Week4_Task_2_Convolutional_Neural_Networks_(CNN).ipynb

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        "<a href=\"https://colab.research.google.com/github/TheKnight909/T5/blob/main/Week4_Task_2_Convolutional_Neural_Networks_(CNN).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "# Exam on Convolutional Neural Networks (CNN)\n",
        "\n",
        "Welcome to the Convolutional Neural Networks (CNN) practical exam. In this exam, you will work on an image classification task to predict weather the parkinglot is occupied or empty. You are provided with a dataset of parkinglot images, and your task is to build, train, and evaluate a CNN model.\n",
        "\n",
        "---\n",
        "\n",
        "## Dataset Overview\n",
        "### **Dataset:**\n",
        "* Just run the command under the `Load Data` section to get the data downloaded and unzipped or you can access it [here](www.kaggle.com/datasets/khaledzsa/parkinglot-occupation)\n",
        "### **Dataset Name:** ParkingLot Occupation\n",
        "\n",
        "### **Description:**  \n",
        "The dataset contains images of labeld parkinglot images for classification purposes. Each image belongs to one of the 2 classes, representing the label.\n",
        "\n",
        "### **Labels:**\n",
        "* `empty`\n",
        "* `occupied`\n"
      ]
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        "## Load Data\n",
        "Run the following command to get the data and unzip it, alternatively you can access the data [here](www.kaggle.com/datasets/khaledzsa/parkinglot-occupation)."
      ]
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            "Dataset URL: https://www.kaggle.com/datasets/khaledzsa/parkinglot-occupation\n",
            "License(s): unknown\n",
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            "  inflating: ParkingLot_Occupation/train/occupied/roi_f9606124483a4335ae7e273793bb1026_occupied.jpg  \n",
            "  inflating: ParkingLot_Occupation/train/occupied/roi_f9e22ea7744340b49a751030fa172000_occupied.jpg  \n",
            "  inflating: ParkingLot_Occupation/train/occupied/roi_fafdc0430ec74652a4de3a189c0caef5_occupied.jpg  \n",
            "  inflating: ParkingLot_Occupation/train/occupied/roi_fafea706182d4cbc96080d9dcce088c6_occupied.jpg  \n",
            "  inflating: ParkingLot_Occupation/train/occupied/roi_fb3602cdcbb442c6aeb043c5b5d6eda7_occupied.jpg  \n",
            "  inflating: ParkingLot_Occupation/train/occupied/roi_fb7b69ea76104e4383d065b2f73d1e11_occupied.jpg  \n",
            "  inflating: ParkingLot_Occupation/train/occupied/roi_fb87e47984324725ba731a37fb4e862b_occupied.jpg  \n",
            "  inflating: ParkingLot_Occupation/train/occupied/roi_fbff5fb7215746e2afcfa5120afe0a4f_occupied.jpg  \n",
            "  inflating: ParkingLot_Occupation/train/occupied/roi_fc601e13b1394ac7ad6a3e7df5483e97_occupied.jpg  \n",
            "  inflating: ParkingLot_Occupation/train/occupied/roi_fc96cddfd79e4ac3a14c328e65e57b22_occupied.jpg  \n",
            "  inflating: ParkingLot_Occupation/train/occupied/roi_fca178acbce7403cae229b5e1109fdd2_occupied.jpg  \n",
            "  inflating: ParkingLot_Occupation/train/occupied/roi_fd0fd206204a43ea94f27117cbb0e1ed_occupied.jpg  \n",
            "  inflating: ParkingLot_Occupation/train/occupied/roi_fe9bf836aa704f678625e0039b57d068_occupied.jpg  \n",
            "  inflating: ParkingLot_Occupation/train/occupied/roi_fed6819dfb2a4585957bf8dd12b2206b_occupied.jpg  \n",
            "  inflating: ParkingLot_Occupation/train/occupied/roi_ff41a85b821749d8aebe8eb58e161700_occupied.jpg  \n",
            "  inflating: ParkingLot_Occupation/train/occupied/roi_ffa479398c4e41ceaf7d330b6cc6cef8_occupied.jpg  \n",
            "  inflating: ParkingLot_Occupation/train/occupied/roi_fff91c7d78e2472193ae94906cf048fb_occupied.jpg  \n"
          ]
        }
      ],
      "source": [
        "# !kaggle datasets download -d khaledzsa/parkinglot-occupation\n",
        "# !unzip parkinglot-occupation.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9bb9f30e",
      "metadata": {
        "id": "9bb9f30e"
      },
      "source": [
        "## Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "22207f85",
      "metadata": {
        "id": "22207f85"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "from glob import glob\n",
        "from PIL import Image\n",
        "import cv2\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3aa9945d",
      "metadata": {
        "id": "3aa9945d"
      },
      "source": [
        "## Data Preprocessing\n",
        "In this section, preprocess the dataset by:\n",
        "- Loading the images from the file paths.\n",
        "- Resizing the images to a consistent size.\n",
        "- Normalizing pixel values.\n",
        "\n",
        "Add more if needed!"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_dir = '/content/ParkingLot_Occupation'\n",
        "train_dir = os.path.join(base_dir, 'train')\n",
        "test_dir = os.path.join(base_dir, 'test')\n",
        "\n",
        "train_empty_dir = os.path.join(train_dir, 'empty')\n",
        "train_occupied_dir = os.path.join(train_dir, 'occupied')\n",
        "test_empty_dir = os.path.join(test_dir, 'empty')\n",
        "test_occupied_dir = os.path.join(test_dir, 'occupied')\n",
        "\n",
        "def load_images_from_folder(folder):\n",
        "    images = []\n",
        "    for filename in glob(os.path.join(folder, '*.jpg')):\n",
        "        img = Image.open(filename)\n",
        "        img = img.resize((128, 128))\n",
        "        img = np.array(img)\n",
        "        if len(img.shape) == 2:\n",
        "            img = np.expand_dims(img, axis=-1)\n",
