mirror of
https://github.com/jung-geun/PSO.git
synced 2025-12-20 04:50:45 +09:00
51 lines
1.3 KiB
Python
51 lines
1.3 KiB
Python
import os
|
|
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
|
|
|
|
import tensorflow as tf
|
|
tf.random.set_seed(777) # for reproducibility
|
|
|
|
from sklearn.datasets import load_iris
|
|
from sklearn.model_selection import train_test_split
|
|
|
|
from tensorflow import keras
|
|
from tensorflow.keras.models import Sequential
|
|
from tensorflow.keras import layers
|
|
|
|
from pso import Optimizer
|
|
|
|
import gc
|
|
|
|
def make_model():
|
|
model = Sequential()
|
|
model.add(layers.Dense(10, activation='relu', input_shape=(4,)))
|
|
model.add(layers.Dense(10, activation='relu'))
|
|
model.add(layers.Dense(3, activation='softmax'))
|
|
|
|
return model
|
|
|
|
def load_data():
|
|
iris = load_iris()
|
|
x = iris.data
|
|
y = iris.target
|
|
|
|
y = keras.utils.to_categorical(y, 3)
|
|
|
|
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, shuffle=True, stratify=y)
|
|
|
|
return x_train, x_test, y_train, y_test
|
|
|
|
model = make_model()
|
|
x_train, x_test, y_train, y_test = load_data()
|
|
|
|
loss = 'categorical_crossentropy'
|
|
|
|
pso_iris = Optimizer(model, loss=loss, n_particles=50, c0=0.5, c1=0.8, w_min=0.75, w_max=1.3)
|
|
|
|
weight, score = pso_iris.fit(
|
|
x_train, y_train, epochs=500, save=True, save_path="./result/iris", renewal="acc", empirical_balance=False, Dispersion=False, check_point=50)
|
|
|
|
pso_iris.model_save("./result/iris")
|
|
pso_iris.save_info("./result/iris/")
|
|
|
|
gc.collect()
|