mirror of
https://github.com/jung-geun/PSO.git
synced 2025-12-20 04:50:45 +09:00
135 lines
4.0 KiB
Python
135 lines
4.0 KiB
Python
# %%
|
|
import os
|
|
|
|
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
|
|
|
|
import tensorflow as tf
|
|
|
|
tf.random.set_seed(777) # for reproducibility
|
|
|
|
import gc
|
|
from datetime import date
|
|
|
|
import numpy as np
|
|
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
|
|
|
|
from pso import Optimizer
|
|
|
|
|
|
def get_data():
|
|
(x_train, y_train), (x_test, y_test) = mnist.load_data()
|
|
|
|
x_train, x_test = x_train / 255.0, x_test / 255.0
|
|
x_train = x_train.reshape((60000, 28, 28, 1))
|
|
x_test = x_test.reshape((10000, 28, 28, 1))
|
|
|
|
print(f"x_train : {x_train[0].shape} | y_train : {y_train[0].shape}")
|
|
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(MaxPooling2D(pool_size=(3, 3)))
|
|
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"))
|
|
|
|
return model
|
|
|
|
|
|
# %%
|
|
model = make_model()
|
|
x_test, y_test = get_data_test()
|
|
# loss = 'binary_crossentropy'
|
|
# loss = 'categorical_crossentropy'
|
|
# loss = 'sparse_categorical_crossentropy'
|
|
# loss = 'kullback_leibler_divergence'
|
|
# loss = 'poisson'
|
|
# loss = 'cosine_similarity'
|
|
# loss = 'log_cosh'
|
|
# 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",
|
|
]
|
|
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]
|
|
w_min = [0.5, 0.6, 0.7]
|
|
w_max = [1.1, 1.2, 1.3]
|
|
negative_swarm = [0.25, 0.3, 0.5]
|
|
eb = [True, False]
|
|
dispersion = [True, False]
|
|
|
|
if __name__ == "__main__":
|
|
try:
|
|
for loss_ in loss:
|
|
for n in n_particles:
|
|
for c_0 in c0:
|
|
for c_1 in c1:
|
|
for w_m in w_min:
|
|
for w_M in w_max:
|
|
for n_s in negative_swarm:
|
|
pso_mnist = Optimizer(
|
|
model,
|
|
loss=loss_,
|
|
n_particles=n,
|
|
c0=c_0,
|
|
c1=c_1,
|
|
w_min=w_m,
|
|
w_max=w_M,
|
|
negative_swarm=n_s,
|
|
)
|
|
|
|
best_score = pso_mnist.fit(
|
|
x_test,
|
|
y_test,
|
|
epochs=200,
|
|
save=True,
|
|
save_path="./result/mnist",
|
|
renewal="acc",
|
|
empirical_balance=False,
|
|
Dispersion=False,
|
|
check_point=25,
|
|
)
|
|
|
|
del pso_mnist
|
|
gc.collect()
|
|
tf.keras.backend.clear_session()
|
|
|
|
except KeyboardInterrupt:
|
|
print("KeyboardInterrupt")
|
|
finally:
|
|
print("Finish")
|