mirror of
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51 lines
1.3 KiB
Python
51 lines
1.3 KiB
Python
import os
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
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import tensorflow as tf
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tf.random.set_seed(777) # for reproducibility
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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.models import Sequential
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from tensorflow.keras import layers
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from pso import Optimizer
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import gc
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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(model, loss=loss, n_particles=50, c0=0.5, c1=0.8, w_min=0.7, w_max=1.3)
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weight, score = pso_iris.fit(
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x_train, y_train, epochs=500, save=True, save_path="./result/iris", renewal="acc", empirical_balance=False, Dispersion=False, check_point=50)
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pso_iris.model_save("./result/iris")
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pso_iris.save_info("./result/iris/")
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gc.collect()
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