refactor: re-structure folder

2026-09-21 09:04:44 +07:00
parent 7492704c8e
commit 5ac0a227c4
26 changed files with 911 additions and 944 deletions
@@ -0,0 +1,93 @@
+import sys
+import os
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+# Tambahkan root directory ke path agar dapat import dari src
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+
+from src.data_loader import load_mnist_for_mlp
+from src.models import create_mlp_model, create_regularized_mlp_model
+from src.utils import ensure_dir
+
+
+def main():
+    print("=" * 60)
+    print("TUGAS 2: EKSPERIMEN REGULARISASI (BASELINE vs BATCHNORM + DROPOUT)")
+    print("=" * 60)
+
+    # 1. Load Data
+    (x_train, y_train), (x_test, y_test) = load_mnist_for_mlp()
+
+    EPOCHS = 20
+    BATCH_SIZE = 128
+
+    # 2. Baseline Model (Without Regularization)
+    print("\n" + "=" * 60)
+    print("TRAINING BASELINE MODEL (WITHOUT REGULARIZATION)")
+    print("=" * 60)
+    model_base = create_mlp_model([256, 128, 64])
+    history_base = model_base.fit(
+        x_train, y_train,
+        epochs=EPOCHS,
+        batch_size=BATCH_SIZE,
+        validation_split=0.1,
+        verbose=1
+    )
+
+    # 3. Regularized Model (BatchNorm + Dropout 0.3)
+    print("\n" + "=" * 60)
+    print("TRAINING REGULARIZED MODEL (WITH BATCHNORM + DROPOUT)")
+    print("=" * 60)
+    model_reg = create_regularized_mlp_model(hidden_layers=[256, 128, 64], dropout_rate=0.3)
+    history_reg = model_reg.fit(
+        x_train, y_train,
+        epochs=EPOCHS,
+        batch_size=BATCH_SIZE,
+        validation_split=0.1,
+        verbose=1
+    )
+
+    # 4. Save Model Artifact
+    results_model_dir = os.path.join("results", "models")
+    ensure_dir(results_model_dir)
+    model_reg_path = os.path.join(results_model_dir, "model_c_regularized.keras")
+    model_reg.save(model_reg_path)
+    print(f"\n[INFO] Model teregularisasi disimpan di: {model_reg_path}")
+
+    # 5. Plot Comparison Curves
+    results_fig_dir = os.path.join("results", "figures")
+    ensure_dir(results_fig_dir)
+    fig_path = os.path.join(results_fig_dir, "loss_regularization.png")
+
+    plt.figure(figsize=(14, 5))
+
+    # Subplot 1: Baseline
+    plt.subplot(1, 2, 1)
+    plt.plot(history_base.history['loss'], label='Training Loss', color='blue')
+    plt.plot(history_base.history['val_loss'], label='Validation Loss', color='orange', linestyle='--')
+    plt.title('Model C Without Regularization')
+    plt.xlabel('Epoch')
+    plt.ylabel('Loss')
+    plt.legend()
+    plt.grid(True, alpha=0.3)
+
+    # Subplot 2: Regularized
+    plt.subplot(1, 2, 2)
+    plt.plot(history_reg.history['loss'], label='Training Loss', color='blue')
+    plt.plot(history_reg.history['val_loss'], label='Validation Loss', color='green', linestyle='--')
+    plt.title('Model C With BatchNorm + Dropout(0.3)')
+    plt.xlabel('Epoch')
+    plt.ylabel('Loss')
+    plt.legend()
+    plt.grid(True, alpha=0.3)
+
+    plt.tight_layout()
+    plt.savefig(fig_path, dpi=130)
+    print(f"[INFO] Grafik perbandingan loss disimpan di: {fig_path}")
+
+
+if __name__ == "__main__":
+    main()
+