        "            img = np.repeat(img, 3, axis=-1)\n",
        "        images.append(img)\n",
        "    return np.array(images)\n",
        "\n",
        "train_empty_images = load_images_from_folder(train_empty_dir)\n",
        "train_occupied_images = load_images_from_folder(train_occupied_dir)\n",
        "\n",
        "test_empty_images = load_images_from_folder(test_empty_dir)\n",
        "test_occupied_images = load_images_from_folder(test_occupied_dir)\n",
        "\n",
        "X_train_val = np.concatenate((train_occupied_images, train_empty_images), axis=0)\n",
        "y_train_val = np.array([1] * len(train_occupied_images) + [0] * len(train_empty_images))\n",
        "\n",
        "X_test = np.concatenate((test_occupied_images, test_empty_images), axis=0)\n",
        "y_test = np.array([1] * len(test_occupied_images) + [0] * len(test_empty_images))\n",
        "\n",
        "print(\"X_train_val shape:\", X_train_val.shape)\n",
        "print(\"y_train_val shape:\", y_train_val.shape)\n",
        "print(\"X_test shape:\", X_test.shape)\n",
        "print(\"y_test shape:\", y_test.shape)\n"
      ],
      "metadata": {
        "id": "_xFpM2t0lbIk",
        "outputId": "309caa29-e527-40ec-e769-050fe75aa359",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "_xFpM2t0lbIk",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train_val shape: (1382, 128, 128, 3)\n",
            "y_train_val shape: (1382,)\n",
            "X_test shape: (344, 128, 128, 3)\n",
            "y_test shape: (344,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "541f3c2b",
      "metadata": {
        "id": "541f3c2b"
      },
      "source": [
        "## Data Splitting\n",
        "In this section, we will split our dataset into three parts:\n",
        "\n",
        "* `train` Folder: Training set (85%).\n",
        "* `train` Folder: Validation set (15%).\n",
        "* `test` Folder: Test set (100%)."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_train_val, y_train_val, test_size=0.15, random_state=42)\n",
        "\n",
        "X_train = X_train / 255.0\n",
        "X_test = X_test / 255.0\n",
        "X_val = X_val / 255.0"
      ],
      "metadata": {
        "id": "qiMfzVu5j3P-"
      },
      "id": "qiMfzVu5j3P-",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "83974596",
      "metadata": {
        "id": "83974596"
      },
      "source": [
        "## Building the CNN Model\n",
        "In this section, define the architecture of the CNN model. The architecture may consist of:\n",
        "- Convolutional layers with max-pooling\n",
        "- Dropout layers\n",
        "- Flatten layer\n",
        "- Dense layers\n",
        "- Output layer\n",
        "\n",
        "Add and remove any of these as needed!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "977da65a",
      "metadata": {
        "id": "977da65a",
        "outputId": "1c977218-d66a-4184-feac-9ecb5141dc38",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        }
      ],
      "source": [
        "model = Sequential([\n",
        "    Conv2D(32, (3, 3), activation='relu', input_shape=(128, 128, 3)),\n",
        "    MaxPooling2D((2, 2)),\n",
        "    Flatten(),\n",
        "    Dense(64, activation='relu'),\n",
        "    Dropout(0.4),\n",
        "    Dense(2, activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(optimizer=Adam(learning_rate=0.001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fe1c0940",
      "metadata": {
        "id": "fe1c0940"
      },
      "source": [
        "## Training the Model\n",
        "Train the CNN model using the training data and validate it on the validation set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ce31faf9",
      "metadata": {
        "id": "ce31faf9",
        "outputId": "1f23d78b-8ac5-4901-d2e3-70653e6ff496",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 190ms/step - accuracy: 0.7358 - loss: 0.7943 - val_accuracy: 0.9423 - val_loss: 0.1596\n",
            "Epoch 2/10\n",
            "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - accuracy: 0.9322 - loss: 0.1690 - val_accuracy: 0.9567 - val_loss: 0.0928\n",
            "Epoch 3/10\n",
            "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 22ms/step - accuracy: 0.9641 - loss: 0.1083 - val_accuracy: 0.9615 - val_loss: 0.0998\n",
            "Epoch 4/10\n",
            "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.9528 - loss: 0.1701 - val_accuracy: 0.9760 - val_loss: 0.0675\n",
            "Epoch 5/10\n",
            "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - accuracy: 0.9600 - loss: 0.1287 - val_accuracy: 0.9663 - val_loss: 0.0905\n",
            "Epoch 6/10\n",
            "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - accuracy: 0.9701 - loss: 0.1020 - val_accuracy: 0.9712 - val_loss: 0.0766\n",
            "Epoch 7/10\n",
            "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.9701 - loss: 0.0720 - val_accuracy: 0.9856 - val_loss: 0.0506\n",
            "Epoch 8/10\n",
            "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.9890 - loss: 0.0529 - val_accuracy: 0.9712 - val_loss: 0.0678\n",
            "Epoch 9/10\n",
            "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.9716 - loss: 0.0622 - val_accuracy: 0.9808 - val_loss: 0.0470\n",
            "Epoch 10/10\n",
            "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.9780 - loss: 0.0554 - val_accuracy: 0.9808 - val_loss: 0.0472\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "early_stoping=EarlyStopping(monitor=\"val_loss\", patience=6)\n",
        "\n",
