Week 2 - Pipeline CNN, eksperimen, laporan akhir
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"""
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Tugas Anggota 2 (Week 2): CNN Evaluation + Visualization
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=========================================================
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PENTING - KONSISTENSI KONFIGURASI DENGAN ANGGOTA 1:
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Skrip ini TIDAK melatih ulang model dari nol. Sebagai gantinya, skrip ini
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memuat bobot model CNN yang sudah dilatih oleh Anggota 1
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(results/models/model_cnn_mnist.keras) pada 05_cnn_full_pipeline.py.
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Ini adalah cara paling aman untuk menjamin "tidak ada konfigurasi berbeda"
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antara Anggota 1 dan Anggota 2, karena:
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- Dataset yang sama -> load_mnist_for_cnn() (src/data_loader.py)
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- Preprocessing yang sama -> normalisasi 0-1, reshape (28, 28, 1)
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- Epoch yang sama -> 10 epoch (bobot hasil training Anggota 1)
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- Batch size yang sama -> 128 (dipakai saat training oleh Anggota 1)
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- Optimizer yang sama -> Adam (tersimpan di dalam file .keras)
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Konstanta di bawah HANYA didokumentasikan ulang (bukan dipakai untuk
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melatih ulang) agar terlihat eksplisit bahwa konfigurasi cocok dengan
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skrip Anggota 1.
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Cakupan tugas (sesuai skema kelompok):
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(1) Evaluasi menyeluruh pada test set (classification report per kelas)
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(2) Visualisasi confusion matrix (raw & ternormalisasi/persentase)
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(3) Visualisasi akurasi per kelas (digit mana yang paling sulit)
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(4) Visualisasi distribusi confidence prediksi (benar vs salah)
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(5) Visualisasi contoh gambar yang salah diklasifikasikan
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"""
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import os
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import sys
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# Sembunyikan log TensorFlow yang berlebihan
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os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import seaborn as sns
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from sklearn.metrics import classification_report, confusion_matrix
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import tensorflow as tf
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from tensorflow import keras
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# Tambahkan root directory ke path
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sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
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from src.data_loader import load_mnist_for_cnn
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from src.utils import ensure_dir
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# ---------------------------------------------------------------------------
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# KONFIGURASI (harus identik dengan Anggota 1 / 05_cnn_full_pipeline.py)
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# ---------------------------------------------------------------------------
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SEED = 42
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EPOCHS = 10 # epoch training model (didokumentasikan, tidak dipakai ulang)
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BATCH_SIZE = 128 # batch size training model (didokumentasikan, tidak dipakai ulang)
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OPTIMIZER = "adam" # optimizer training model (tersimpan di file .keras)
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MODEL_PATH = os.path.join("results", "models", "model_cnn_mnist.keras")
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FIGURES_DIR = os.path.join("results", "figures")
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REPORTS_DIR = os.path.join("results", "reports")
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CLASS_NAMES = [str(i) for i in range(10)]
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def main():
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tf.random.set_seed(SEED)
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np.random.seed(SEED)
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ensure_dir(FIGURES_DIR)
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ensure_dir(REPORTS_DIR)
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print("=" * 75)
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print("TUGAS ANGGOTA 2: CNN EVALUATION + VISUALIZATION")
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print("=" * 75)
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print(
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f"[KONFIGURASI - selaras dengan Anggota 1] "
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f"seed={SEED} | epochs={EPOCHS} | batch_size={BATCH_SIZE} | optimizer={OPTIMIZER}"
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)
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# 1. Load Data (dataset & preprocessing sama persis dengan Anggota 1)
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print("\n[1/6] Memuat dataset MNIST (load_mnist_for_cnn - sama dengan Anggota 1)...")
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(x_train, y_train), (x_test, y_test) = load_mnist_for_cnn()
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print(f" -> Data testing : {x_test.shape[0]:,} gambar {x_test.shape[1:]}")
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# 2. Load Model Terlatih dari Anggota 1
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print(f"\n[2/6] Memuat model terlatih dari: {MODEL_PATH}")
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if not os.path.exists(MODEL_PATH):
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raise FileNotFoundError(
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f"Model tidak ditemukan di '{MODEL_PATH}'. "
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f"Jalankan dahulu experiments/05_cnn_full_pipeline.py (skrip Anggota 1)."
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)
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model = keras.models.load_model(MODEL_PATH)
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print(f" -> Model '{model.name}' berhasil dimuat ({model.count_params():,} parameter).")
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# 3. Evaluasi Umum pada Test Set
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print("\n[3/6] Mengevaluasi model pada test set...")
