffle(len(self.x)).batch(batch_size)
tf.data.Dataset.from_tensor_slices((self.x, self.y))
.shuffle(len(self.x))
.batch(batch_size)
)
def getDataset(self):
@@ -353,7 +363,13 @@ class Optimizer:
raise ValueError("x, y shape error")
if log not in [0, 1, 2]:
raise ValueError("log not in [0, 1, 2]")
raise ValueError(
"""log not in [0, 1, 2]
0 : log 기록 안함
1 : csv
2 : tensorboard
"""
)
if renewal not in ["acc", "loss", "mse"]:
raise ValueError("renewal not in ['acc', 'loss', 'mse']")
@@ -394,7 +410,19 @@ class Optimizer:
self.train_summary_writer[i] = tf.summary.create_file_writer(
train_log_dir + f"/{i}"
)
port = find_free_port()
tensorboard_precess = subprocess.Popen(
[
"tensorboard",
"--logdir",
self.log_path,
"--port",
str(port),
]
)
tensorboard_url = f"http://localhost:{port}"
print(f"tensorboard url : {tensorboard_url}")
atexit.register(tensorboard_precess.kill)
elif check_point is not None or log == 1:
if not os.path.exists(self.log_path):
os.makedirs(self.log_path, exist_ok=True)
@@ -408,7 +436,7 @@ class Optimizer:
model_.compile(
loss=self.loss,
optimizer="adam",
metrics=["accuracy", "mse"]
metrics=["accuracy", "mse"],
)
model_.fit(x, y, epochs=1, verbose=0)
score = model_.evaluate(x, y, verbose=1)
@@ -463,8 +491,7 @@ class Optimizer:
if dispersion:
ts = weight_min + np.random.rand() * (weight_max - weight_min)
g_, g_sh, g_len = self._encode(
Particle.g_best_weights)
g_, g_sh, g_len = self._encode(Particle.g_best_weights)
decrement = (epochs - epoch + 1) / epochs
g_ = (1 - decrement) * g_ + decrement * ts
g_best = self._decode_(g_, g_sh, g_len)
@@ -472,8 +499,7 @@ class Optimizer:
if empirical_balance:
if np.random.rand() < np.exp(-(epoch) / epochs):
w_p_ = self._f(
x_batch, y_batch, self.particles[i].get_best_weights(
)
x_batch, y_batch, self.particles[i].get_best_weights()
)
w_g_ = self._f(x_batch, y_batch, g_best)
w_p = w_p_ / (w_p_ + w_g_)
@@ -491,9 +517,7 @@ class Optimizer:
1
/ (
self.n_particles
* np.linalg.norm(
weight_min - weight_max
)
* np.linalg.norm(weight_min - weight_max)
)
* sigma_post
)
@@ -543,9 +567,7 @@ class Optimizer:
if log == 2:
with self.train_summary_writer[i].as_default():
tf.summary.scalar("loss", score[0], step=epoch + 1)
tf.summary.scalar(
"accuracy", score[1], step=epoch + 1
)
tf.summary.scalar("accuracy", score[1], step=epoch + 1)
tf.summary.scalar("mse", score[2], step=epoch + 1)
if renewal == "loss":
@@ -555,6 +577,11 @@ class Optimizer:
min_loss, max_acc, min_mse = score
best_particle_index = i
elif score[0] == min_loss:
if score[1] > max_acc:
min_loss, max_acc, min_mse = score
best_particle_index = i
elif renewal == "acc":
# 최고 점수 보다 높거나 같을 경우
if score[1] > max_acc:
@@ -562,14 +589,22 @@ class Optimizer:
min_loss, max_acc, min_mse = score
best_particle_index = i
elif score[1] == max_acc:
if score[2] < min_mse:
min_loss, max_acc, min_mse = score
best_particle_index = i
elif renewal == "mse":
if score[2] < min_mse:
min_loss, max_acc, min_mse = score
best_particle_index = i
elif score[2] == min_mse:
if score[1] > max_acc:
min_loss, max_acc, min_mse = score
best_particle_index = i
particle_sum += score[1]
if log == 1:
@@ -587,34 +622,28 @@ class Optimizer:
if renewal == "loss":
if min_loss <= Particle.g_best_score[0]:
if min_loss < Particle.g_best_score[0]:
self.particles[best_particle_index].update_global_best(
)
self.particles[best_particle_index].update_global_best()
else:
if max_acc > Particle.g_best_score[1]:
self.particles[best_particle_index].update_global_best(
)
self.particles[best_particle_index].update_global_best()
elif renewal == "acc":
if max_acc >= Particle.g_best_score[1]:
# 최고 점수 보다 높을 경우
if max_acc > Particle.g_best_score[1]:
# 최고 점수 갱신
self.particles[best_particle_index].update_global_best(
)
self.particles[best_particle_index].update_global_best()
# 최고 점수 와 같을 경우
else:
# 최저 loss 보다 낮을 경우
if min_loss < Particle.g_best_score[0]:
self.particles[best_particle_index].update_global_best(
)
self.particles[best_particle_index].update_global_best()
elif renewal == "mse":
if min_mse <= Particle.g_best_score[2]:
if min_mse < Particle.g_best_score[2]:
self.particles[best_particle_index].update_global_best(
)
self.particles[best_particle_index].update_global_best()
else:
if max_acc > Particle.g_best_score[1]:
self.particles[best_particle_index].update_global_best(
)
self.particles[best_particle_index].update_global_best()
# 최고 점수 갱신
epochs_pbar.set_description(
f"best - loss: {Particle.g_best_score[0]:.4f} - acc: {Particle.g_best_score[1]:.4f} - mse: {Particle.g_best_score[2]:.4f}"
@@ -623,9 +652,12 @@ class Optimizer:
if check_point is not None:
if epoch % check_point == 0:
os.makedirs(
f"./logs/{log_name}/{self.day}", exist_ok=True)
f"./logs/{log_name}/{self.day}",
exist_ok=True,
)
self._check_point_save(
f"./logs/{log_name}/{self.day}/ckpt-{epoch}")
f"./logs/{log_name}/{self.day}/ckpt-{epoch}"
)
tf.keras.backend.reset_uids()
tf.keras.backend.clear_session()
@@ -661,7 +693,7 @@ class Optimizer:
model.compile(
loss=self.loss,
optimizer="adam",
metrics=["accuracy", "mse"]
metrics=["accuracy", "mse"],
)
return model
@@ -741,8 +773,7 @@ class Optimizer:
x, y = valid_data
model = self.get_best_model()
score = model.evaluate(x, y, verbose=1)
print(
f"model score - loss: {score[0]} - acc: {score[1]} - mse: {score[2]}")
print(f"model score - loss: {score[0]} - acc: {score[1]} - mse: {score[2]}")
model.save(
f"./{self.log_path}/model_{score[0 if self.renewal == 'loss' else 1 if self.renewal == 'acc' else 2 ]}.h5"
)