pothole_detection_model_prototype_5_adam.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-GdFlDcHqCs9",
        "outputId": "e08fddea-f801-4263-e4d8-c11eee8333e1"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OVNTgKpZr8LA",
        "outputId": "e60d0976-08c0-4adc-bca2-4160425ab741"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Pothole-Detection'...\n",
            "remote: Enumerating objects: 29946, done.\u001b[K\n",
            "remote: Counting objects: 100% (53/53), done.\u001b[K\n",
            "remote: Compressing objects: 100% (38/38), done.\u001b[K\n",
            "remote: Total 29946 (delta 14), reused 43 (delta 13), pack-reused 29893\u001b[K\n",
            "Receiving objects: 100% (29946/29946), 1.20 GiB | 17.14 MiB/s, done.\n",
            "Resolving deltas: 100% (1433/1433), done.\n",
            "Checking out files: 100% (30016/30016), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone \"https://github.com/srajan-kiyotaka/Pothole-Detection.git\" "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "z9QvRrgEm7Fv",
        "outputId": "3a5c08f2-9231-425d-e933-a792e5547cc1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Image ID  Pothole  Number of Potholes Level  Unnamed: 4\n",
              "0   pothole_image_1299        1                   3     B         NaN\n",
              "1  pothole_image_10064        1                   1     A         NaN\n",
              "2  pothole_image_10079        1                   1     A         NaN\n",
              "3  pothole_image_10084        1                   1     A         NaN\n",
              "4  pothole_image_10087        1                   2     A         NaN\n",
              "5  pothole_image_10110        1                   1     A         NaN\n",
              "6  pothole_image_10119        1                   1     A         NaN\n",
              "7  pothole_image_10120        1                   1     A         NaN\n",
              "8  pothole_image_10135        1                   1     A         NaN\n",
              "9  pothole_image_10139        1                   1     A         NaN"
            ],
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              "\n",
              "  <div id=\"df-f6a98fe5-6652-4303-bfb8-a560c3182f87\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Image ID</th>\n",
              "      <th>Pothole</th>\n",
              "      <th>Number of Potholes</th>\n",
              "      <th>Level</th>\n",
              "      <th>Unnamed: 4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>pothole_image_1299</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>B</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>pothole_image_10064</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>A</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>pothole_image_10079</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>A</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>pothole_image_10084</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>A</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>pothole_image_10087</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>A</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>pothole_image_10110</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>A</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>pothole_image_10119</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>A</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>pothole_image_10120</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>A</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>pothole_image_10135</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>A</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>pothole_image_10139</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>A</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        buttonEl.style.display =\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f6a98fe5-6652-4303-bfb8-a560c3182f87');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# Importing the csv file\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "features = pd.read_csv(r\"/content/Pothole-Detection/Dataset_Info.csv\")\n",
        "\n",
        "features.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DFVSPuZFm8Ex",
        "outputId": "9390fa23-fd66-46f1-9cd9-ec7fe2b019cc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Image ID                  0\n",
              "Pothole                   0\n",
              "Number of Potholes        0\n",
              "Level                     0\n",
              "Unnamed: 4            30000\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "features.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "E-HnvxGFnFeC"
      },
      "outputs": [],
      "source": [
        "features = features.drop('Unnamed: 4', axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "2bb-uXLT2CSX",
        "outputId": "dfef20d6-dd60-47cd-8bf3-07b144aa078b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Image ID  Pothole  Number of Potholes Level\n",
