iris.py 파일 삭제

test/iris.py

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+import gc
+import os
+import sys
+
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from tensorflow import keras
+from tensorflow.keras import layers
+from tensorflow.keras.models import Sequential
+
+from pso import optimizer
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+
+
+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()
+
+
+pso_iris = optimizer(
+    model=model,
+    loss="categorical_crossentropy",
+    n_particles=100,
+    c0=0.5,
+    c1=0.3,
+    w_min=0.1,
+    w_max=0.9,
+    negative_swarm=0,
+    mutation_swarm=0.1,
+    convergence_reset=True,
+    convergence_reset_patience=10,
+    convergence_reset_monitor="loss",
+    convergence_reset_min_delta=0.001,
+)
+
+best_score = pso_iris.fit(
+    x_train,
+    y_train,
+    epochs=500,
+    save_info=True,
+    log=2,
+    log_name="iris",
+    renewal="loss",
+    check_point=25,
+    validate_data=(x_test, y_test),
+)
+
+gc.collect()
+print("Done!")
+sys.exit(0)