mirror of
https://github.com/jung-geun/PSO.git
synced 2025-12-20 04:50:45 +09:00
79 lines
1.5 KiB
Python
79 lines
1.5 KiB
Python
# %%
|
|
import os
|
|
|
|
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
|
|
|
|
import numpy as np
|
|
import tensorflow as tf
|
|
|
|
# from pso_tf import PSO
|
|
from pso import Optimizer
|
|
from tensorflow import keras
|
|
from tensorflow.keras import layers
|
|
from tensorflow.keras.models import Sequential
|
|
|
|
print(tf.__version__)
|
|
print(tf.config.list_physical_devices())
|
|
|
|
|
|
def get_data():
|
|
x = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
|
|
y = np.array([[0], [1], [1], [0]])
|
|
return x, y
|
|
|
|
|
|
def make_model():
|
|
leyer = []
|
|
leyer.append(layers.Dense(2, activation="sigmoid", input_shape=(2,)))
|
|
# leyer.append(layers.Dense(2, activation='sigmoid'))
|
|
leyer.append(layers.Dense(1, activation="sigmoid"))
|
|
|
|
model = Sequential(leyer)
|
|
|
|
return model
|
|
|
|
|
|
# %%
|
|
model = make_model()
|
|
x_test, y_test = get_data()
|
|
|
|
loss = [
|
|
"mean_squared_error",
|
|
"mean_squared_logarithmic_error",
|
|
"binary_crossentropy",
|
|
"categorical_crossentropy",
|
|
"sparse_categorical_crossentropy",
|
|
"kullback_leibler_divergence",
|
|
"poisson",
|
|
"cosine_similarity",
|
|
"log_cosh",
|
|
"huber_loss",
|
|
"mean_absolute_error",
|
|
"mean_absolute_percentage_error",
|
|
]
|
|
|
|
pso_xor = Optimizer(
|
|
model,
|
|
loss=loss[0],
|
|
n_particles=75,
|
|
c0=0.35,
|
|
c1=0.8,
|
|
w_min=0.6,
|
|
w_max=1.2,
|
|
negative_swarm=0.25,
|
|
mutation_swarm=0.25,
|
|
)
|
|
best_score = pso_xor.fit(
|
|
x_test,
|
|
y_test,
|
|
epochs=200,
|
|
save=True,
|
|
save_path="./result/xor",
|
|
renewal="acc",
|
|
empirical_balance=False,
|
|
Dispersion=False,
|
|
check_point=25,
|
|
)
|
|
|
|
# %%
|