              "0   pothole_image_1299        1                   3     B\n",
              "1  pothole_image_10064        1                   1     A\n",
              "2  pothole_image_10079        1                   1     A\n",
              "3  pothole_image_10084        1                   1     A\n",
              "4  pothole_image_10087        1                   2     A\n",
              "5  pothole_image_10110        1                   1     A\n",
              "6  pothole_image_10119        1                   1     A\n",
              "7  pothole_image_10120        1                   1     A\n",
              "8  pothole_image_10135        1                   1     A\n",
              "9  pothole_image_10139        1                   1     A"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>Image ID</th>\n",
              "      <th>Pothole</th>\n",
              "      <th>Number of Potholes</th>\n",
              "      <th>Level</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>pothole_image_1299</td>\n",
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              "      <td>3</td>\n",
              "      <td>B</td>\n",
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              "      <th>1</th>\n",
              "      <td>pothole_image_10064</td>\n",
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              "      <th>2</th>\n",
              "      <td>pothole_image_10079</td>\n",
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              "      <th>3</th>\n",
              "      <td>pothole_image_10084</td>\n",
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              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>pothole_image_10087</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>pothole_image_10110</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>pothole_image_10119</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>pothole_image_10120</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>pothole_image_10135</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>pothole_image_10139</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>A</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
          "execution_count": 6
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      ],
      "source": [
        "features.head(10)"
      ]
    },
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        },
        "id": "UXRkxcOl2EwW",
        "outputId": "a810167d-3c64-4f66-edf1-e029bc62b019"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Image ID              0\n",
              "Pothole               0\n",
              "Number of Potholes    0\n",
              "Level                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "features.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OhnJtc4F-IC2",
        "outputId": "aa461b3f-c04e-4183-e22c-7dddeb48a1a0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Normal Road  Pothole  Number of Potholes  A  B  C  S\n",
            "0            0        1                   3  0  1  0  0\n",
            "1            0        1                   1  1  0  0  0\n",
            "2            0        1                   1  1  0  0  0\n",
            "3            0        1                   1  1  0  0  0\n",
            "4            0        1                   2  1  0  0  0\n",
            "5            0        1                   1  1  0  0  0\n",
            "6            0        1                   1  1  0  0  0\n",
            "7            0        1                   1  1  0  0  0\n",
            "8            0        1                   1  1  0  0  0\n",
            "9            0        1                   1  1  0  0  0\n"
          ]
        }
      ],
      "source": [
        "all_image_names = features['Image ID']\n",
        "features = features.drop('Image ID', axis = 1)\n",
        "\n",
        "pothole_or_not = features['Pothole']\n",
        "pothole_info = features['Number of Potholes']\n",
        "pothole_level = features['Level']\n",
        "\n",
        "pothole_or_not = pd.get_dummies(pothole_or_not, columns = ['Pothole'])\n",
        "pothole_or_not.columns = ['Normal Road', 'Pothole']\n",
        "pothole_level = pd.get_dummies(pothole_level, columns = ['Level'])\n",
        "\n",
        "features = pothole_or_not.join(pothole_info)\n",
        "features = features.join(pothole_level)\n",
        "\n",
        "print(features.head(10))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "F2dq6N4H-H5x"
      },
      "outputs": [],
      "source": [
        "from sklearn.utils import shuffle\n",
        "\n",
        "image_names_shuffled, labels_shuffled = shuffle(all_image_names, features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "1j_72-_L-IJ_"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train_image_names, X_test_image_names, y_train, y_test = train_test_split(\n",
        "    image_names_shuffled, labels_shuffled, test_size=0.3, random_state=1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gxBC_7wIuD7u",
        "outputId": "512322fe-bf55-4fcd-ab13-b27cda442549"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "XG32217H-IHp"
      },
      "outputs": [],
      "source": [
        "train_pothole_or_not = y_train[['Normal Road', 'Pothole']]\n",
        "train_pothole_info = y_train['Number of Potholes']\n",
        "train_pothole_level = y_train[['A', 'B', 'C', 'S']]\n",
        "\n",
        "test_pothole_or_not = y_test[['Normal Road', 'Pothole']]\n",
        "test_pothole_info = y_test['Number of Potholes']\n",
        "test_pothole_level = y_test[['A', 'B', 'C', 'S']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vJnzrZS9dRSY",
        "outputId": "78b151c1-ab6f-4389-c734-08d5aa331712"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(21000, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "y_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "k83Y2bnU2Hzv"
      },
      "outputs": [],
      "source": [
        "train_pothole_or_not = np.array(train_pothole_or_not)\n",
        "train_pothole_info = np.array(train_pothole_info)\n",
        "train_pothole_level = np.array(train_pothole_level)\n",
        "\n",
        "test_pothole_or_not = np.array(test_pothole_or_not)\n",
        "test_pothole_info = np.array(test_pothole_info)\n",
        "test_pothole_level = np.array(test_pothole_level)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "g6cEmpAf49jV"
      },
      "outputs": [],
      "source": [
        "import cv2\n",
        "\n",
        "def get_image(file_location):\n",
        "    img = cv2.imread(file_location)\n",
        "    img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
        "    img = img/255\n",
        "    img = cv2.resize(img,(200, 200))\n",
        "    return img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "kduuFCaW3j1o"
      },
      "outputs": [],
      "source": [
        "import keras\n",
        "import numpy as np\n",
        "\n",
        "class My_Custom_Generator(keras.utils.all_utils.Sequence) :\n",
        "  \n",
        "  def __init__(self, image_filenames, pothole, pothole_number, pothole_level, batch_size = 128) :\n",
        "    self.image_filenames = image_filenames\n",
        "    self.pothole = pothole\n",
        "    self.pothole_number = pothole_number\n",
        "    self.pothole_level = pothole_level\n",
        "    self.batch_size = batch_size\n",
        "    \n",
        "    \n",
        "  def __len__(self) :\n",
        "    return (np.ceil(len(self.image_filenames) / float(self.batch_size))).astype(np.int)\n",
        "  \n",
        "  \n",
        "  def __getitem__(self, idx) :\n",
        "    batch_x = self.image_filenames[idx * self.batch_size : (idx+1) * self.batch_size]\n",
        "    a = self.pothole[idx * self.batch_size : (idx+1) * self.batch_size]\n",
        "    # b = self.pothole_number[idx * self.batch_size : (idx+1) * self.batch_size]\n",
        "    c = self.pothole_level[idx * self.batch_size : (idx+1) * self.batch_size]\n",
        "    batch_y = [np.array(a), np.array(c)] #, np.array(c)]\n",
        "\n",
        "    return np.array([\n",
        "            get_image(f'/content/Pothole-Detection/Unified Dataset/{str(file_name)}.jpg')\n",
        "               for file_name in batch_x]), batch_y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "u9atomppSxXV"
      },
      "outputs": [],
      "source": [
        "batch_size = 90\n",
        "\n",
        "my_training_batch_generator = My_Custom_Generator(X_train_image_names, train_pothole_or_not, train_pothole_info, train_pothole_level, batch_size)\n",
        "my_validation_batch_generator = My_Custom_Generator(X_test_image_names, test_pothole_or_not, test_pothole_info, test_pothole_level, batch_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j3oMzubsTJQU",
        "outputId": "1127892f-dc28-42d0-ed2b-0fd6ccbb5a09"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " Input Layer (InputLayer)       [(None, 200, 200, 1  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " conv_1 (Conv2D)                (None, 193, 193, 64  4160        ['Input Layer[0][0]']            \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " act_1 (Activation)             (None, 193, 193, 64  0           ['conv_1[0][0]']                 \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " pool_1 (MaxPooling2D)          (None, 186, 186, 64  0           ['act_1[0][0]']                  \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv_2 (Conv2D)                (None, 183, 183, 32  32800       ['pool_1[0][0]']                 \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " act_2 (Activation)             (None, 183, 183, 32  0           ['conv_2[0][0]']                 \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " pool_2 (MaxPooling2D)          (None, 176, 176, 32  0           ['act_2[0][0]']                  \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv_3 (Conv2D)                (None, 169, 169, 16  32784       ['pool_2[0][0]']                 \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " act_3 (Activation)             (None, 169, 169, 16  0           ['conv_3[0][0]']                 \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " pool_3 (MaxPooling2D)          (None, 154, 154, 16  0           ['act_3[0][0]']                  \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv_4 (Conv2D)                (None, 151, 151, 8)  2056        ['pool_3[0][0]']                 \n",
