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23-07-07

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dev container 설정 - tqdm + tensorflow 자동 설치 env name = pso 로 자동 생성
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jung-geun
2023-07-07 18:30:08 +09:00
parent c163de6cb6
commit 7410ed9e04
12 changed files with 124 additions and 135 deletions
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84
auto_tunning.py
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@@ -1,30 +1,25 @@
# %%
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
import tensorflow as tf
tf.random.set_seed(777) # for reproducibility
from tensorflow import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
# from pso_tf import PSO
from pso import Optimizer
import gc
from datetime import date
import numpy as np
from datetime import date
from keras import backend as K
from keras.datasets import mnist
from keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D
from keras.models import Sequential
from tensorflow import keras
from tqdm import tqdm
import gc
from pso import Optimizer
# print(tf.__version__)
# print(tf.config.list_physical_devices())
# print(f"Num GPUs Available: {len(tf.config.list_physical_devices('GPU'))}")
def get_data():
(x_train, y_train), (x_test, y_test) = mnist.load_data()
@@ -37,26 +32,30 @@ def get_data():
print(f"x_test : {x_test[0].shape} | y_test : {y_test[0].shape}")
return x_train, y_train, x_test, y_test
def get_data_test():
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_test = x_test.reshape((10000, 28, 28, 1))
return x_test, y_test
def make_model():
model = Sequential()
model.add(Conv2D(32, kernel_size=(5, 5),
activation='relu', input_shape=(28, 28, 1)))
model.add(
Conv2D(32, kernel_size=(5, 5), activation="relu", input_shape=(28, 28, 1))
)
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
model.add(Conv2D(64, kernel_size=(3, 3), activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.add(Dense(128, activation="relu"))
model.add(Dense(10, activation="softmax"))
return model
# %%
model = make_model()
x_test, y_test = get_data_test()
@@ -67,12 +66,23 @@ x_test, y_test = get_data_test()
# loss = 'poisson'
# loss = 'cosine_similarity'
# loss = 'log_cosh'
# loss = 'huber_loss'
# loss = 'huber_loss'
# loss = 'mean_absolute_error'
# loss = 'mean_absolute_percentage_error'
# loss = 'mean_squared_error'
loss = ['mse', 'categorical_crossentropy', 'binary_crossentropy', 'kullback_leibler_divergence', 'poisson', 'cosine_similarity', 'log_cosh', 'huber_loss', 'mean_absolute_error', 'mean_absolute_percentage_error']
loss = [
"mse",
"categorical_crossentropy",
"binary_crossentropy",
"kullback_leibler_divergence",
"poisson",
"cosine_similarity",
"log_cosh",
"huber_loss",
"mean_absolute_error",
"mean_absolute_percentage_error",
]
n_particles = [50, 75, 100]
c0 = [0.25, 0.35, 0.45, 0.55]
c1 = [0.5, 0.6, 0.7, 0.8, 0.9]
@@ -93,31 +103,31 @@ if __name__ == "__main__":
for n_s in negative_swarm:
pso_mnist = Optimizer(
model,
loss=loss_,
loss=loss_,
n_particles=n,
c0=c_0,
c1=c_1,
c0=c_0,
c1=c_1,
w_min=w_m,
w_max=w_M,
negative_swarm=n_s
)
negative_swarm=n_s,
)
best_score = pso_mnist.fit(
x_test,
y_test,
epochs=200,
save=True,
save_path="./result/mnist",
renewal="acc",
save_path="./result/mnist",
renewal="acc",
empirical_balance=False,
Dispersion=False,
check_point=25
)
Dispersion=False,
check_point=25,
)
del pso_mnist
gc.collect()
gc.collect()
tf.keras.backend.clear_session()
except KeyboardInterrupt:
print("KeyboardInterrupt")
finally: