mirror of
https://github.com/jung-geun/PSO.git
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60 lines
1.5 KiB
Python
60 lines
1.5 KiB
Python
# %%
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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 pso_tf import PSO
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from pso import Optimizer
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from tensorflow import keras
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import numpy as np
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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 datetime import datetime
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print(tf.__version__)
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print(tf.config.list_physical_devices())
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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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leyer = []
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leyer.append(layers.Dense(2, activation='sigmoid', input_shape=(2,)))
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# leyer.append(layers.Dense(2, activation='sigmoid'))
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leyer.append(layers.Dense(1, activation='sigmoid'))
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model = Sequential(leyer)
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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 = 'binary_crossentropy'
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# loss = 'categorical_crossentropy'
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# loss = 'sparse_categorical_crossentropy'
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# loss = 'kullback_leibler_divergence'
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# loss = 'poisson'
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# loss = 'cosine_similarity'
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# loss = 'log_cosh'
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# loss = 'huber_loss'
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# loss = 'mean_absolute_error'
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# loss = 'mean_absolute_percentage_error'
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loss = 'mean_squared_error'
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pso_xor = Optimizer(model,
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loss=loss, n_particles=75, c0=0.35, c1=0.8, w_min=0.6, w_max=1.2, negative_swarm=0.25)
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best_score = pso_xor.fit(
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x_test, y_test, epochs=200, save=True, save_path="./result/xor", renewal="acc", empirical_balance=False, Dispersion=False, check_point=25)
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# %%
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