            "                                                                                                  \n",
            " act_4 (Activation)             (None, 151, 151, 8)  0           ['conv_4[0][0]']                 \n",
            "                                                                                                  \n",
            " pool_4 (MaxPooling2D)          (None, 136, 136, 8)  0           ['act_4[0][0]']                  \n",
            "                                                                                                  \n",
            " flat_1 (Flatten)               (None, 379456)       0           ['pool_3[0][0]']                 \n",
            "                                                                                                  \n",
            " flat_2 (Flatten)               (None, 147968)       0           ['pool_4[0][0]']                 \n",
            "                                                                                                  \n",
            " dense_1 (Dense)                (None, 128)          48570496    ['flat_1[0][0]']                 \n",
            "                                                                                                  \n",
            " dense_4 (Dense)                (None, 128)          18940032    ['flat_2[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_1 (BatchNormalization)   (None, 128)          512         ['dense_1[0][0]']                \n",
            "                                                                                                  \n",
            " batch_5 (BatchNormalization)   (None, 128)          512         ['dense_4[0][0]']                \n",
            "                                                                                                  \n",
            " add_2 (Add)                    (None, 128)          0           ['batch_1[0][0]',                \n",
            "                                                                  'batch_5[0][0]']                \n",
            "                                                                                                  \n",
            " batch_6 (BatchNormalization)   (None, 128)          512         ['add_2[0][0]']                  \n",
            "                                                                                                  \n",
            " dense_5 (Dense)                (None, 256)          33024       ['batch_6[0][0]']                \n",
            "                                                                                                  \n",
            " drop_2 (Dropout)               (None, 256)          0           ['dense_5[0][0]']                \n",
            "                                                                                                  \n",
            " dense_6 (Dense)                (None, 72)           18504       ['drop_2[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_7 (BatchNormalization)   (None, 72)           288         ['dense_6[0][0]']                \n",
            "                                                                                                  \n",
            " dense_2 (Dense)                (None, 256)          33024       ['batch_1[0][0]']                \n",
            "                                                                                                  \n",
            " dense_7 (Dense)                (None, 64)           4672        ['batch_7[0][0]']                \n",
            "                                                                                                  \n",
            " batch_2 (BatchNormalization)   (None, 256)          1024        ['dense_2[0][0]']                \n",
            "                                                                                                  \n",
            " drop_3 (Dropout)               (None, 64)           0           ['dense_7[0][0]']                \n",
            "                                                                                                  \n",
            " dense_3 (Dense)                (None, 64)           16448       ['batch_2[0][0]']                \n",
            "                                                                                                  \n",
            " dense_8 (Dense)                (None, 32)           2080        ['drop_3[0][0]']                 \n",
            "                                                                                                  \n",
