mirror of
https://github.com/jung-geun/PSO.git
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66 lines
1.3 KiB
Python
66 lines
1.3 KiB
Python
import os
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
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import gc
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import numpy as np
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import tensorflow as tf
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from pso import Optimizer
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from sklearn.datasets import load_iris
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from sklearn.model_selection import train_test_split
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from tensorflow import keras
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from tensorflow.keras import layers
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from tensorflow.keras.models import Sequential
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def make_model():
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model = Sequential()
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model.add(layers.Dense(10, activation='relu', input_shape=(4,)))
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model.add(layers.Dense(10, activation='relu'))
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model.add(layers.Dense(3, activation='softmax'))
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return model
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def load_data():
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iris = load_iris()
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x = iris.data
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y = iris.target
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y = keras.utils.to_categorical(y, 3)
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x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, shuffle=True, stratify=y)
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return x_train, x_test, y_train, y_test
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model = make_model()
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x_train, x_test, y_train, y_test = load_data()
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loss = ['categorical_crossentropy']
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pso_iris = Optimizer(
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model,
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loss=loss[0],
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n_particles=100,
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c0=0.4,
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c1=0.8,
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w_min=0.7,
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w_max=1.0,
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negative_swarm=0,
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mutation_swarm=0,
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)
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best_score = pso_iris.fit(
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x_train,
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y_train,
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epochs=200,
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save=True,
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save_path="./result/iris",
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renewal="acc",
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empirical_balance=False,
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Dispersion=False,
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check_point=25
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)
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gc.collect()
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