        "history = model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs= 10,callbacks=early_stoping)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "25ad1b22",
      "metadata": {
        "id": "25ad1b22"
      },
      "source": [
        "## Evaluate the Model\n",
        "Evaluate the performance of the model on the test set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "eb9ed70f",
      "metadata": {
        "id": "eb9ed70f",
        "outputId": "29eab0da-c30c-4888-a8f4-ff7d36bb88ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 721
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 60ms/step - accuracy: 0.9502 - loss: 0.1107\n",
            "Test accuracy: 0.9738371968269348\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.99      0.98       181\n",
            "           1       0.99      0.95      0.97       163\n",
            "\n",
            "    accuracy                           0.97       344\n",
            "   macro avg       0.98      0.97      0.97       344\n",
            "weighted avg       0.97      0.97      0.97       344\n",
            "\n",
            "Confusion Matrix:\n",
            "[[180   1]\n",
            " [  8 155]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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/ZrwrvvJcBkcMukyNSiXJnVTjuSrqwh8M8WNmxctoc9m4L4/8kqpnD+2UFFXazRIXbvFoH0fbOM1AKCIijaNVh5FL/nMJWw5vqdG+bpu7vNWidFbGSi0ZpcHDjPuJZOd7efe7fWzYEw4dP2YW4AsefTmqw2ahR2pseehoF0ef9nHEuRvmDp0iIiI10arDSIonhe2520n2JFeezjs67aiZGmMdsU2upcAwDN5av5fZb39PbnHluTli3Xb6tosr7WaJp2+7OLqnxuC0q5tFRESallYdRv5+xt/x2D3NcvbGzLwS/vDGd3yYkQVA77axnNWvbaS7pWOip8mFJxERkaq06jAS7Yg2u4RaMwyD19fu4d7/fE9eSQCHzcLNY3tw/endcGhwqYiINEOtOow0N/tzS/j9G9/x0aZwa0j/DnH87ZJB9G6rOxmLiEjzpTDSDBiGwatrfuJP72wkvySA02bllnE9uPbnJ6k1REREmj2FkSZuX24xs17/jk82ZwMwqGM8f71kED3TYk2uTEREpH4ojDRRhmHw8te7+fM7GeR7w60ht/1fT645tasmHhMRkRZFYaQJ2nO4mJmvfcunPx4AYHB6An+9eCA91BoiIiItkMJIE2IYBi+u3s39SzMo8AZw2q3cflZPfv2zk7BZdZmuiIi0TAojTcRPh4qY+dp3fLYl3BoytFMCD148iO6pMSZXJiIi0rAURkwWChks+WoXc97NoNAXxGW38rvxvbhqTFe1hoiISKugMGKi3QeLuPO1b/l8aw4Awzsn8uDFAzkpRa0hIiLSeiiMmCAUMnjhy53MfW8TRb4gboeVO8b3ZuopXdQaIiIirY7CSCPblVPE7179hi+3HwRgZJckHrx4IF2Sm9/U9CIiIvVBYaSRhEIGz63awQPLNlPsD+Jx2Ljz7F5cMboLVrWGiIhIK6Yw0gh2HCjkjte+5avS1pBRXcOtIZ3bqDVEREREYaQBhUIGz3y+gwff30SJP0SU08asCb2ZMqqzWkNERERKKYw0kG3ZBdzx6rd8vfMQAKNPasODFw8kPSnK5MpERESaFoWRehYMGSxauZ2/vr8ZbyBEtNPGrHP6MHlkJ7WGiIiIVEFhpB5tzS7gd698w9pdhwH4Wfdk5l40gI6Jag0RERGpjsJIPQiGDP716TYeWv4DvkCIGJedP/yiD5eNSMdiUWuIiIjIsSiMnKAtWfnc/sq3rN99GIBTeyQz96KBdEjwmFuYiIhIM6EwUkeBYIiFn27n7x+GW0NiXXbuPrcvlwzvqNYQERGRWlAYqYMfMvP53Svf8M1PuQCc1jOFuRcNoF28WkNERERqS2GkFgLBEE/+bxuPfPgjvmCIWLede87ty8XD1BoiIiJSVwojNbRpfx6/e+VbvtsTbg0Z2zuVv1wwgLbxbpMrExERad4URo7DHwzxxCdb+cdHP+IPGsS57cye2I8Lh3ZQa4iIiEg9UBg5hox9edz+yjd8vzcPgHF9Urn/ggGkxak1REREpL4ojFTBFwjx+CdbmP/RFgIhg3iPg3v/Xz/OG9xerSEiIiL1TGHkCN/vzeX2V74lY1+4NeSsvmn8+YL+pMaqNURERKQhKIyU8gVCzP94C49/HG4NSYxycO95/Zk4sJ1aQ0RERBqQwgiwYU8ut7/yDZv25wNwdr+23Hd+f1JiXSZXJiIi0vK16jDiDQSZ/9EWHv9kK8GQQVK0kz+d149fDFBriIiISGNptWGkxB/k/MdWRlpDfjGgHfee14/kGLWGiIiINKZWG0bcDhsjuiSRne/lT+f15xcD25ldkoiISKtkMQzDMLuI48nLyyM+Pp7c3Fzi4uLq7XULvQFK/EHaqDVERESk3tX0/N1qW0YAol12ol2t+lsgIiJiOqvZBYiIiEjrpjAiIiIiplIYEREREVMpjIiIiIipFEZERETEVAojIiIiYiqFERERETGVwoiIiIiYSmFERERETKUwIiIiIqaqUxh57LHH6NKlC263m1GjRvHVV18dc/958+bRq1cvPB4P6enp3HbbbZSUlNSpYBEREWlZah1GXnrpJWbMmMHs2bNZu3YtgwYNYvz48WRlZVW5/5IlS5g5cyazZ88mIyODp556ipdeeonf//73J1y8iIiINH+1DiMPP/ww11xzDVdddRV9+/bliSeeICoqiqeffrrK/T///HPGjBnD5MmT6dKlC2eddRaTJk06bmuKiIiItA61CiM+n481a9Ywbty48hewWhk3bhyrVq2q8phTTjmFNWvWRMLHtm3bePfddznnnHOqfR+v10teXl6lRURERFome212PnDgAMFgkLS0tErb09LS2LRpU5XHTJ48mQMHDvCzn/0MwzAIBAJcf/31x+ymmTNnDvfee29tShMREZFmqsGvpvnkk0/4y1/+wuOPP87atWt5/fXXWbp0Kffdd1+1x8yaNYvc3NzIsnv37oYuU0RERExSq5aR5ORkbDYbmZmZlbZnZmbStm3bKo+5++67ufzyy5k2bRoAAwYMoLCwkGuvvZY//OEPWK1H5yGXy4XL5apNaSIiItJM1aplxOl0MmzYMFasWBHZFgqFWLFiBaNHj67ymKKioqMCh81mA8AwjNrWKyIiIi1MrVpGAGbMmMHUqVMZPnw4I0eOZN68eRQWFnLVVVcBcMUVV9ChQwfmzJkDwMSJE3n44YcZMmQIo0aNYsuWLdx9991MnDgxEkpERESk9ap1GLn00kvJzs7mnnvuYf/+/QwePJhly5ZFBrXu2rWrUkvIXXfdhcVi4a677mLPnj2kpKQwceJE7r///vr7FCIiItJsWYxm0FeSl5dHfHw8ubm5xMXFmV2OiIiI1EBNz9+6N42IiIiYSmFERERETKUwIiIiIqZSGBERERFTKYyIiIiIqRRGRERExFQKIyIiImIqhRERERExlcKIiIiImEphREREREylMCIiIiKmUhgRERERUymMiIiIiKkURkRERMRUCiMiIiJiKoURERERMZXCiIiIiJhKYURERERMpTAiIiIiplIYEREREVMpjIiIiIipFEZERETEVAojIiIiYiqFERERETGVwoiIiIiYSmFERERETKUwIiIiIqZSGBERERFTKYyIiIiIqRRGRERExFQKIyIiImIqhRERERExlcKIiIiImEphREREREylMCIiIiKmUhgRERERUymMiIiIiKkURkRERMRUCiMiIiJiKoURERERMZXCiIiIiJhKYURERERMpTAiIiIiplIYEREREVMpjIiIiIipFEZERETEVAojIiIiYiqFERERETGVwoiIiIiYym52ASIi0jQFg0H8fr/ZZUgT5nA4sNlsJ/w6CiMiIlKJYRjs37+fw4cPm12KNAMJCQm0bdsWi8VS59dQGBERkUrKgkhqaipRUVEndJKRlsswDIqKisjKygKgXbt2dX4thREREYkIBoORINKmTRuzy5EmzuPxAJCVlUVqamqdu2w0gFVERCLKxohERUWZXIk0F2W/KycyvkhhREREjqKuGamp+vhdURgRERERUymMiIiIiKkURkRERMRUCiMiIiINQBPG1ZzCiIiItAjLli3jZz/7GQkJCbRp04Zzzz2XrVu3Rp7/6aefmDRpEklJSURHRzN8+HC+/PLLyPP/+c9/GDFiBG63m+TkZC644ILIcxaLhTfffLPS+yUkJPDMM88AsGPHDiwWCy+99BKnnXYabrebxYsXk5OTw6RJk+jQoQNRUVEMGDCAf//735VeJxQK8eCDD9K9e3dcLhedOnXi/vvvB2Ds2LHceOONlfbPzs7G6XSyYsWK+vi2NQl1CiOPPfYYXbp0we12M2rUKL766qtj7n/48GGmT59Ou3btcLlc9OzZk3fffbdOBYuISOMyDIMiX6DRF8MwalVnYWEhM2bM4Ouvv2bFihVYrVYuuOACQqEQBQUFnHbaaezZs4e3336bb775hjvuuINQKATA0qVLueCCCzjnnHNYt24dK1asYOTIkbX+Xs2cOZNbbrmFjIwMxo8fT0lJCcOGDWPp0qVs2LCBa6+9lssvv7zSeXPWrFnMnTuXu+++m40bN7JkyRLS0tIAmDZtGkuWLMHr9Ub2f+GFF+jQoQNjx46tdX1NVa0nPXvppZeYMWMGTzzxBKNGjWLevHmMHz+ezZs3k5qaetT+Pp+P//u//yM1NZVXX32VDh06sHPnThISEuqjfhERaWDF/iB973m/0d9345/GE+Ws+WnqoosuqvT1008/TUpKChs3buTzzz8nOzub1atXk5SUBED37t0j+95///1cdtll3HvvvZFtgwYNqnXNt956KxdeeGGlbbfffntk/aabbuL999/n5ZdfZuTIkeTn5/PII48wf/58pk6dCkC3bt342c9+BsCFF17IjTfeyFtvvcUvf/lLAJ555hmuvPLKFnX5da1bRh5++GGuueYarrrqKvr27csTTzxBVFQUTz/9dJX7P/