PSO/iris.py

import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

import tensorflow as tf

tf.random.set_seed(777)  # for reproducibility

import numpy as np

np.random.seed(777)

import gc

from pso import Optimizer
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential


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[0], 
    n_particles=100, 
    c0=0.4, 
    c1=0.8, 
    w_min=0.7,
    w_max=1.0, 
    negative_swarm=0.25
    )

best_score = pso_iris.fit(
    x_train, 
    y_train, 
    epochs=200, 
    save=True, 
    save_path="./result/iris", 
    renewal="acc", 
    empirical_balance=False, 
    Dispersion=False, 
    check_point=25
    )

gc.collect()