코드 변경 내용을 요약한 커밋 메시지입니다.

test/seeds.py

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+# %%
+import json
+import os
+import sys
+
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+from keras.layers import Dense
+from keras.models import Sequential
+from keras.utils import to_categorical
+from sklearn.model_selection import train_test_split
+from tensorflow import keras
+
+from pso import optimizer
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+
+
+def get_data():
+    with open("data/seeds/seeds_dataset.txt", "r", encoding="utf-8") as f:
+        data = f.readlines()
+        df = pd.DataFrame([d.split() for d in data])
+        df.columns = [
+            "area",
+            "perimeter",
+            "compactness",
+            "length_of_kernel",
+            "width_of_kernel",
+            "asymmetry_coefficient",
+            "length_of_kernel_groove",
+            "target",
+        ]
+
+        df = df.astype(float)
+        df["target"] = df["target"].astype(int)
+
+        x = df.iloc[:, :-1].values.round(0).astype(int)
+        y = df.iloc[:, -1].values
+
+    y_class = to_categorical(y)
+
+    x_train, x_test, y_train, y_test = train_test_split(
+        x, y_class, test_size=0.2, shuffle=True
+    )
+
+    return x_train, y_train, x_test, y_test
+
+
+def make_model():
+    model = Sequential()
+    model.add(Dense(16, activation="relu", input_shape=(7,)))
+    model.add(Dense(32, activation="relu"))
+    model.add(Dense(4, activation="softmax"))
+
+    return model
+
+
+# %%
+model = make_model()
+x_train, y_train, x_test, y_test = get_data()
+
+loss = [
+    "mean_squared_error",
+    "categorical_crossentropy",
+    "sparse_categorical_crossentropy",
+    "binary_crossentropy",
+    "kullback_leibler_divergence",
+    "poisson",
+    "cosine_similarity",
+    "log_cosh",
+    "huber_loss",
+    "mean_absolute_error",
+    "mean_absolute_percentage_error",
+]
+
+# rs = random_state()
+
+pso_mnist = optimizer(
+    model,
+    loss="categorical_crossentropy",
+    n_particles=100,
+    c0=0.5,
+    c1=1.0,
+    w_min=0.7,
+    w_max=1.2,
+    negative_swarm=0.0,
+    mutation_swarm=0.3,
+    convergence_reset=True,
+    convergence_reset_patience=10,
+    convergence_reset_monitor="mse",
+    convergence_reset_min_delta=0.0005,
+)
+
+best_score = pso_mnist.fit(
+    x_train,
+    y_train,
+    epochs=500,
+    save_info=True,
+    log=2,
+    log_name="seeds",
+    renewal="acc",
+    check_point=25,
+    empirical_balance=False,
+    dispersion=False,
+    back_propagation=False,
+    validate_data=(x_test, y_test),
+)
+
+print("Done!")
+
+sys.exit(0)