300xw8eJA333yTMWPG0KVLF0477bQ6/ZBFRESq8+OPPzJp0iROOukk4uLi6NKlCwC7du1i/fr1DBkyJBJEjrR+/XrOPPPME65h+PDhlb4OBoPcd999DBgwgKSkJGJiYnj//ffZtWsXABkZGXi93mrf2+12c/nll0fOsWvXrmXDhg1ceeWVJ1xrU1KrlhGfz8eaNWuYNWtWZJvVamXcuHGsWrWqymPefvttRo8ezfTp03nrrbdISUlh8uTJ3HnnnfVypz8REWlYHoeNjX8ab8r71sbEiRPp3LkzCxcupH379oRCIfr374/P54tMW17tex3neYvFclS3UVUDVKOjoyt9/de//pVHHnmEefPmMWDAAKKjo7n11lvx+Xw1el8Id9UMHjyYn376iUWLFjF27Fg6d+583OOak1qFkQMHDhAMBiN9WWXS0tLYtGlTlcds27aNjz76iClTpvDuu++yZcsWfvOb3+D3+5k9e3aVx3i93kr9Y3l5ebUpU0RE6pHFYqlVd4kZcnJy2Lx5MwsXLuTUU08F4LPPPos8P3DgQP71r39x8ODBKltHBg4cyIoVK7jqqquqfP2UlBT27dsX+frHH3+kqKjouHWtXLmS8847j1/96ldAeLDqDz/8QN++fQHo0aMHHo+HFStWMG3atCpfY8CAAQwfPpyFCxeyZMkS5s+ff9z3bW4a/GqaUChEamoqCxYsYNiwYVx66aX84Q9/4Iknnqj2mDlz5hAfHx9Z0tPTG7pMERFpxhITE2nTpg0LFixgy5YtfPTRR8yYMSPy/KRJk2jbti3nn38+K1euZNu2bbz22muRVv3Zs2fz73//m9mzZ5ORkcF3333HAw88EDl+7NixzJ8/n3Xr1vH1119z/fXX43A4jltXjx49WL58OZ9//jkZGRlcd911ZGZmRp53u93ceeed3HHHHTz33HNs3bqVL774gqeeeqrS60ybNo25c+diGEalq3xailqFkeTkZGw2W6VvJEBmZiZt27at8ph27drRs2fPSl0yffr0Yf/+/ZFmqiPNmjWL3NzcyLJ79+7alCkiIq2M1WrlxRdfZM2aNfTv35/bbruNv/71r5HnnU4nH3zwAampqZxzzjkMGDCAuXPnRs5Np59+Oq+88gpvv/02gwcPZuzYsZWueHnooYdIT0/n1FNPZfLkydx+++01upngXXfdxdChQxk/fjynn356JBBVdPfdd/Pb3/6We+65hz59+nDppZeSlZVVaZ9JkyZht9uZNGkSbrf7BL5TTZPFqOW1U6NGjWLkyJE8+uijQLjlo1OnTtx4443MnDnzqP1///vfs2TJErZt24bVGs4+jzzyCA888AB79+6t0Xvm5eURHx9Pbm4ucXFxtSlXRERqoaSkhO3bt9O1a9cWedJrrnbs2EG3bt1YvXo1Q4cONbucSo71O1PT83etu2lmzJjBwoULefbZZ8nIyOCGG26gsLAw0s92xRVXVBrgesMNN3Dw4EFuueUWfvjhB5YuXcpf/vIXpk+fXtu3FhERaVX8fj/79+/nrrvu4uSTT25yQaS+1HpE0qWXXkp2djb33HMP+/fvZ/DgwSxbtiwyqHXXrl2RFhCA9PR03n//fW677TYGDhxIhw4duOWWW7jzzjvr71OIiIi0QCtXruSMM86gZ8+evPrqq2aX02Bq3U1jBnXTiIg0DnXTSG2Z0k0jIiIiUp8URkRERMRUCiMiIiJiKoURERERMZXCiIiIiJhKYURERERMpTAiIiItwumnn86tt95qdhlSBwojIiIiYiqFERERETGVwoiIiLQ4hw4d4oorriAxMZGoqCgmTJjAjz/+GHl+586dTJw4kcTERKKjo+nXrx/vvvtu5NgpU6aQkpKCx+OhR48eLFq0yKyP0irU+t40IiLSyhgG+Isa/30dUWCx1OnQK6+8kh9//JG3336buLg47rzzTs455xw2btyIw+Fg+vTp+Hw+/ve//xEdHc3GjRuJiYkB4O6772bjxo289957JCcns2XLFoqLi+vzk8kRFEZEROTY/EXwl/aN/76/3wvO6FofVhZCVq5cySmnnALA4sWLSU9P58033+SSSy5h165dXHTRRQwYMACAk046KXL8rl27GDJkCMOHDwegS5cuJ/5Z5JjUTSMiIi1KRkYGdrudUaNGRba1adOGXr16kZGRAcDNN9/Mn//8Z8aMGcPs2bP59ttvI/vecMMNvPjiiwwePJg77riDzz//vNE/Q2ujlhERETk2R1S4lcKM920g06ZNY/z48SxdupQPPviAOXPm8NBDD3HTTTcxYcIEdu7cybvvvsvy5cs588wzmT59On/7298arJ7WTi0jIiJybBZLuLuksZc6jhfp06cPgUCAL7/8MrItJyeHzZs307dv38i29PR0rr/+el5//XV++9vfsnDhwshzKSkpTJ06lRdeeIF58+axYMGCun//5LjUMiIiIi1Kjx49OO+887jmmmt48skniY2NZebMmXTo0IHzzjsPgFtvvZUJEybQs2dPDh06xMcff0yfPn0AuOeeexg2bBj9+vXD6/XyzjvvRJ6ThqGWERERaXEWLVrEsGHDOPfccxk9ejSGYfDuu+/icDgACAaDTJ8+nT59+nD22WfTs2dPHn/8cQCcTiezZs1i4MCB/PznP8dms/Hiiy+a+XFaPIthGIbZRRxPXl4e8fHx5ObmEhcXZ3Y5IiItVklJCdu3b6dr16643W6zy5Fm4Fi/MzU9f6tlREREREylMCIiIiKmUhgRERERUymMiIiIiKkURkRERMRUCiMiIiJiKoURERERMZXCiIiIiJhKYURERERMpTAiIiICdOnShXnz5pldRqukMCIiIiKmUhgRERFp5oLBIKFQyOwy6kxhREREmr0FCxbQvn37o07I5513HldffTVbt27lvPPOIy0tjZiYGEaMGMGHH35Y5/d7+OGHGTBgANHR0aSnp/Ob3/yGgoKCSvusXLmS008/naioKBITExk/fjyHDh0CIBQK8eCDD9K9e3dcLhedOnXi/vvvB+CTTz7BYrFw+PDhyGutX78ei8XCjh07AHjmmWdISEjg7bffpm/fvrhcLnbt2sXq1av5v//7P5KTk4mPj+e0005j7dq1leo6fPgw1113HWlpabjdbvr3788777xDYWEhcXFxvPrqq5X2f/PNN4mOjiY/P7/O36/jURgREZFjMgyDIn9Roy+1uan8JZdcQk5ODh9//HFk28GDB1m2bBlTpkyhoKCAc845hxUrVrBu3TrOPvtsJk6cyK5du+r0PbFarfzjH//g+++/59lnn+Wjjz7ijjvuiDy/fv16zjzzTPr27cuqVav47LPPmDhxIsFgEIBZs2Yxd+5c7r77bjZu3MiSJUtIS0urVQ1FRUU88MAD/Otf/+L7778nNTWV/Px8pk6dymeffcYXX3xBjx49OOeccyJBIhQKMWHCBFauXMkLL7zAxo0bmTt3LjabjejoaC677DIWLVpU6X0WLVrExRdfTGxsbJ2+VzVhb7BXFhGRFqE4UMyoJaMa/X2/nPwlUY6oGu2bmJjIhAkTWLJkCWeeeSYAr776KsnJyZxxxhlYrVYGDRoU2f++++7jjTfe4O233+bGG2+sdW233nprZL1Lly78+c9/5vrrr+fxxx8H4MEHH2T48OGRrwH69esHQH5+Po888gjz589n6tSpAHTr1o2f/exntarB7/fz+OOPV/pcY8eOrbTPggULSEhI4L///S/nnnsuH374IV999RUZGRn07NkTgJNOOimy/7Rp0zjllFPYt28f7dq1Iysri3ffffeEWpFqQi0jIiLSIkyZMoXXXnsNr9cLwOLFi7nsssuwWq0UFBRw++2306dPHxISEoiJiSEjI6POLSMffvghZ555Jh06dCA2NpbLL7+cnJwcioqKgPKWkapkZGTg9Xqrfb6mnE4nAwcOrLQtMzOTa665hh49ehAfH09cXBwFBQWRz7l+/Xo6duwYCSJHGjlyJP369ePZZ58F4IUXXqBz5878/Oc/P6Faj0ctIyIickweu4cvJ39pyvvWxsSJEzEMg6VLlzJixAg+/fRT/v73vwNw++23s3z5cv72t7/RvXt3PB4PF198MT6fr9Z17dixg3PPPZcbbriB+++/n6SkJD777DN+/etf4/P5iIqKwuOpvvZjPQfhLiCgUjeV3++v8nUsFkulbVOnTiUnJ4dHHnmEzp0743K5GD16dORzHu+9Idw68thjjzFz5kwWLVrEVVddddT71DeFEREROSaLxVLj7hIzud1uLrzwQhYvXsyWLVvo1asXQ4cOBcKDSa+88kouuOACAAoKCiKDQWtrzZo1hEIhHnrooUhwePnllyvtM3DgQFasWMG999571PE9evTA4/GwYsUKpk2bdtTzKSkpAOzbt4/ExEQg3KJREytXruTxxx/nnHPOAWD37t0cOHCgUl0//fQTP/zwQ7WtI7/61a+44447+Mc//sHGjRsjXUkNSd00IiLSYkyZMoWlS5fy9NNPM2XKlMj2Hj168Prrr7N+/Xq++eYbJk+eXOdLYbt3747f7+fRRx9l27ZtPP/88zzxxBOV9pk1axarV6/mN7/5Dd9++y2bNm3in//8JwcOHMDtdnPnnXdyxx138Nxzz7F161a++OILnnrqqcjrp6en88c//pEff/yRpUuX8tBDD9Woth49evD888+TkZHBl19+yZQpUyq1hpx22mn8/Oc/56KLLmL58uVs376d9957j2XLlkX2SUxM5MILL+R3v/sdZ511Fh07dqzT96k2FEZERKTFGDt2LElJSWzevJnJkydHtj/88MMkJiZyyimnMHHiRMaPHx9pNamtQYMG8fDDD/PAAw/Qv39/Fi9ezJw5cyrt07NnTz744AO++eYbRo4cyejRo3nrrbew28MdEnfffTe//e1vueeee+jTpw+XXnopWVlZADgcDv7973+zadMmBg4cyAMPPMCf//znGtX21FNPcejQIYYOHcrll1/OzTffTGpqaqV9XnvtNUaMGMGkSZPo27cvd9xxR+QqnzJlXU5XX311nb5HtWUxanPtlEny8vKIj48nNzeXuLg4s8sREWmxSkpK2L59O127dsXtdptdjpjk+eef57bbbmPv3r04nc5j7nus35manr81ZkRERESA8Nwl+/btY+7cuVx33XXHDSL1Rd00IiIiFSxevJiYmJgql7K5QlqqBx98kN69e9O2bVtmzZrVaO/bertpQkHY8DqsfRYm/RtcDTeznIhIc6FumvCkZJmZmVU+53A46Ny5cyNX1LSpm+ZE/Xcu5GyBtc/D6N+YXY2IiDQBsbGxDTr1uRyt9XbTWG1wyk3h9VWPQfDoCWVERESk4bXeMAIw8DKIToW8n+D7N8yuRkREpFVq3WHE4YZR14XXVz4CTX/4jIiISIvTusMIwIhfgyMaMjfA1hVmVyMiItLqKIx4EmHYleH1lY+YWoqIiEhrpDACcPINYLXD9v/B3nVmVyMiIibo0qUL8+bNq9G+FouFN998s0HraU0URgAS0qH/ReH1lf8wtxYREZFWRmGkzCk3hx83vgkHt5taioiISGuiMFKmbX/oPg6MUHjeERERaTYWLFhA+/btCYVClbafd955XH311WzdupXzzjuPtLQ0YmJiGDFiBB9++GG9vf93333H2LFj8Xg8tGnThmuvvZaCgoLI85988gkjR44kOjqahIQExowZw86dOwH45ptvOOOMM4iNjSUuLo5hw4bx9ddf11ttzYHCSEVjbgk/rnsBCnPMrUVEpIkwDINQUVGjL7W5W8kll1xCTk4OH3/8cWTbwYMHWbZsGVOmTKGgoIBzzjmHFStWsG7dOs4++2wmTpzIrl27Tvj7U1hYyPjx40lMTGT16tW88sorfPjhh9x4440ABAIBzj//fE477TS+/fZbVq1axbXXXovFYgFgypQpdOzYkdWrV7NmzRpmzpyJw+E44bqak9Y9HfyRupwK7QbDvvWweiGcPtPsikRETGcUF7N56LBGf99ea9dgiYqq0b6JiYlMmDCBJUuWcOaZZwLw6quvkpyczBlnnIHVamXQoEGR/e+77z7eeOMN3n777UhoqKslS5ZQUlLCc889R3R0NADz589n4sSJPPDAAzgcDnJzczn33HPp1q0bAH369Ikcv2vXLn73u9/Ru3dvAHr06HFC9TRHahmpyGIpbx358knwFZlbj4iI1NiUKVN47bXX8Hq9QPjuu5dddhlWq5WCggJuv/12+vTpQ0JCAjExMWRkZNRLy0hGRgaDBg2KBBGAMWPGEAqF2Lx5M0lJSVx55ZWMHz+eiRMn8sgjj7Bv377IvjNmzGDatGmMGzeOuXPnsnXr1hOuqblRy8iR+vw/SOwCh3bA+sUw8hqzKxIRMZXF46HX2jWmvG9tTJw4EcMwWLp0KSNGjODTTz/l73//OwC33347y5cv529/+xvdu3fH4/Fw8cUX4/P5GqL0oyxatIibb76ZZcuW8dJLL3HXXXexfPlyTj75ZP74xz8yefJkli5dynvvvcfs2bN58cUXueCCCxqltqZAYeRINjuMvhHevR1WzYdhV4W3iYi0UhaLpcbdJWZyu91ceOGFLF68mC1bttCrVy+GDh0KwMqVK7nyyisjJ/iCggJ27NhRL+/bp08fnnnmGQoLCyOtIytXrsRqtdKrV6/IfkOGDGHIkCHMmjWL0aNHs2TJEk4++WQAevbsSc+ePbntttuYNGkSixYtalVhRN00VRk8BaLahFtHMt42uxoREamhKVOmsHTpUp5++mmmTJkS2d6jRw9ef/111q9fzzfffMPkyZOPuvLmRN7T7XYzdepUNmzYwMcff8xNN93E5ZdfTlpaGtu3b2fWrFmsWrWKnTt38sEHH/Djjz/Sp08fiouLufHGG/nkk0/YuXMnK1euZPXq1ZXGlLQGCiNVcUbByGvD67qBnohIszF27FiSkpLYvHkzkydPjmx/+OGHSUxM5JRTTmHixImMHz8+0mpyoqKionj//fc5ePAgI0aM4OKLL+bMM89k/vz5kec3bdrERRddRM+ePbn22muZPn061113HTabjZycHK644gp69uzJL3/5SyZMmMC9995bL7U1FxajNtdOmSQvL4/4+Hhyc3OJi4trnDctzIG/94NAMVzxNpx0WuO8r4iIiUpKSti+fTtdu3bF7XabXY40A8f6nanp+VstI9WJbgNDLw+vf64p4kVERBpKncLIY489RpcuXXC73YwaNYqvvvqqRse9+OKLWCwWzj///Lq8beMbPR0sVtjyIezfYHY1IiLSCBYvXkxMTEyVS79+/cwur0Wq9WUiL730EjNmzOCJJ55g1KhRzJs3j/Hjx7N582ZSU1OrPW7Hjh3cfvvtnHrqqSdUcKNK7AJ9z4fvXw+3jly4wOyKRESkgf2///f/GDVqVJXPtbaZURtLrVtGHn74Ya655hquuuoq+vbtyxNPPEFUVBRPP/10tccEg0GmTJnCvffey0knnXRCBTe6MaU30PvuVTi829xaRESkwcXGxtK9e/cql86dO5tdXotUqzDi8/lYs2YN48aNK38Bq5Vx48axatWqao/705/+RGpqKr/+9a9r9D5er5e8vLxKi2naD4Gup4ERhC/+aV4dIiIiLVStwsiBAwcIBoOkpaVV2p6Wlsb+/furPOazzz7jqaeeYuHChTV+nzlz5hAfHx9Z0tPTa1Nm/StrHVnzDBQfMrUUEZHG0AwutJQmoj5+Vxr0apr8/Hwuv/xyFi5cSHJyco2PmzVrFrm5uZFl926Tu0e6nQlp/cFfCKufMrcWEZEGVDYmoqhI9+aSmin7XTmR8TS1GsCanJyMzWYjMzOz0vbMzEzatm171P5bt25lx44dTJw4MbKtbMY7u93O5s2bI3cwrMjlcuFyuWpTWsMqu4He69eEb6A3+kZw6Pp7EWl5bDYbCQkJZGVlAeEJu8pudS9SkWEYFBUVkZWVRUJCAjabrc6vVasw4nQ6GTZsGCtWrIhcnhsKhVixYkWVt2Du3bs33333XaVtd911F/n5+TzyyCPmd7/URr8L4MN7Ie8n+PZFGHal2RWJiDSIsj8uywKJyLEkJCRU2SBRG7W+tHfGjBlMnTqV4cOHM3LkSObNm0dhYSFXXXUVAFdccQUdOnRgzpw5uN1u+vfvf1TRwFHbmzybIzzvyPuz4PNHYcjlYK17ChQRaaosFgvt2rUjNTUVv99vdjnShDkcjhNqESlT6zBy6aWXkp2dzT333MP+/fsZPHgwy5Ytiwxq3bVrF1ZrC53YdegV8N8HIGcLbH4X+kw8/jEiIs2UzWarlxONyPHo3jS1teI++PRv0HEE/Hp5eDyJiIiIHEX3pmkoo64Dmwt+Wg27vjC7GhERkWZPYaS2YlJh8KTw+spHzK1FRESkBVAYqYvRNwEW+OE9yNpkdjUiIiLNmsJIXSR3hz7nhtc/f9TcWkRERJo5hZG6OuWW8OO3L0HePnNrERERacYURuoqfQR0OgVCfvhSN9ATERGpK4WREzGmtHXk60VQkmtuLSIiIs2UwsiJ6HEWpPQGb174jr4iIiJSawojJ8JqhVNuCq9/8U8I+MytR0REpBlSGDlRAy6B2HaQvw++e8XsakRERJodhZETZXfByTeE1z//B4RC5tYjIiLSzCiM1IdhV4IrDrI3wY8fmF2NiIhIs6IwUh/c8eFAApoiXkREpJYURurLyTeA1QG7Pofdq82uRkREpNlQGKkvce1h4KXh9c/VOiIiIlJTCiP1qewy34x34MAWc2sRERFpJhRG6lNqb+h5NmDAKt1AT0REpCYURupb2RTx6/8NBVnm1iIiItIMKIzUt06joeMICHrhyyfNrkZERKTJUxipbxZLeevI6oXgLTC3HhERkSZOYaQh9DoHkrqF7+S79jmzqxEREWnSFEYagtVW4QZ6j0PQb249IiIiTZjCSEMZNAmiUyB3N3z/htnViIiINFkKIw3F4YZR14fXVz4ChmFuPSIiIk2UwkhDGvFrcERD5gbYusLsakRERJokhZGG5EmEYVPD6yv/YW4tIiIiTZTCSEM7+TdgscH2/8LedWZXIyIi0uQojDS0hHQYcHF4Xa0jIiIiR1EYaQxll/lufBMObje1FBERkaZGYaQxtB0A3c4EIxSed0REREQiFEYaS9kU8Wufh8Icc2sRERFpQhRGGkvXn0O7wRAoDt+zRkRERACFkcZjscCYm8PrXz4JviJz6xEREWkiFEYaU5/zIKEzFB+E9YvNrkZERKRJUBhpTDZ7+ZU1q+ZDMGBuPSIiIk2AwkhjGzwFPElwaAdkvG12NSIiIqZTGGlszigYeW14XTfQExERURgxxchrwO6Bfethx6dmVyMiImIqhREzRCfDkF+F11c+Ym4tIiIiJlMYMcvo6WCxwpYPYf8Gs6sRERExjcKIWZK6Qt/zwuuf6wZ6IiLSeimMmOmU0knQNrwGh3ebW4uIiIhJFEbM1GFoeJr4UAC++KfZ1YiIiJhCYcRsZTfQW/MMFB8ytRQREREzKIyYrduZkNYf/IWw+imzqxEREWl0CiNms1jKx458+ST4S8ytR0REpJEpjDQF/S+EuI5QmAXfvmh2NSIiIo1KYaQpsDnC844AfP4ohILm1iMiItKIFEaaiqFXgDsecrbA5nfNrkZERKTRKIw0Fa4YGDEtvK4b6ImISCuiMNKUjLwObC74aTXs+sLsakRERBqFwkhTEpsGgyeF13UDPRERaSUURpqa0TcBFvjhPcjaZHY1IiIiDU5hpKlJ7g69fxFeX/WoubWIiIg0AoWRpmjMreHHb16CvH2mliIiItLQFEaaovQR0OkUCPnhS91AT0REWjaFkaZqTOkU8V8vgpI8c2sRERFpQAojTVWP8ZDcC7x54Tv6ioiItFAKI02V1VreOvLF4xDwmVuPiIhIA1EYacoGXAKx7SB/H3z3itnViIiINAiFkabM7oJR14fXP/8HhELm1iMiItIA6hRGHnvsMbp06YLb7WbUqFF89dVX1e67cOFCTj31VBITE0lMTGTcuHHH3F+OMPwqcMZC9ibYstzsakREROpdrcPISy+9xIwZM5g9ezZr165l0KBBjB8/nqysrCr3/+STT5g0aRIff/wxq1atIj09nbPOOos9e/accPGtgjs+HEhAU8SLiEiLZDGM2t0edtSoUYwYMYL58+cDEAqFSE9P56abbmLmzJnHPT4YDJKYmMj8+fO54ooravSeeXl5xMfHk5ubS1xcXG3KbRny9sK8geF5R379YXgeEhERkSaupufvWrWM+Hw+1qxZw7hx48pfwGpl3LhxrFq1qkavUVRUhN/vJykpqdp9vF4veXl5lZZWLa49DPxleP1ztY6IiEjLUqswcuDAAYLBIGlpaZW2p6WlsX///hq9xp133kn79u0rBZojzZkzh/j4+MiSnp5emzJbplNuCj9mvAM5W82tRUREpB416tU0c+fO5cUXX+SNN97A7XZXu9+sWbPIzc2NLLt3727EKpuo1D7Q82zAgM91Az0REWk5ahVGkpOTsdlsZGZmVtqemZlJ27Ztj3ns3/72N+bOncsHH3zAwIEDj7mvy+UiLi6u0iLAmFvCj+uXQEHVA4ZFRESam1qFEafTybBhw1ixYkVkWygUYsWKFYwePbra4x588EHuu+8+li1bxvDhw+tebWvXaTR0GA5BL3z5pNnViIiI1Itad9PMmDGDhQsX8uyzz5KRkcENN9xAYWEhV10Vvvz0iiuuYNasWZH9H3jgAe6++26efvppunTpwv79+9m/fz8FBQX19ylaC4ulvHVk9b/Aq++hiIg0f/baHnDppZeSnZ3NPffcw/79+xk8eDDLli2LDGrdtWsXVmt5xvnnP/+Jz+fj4osvrvQ6s2fP5o9//OOJVd8a9f4FJHWDg1th3fNw8g1mVyQiInJCaj3PiBla/TwjR/p6EbxzK8Snw83rwOYwuyIREZGjNMg8I9JEDJoE0SmQuxu+f8PsakRERE6Iwkhz5HDDqOvC6yv/AU2/cUtERKRaCiPN1fBfgyMaMr+DrR+ZXY2IiEidKYw0V1FJMGxqeF030BMRkWZMYaQ5O/kGsNhg+39h73qzqxEREamTVh1Ggvn5GD6f2WXUXUIn6H9ReP3zf5hbi4iISB216jCS/fe/s2X82Rx8/gVCxcVml1M3Y24OP37/Bmx8C4IBc+sRERGppVYbRgy/n4L/fUpg3z4y77+fLWeO48CTCwjm55tdWu20HQDd/w+MELx8BczrDx/dD4d3mV2ZiIhIjbTqSc9CXi+5b7xBzsJ/4d+zBwBrbCyJUyaTdMUV2JOS6u29GlTxIfj0YVi/GIpySjdaoPuZMOzK8N1+NTGaiIg0spqev1t1GCljBALkvfsuBxYswLdlKwAWt5uEX15Cm6uvxnGcOxI3GQEvbFoKa54JD2otE5MGQ34FQ6+AxC5mVSciIq2MwkgdGKEQ+StWkPPkAko2bAhvdDhIOP882kybhrNz5wZ773p3cBusfQ7WvQCF2eXbu42FoVOh1zlgd5pXn4iItHgKIyfAMAwKP/+cnCcXUPTVV+GNVitxZ59Nm+uuxd2rV4PXUG8CPvjhvXBrScXJ0aJTYPCUcGtJm26mlSciIi2Xwkg9KVq7jpwFCyj45JPItpjTT6fNddcSNWRIo9Zywg7tgLXPh+/2W5BZvr3raeEJ1HqfC3aXaeWJiEjLojBSz0o2bSJnwQLy3lsWuRdM1KhRJF93LVGjR2OxWEypq06Cfvjh/XBryZYPgdJfgag2MHgyDL0SkrubWKCIiLQECiMNxLt9OzlPPUXuW2+D3w+Ae8AAkq+7lpixY7FYm9nV0od3lbaWvAD5e8u3dzk1PLakz8TwjflERERqSWGkgfn37SNn0SIOv/wKRkkJAK4e3Wlz7bXETZiAxW43ucJaCgZgy/Jwa8mPH4TnLQHwJMKgyeFunJRmNFbGbIYBzam1TESkASiMNJJATg4Hn3ueQ4sXEyooAMDRsSNtpk0j/oLzsbqa4RiM3J/CLSVrn4e8n8q3dzolHEr6ngcOj3n1NTX+EsjcAHvXwZ614ccDmyGuI6T1g7b9w49pAyCpK1htZlcsItIoFEYaWTA/n0NL/s3BZ58lePAgAPaUFJKuuorES3+JNTra5ArrIBSELSvCrSU/LAMjGN7ujodBk8LdOGl9TS2x0QX9kJUBe9eWh4+sjRCq4TT8jihI7VMeTtL6hRdPQoOWLSJiBoURk4SKizn86mvkPPUUgf37AbDFx5N4xeUkTZmCLSHB3ALrKm8vrFscnrskt8JU8+mjwqGk3wXgjDKvvoYQCsKBH8Ohoyx87P8OAiVH7xvVBtoPhQ5Dof2QcODI/Qkyvw8fk/l9OMQEqrkHUnw6pPWv0JLSH5JOUiuKiDRrCiMmM3w+cv/zH3IWLMS3cycA1qgoEiZdRtLUqThSU02usI5CQdj2cbi1ZPN75S0CrngY+Mvw9PNt+5tZYd0YBhzaXt7Nsncd7PsGfAVH7+uKh/aDw6GjLHzEpx9/jEgoGJ6MriycZG4IP+burnp/uyccatr2VyuKiDRLCiNNhBEMkv/BBxx4cgHeTZsAsDidxF90IW1+PQ1nxw4mV3gC8veH74ez9rnwHCZlOgwPh5L+F4KzCXZPGQbk7ak8xmPvOig5fPS+jmhoNygcOMrCR2JXqM+rpooPQebG0nCyAfZvqHkrSlq/8M0S1YoiIk2QwkgTYxgGBf/9LzlPPEnx+vXhjTYb8eeeS5trr8HVrRnPghoKhe+Fs+YZ2PROeWuJMxYGXhIOJu0GmVdfQXblMR5710Fh1tH72ZzhE3v7oeXBI7mnOSf5slaUsnBS1pJyvFaUsnASaUVJbNy6RUQqUBhpogzDoGj1anKeXEDhypXhjRYLsePG0ea66/D072dugSeqIAvWL4G1z4ZPpmXaDwmPLRlwMbhiG+79iw/B3vUVwse6ylcElbHYILUvdCht8Wg/NPx1U79fT/Hh0mDyPWSWdfdsrL4VJa5j+RgUtaKISCNTGGkGir/bQM6CBeQvXx7ZFj1mDMnXX4dn+PDmNavrkUIh2PlZuLVk49sQCk8QhyM6HEiGXRkOASfyGb0FsP/bCl0taysHoAhLuIWj4hiPtgNazuXJoSAc3F4eTspaUioONK5IrSgi0kgURpoR75Yt5CxcSO47SyEYvnzWM3QoydddS/TPf968QwlA4QH45t/hYJKzpXx724HhUDLgEnAf5+da3VweZZOzVZTYtfIYj7YDj//6LVHx4fBlx/s3lI9HqVErSr/SlpT+4ZsoqhVFROpIYaQZ8v30U3iq+ddex/D5AHD17k3yddcSe9ZZWGzN/KRgGLDz89LWkrcg6A1vd0SFB7sOuwo6DAuPOanpXB5xHcqDR9kSldSoH6tZibSibCi/mmf/huO3orQdULoMDM8t05BdbSLSYiiMNGP+rCwOPvssh//9IqGiIgCcXbrQ5ppriJ94LhZnEx/XUBNFB+GbF8PB5MDm8u3xncKDS6ucyyO5vJulbJBpbFqjldyilbWiROZFKb2ix19U9f5JJ1UOKG0HQGw7TYEvIpUojLQAwcOHOfjCYg4+/zyh3FwA7O3a0ebqq0m4+CKsnuYz5sEwDAyvl1BhYflSVESooIDQzvWENn9EaOc6jEAAV0IAT3s3ts6DK4eP+I462TWmimNR9ldY8vdVvb8n6eiAktwDbI7GrVtEmgyFkRYkWFDI4ZdfJmfR0wSzDwBgS0oiaepUEidPwhZb/03mhmFgFBeXh4bSABEsLMQoKiJ4ZKgoLCRUWFRp30rPFRVFxsPUiNWKq1cvooYMwTN0KFFDh+Bo377eP6fUQeGByuFk/3dw4Ify2wVUZHNW6OYZWD5g1h3f+HWLSKNTGGmBQl4vuW+8Sc6//oX/p/DlqtbYWBKnTCbp8suxuNyEigorBIMK60eEiqO+ruI5GuhXwxIVhTUqCmt0FNboaGxR0Viio7BFR2MYBiXffod/z56jjrO3bUvU0KGRcOLq2bP53R25pfKXQHbGESFlA/jyq94/oXPlgNJ2gFq+RFoghZEWzAgEyHv3XQ4sWIBvy9aGfTOLJRwcosLBwRodXXn9yK+PtV90FFaPp0YDcf2ZmRSvXUvR2nUUr11LyaZNR7WsWKOi8AwehGfIUDxDh+AZNBhbTBOc8bWZMoJBAllZWD0erPHxtb+qKxSCwzsrB5RjTdzmjq8cTtoOgOReTX/uFxGplsJIK2CEQhR89BEHnniSkg0bwhut1hoGhyisUdGR1omKz9mio7FEhVsqLB4Plvqc+ryOQoWFFH/3HUVr11K8dh3F69cTKjjivjHq2qkTIxDAt3s3vq1b8W7ZinfrVrxbt+Dbth2jJDyQ2OJ0Yk9JCS+pqdU8pmBLSDh+aCk6WDqzbIUWlOyMqq+WsjogpXflgNK2v+ZEEWkmFEZaEcMwCOXmYnG5sLjdzX9ekhowgkG8W7ZUaj2pvmtnSKT1xN2rV6vt2jF8Pny7dpUGji2R8OHbvh3D76/6ILsdAlWEhGpYHA5sKck4UlKxp6ZgP/KxNLjYEhIqh9yAF7I3V25B2f8tlORW/Ubx6UcElAHhrp9W8Lsv0pwojEir48/Monjd2kjrSUlGRpVdO+5BA4kaEh574hk8CFtMjEkVN4yQ14tvxw68W7ZUau3w7dxZbbCweDy4TjoJV/duOLt1x9W9G65u3XB07IgRDBLMzsaflUUgO5tAVnbpY1alx+ChQzUv0uHAnpxcGlTCi+PIlpaUFGzWAixZpXOh7P82HFQO76z6NV1x4YnaKgaUlN7gcNfhuygi9UFhRFq9UFERxd9+VxpQSrt28o8YUGm14urZM9J6EjV0CPb27ZtF61KouBjvtm2VA8eWLfh27w6P16iCNToaZ/duuLp1x9WtWyR8ONq3O+HuOMPnI3DgAIGsLPzZRwaW8q+DBw/W/EXt9nBoiYSUZOyJcdidXhy2XOyBvdhLtmHL34zF8B19vMUGKb3CwSS1T/i2AMm9ILEL2FpnC5lIY1IYETmCEQrh/XFLpdaTsquSKrKnpeEZOiTSeuLubW7XTrCgEN+2reXdK6XBw79nT7VXPFnj4nB1rxw4XN27YU9LMz1oGT4fgZycSmEl0upSIbgEc3JqfkWXzYY9MR57nBt7VAi7owi7kYPdUYjdHcTuCeGMDWBzlL6e1RGe6j65Z3hJ6VW63gOcGgQtUl8URkRqwJ+VFR4QW9p6UpKRcVRXhiUqCk8jdO0E8/LCg0e3lAcO79atBPZVM8kY4flmXN26lbd2lHav2JKTTQ8dJ8rw+wkcPFi5Oygrm0B2+NGfXdo9lHOw2pagIznirbjjS3DFleBO9ONOCGCPClYeahKfXiGk9CxvTYlO1pgUkVpSGBGpg1BREcXfbShvPVlX/107gUOHKnetbN2Cd8tWAllZ1R5jT0k5KnA4u3XDnqT78BiBAIGcg1WOY4m0vGRmEjxwoMrjrR477mQ7rthC3DF5uBICuOL9R98f0JNYHlIqtqYkdNLNBEWqoTAiUg+MUKjCVTu169oJHj581JUr3q1bw90P1bC3axfuWqnY2tHtJGzxmrH0RAUOHcK7eTMlmzbhzdhEyebNeLduhaquJLJZcaXG4Eq24I4pwO3KwpXgx+6uogXG7oY23Y9uTWnTHRzN55YNjanstNPcW+/k+BRGRBpITbp2sNmOOf29o2PHo7pXnCed1OKu7GnqDJ8P77ZtlGRswrtpUziobNpEMLfqS4rtSbG42sXhbmPgjs7FZfsJZ1QxlirH/logsXPVrSkt/M7SZRPm+ffswb93b3jZswf/ntLHffvAZsPZpQvOLp1xdumCq0uX0q+7KHy3IAojIo0kVFxc4aqdtRSv/4ZQXh5YrTjT03FWGkjaDVfXrlijoswuW6phGAaB/fvDwWTz5khQ8e3aVeWAWovLhatzGu52MbgSg7ijDuGy7MAWPFz9m0QlVxg0W9aa0gviOkATmGTweAyfD39mZnm42Lu3cvDYv79W89McyZaYiLNr10g4KQsszs6dsbpc9fhJpKEpjIiYxAiFCOzbhy05Wf9xtiChwkJKfvihtAVlMyWbMvD+8CNGcXGV+zs6dsDdJQ1Xmgd3QgC35wB233YseUd385UfFBW+oqds0GxZl09St0adFj9UUoJ/776jQ0bpeiAz8/hXOjkcONq2xdGhA4727XF0aI+jffm64ffj27ED346d+LZvL13fccyxU1gsONq1CweTimGlaxcc7drV6FYT0rgURkREGpgRDOLbuQvv5k2UZGyiZPMmvJs2h0/WVbDGx+Pu0R1351RcaS7ccV6cjkysh7dAzlYIVTMTrsUWnhslpRdEp4DdFV5srvCYFbuzdL1su7N0e8X18n2C3iD+7MP4Mw/gzzqAf28m/n17wy0de/dWO9i3UkkuV2mwKA0YZesdwo/25OQ6hYNQYSG+nTvDE/eVBpSywHLUYPKK9TgcODp3Ku/y6dwZZ8d2ODumYYtxYQn6IFAcvqljoHTxF1dYLwk/H/BWvf2o47zh7RZreHCzOyH8eNRyxHZ3Qqu635LCiIiISQKHDoVbUDI2RYKKd9u2qrsu7HZcJ52Eu3dPXOmpuFMduGKLsHt3w4HNkP1D9Xc/roJhQMhvwV9ow19oL3204S+yRbYFfcfvCrI6wBFnxRFnx5HgxJHgwpHowZEUjSM5BltcDBZHdYGndN3uLn2uYngqXQyjZoGg9MRv+IsIHs7Hl5mLL7sAX3YhvpwSfAf9+A4HMaofooXVEZ5npuLiigvgjAlidZhwCnTGVAgvCccOLxVDjDO62V1erjAiItKEhHw+fFu2lHfxbApf2RPKy6tyf3taGq7evXD37o27U1tcKVaczsNQcpjg4Tz82YfwZ+fiz8nHn1OI/1AR/kMl+A/7CPmOP++K1RnCER3AERXEER3EGR3EHh3EWbrN6jSazXnPCIG/yIYv3166lK/7C21A9R/EHmPD2caNMyUKZ2oszrQ4nG2TcLZtg8UVFQ5UDk9pwHKHby9g95Q+uiEUhJLDUHzoiKWKbSW5wAmccq2O47e8lG1zV3jOHW/a5ecKIyIiTZxhGAT27TsqoPh37apyf