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test_loss, test_acc = model.evaluate(x_test, y_test, verbose=0)
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y_pred_probs = model.predict(x_test, verbose=0)
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y_pred = np.argmax(y_pred_probs, axis=1)
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y_confidence = np.max(y_pred_probs, axis=1)
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is_correct = (y_pred == y_test)
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print(f" -> Test Accuracy : {test_acc * 100:.2f}%")
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print(f" -> Test Loss : {test_loss:.4f}")
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print(f" -> Total Salah : {np.sum(~is_correct):,} dari {len(y_test):,} gambar")
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# -----------------------------------------------------------------
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# (1) Classification Report per Kelas
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# -----------------------------------------------------------------
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print("\n[4/6] Membuat classification report per kelas...")
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report_dict = classification_report(
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y_test, y_pred, target_names=CLASS_NAMES, digits=4, output_dict=True
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)
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report_text = classification_report(y_test, y_pred, target_names=CLASS_NAMES, digits=4)
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print("\n" + "-" * 75)
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print("CLASSIFICATION REPORT (Precision / Recall / F1-Score per Digit):")
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print("-" * 75)
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print(report_text)
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report_txt_path = os.path.join(REPORTS_DIR, "classification_report_cnn.txt")
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with open(report_txt_path, "w") as f:
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f.write("CLASSIFICATION REPORT - CNN MNIST (Anggota 2: CNN Evaluation)\n")
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f.write(f"Test Accuracy: {test_acc * 100:.2f}% | Test Loss: {test_loss:.4f}\n")
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f.write("=" * 65 + "\n")
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f.write(report_text)
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print(f" -> Laporan teks tersimpan: {report_txt_path}")
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report_df = pd.DataFrame(report_dict).transpose()
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report_csv_path = os.path.join(REPORTS_DIR, "classification_report_cnn.csv")
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report_df.to_csv(report_csv_path)
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print(f" -> Laporan CSV tersimpan : {report_csv_path}")
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# Visualisasi precision/recall/f1 per kelas (bar chart berkelompok)
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per_class_df = report_df.loc[CLASS_NAMES, ["precision", "recall", "f1-score"]]
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fig, ax = plt.subplots(figsize=(11, 5))
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per_class_df.plot(kind="bar", ax=ax, width=0.75,
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color=["#3498db", "#2ecc71", "#e74c3c"])
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ax.set_title("Precision, Recall, dan F1-Score per Digit (CNN)", fontsize=13, fontweight="bold")
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ax.set_xlabel("Digit", fontsize=11)
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ax.set_ylabel("Skor", fontsize=11)
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ax.set_ylim(0.90, 1.005)
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ax.set_xticklabels(CLASS_NAMES, rotation=0)
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ax.legend(["Precision", "Recall", "F1-Score"], loc="lower right")
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ax.grid(True, axis="y", alpha=0.3)
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plt.tight_layout()
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metrics_bar_path = os.path.join(FIGURES_DIR, "per_class_metrics_cnn.png")
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plt.savefig(metrics_bar_path, dpi=150)
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plt.close()
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print(f" -> Grafik metrik per kelas tersimpan: {metrics_bar_path}")
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# -----------------------------------------------------------------
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# (2) Confusion Matrix: Raw Count & Ternormalisasi (%)
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# -----------------------------------------------------------------
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print("\n[5/6] Membuat visualisasi confusion matrix (raw & normalisasi)...")
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cm = confusion_matrix(y_test, y_pred)
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cm_norm = cm.astype("float") / cm.sum(axis=1, keepdims=True) * 100
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fig, axes = plt.subplots(1, 2, figsize=(17, 7))
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sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", cbar=True,
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xticklabels=CLASS_NAMES, yticklabels=CLASS_NAMES,
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annot_kws={"size": 9}, ax=axes[0])
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axes[0].set_title("Confusion Matrix (Jumlah Gambar)", fontsize=12, fontweight="bold")
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axes[0].set_xlabel("Predicted Digit")
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axes[0].set_ylabel("True Digit")
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sns.heatmap(cm_norm, annot=True, fmt=".1f", cmap="Oranges", cbar=True,
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xticklabels=CLASS_NAMES, yticklabels=CLASS_NAMES,
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annot_kws={"size": 9}, ax=axes[1])
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axes[1].set_title("Confusion Matrix (Persentase per Baris / Recall %)", fontsize=12, fontweight="bold")
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axes[1].set_xlabel("Predicted Digit")
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axes[1].set_ylabel("True Digit")
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plt.suptitle("Evaluasi CNN pada MNIST Test Set (10.000 Gambar)", fontsize=14, fontweight="bold", y=1.02)
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plt.tight_layout()
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cm_dual_path = os.path.join(FIGURES_DIR, "confusion_matrix_cnn_detail.png")
