Upload train_last.py

train_last.py

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+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, roc_curve, auc, classification_report
+import tensorflow as tf
+from tensorflow.keras import Sequential
+from tensorflow.keras.layers import Conv2D, BatchNormalization, Dropout, Flatten, Dense, Input
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping
+from tensorflow.keras.optimizers import SGD
+from tensorflow.keras.utils import to_categorical
+
+# Фиксация случайных seed для воспроизводимости
+def set_seeds(seed=42):
+    np.random.seed(seed)
+    tf.random.set_seed(seed)
+set_seeds(42)
+
+# ------------------- 1. Загрузка и балансировка -------------------
+df_esp = pd.read_csv('dataset_clean_esp.csv')
+df_power = pd.read_csv('dataset_clean_transistor.csv')
+
+df_esp['label'] = 'ESP32'
+df_power['label'] = 'TRANSISTOR'
+
+df = pd.concat([df_esp, df_power], ignore_index=True)
+print("Распределение до балансировки:\n", df['label'].value_counts())
+
+min_count = min(df['label'].value_counts())
+df_balanced = pd.concat([
+    df[df['label'] == 'ESP32'].sample(min_count, random_state=42),
+    df[df['label'] == 'TRANSISTOR'].sample(min_count, random_state=42)
+])
+print("После балансировки:\n", df_balanced['label'].value_counts())
+
+# ------------------- 2. Признаки, фильтрация, нормализация -------------------
+dist_cols = [f'z{i}' for i in range(64)]
+df_balanced = df_balanced[(df_balanced[dist_cols] < 0.375).all(axis=1)]
+
+X = df_balanced[dist_cols].values.reshape(-1, 8, 8, 1).astype('float32')
+y = df_balanced['label'].values
+print(f"X range: {X.min():.3f} - {X.max():.3f}")
+
+# ------------------- 3. One-hot encoding -------------------
+y_binary = (y == 'TRANSISTOR').astype(int)
+y_cat = to_categorical(y_binary, num_classes=2)
+
+# ------------------- 4. Разделение -------------------
+X_train, X_temp, y_train, y_temp = train_test_split(X, y_cat, test_size=0.3, random_state=42, stratify=y_binary)
+X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42, stratify=np.argmax(y_temp, axis=1))
+print(f"Train: {X_train.shape}, Val: {X_val.shape}, Test: {X_test.shape}")
+
+# ------------------- 5. Аугментация -------------------
+datagen = ImageDataGenerator(
+    rotation_range=5,
+    width_shift_range=0.05,
+    height_shift_range=0.05,
+    horizontal_flip=True,
+    fill_mode='nearest'
+)
+datagen.fit(X_train)
+
+# ------------------- 6. Модель (ВАША ОРИГИНАЛЬНАЯ) -------------------
+model = Sequential([
+    Input(shape=(8,8,1)),
+    Conv2D(3, (3,3), padding='same', activation='relu'),
+    BatchNormalization(),
+    Dropout(0.2),
+    Conv2D(4, (3,3), padding='valid', activation='relu'),
+    BatchNormalization(),
+    Dropout(0.2),
+    Flatten(),
+    Dense(16, activation='relu'),
+    Dropout(0.3),
+    Dense(2, activation='softmax')
+])
+
+model.compile(
+    optimizer=SGD(learning_rate=0.0005, momentum=0.95, nesterov=True),
+    loss='categorical_crossentropy',
+    metrics=['accuracy']
+)
+model.summary()
+
+# ------------------- 7. Callbacks -------------------
+reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=10, min_lr=1e-6, verbose=1)
+early_stop = EarlyStopping(monitor='val_loss', patience=25, restore_best_weights=True, verbose=1)
+
+# ------------------- 8. Обучение -------------------
+history = model.fit(
