mirror of
https://github.com/jung-geun/PSO.git
synced 2025-12-19 20:44:39 +09:00
78 lines
1.4 KiB
Python
78 lines
1.4 KiB
Python
# %%
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import os
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import sys
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os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
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import numpy as np
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import tensorflow as tf
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from tensorflow import keras
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from tensorflow.keras import layers
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from tensorflow.keras.layers import Dense
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from tensorflow.keras.models import Sequential
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from pso import Optimizer
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def get_data():
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x = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
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y = np.array([[0], [1], [1], [0]])
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return x, y
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def make_model():
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model = Sequential()
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model.add(layers.Dense(2, activation="sigmoid", input_shape=(2,)))
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model.add(layers.Dense(1, activation="sigmoid"))
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return model
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# %%
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model = make_model()
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x_test, y_test = get_data()
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loss = [
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"mean_squared_error",
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"mean_squared_logarithmic_error",
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"binary_crossentropy",
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"categorical_crossentropy",
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"sparse_categorical_crossentropy",
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"kullback_leibler_divergence",
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"poisson",
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"cosine_similarity",
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"log_cosh",
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"huber_loss",
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"mean_absolute_error",
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"mean_absolute_percentage_error",
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]
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pso_xor = Optimizer(
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model,
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loss=loss[0],
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n_particles=50,
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c0=0.35,
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c1=0.8,
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w_min=0.6,
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w_max=1.2,
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negative_swarm=0.1,
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mutation_swarm=0.2,
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particle_min=-3,
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particle_max=3,
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)
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best_score = pso_xor.fit(
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x_test,
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y_test,
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epochs=200,
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save=True,
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save_path="./result/xor",
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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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print("Done!")
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sys.exit(0)
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# %%
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