            " pothole (Dense)                (None, 2)            130         ['dense_3[0][0]']                \n",
            "                                                                                                  \n",
            " pothole_level (Dense)          (None, 4)            132         ['dense_8[0][0]']                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 67,693,190\n",
            "Trainable params: 67,691,766\n",
            "Non-trainable params: 1,424\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "from keras.models import Sequential, Model\n",
        "from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Dropout, Input, Activation, Add, BatchNormalization\n",
        "\n",
        "input_ = Input(shape = (200, 200, 1), name = \"Input Layer\")\n",
        "\n",
        "conv_1 = Conv2D(64, kernel_size = (8, 8), name = \"conv_1\")(input_)\n",
        "act_1 = Activation(\"relu\", name = \"act_1\")(conv_1)\n",
        "pool_1 = MaxPooling2D(pool_size = (8, 8), strides = (1, 1), padding = \"valid\", name = \"pool_1\")(act_1)\n",
        "\n",
        "conv_2 = Conv2D(32, kernel_size = (4, 4), name = \"conv_2\")(pool_1)\n",
        "act_2 = Activation(\"relu\", name = \"act_2\")(conv_2)\n",
        "pool_2 = MaxPooling2D(pool_size = (8, 8), strides = (1, 1), padding = \"valid\", name = \"pool_2\")(act_2)\n",
        "\n",
        "conv_3 = Conv2D(16, kernel_size = (8, 8), name = \"conv_3\")(pool_2)\n",
        "act_3 = Activation(\"relu\", name = \"act_3\")(conv_3)\n",
        "pool_3 = MaxPooling2D(pool_size = (16, 16), strides = (1, 1), padding = \"valid\", name = \"pool_3\")(act_3)\n",
        "\n",
        "flat_1 = Flatten(name = \"flat_1\")(pool_3)\n",
        "\n",
        "dense_1 = Dense(128, activation = \"relu\", name = \"dense_1\")(flat_1)\n",
        "batch_1 = BatchNormalization(name = \"batch_1\")(dense_1)\n",
        "dense_2 = Dense(256, activation = \"relu\", name = \"dense_2\")(batch_1)\n",
        "batch_2 = BatchNormalization(name = \"batch_2\")(dense_2)\n",
        "dense_3 = Dense(64, activation = \"relu\", name = \"dense_3\")(batch_2)\n",
        "isPothole = Dense(2, activation = \"softmax\", name = \"pothole\")(dense_3)\n",
        "\n",
        "conv_4 = Conv2D(8, kernel_size = (4, 4), name = \"conv_4\")(pool_3)\n",
        "act_4 = Activation(\"relu\", name = \"act_4\")(conv_4)\n",
        "pool_4 = MaxPooling2D(pool_size = (16, 16), strides = (1, 1), padding = \"valid\", name = \"pool_4\")(act_4)\n",
        "\n",
        "# flat_2 = Flatten(name = \"flat_2\")(pool_4)\n",
        "\n",
        "# dense_4 = Dense(128, activation = \"relu\", name = \"dense_4\")(flat_2)\n",
        "# batch_3 = BatchNormalization(name = \"batch_3\")(dense_4)\n",
        "# add_1 = Add(name = \"add_1\")([batch_1, batch_3])\n",
        "\n",
        "# dense_5 = Dense(64, activation = \"relu\", name = \"dense_5\")(add_1)\n",
        "# batch_4 = BatchNormalization(name = \"batch_4\")(dense_5)\n",
        "# pothole_num = Dense(1, activation = \"relu\", name = \"pothole_number\")(batch_2)\n",
        "\n",
        "# conv_5 = Conv2D(16, kernel_size = (8, 8), name = \"conv_5\")(pool_4)\n",
        "# act_5 = Activation(\"relu\", name = \"act_5\")(conv_5)\n",
        "# pool_5 = MaxPooling2D(pool_size = (4, 4), strides = (1, 1), padding = \"valid\", name = \"pool_5\")(act_5)\n",
        "\n",
        "flat_2 = Flatten(name = \"flat_2\")(pool_4)\n",
        "\n",
        "dense_4 = Dense(128, activation = \"relu\", name = \"dense_4\")(flat_2)\n",
        "batch_5 = BatchNormalization(name = \"batch_5\")(dense_4)\n",
        "add_2 = Add(name = \"add_2\")([batch_1, batch_5])\n",
        "batch_6 = BatchNormalization(name = \"batch_6\")(add_2)\n",
        "dense_5 = Dense(256, activation = \"relu\", name = \"dense_5\")(batch_6)\n",
        "drop_2 = Dropout(rate = 0.4, name = \"drop_2\")(dense_5)\n",
        "# add_3 = Add(name = \"add_3\")([batch_7, batch_2])\n",
        "dense_6 = Dense(72, activation = \"relu\", name = \"dense_6\")(drop_2)\n",
        "batch_7 = BatchNormalization(name = \"batch_7\")(dense_6)\n",
        "dense_7 = Dense(64, activation = \"relu\", name = \"dense_7\")(batch_7)\n",
        "drop_3 = Dropout(rate = 0.4, name = \"drop_3\")(dense_7)\n",
        "dense_8 = Dense(32, activation = \"relu\", name = \"dense_8\")(drop_3)\n",
        "pothole_level = Dense(4, activation = \"softmax\", name = \"pothole_level\")(dense_8)\n",
        "\n",
        "model = Model(inputs = input_, outputs = [isPothole, pothole_level])\n",
        "\n",
        "opt = tf.keras.optimizers.Adam(learning_rate = 0.001)\n",
        "\n",
        "model.compile(\n",
        "              loss = {\n",
        "                  'pothole' : 'categorical_crossentropy',\n",
        "                #   'pothole_number' : 'categorical_crossentropy'\n",
        "                  'pothole_level' : 'categorical_crossentropy'\n",
        "              },\n",
        "              optimizer = opt,\n",