4vHA4YRuZvysdjatKncjdKhtCulfbgrxRYVBUFvuBUi6CttnfCVbqu4fuQ+ZV97w/sESkqf8x6x7j3i+ArrAW+4e8PuKj/ROzylrSae8hN+xecjQaCacFDx+Qr7hIIW/D/9VKHLZwe+7eHHY96awGbD2bFjpXEpZet1nsk4FARvXjWh5XAVgeZQOOgUHay+C69GLOFAcryWl+7jIDbtBN7naAojIiLNVLCgAO8PP1SeE+WHH8pDiMWCPTW1+jEb7dph9WiOk+MJ5uZGxqeULd7SMSpGUVG1x1k8nshVPvbklPDGslPpkY8YkXlVyp+j2n3DX1bxWqEA+L3lYS8S+sq/NgJeCPpLg58/vE/IX0VjjKXy25ZK/dM8nKPPP8Z3rPZqev7WnaJERJoYW0wMUUOHEjV0aGRb2WBZi92Go21bLM7WMwiyodji4/EMHIhn4MBK2w3DIJCVFWlBqbT89BNGcTHejAy8GRkmVV5TjtKlZtoEzbvDtcKIiEgzYLHZcJ3U1ewyWgWLxYIjLQ1HWhrRJ4+q9Jzh9+PfsyfcgrJ9B8HDh8uHpJR23ZR34VgqbS9/rGb/Gh5X5/2Pc4yj1zDMojAiIiJSQxaHIzJ2hNPNrqblaPpT/YmIiEiLpjAiIiIiplIYEREREVMpjIiIiIipFEZERETEVAojIiIiYiqFERERETGVwoiIiIiYSmFERERETKUwIiIiIqZSGBERERFTKYyIiIiIqRRGRERExFTN4q69hmEAkJeXZ3IlIiIiUlNl5+2y83h1mkUYyc/PByA9Pd3kSkRERKS28vPziY+Pr/Z5i3G8uNIEhEIh9u7dS2xsLBaLpd5eNy8vj/T0dHbv3k1cXFy9va7UjX4eTY9+Jk2Lfh5Ni34ex2cYBvn5+bRv3x6rtfqRIc2iZcRqtdKxY8cGe/24uDj9IjUh+nk0PfqZNC36eTQt+nkc27FaRMpoAKuIiIiYSmFERERETNWqw4jL5WL27Nm4XC6zSxH082iK9DNpWvTzaFr086g/zWIAq4iIiLRcrbplRERERMynMCIiIiKmUhgRERERUymMiIiIiKladRh57LHH6NKlC263m1GjRvHVV1+ZXVKrNGfOHEaMGEFsbCypqamcf/75bN682eyypNTcuXOxWCzceuutZpfSau3Zs4df/epXtGnTBo/Hw4ABA/j666/NLqvVCgaD3H333XTt2hWPx0O3bt247777jnv/Faleqw0jL730EjNmzGD27NmsXbuWQYMGMX78eLKysswurdX573//y/Tp0/niiy9Yvnw5fr+fs846i8LCQrNLa/VWr17Nk08+ycCBA80updU6dOgQY8aMweFw8N5777Fx40YeeughEhMTzS6t1XrggQf45z//yfz588nIyOCBBx7gwQcf5NFHHzW7tGar1V7aO2rUKEaMGMH8+fOB8P1v0tPTuemmm5g5c6bJ1bVu2dnZpKam8t///pef//znZpfTahUUFDB06FAef/xx/vznPzN48GDmzZtndlmtzsyZM1m5ciWffvqp2aVIqXPPPZe0tDSeeuqpyLaLLroIj8fDCy+8YGJlzVerbBnx+XysWbOGcePGRbZZrVbGjRvHqlWrTKxMAHJzcwFISkoyuZLWbfr06fziF7+o9O9EGt/bb7/N8OHDueSSS0hNTWXIkCEsXLjQ7LJatVNOOYUVK1bwww8/APDNN9/w2WefMWHCBJMra76axY3y6tuBAwcIBoOkpaVV2p6WlsamTZtMqkog3EJ16623MmbMGPr37292Oa3Wiy++yNq1a1m9erXZpbR627Zt45///CczZszg97//PatXr+bmm2/G6XQydepUs8trlWbOnEleXh69e/fGZrMRDAa5//77mTJlitmlNVutMoxI0zV9+nQ2bNjAZ599ZnYprdbu3bu55ZZbWL58OW632+xyWr1QKMTw4cP5y1/+AsCQIUPYsGEDTzzxhMKISV5++WUWL17MkiVL6NevH+vXr+fWW2+lffv2+pnUUasMI8nJydhsNjIzMyttz8zMpG3btiZVJTfeeCPvvPMO//vf/+jYsaPZ5bRaa9asISsri6FDh0a2BYNB/ve//zF//ny8Xi82m83ECluXdu3a0bdv30rb+vTpw2uvvWZSRfK73/2OmTNnctlllwEwYMAAdu7cyZw5cxRG6qhVjhlxOp0MGzaMFStWRLaFQiFWrFjB6NGjTaysdTIMgxtvvJE33niDjz76iK5du5pdUqt25pln8t1337F+/frIMnz4cKZMmcL69esVRBrZmDFjjrrU/YcffqBz584mVSRFRUVYrZVPnzabjVAoZFJFzV+rbBkBmDFjBlOnTmX48OGMHDmSefPmUVhYyFVXXWV2aa3O9OnTWbJkCW+99RaxsbHs378fgPj4eDwej8nVtT6xsbFHjdeJjo6mTZs2Gsdjgttuu41TTjmFv/zlL/zyl7/kq6++YsGCBSxYsMDs0lqtiRMncv/999OpUyf69evHunXrePjhh7n66qvNLq35MlqxRx991OjUqZPhdDqNkSNHGl988YXZJbVKQJXLokWLzC5NSp122mnGLbfcYnYZrdZ//vMfo3///obL5TJ69+5tLFiwwOySWrW8vDzjlltuMTp16mS43W7jpJNOMv7whz8YXq/X7NKarVY7z4iIiIg0Da1yzIiIiIg0HQojIiIiYiqFERERETGVwoiIiIiYSmFERERETKUwIiIiIqZSGBERERFTKYyIiIiIqRRGRERExFQKIyIiImIqhRERERExlcKIiIiImOr/A2DrMzqSShIgAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "test_loss, test_acc = model.evaluate(X_test, y_test)\n",
        "print(f\"Test accuracy: {test_acc}\")\n",
        "\n",
        "y_pred = np.argmax(model.predict(X_test), axis=-1)\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "print(\"Confusion Matrix:\")\n",
        "print(conf_matrix)\n",
        "\n",
        "\n",
        "pd.DataFrame(history.history).plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a37fca33",
      "metadata": {
        "id": "a37fca33"
      },
      "source": [
        "## Make Predictions\n",
        "Use the trained model to make predictions on new or unseen parkinglot images."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f1ab3608",
      "metadata": {
        "id": "f1ab3608"
      },
      "source": [
        "if you need new, we prepared some data for you [here](www.kaggle.com/datasets/khaledzsa/parkinglot-occupation), or you can simply run the following command to get the data and unzip it.\n",
        "\n",
        "<small>Note: please note that the file contain MetaData to tell you what each image contains <b>THIS IS JUST FOR YOU TO MAKE SURE</b></smmall>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8d59c9e4",
      "metadata": {
        "id": "8d59c9e4"
      },
      "source": [
        "If you ran the command above, you should have a file called ParkingLot_Occupation in your current directory. which contains the testing dataset.\n",
        "If you ran the command above <span style='color:red;'>DON'T RUN THIS CELL</span>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a7a5c981",
      "metadata": {
        "id": "a7a5c981"
      },
      "outputs": [],
      "source": [
        "# !kaggle datasets download -d khaledzsa/parkinglot-occupation\n",
        "# !unzip parkinglot-occupation.zip"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img = Image.open('/content/ParkingLot_Occupation/test/occupied/roi_012c86aba60740829d6d1e6d97d283fc_occupied.jpg')\n",
        "img = img.resize((128, 128))\n",
        "img_array = np.array(img) / 255.0\n",
        "img_input = img_array.reshape(1, 128, 128, 3)\n",
        "\n",
        "prediction = model.predict(img_input)\n",
        "predicted_class = np.argmax(prediction)\n",
        "\n",
        "plt.imshow(img_array)\n",
        "plt.title(f\"Predicted Class: {predicted_class}\")\n",
        "plt.axis('off')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "YfeyzzFIrBh6",
        "outputId": "177744d5-ea29-4494-bf00-b995b6fa72f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        }
      },
      "id": "YfeyzzFIrBh6",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8430600d",
      "metadata": {
        "id": "8430600d"
      },
      "source": [
        "## Model Performance Visualization\n",
        "Visualize performance metrics such as accuracy and loss over the epochs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "50624cfe",
