23-05-29

EBPSO 알고리즘 구현 - 선택지로 추가
random 으로 분산시키는 방법 구현 - 선택지로 추가
iris 기준 98퍼센트로 나오나 정확한 결과를 지켜봐야 할것으로 보임

iris.py

@@ -0,0 +1,48 @@
+import os
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+
+import tensorflow as tf
+tf.random.set_seed(777)  # for reproducibility
+
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+
+from tensorflow import keras
+from tensorflow.keras.models import Sequential
+from tensorflow.keras import layers
+
+from pso import Optimizer
+
+import gc
+
+def make_model():
+    model = Sequential()
+    model.add(layers.Dense(10, activation='relu', input_shape=(4,)))
+    model.add(layers.Dense(10, activation='relu'))
+    model.add(layers.Dense(3, activation='softmax'))
+
+    return model
+
+def load_data():
+    iris = load_iris()
+    x = iris.data
+    y = iris.target
+
+    y = keras.utils.to_categorical(y, 3)
+
+    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, shuffle=True, stratify=y)
+
+    return x_train, x_test, y_train, y_test
+
+model = make_model()
+x_train, x_test, y_train, y_test = load_data()
+
+loss = 'categorical_crossentropy'
+
+pso_iris = Optimizer(model, loss=loss, n_particles=50, c0=0.4, c1=0.8, w_min=0.7, w_max=1.3)
+weight, score = pso_iris.fit(
+    x_train, y_train, epochs=500, save=True, save_path="./result/iris", renewal="acc", empirical_balance=True, Dispersion=True, check_point=50)
+pso_iris.model_save("./result/iris")
+pso_iris.save_info("./result/iris/")
+
+gc.collect()