Task 2 - push

2026-09-20 10:23:20 +07:00
parent 8f32804f5f
commit e6e31080a1
2 changed files with 347 additions and 0 deletions
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+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+import matplotlib.pyplot as plt
+
+# 1. LOAD & PREPROCESS MNIST DATASET
+(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
+
+x_train = x_train.astype("float32") / 255.0
+x_test = x_test.astype("float32") / 255.0
+x_train = x_train.reshape(-1, 784)
+x_test = x_test.reshape(-1, 784)
+
+# Model C Architecture from Task 1
+BEST_ARCHITECTURE = [256, 128, 64]
+EPOCHS = 20  # 20 epochs gives enough room to observe overfitting
+BATCH_SIZE = 128
+
+
+# ============================================================
+# MODEL 1: WITHOUT REGULARIZATION (Baseline Model C from Task 1)
+# ============================================================
+model_base = keras.Sequential([
+    layers.Input(shape=(784,)),
+    layers.Dense(256, activation="relu"),
+    layers.Dense(128, activation="relu"),
+    layers.Dense(64, activation="relu"),
+    layers.Dense(10, activation="softmax")
+])
+
+model_base.compile(
+    optimizer="adam",
+    loss="sparse_categorical_crossentropy",
+    metrics=["accuracy"]
+)
+
+print("=" * 60)
+print("TRAINING BASELINE MODEL (WITHOUT REGULARIZATION)")
+print("=" * 60)
+
+history_base = model_base.fit(
+    x_train, y_train,
+    epochs=EPOCHS,
+    batch_size=BATCH_SIZE,
+    validation_split=0.1,
+    verbose=1
+)
+
+
+# ============================================================
+# MODEL 2: WITH REGULARIZATION (Batch Normalization + Dropout 0.3)
+# ============================================================
+model_reg = keras.Sequential([
+    layers.Input(shape=(784,)),
+    
+    layers.Dense(256),
+    layers.BatchNormalization(),
+    layers.Activation("relu"),
+    layers.Dropout(0.3),
+    
+    layers.Dense(128),
+    layers.BatchNormalization(),
+    layers.Activation("relu"),
+    layers.Dropout(0.3),
+    
+    layers.Dense(64),
+    layers.BatchNormalization(),
+    layers.Activation("relu"),
+    layers.Dropout(0.3),
+    
+    layers.Dense(10, activation="softmax")
+])
+
+model_reg.compile(
+    optimizer="adam",
+    loss="sparse_categorical_crossentropy",
+    metrics=["accuracy"]
+)
+
+print("\n" + "=" * 60)
+print("TRAINING REGULARIZED MODEL (WITH BATCHNORM + DROPOUT)")
+print("=" * 60)
+
+history_reg = model_reg.fit(
+    x_train, y_train,
+    epochs=EPOCHS,
+    batch_size=BATCH_SIZE,
+    validation_split=0.1,
+    verbose=1
+)
+
+# Optional: Save the trained regularized model to disk
+model_reg.save("model_c_regularized.keras")
+print("\n[INFO] Regularized model saved as 'model_c_regularized.keras' in your project directory.")
+
+
+# ============================================================
+# PLOTTING LOSS CURVES FOR COMPARISON
+# ============================================================
+plt.figure(figsize=(14, 5))
+
+# Plot 1: Baseline (Without Regularization)
+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)
+
+# Plot 2: Regularized (With BatchNorm + Dropout)
+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)
+
+plt.tight_layout()
+plt.show()