      "metadata": {
        "id": "50624cfe",
        "outputId": "092ef7e8-7b0a-4314-b431-bdddb1b39c87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 5))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(history.history['accuracy'], label='train_accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='val_accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Accuracy Over Epochs')\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.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Loss Over Epochs')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8d4f3348",
      "metadata": {
        "id": "8d4f3348"
      },
      "source": [
        "## Save the Model\n",
        "Save the trained CNN model for submission."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fe6b2df0",
      "metadata": {
        "id": "fe6b2df0",
        "outputId": "c373c92d-0652-41ca-9ed9-546108ccb0e6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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": [
        "model.save('traffic_sign_cnn_model.h5')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ed90995f",
      "metadata": {
        "id": "ed90995f"
      },
      "source": [
        "## Project Questions:\n",
        "\n",
        "1. **Data Preprocessing**: Explain why you chose your specific data preprocessing techniques (e.g., resizing images, normalization, data augmentation). How do these preprocessing steps improve the performance of your CNN model?\n",
        "2. **Model Architecture**: Describe the architecture of your CNN model (e.g., number of convolutional layers, kernel sizes, pooling layers). Why did you choose this structure, and how do you expect each layer to contribute to feature extraction?\n",
        "3. **Activation Functions**: Justify your choice of activation functions. How do they influence the training and output of your CNN?\n",
        "4. **Training Process**: Discuss your choice of batch size, number of epochs, and optimizer. How did these decisions impact the training process and the convergence of the model?\n",
        "5. **Loss Function and Metrics**: Explain why you chose the specific loss function and evaluation metrics for this classification task. How do they align with the goal of correctly classifying parkinglot?\n",
        "6. **Regularization Techniques**: If you used regularization methods like dropout or batch normalization, explain why you implemented them and how they helped prevent overfitting in your model.\n",
        "7. **Model Evaluation**: Justify the method you used to evaluate your model's performance on the test set. Why did you select these evaluation techniques, and what insights did they provide about your model's accuracy and generalization ability?\n",
        "8. **Model Visualization**: Explain the significance of the performance visualizations (e.g., accuracy and loss curves). What do they tell you about your model's training process and its ability to generalize?\n",
        "9. **Overfitting and Underfitting**: Analyze whether the model encountered any overfitting or underfitting during training. What strategies could you implement to mitigate these issues?"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "02e7f114",
      "metadata": {
        "id": "02e7f114"
      },
      "source": [
        "### Answer Here:\n",
        "\n",
        "**Data Preprocessing:**\n",
        "Resized images to (128, 128) for uniformity and normalized pixel values to speed up training. This helps the model learn faster and more accurately.\n",
        "\n",
        "**Model Architecture:**\n",
        "The model includes 2 convolutional layers for feature extraction, max-pooling for dimensionality reduction, followed by flattening and dense layers for classification. And after many tryies i think this is good\n",
        "\n",
        "**Activation Functions:**\n",
        "ReLU is used for hidden layers to introduce non-linearity, aiding in learning complex patterns. Softmax is used in the output layer to convert predictions into probabilities for classification.\n",
        "\n",
        "**Training Process:**\n",
        "A batch size of 32 and 10 epochs were chosen to balance training time and performance. The Adam optimizer adjusts learning rates automatically, helping the model converge faster.\n",
        "\n",
        "**Loss Function and Metrics:**\n",
        "Sparse categorical cross-entropy is used, which is suitable for multi-class classification. Accuracy is used as the metric to measure the model’s performance in classifying images.\n",
        "\n",
        "**Regularization Techniques:**\n",
        "Dropout is applied to prevent overfitting by randomly dropping neurons during training, which helps the model generalize better.\n",
        "\n",
        "**Model Evaluation:**\n",
        "The model's performance is evaluated using accuracy because it is balanced dataset and a confusion matrix on the test set to assess how well it generalizes to new data.\n",
        "\n",
        "**Model Visualization:**\n",
        "Accuracy and loss curves over epochs indicate the learning progress. Stable curves suggest good training, while divergence may indicate overfitting or underfitting.\n",
        "\n",
        "**Overfitting and Underfitting:**\n",
        "there is none alhamdullah, If there is overfitting, i will increase dropout or simplify the model.And  If there is underfitting, increase model complexity or train for more epochs."
      ]
    }
  ],
  "metadata": {
    "language_info": {
      "name": "python"
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}