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plt.savefig(cm_dual_path, dpi=150, bbox_inches="tight")
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plt.close()
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print(f" -> Confusion matrix detail tersimpan: {cm_dual_path}")
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# -----------------------------------------------------------------
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# (3) Akurasi per Kelas (diagonal confusion matrix ternormalisasi)
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# -----------------------------------------------------------------
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per_class_acc = np.diag(cm_norm)
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fig, ax = plt.subplots(figsize=(10, 5))
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colors = ["#e74c3c" if a < per_class_acc.mean() else "#2ecc71" for a in per_class_acc]
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bars = ax.bar(CLASS_NAMES, per_class_acc, color=colors)
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ax.axhline(per_class_acc.mean(), color="gray", linestyle="--", linewidth=1.5,
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label=f"Rata-rata: {per_class_acc.mean():.2f}%")
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for bar, acc in zip(bars, per_class_acc):
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ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() - 1.2,
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f"{acc:.1f}%", ha="center", va="top", fontsize=9, color="white", fontweight="bold")
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ax.set_title("Akurasi per Digit pada Test Set (CNN)", fontsize=13, fontweight="bold")
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ax.set_xlabel("Digit")
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ax.set_ylabel("Akurasi (%)")
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ax.set_ylim(min(per_class_acc) - 5, 100.5)
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ax.legend()
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ax.grid(True, axis="y", alpha=0.3)
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plt.tight_layout()
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per_class_acc_path = os.path.join(FIGURES_DIR, "per_class_accuracy_cnn.png")
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plt.savefig(per_class_acc_path, dpi=150)
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plt.close()
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print(f" -> Grafik akurasi per kelas tersimpan: {per_class_acc_path}")
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hardest_idx = np.argsort(per_class_acc)[:3]
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print(" -> 3 digit paling sulit dikenali:")
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for idx in hardest_idx:
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print(f" Digit {idx}: akurasi {per_class_acc[idx]:.2f}%")
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# -----------------------------------------------------------------
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# (4) Distribusi Confidence Prediksi (Benar vs Salah)
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# -----------------------------------------------------------------
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print("\n[6/6] Membuat visualisasi distribusi confidence & sampel kesalahan...")
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fig, ax = plt.subplots(figsize=(9, 5.5))
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ax.hist(y_confidence[is_correct], bins=30, alpha=0.65, color="#2ecc71",
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label=f"Prediksi Benar (n={np.sum(is_correct):,})", density=True)
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ax.hist(y_confidence[~is_correct], bins=30, alpha=0.75, color="#e74c3c",
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label=f"Prediksi Salah (n={np.sum(~is_correct):,})", density=True)
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ax.set_title("Distribusi Confidence Prediksi Model CNN", fontsize=13, fontweight="bold")
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ax.set_xlabel("Confidence (Probabilitas Softmax Maksimum)")
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ax.set_ylabel("Kepadatan (Density)")
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ax.legend()
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ax.grid(True, alpha=0.3)
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plt.tight_layout()
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confidence_path = os.path.join(FIGURES_DIR, "confidence_distribution_cnn.png")
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plt.savefig(confidence_path, dpi=150)
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plt.close()
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print(f" -> Distribusi confidence tersimpan: {confidence_path}")
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avg_conf_correct = y_confidence[is_correct].mean() * 100
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avg_conf_wrong = y_confidence[~is_correct].mean() * 100
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print(f" -> Rata-rata confidence saat BENAR : {avg_conf_correct:.2f}%")
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print(f" -> Rata-rata confidence saat SALAH : {avg_conf_wrong:.2f}%")
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# -----------------------------------------------------------------
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# (5) Contoh Gambar yang Salah Diklasifikasikan
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# -----------------------------------------------------------------
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wrong_idx = np.where(~is_correct)[0]
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rng = np.random.default_rng(SEED)
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n_show = min(25, len(wrong_idx))
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sample_idx = rng.choice(wrong_idx, size=n_show, replace=False)
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fig, axes = plt.subplots(5, 5, figsize=(11, 12))
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for ax, idx in zip(axes.flat, sample_idx):
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ax.imshow(x_test[idx].reshape(28, 28), cmap="gray")
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conf = y_confidence[idx] * 100
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ax.set_title(f"True: {y_test[idx]} | Pred: {y_pred[idx]}\nConf: {conf:.1f}%",
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fontsize=9, color="#c0392b")
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ax.axis("off")
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for ax in axes.flat[n_show:]:
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ax.axis("off")
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plt.suptitle(f"Contoh {n_show} Gambar Salah Klasifikasi dari {len(wrong_idx)} Total Kesalahan",
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fontsize=13, fontweight="bold", y=1.0)
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plt.tight_layout()