+    datagen.flow(X_train, y_train, batch_size=32),
+    validation_data=(X_val, y_val),
+    epochs=100,
+    callbacks=[reduce_lr, early_stop],
+    verbose=1
+)
+
+# ------------------- 9. Оценка -------------------
+test_loss, test_acc = model.evaluate(X_test, y_test, verbose=0)
+print(f"\nTest accuracy: {test_acc:.4f}")
+
+# ============================================================================
+# 10. ГРАФИКИ (КАЖДЫЙ ОТДЕЛЬНО, ОРИГИНАЛЬНЫЕ МЕТРИКИ)
+# ============================================================================
+
+# График 1: Accuracy
+plt.figure(figsize=(10, 6))
+plt.plot(history.history['accuracy'], 'b-', linewidth=2, label='Train accuracy')
+plt.plot(history.history['val_accuracy'], 'orange', linewidth=2, label='Validation accuracy')
+plt.title('Model Accuracy', fontsize=16, fontweight='bold')
+plt.xlabel('Epoch', fontsize=12)
+plt.ylabel('Accuracy', fontsize=12)
+plt.legend(loc='lower right', fontsize=11)
+plt.grid(True, alpha=0.3)
+plt.ylim(0, 1)
+plt.tight_layout()
+plt.savefig('accuracy_plot.png', dpi=150, bbox_inches='tight')
+plt.show()
+
+# График 2: Loss
+plt.figure(figsize=(10, 6))
+plt.plot(history.history['loss'], 'b-', linewidth=2, label='Train loss')
+plt.plot(history.history['val_loss'], 'orange', linewidth=2, label='Validation loss')
+plt.title('Model Loss', fontsize=16, fontweight='bold')
+plt.xlabel('Epoch', fontsize=12)
+plt.ylabel('Loss', fontsize=12)
+plt.legend(loc='upper right', fontsize=11)
+plt.grid(True, alpha=0.3)
+plt.tight_layout()
+plt.savefig('loss_plot.png', dpi=150, bbox_inches='tight')
+plt.show()
+
+# График 3: ROC Curve
+y_pred_prob = model.predict(X_test)[:,1]
+y_true = np.argmax(y_test, axis=1)
+fpr, tpr, _ = roc_curve(y_true, y_pred_prob)
+roc_auc = auc(fpr, tpr)
+
+plt.figure(figsize=(10, 6))
+plt.plot(fpr, tpr, 'g-', linewidth=2, label=f'ROC curve (AUC = {roc_auc:.3f})')
+plt.plot([0, 1], [0, 1], 'r--', linewidth=1.5, label='Random classifier')
+plt.title('ROC Curve', fontsize=16, fontweight='bold')
+plt.xlabel('False Positive Rate', fontsize=12)
+plt.ylabel('True Positive Rate', fontsize=12)
+plt.legend(loc='lower right', fontsize=11)
+plt.grid(True, alpha=0.3)
+plt.fill_between(fpr, tpr, alpha=0.2, color='green')
+plt.tight_layout()
+plt.savefig('roc_curve.png', dpi=150, bbox_inches='tight')
+plt.show()
+
+# График 4: Confusion Matrix
+y_pred = (y_pred_prob > 0.5).astype(int)
+cm = confusion_matrix(y_true, y_pred)
+disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['ESP32', 'TRANSISTOR'])
+fig, ax = plt.subplots(figsize=(7, 6))
+disp.plot(cmap='Blues', values_format='d', ax=ax)
+plt.title('Confusion Matrix', fontsize=16, fontweight='bold', pad=15)
+plt.tight_layout()
+plt.savefig('confusion_matrix.png', dpi=150, bbox_inches='tight')
+plt.show()
+
+# ------------------- 11. Classification Report -------------------
+print("\n" + "="*60)
+print("CLASSIFICATION REPORT")
+print("="*60)
+print(classification_report(y_true, y_pred, target_names=['ESP32', 'TRANSISTOR']))
+
+# ============================================================================
+# 12. ТЕПЛОВЫЕ КАРТЫ (ГОРИЗОНТАЛЬНО: 2 ESP32 + 2 TRANSISTOR)
+# ============================================================================
+esp_idx = np.where(np.argmax(y_cat, axis=1) == 0)[0]
+tr_idx = np.where(np.argmax(y_cat, axis=1) == 1)[0]
+
+# Перемешиваем для разнообразия
+np.random.seed(42)
