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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ac80a687",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.applications import MobileNetV2\n",
    "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import os\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e45a6baa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters\n",
    "IMG_SIZE = 128\n",
    "BATCH_SIZE = 32\n",
    "EPOCHS = 15\n",
    "DATA_DIR = r\"C:\\Users\\Cherilyn\\Desktop\\trash_sort\\src\\data\\trashnet\"\n",
    "MODEL_PATH = 'models/trashsort_cnn.h5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "957a1e3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current working directory: c:\\Users\\Cherilyn\\Desktop\\trash_sort\\src\n"
     ]
    }
   ],
   "source": [
    "# Verify current working directory (optional)\n",
    "print(\"Current working directory:\", os.getcwd())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2213192b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1914 images belonging to 5 classes.\n",
      "Found 476 images belonging to 5 classes.\n",
      "Detected classes: ['cardboard', 'glass', 'metal', 'paper', 'plastic']\n"
     ]
    }
   ],
   "source": [
    "# Data generators\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1./255,\n",
    "    validation_split=0.2,\n",
    "    horizontal_flip=True,\n",
    "    rotation_range=15\n",
    ")\n",
    "\n",
    "train_gen = train_datagen.flow_from_directory(\n",
    "    DATA_DIR,\n",
    "    target_size=(IMG_SIZE, IMG_SIZE),\n",
    "    batch_size=BATCH_SIZE,\n",
    "    class_mode='categorical',\n",
    "    subset='training',\n",
    "    shuffle=True\n",
    ")\n",
    "\n",
    "val_gen = train_datagen.flow_from_directory(\n",
    "    DATA_DIR,\n",
    "    target_size=(IMG_SIZE, IMG_SIZE),\n",
    "    batch_size=BATCH_SIZE,\n",
    "    class_mode='categorical',\n",
    "    subset='validation',\n",
    "    shuffle=False\n",
    ")\n",
    "\n",
    "# Get class names from generator\n",
    "CLASS_NAMES = list(train_gen.class_indices.keys())\n",
    "print(\"Detected classes:\", CLASS_NAMES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7a66c30c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AttributeError: module 'ml_dtypes' has no attribute 'float4_e2m1fn'\n"
     ]
    }
   ],
   "source": [
    "# Build model using MobileNetV2\n",
    "base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(IMG_SIZE, IMG_SIZE, 3))\n",
    "base_model.trainable = False  # Freeze base model\n",
    "\n",
    "x = base_model.output\n",
    "x = GlobalAveragePooling2D()(x)\n",
    "x = Dropout(0.3)(x)\n",
    "predictions = Dense(len(CLASS_NAMES), activation='softmax')(x)\n",
    "\n",
    "model = Model(inputs=base_model.input, outputs=predictions)\n",
    "model.compile(optimizer=Adam(learning_rate=1e-3), loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8c4c5d15",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Cherilyn\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
      "  self._warn_if_super_not_called()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 331ms/step - accuracy: 0.4190 - loss: 1.5131 - val_accuracy: 0.6471 - val_loss: 0.9059\n",
      "Epoch 2/15\n",
      "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 285ms/step - accuracy: 0.7037 - loss: 0.7874 - val_accuracy: 0.6849 - val_loss: 0.8123\n",
      "Epoch 3/15\n",
      "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 306ms/step - accuracy: 0.7438 - loss: 0.6373 - val_accuracy: 0.7290 - val_loss: 0.7049\n",
      "Epoch 4/15\n",
      "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 375ms/step - accuracy: 0.7903 - loss: 0.5633 - val_accuracy: 0.7311 - val_loss: 0.7072\n",
      "Epoch 5/15\n",
      "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 367ms/step - accuracy: 0.8171 - loss: 0.4796 - val_accuracy: 0.7395 - val_loss: 0.6687\n",
      "Epoch 6/15\n",
      "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 303ms/step - accuracy: 0.8351 - loss: 0.4439 - val_accuracy: 0.7458 - val_loss: 0.6819\n",
      "Epoch 7/15\n",
      "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 357ms/step - accuracy: 0.8358 - loss: 0.4349 - val_accuracy: 0.7542 - val_loss: 0.7027\n",
      "Epoch 8/15\n",
      "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 335ms/step - accuracy: 0.8482 - loss: 0.4211 - val_accuracy: 0.7731 - val_loss: 0.6575\n",
      "Epoch 9/15\n",
      "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 357ms/step - accuracy: 0.8607 - loss: 0.4064 - val_accuracy: 0.7416 - val_loss: 0.7120\n",
      "Epoch 10/15\n",
      "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 329ms/step - accuracy: 0.8593 - loss: 0.3811 - val_accuracy: 0.7710 - val_loss: 0.6982\n",
      "Epoch 11/15\n",
      "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 321ms/step - accuracy: 0.8695 - loss: 0.3926 - val_accuracy: 0.7836 - val_loss: 0.6148\n",
      "Epoch 12/15\n",
      "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 331ms/step - accuracy: 0.8711 - loss: 0.3419 - val_accuracy: 0.7626 - val_loss: 0.6785\n",
      "Epoch 13/15\n",
      "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 357ms/step - accuracy: 0.8709 - loss: 0.3318 - val_accuracy: 0.7710 - val_loss: 0.6565\n",
      "Epoch 14/15\n",
      "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 348ms/step - accuracy: 0.8790 - loss: 0.3214 - val_accuracy: 0.7374 - val_loss: 0.7105\n",
      "Epoch 15/15\n",
      "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 324ms/step - accuracy: 0.8875 - loss: 0.3023 - val_accuracy: 0.7311 - val_loss: 0.7408\n"
     ]
    }
   ],
   "source": [
    "# Train model\n",
    "history = model.fit(\n",
    "    train_gen,\n",
    "    validation_data=val_gen,\n",
    "    epochs=EPOCHS\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c16f29c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    }
   ],
   "source": [
    "# Save model\n",
    "os.makedirs(os.path.dirname(MODEL_PATH), exist_ok=True)\n",
    "model.save(MODEL_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "241fc8ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot accuracy and loss\n",
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(history.history['accuracy'], label='Train Acc')\n",
    "plt.plot(history.history['val_accuracy'], label='Val Acc')\n",
    "plt.legend()\n",
    "plt.title('Accuracy')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(history.history['loss'], label='Train Loss')\n",
    "plt.plot(history.history['val_loss'], label='Val Loss')\n",
    "plt.legend()\n",
    "plt.title('Loss')\n",
    "\n",
    "plt.savefig('training_history.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c021009a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 266ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion Matrix\n",
    "val_gen.reset()\n",
    "Y_pred = model.predict(val_gen, verbose=1)\n",
    "y_pred = np.argmax(Y_pred, axis=1)\n",
    "y_true = val_gen.classes\n",
    "\n",
    "cm = confusion_matrix(y_true, y_pred)\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=CLASS_NAMES, yticklabels=CLASS_NAMES)\n",
    "plt.title('Confusion Matrix')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.tight_layout()\n",
    "plt.savefig('confusion_matrix.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4a2c4514",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "   cardboard       0.96      0.65      0.78        80\n",
      "       glass       0.62      0.80      0.70       100\n",
      "       metal       0.78      0.73      0.75        82\n",
      "       paper       0.71      0.92      0.80       118\n",
      "     plastic       0.77      0.50      0.61        96\n",
      "\n",
      "    accuracy                           0.73       476\n",
      "   macro avg       0.77      0.72      0.73       476\n",
      "weighted avg       0.76      0.73      0.73       476\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print classification report\n",
    "print(classification_report(y_true, y_pred, target_names=CLASS_NAMES))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}