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23-07-06

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dev container 실행 코드 추가
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jung-geun
2023-07-06 22:04:42 +09:00
parent c7384cdf7b
commit c163de6cb6
11 changed files with 192 additions and 132 deletions
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52
xor.py
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@@ -1,10 +1,11 @@
# %%
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
import numpy as np
import tensorflow as tf
# from pso_tf import PSO
from pso import Optimizer
from tensorflow import keras
@@ -14,31 +15,64 @@ from tensorflow.keras.models import Sequential
print(tf.__version__)
print(tf.config.list_physical_devices())
def get_data():
x = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
x = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y = np.array([[0], [1], [1], [0]])
return x, y
def make_model():
leyer = []
leyer.append(layers.Dense(2, activation='sigmoid', input_shape=(2,)))
leyer.append(layers.Dense(2, activation="sigmoid", input_shape=(2,)))
# leyer.append(layers.Dense(2, activation='sigmoid'))
leyer.append(layers.Dense(1, activation='sigmoid'))
leyer.append(layers.Dense(1, activation="sigmoid"))
model = Sequential(leyer)
return model
# %%
model = make_model()
x_test, y_test = get_data()
loss = ['mean_squared_error', 'mean_squared_logarithmic_error', 'binary_crossentropy', 'categorical_crossentropy', 'sparse_categorical_crossentropy', 'kullback_leibler_divergence', 'poisson', 'cosine_similarity', 'log_cosh', 'huber_loss', 'mean_absolute_error', 'mean_absolute_percentage_error']
loss = [
"mean_squared_error",
"mean_squared_logarithmic_error",
"binary_crossentropy",
"categorical_crossentropy",
"sparse_categorical_crossentropy",
"kullback_leibler_divergence",
"poisson",
"cosine_similarity",
"log_cosh",
"huber_loss",
"mean_absolute_error",
"mean_absolute_percentage_error",
]
pso_xor = Optimizer(model,
loss=loss[0], n_particles=75, c0=0.35, c1=0.8, w_min=0.6, w_max=1.2, negative_swarm=0.25, mutation_swarm=0.25)
pso_xor = Optimizer(
model,
loss=loss[0],
n_particles=75,
c0=0.35,
c1=0.8,
w_min=0.6,
w_max=1.2,
negative_swarm=0.25,
mutation_swarm=0.25,
)
best_score = pso_xor.fit(
x_test, y_test, epochs=200, save=True, save_path="./result/xor", renewal="acc", empirical_balance=False, Dispersion=False, check_point=25)
x_test,
y_test,
epochs=200,
save=True,
save_path="./result/xor",
renewal="acc",
empirical_balance=False,
Dispersion=False,
check_point=25,
)
# %%