+esp_shuffled = np.random.permutation(esp_idx)
+tr_shuffled = np.random.permutation(tr_idx)
+
+n_esp = 2
+n_tr = 2
+n_heatmaps = 10
+
+vmin_global, vmax_global = 0.0, 0.35
+
+for hm in range(n_heatmaps):
+    start_esp = (hm * n_esp) % len(esp_shuffled)
+    start_tr = (hm * n_tr) % len(tr_shuffled)
+    
+    current_esp_idx = esp_shuffled[start_esp:start_esp + n_esp]
+    current_tr_idx = tr_shuffled[start_tr:start_tr + n_tr]
+    
+    # Добираем если не хватает
+    if len(current_esp_idx) < n_esp:
+        needed = n_esp - len(current_esp_idx)
+        current_esp_idx = np.concatenate([current_esp_idx, esp_shuffled[:needed]])
+    if len(current_tr_idx) < n_tr:
+        needed = n_tr - len(current_tr_idx)
+        current_tr_idx = np.concatenate([current_tr_idx, tr_shuffled[:needed]])
+    
+    # ГОРИЗОНТАЛЬНЫЙ РИСУНОК: 1 строка, 4 столбца
+    fig, axes = plt.subplots(1, 4, figsize=(12, 4))
+    fig.suptitle('Depth Maps', fontsize=14, fontweight='bold', y=1.02)
+    
+    # ESP32 пример 1
+    ax = axes[0]
+    sample = X[current_esp_idx[0]].reshape(8, 8)
+    im = ax.imshow(sample, cmap='plasma', vmin=vmin_global, vmax=vmax_global)
+    ax.set_title('ESP32 #1', fontsize=11, fontweight='bold')
+    ax.axis('off')
+    
+    # ESP32 пример 2
+    ax = axes[1]
+    sample = X[current_esp_idx[1]].reshape(8, 8)
+    ax.imshow(sample, cmap='plasma', vmin=vmin_global, vmax=vmax_global)
+    ax.set_title('ESP32 #2', fontsize=11, fontweight='bold')
+    ax.axis('off')
+    
+    # TRANSISTOR пример 1
+    ax = axes[2]
+    sample = X[current_tr_idx[0]].reshape(8, 8)
+    ax.imshow(sample, cmap='plasma', vmin=vmin_global, vmax=vmax_global)
+    ax.set_title('TRANSISTOR #1', fontsize=11, fontweight='bold')
+    ax.axis('off')
+    
+    # TRANSISTOR пример 2
+    ax = axes[3]
+    sample = X[current_tr_idx[1]].reshape(8, 8)
+    ax.imshow(sample, cmap='plasma', vmin=vmin_global, vmax=vmax_global)
+    ax.set_title('TRANSISTOR #2', fontsize=11, fontweight='bold')
+    ax.axis('off')
+    
+    # Colorbar справа
+    cbar_ax = fig.add_axes([0.92, 0.15, 0.02, 0.7])
+    cbar = fig.colorbar(im, cax=cbar_ax)
+    cbar.set_label('Normalized distance', fontsize=10)
+    
+    plt.tight_layout(rect=[0, 0, 0.9, 1])
+    plt.savefig(f'heatmap_set_{hm+1:02d}.png', dpi=150, bbox_inches='tight')
+    plt.close()
+    print(f"✅ Сохранена тепловая карта {hm+1}/{n_heatmaps}")
+
+print(f"\n✅ Сохранено {n_heatmaps} тепловых карт (heatmap_set_01.png ... heatmap_set_10.png)")
+
+# ------------------- 13. Сохранение модели -------------------
+converter = tf.lite.TFLiteConverter.from_keras_model(model)
+tflite_model = converter.convert()
+with open('model_quantized.tflite', 'wb') as f:
+    f.write(tflite_model)
+print("Модель сохранена как model_quantized.tflite")
+
+model.save('model_final.h5')
+print("Модель сохранена как model_final.h5")
+
+# ------------------- 14. Итоговая статистика -------------------
+print("\n" + "="*60)
+print("FINAL TRAINING SUMMARY")
+print("="*60)
+print(f"Test accuracy:           {test_acc:.4f} ({test_acc*100:.2f}%)")
+print(f"ROC AUC:                 {roc_auc:.4f}")
+print(f"Best validation accuracy: {max(history.history['val_accuracy']):.4f}")
+print(f"Best validation loss:     {min(history.history['val_loss']):.6f}")
+print(f"Training epochs done:    {len(history.history['accuracy'])}")
+print("="*60)