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misclassified_path = os.path.join(FIGURES_DIR, "misclassified_samples_cnn.png")
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plt.savefig(misclassified_path, dpi=150, bbox_inches="tight")
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plt.close()
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print(f" -> Contoh gambar salah klasifikasi tersimpan: {misclassified_path}")
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# -----------------------------------------------------------------
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# Ringkasan Akhir
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# -----------------------------------------------------------------
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summary_path = os.path.join(REPORTS_DIR, "evaluation_summary_cnn.csv")
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summary_df = pd.DataFrame([{
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"test_accuracy_pct": round(test_acc * 100, 4),
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"test_loss": round(test_loss, 4),
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"total_errors": int(np.sum(~is_correct)),
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"total_samples": int(len(y_test)),
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"avg_confidence_correct_pct": round(avg_conf_correct, 4),
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"avg_confidence_wrong_pct": round(avg_conf_wrong, 4),
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"hardest_digit_1": int(hardest_idx[0]),
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"hardest_digit_1_acc_pct": round(per_class_acc[hardest_idx[0]], 4),
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"hardest_digit_2": int(hardest_idx[1]),
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"hardest_digit_2_acc_pct": round(per_class_acc[hardest_idx[1]], 4),
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}])
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summary_df.to_csv(summary_path, index=False)
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print("\n" + "=" * 75)
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print("RINGKASAN EVALUASI CNN (ANGGOTA 2)")
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print("=" * 75)
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print(f"Test Accuracy : {test_acc * 100:.2f}%")
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print(f"Test Loss : {test_loss:.4f}")
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print(f"Total Kesalahan : {np.sum(~is_correct):,} / {len(y_test):,}")
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print(f"Confidence saat Benar : {avg_conf_correct:.2f}%")
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print(f"Confidence saat Salah : {avg_conf_wrong:.2f}%")
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print(f"Digit Paling Sulit : {hardest_idx[0]} ({per_class_acc[hardest_idx[0]]:.2f}%), "
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f"{hardest_idx[1]} ({per_class_acc[hardest_idx[1]]:.2f}%)")
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print("-" * 75)
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print("File yang dihasilkan:")
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print(f" 1. {metrics_bar_path}")
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print(f" 2. {cm_dual_path}")
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print(f" 3. {per_class_acc_path}")
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print(f" 4. {confidence_path}")
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print(f" 5. {misclassified_path}")
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print(f" 6. {report_txt_path}")
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print(f" 7. {report_csv_path}")
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print(f" 8. {summary_path}")
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print("=" * 75 + "\n")
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,14 @@
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,precision,recall,f1-score,support
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0,0.9938775510204082,0.9938775510204082,0.9938775510204082,980.0
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1,0.992988606485539,0.9982378854625551,0.9956063268892794,1135.0
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2,0.9932038834951457,0.9912790697674418,0.9922405431619786,1032.0
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3,0.9940298507462687,0.9891089108910891,0.9915632754342432,1010.0
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4,0.9908814589665653,0.9959266802443992,0.9933976637887253,982.0
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5,0.9877232142857143,0.992152466367713,0.9899328859060402,892.0
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6,0.9937304075235109,0.9926931106471816,0.9932114882506528,958.0
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7,0.9893307468477207,0.9922178988326849,0.9907722195240408,1028.0
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8,0.9836734693877551,0.9897330595482546,0.9866939611054247,974.0
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9,0.9919436052366566,0.9762140733399405,0.984015984015984,1009.0
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accuracy,0.9912,0.9912,0.9912,0.9912
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macro avg,0.9911382793995284,0.9911440706121668,0.9911311899096779,10000.0
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weighted avg,0.9912088120264272,0.9912,0.9911943463223446,10000.0
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@@ -0,0 +1,19 @@
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CLASSIFICATION REPORT - CNN MNIST (Anggota 2: CNN Evaluation)
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Test Accuracy: 99.12% | Test Loss: 0.0268
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=================================================================
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precision recall f1-score support
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0 0.9939 0.9939 0.9939 980
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1 0.9930 0.9982 0.9956 1135
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2 0.9932 0.9913 0.9922 1032
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3 0.9940 0.9891 0.9916 1010
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4 0.9909 0.9959 0.9934 982
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5 0.9877 0.9922 0.9899 892
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||||
6 0.9937 0.9927 0.9932 958
|
||||
7 0.9893 0.9922 0.9908 1028
|
||||
8 0.9837 0.9897 0.9867 974
|
||||
9 0.9919 0.9762 0.9840 1009
|
||||
|
||||
accuracy 0.9912 10000
|
||||
macro avg 0.9911 0.9911 0.9911 10000
|
||||
weighted avg 0.9912 0.9912 0.9912 10000
|
||||
@@ -0,0 +1,2 @@
|
||||
test_accuracy_pct,test_loss,total_errors,total_samples,avg_confidence_correct_pct,avg_confidence_wrong_pct,hardest_digit_1,hardest_digit_1_acc_pct,hardest_digit_2,hardest_digit_2_acc_pct
|
||||
99.12,0.0268,88,10000,99.5341,76.6475,9,97.6214,3,98.9109
|
||||
|
Reference in New Issue
Block a user