23-07-06

dev container 실행 코드 추가

iris.py

@@ -1,6 +1,6 @@
 import os
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
 
 import gc
 
@@ -16,12 +16,13 @@ from tensorflow.keras.models import Sequential
 
 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'))
+    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
@@ -29,37 +30,40 @@ def load_data():
 
     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)
+    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']
+loss = ["categorical_crossentropy"]
 
 pso_iris = Optimizer(
     model,
-    loss=loss[0], 
-    n_particles=100, 
-    c0=0.4, 
-    c1=0.8, 
+    loss=loss[0],
+    n_particles=100,
+    c0=0.4,
+    c1=0.8,
     w_min=0.7,
-    w_max=1.0, 
+    w_max=1.0,
     negative_swarm=0.1,
     mutation_swarm=0.2,
-    )
+)
 
 best_score = pso_iris.fit(
-    x_train, 
-    y_train, 
-    epochs=200, 
-    save=True, 
-    save_path="./result/iris", 
-    renewal="acc", 
-    empirical_balance=False, 
-    Dispersion=False, 
-    check_point=25
-    )
+    x_train,
+    y_train,
+    epochs=200,
+    save=True,
+    save_path="./result/iris",
+    renewal="acc",
+    empirical_balance=False,
+    Dispersion=False,
+    check_point=25,
+)
 
 gc.collect()