23-06-22

np random seed 고정
각 함수의 설명 추가

xor.py

@@ -5,11 +5,13 @@ os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
 import tensorflow as tf
 tf.random.set_seed(777)  # for reproducibility
 
+import numpy as np
+np.random.seed(777)
+
 # from pso_tf import PSO
 from pso import Optimizer
 from tensorflow import keras
 
-import numpy as np
 
 from tensorflow import keras
 from tensorflow.keras.models import Sequential
@@ -38,20 +40,11 @@ def make_model():
 # %%
 model = make_model()
 x_test, y_test = get_data()
-# loss = 'binary_crossentropy'
-# loss = 'categorical_crossentropy'
-# loss = 'sparse_categorical_crossentropy'
-# loss = 'kullback_leibler_divergence'
-# loss = 'poisson'
-# loss = 'cosine_similarity'
-# loss = 'log_cosh'
-# loss = 'huber_loss' 
-# loss = 'mean_absolute_error'
-# loss = 'mean_absolute_percentage_error'
-loss = 'mean_squared_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, n_particles=75, c0=0.35, c1=0.8, w_min=0.6, w_max=1.2, negative_swarm=0.25)
+    loss=loss[0], n_particles=75, c0=0.35, c1=0.8, w_min=0.6, w_max=1.2, negative_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)