        "              metrics = ['accuracy']\n",
        ")\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "YIeo01tcvYBr",
        "outputId": "837f443f-df9d-45af-9d52-6f7e6ddf5b6b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "keras.utils.all_utils.plot_model(model, \"model.png\", show_shapes = True, show_layer_names = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "YvDD2rbx4eUf"
      },
      "outputs": [],
      "source": [
        "# class Logger(keras.callbacks.Callback):\n",
        "#   def on_epoch_end(self, epoch, logs=None):\n",
        "#     pothole_accuracy = logs.get('pothole_accuracy')\n",
        "#     pothole_number_accuracy = logs.get('pothole_number_accuracy')\n",
        "#     pothole_level_accuracy = logs.get('pothole_level_accuracy')\n",
        "\n",
        "#     val_pothole_accuracy = logs.get('val_pothole_accuracy')\n",
        "#     val_pothole_number_accuracy = logs.get('val_pothole_number_accuracy')\n",
        "#     val_pothole_level_accuracy = logs.get('val_pothole_level_accuracy')\n",
        "\n",
        "#     print('='*30, epoch + 1, '='*30)\n",
        "#     print(f'pothole_accuracy: {pothole_accuracy:.2f}, pothole_number_accuracy: {pothole_number_accuracy:.2f}, pothole_level_accuracy: {pothole_level_accuracy:.2f}')\n",
        "#     print(f'val_pothole_accuracy: {val_pothole_accuracy:.2f}, val_pothole_number_accuracy: {val_pothole_number_accuracy:.2f}, val_pothole_level_accuracy:{val_pothole_level_accuracy:.2f}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P3BLNzCo3Za5",
        "outputId": "ecddaf03-6447-4759-9834-f36122295959"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  # Remove the CWD from sys.path while we load stuff.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:15: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  from ipykernel import kernelapp as app\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "233/233 [==============================] - 218s 852ms/step - loss: 1.0437 - pothole_loss: 0.3847 - pothole_level_loss: 0.6591 - pothole_accuracy: 0.8444 - pothole_level_accuracy: 0.7750 - val_loss: 1.0759 - val_pothole_loss: 0.5077 - val_pothole_level_loss: 0.5682 - val_pothole_accuracy: 0.7780 - val_pothole_level_accuracy: 0.8108\n",
            "Epoch 2/50\n",
            "233/233 [==============================] - 196s 840ms/step - loss: 1.0010 - pothole_loss: 0.3797 - pothole_level_loss: 0.6213 - pothole_accuracy: 0.8412 - pothole_level_accuracy: 0.7857 - val_loss: 1.1029 - val_pothole_loss: 0.4711 - val_pothole_level_loss: 0.6318 - val_pothole_accuracy: 0.8140 - val_pothole_level_accuracy: 0.7816\n",
            "Epoch 3/50\n",
            "233/233 [==============================] - 194s 832ms/step - loss: 0.9951 - pothole_loss: 0.3698 - pothole_level_loss: 0.6252 - pothole_accuracy: 0.8407 - pothole_level_accuracy: 0.7718 - val_loss: 1.2117 - val_pothole_loss: 0.5249 - val_pothole_level_loss: 0.6869 - val_pothole_accuracy: 0.7559 - val_pothole_level_accuracy: 0.7816\n",
            "Epoch 4/50\n",
            "233/233 [==============================] - 194s 832ms/step - loss: 0.8828 - pothole_loss: 0.3123 - pothole_level_loss: 0.5705 - pothole_accuracy: 0.8775 - pothole_level_accuracy: 0.8044 - val_loss: 1.1795 - val_pothole_loss: 0.4216 - val_pothole_level_loss: 0.7580 - val_pothole_accuracy: 0.8167 - val_pothole_level_accuracy: 0.6969\n",
            "Epoch 5/50\n",
            "233/233 [==============================] - 195s 836ms/step - loss: 0.7755 - pothole_loss: 0.2660 - pothole_level_loss: 0.5095 - pothole_accuracy: 0.9000 - pothole_level_accuracy: 0.8344 - val_loss: 8.5472 - val_pothole_loss: 1.9701 - val_pothole_level_loss: 6.5771 - val_pothole_accuracy: 0.7326 - val_pothole_level_accuracy: 0.7816\n",
            "Epoch 6/50\n",
            "233/233 [==============================] - 195s 835ms/step - loss: 0.8471 - pothole_loss: 0.3041 - pothole_level_loss: 0.5430 - pothole_accuracy: 0.8813 - pothole_level_accuracy: 0.8200 - val_loss: 1.5874 - val_pothole_loss: 0.4462 - val_pothole_level_loss: 1.1411 - val_pothole_accuracy: 0.8104 - val_pothole_level_accuracy: 0.7816\n",
            "Epoch 7/50\n",
            "233/233 [==============================] - 195s 838ms/step - loss: 0.7564 - pothole_loss: 0.2606 - pothole_level_loss: 0.4958 - pothole_accuracy: 0.9022 - pothole_level_accuracy: 0.8389 - val_loss: 2.6247 - val_pothole_loss: 0.4619 - val_pothole_level_loss: 2.1628 - val_pothole_accuracy: 0.8002 - val_pothole_level_accuracy: 0.7817\n",
            "Epoch 8/50\n",
            "233/233 [==============================] - 193s 827ms/step - loss: 0.7702 - pothole_loss: 0.2675 - pothole_level_loss: 0.5027 - pothole_accuracy: 0.8974 - pothole_level_accuracy: 0.8308 - val_loss: 1.1113 - val_pothole_loss: 0.4112 - val_pothole_level_loss: 0.7000 - val_pothole_accuracy: 0.8054 - val_pothole_level_accuracy: 0.7821\n",
            "Epoch 9/50\n",
            "233/233 [==============================] - 192s 826ms/step - loss: 0.7066 - pothole_loss: 0.2377 - pothole_level_loss: 0.4689 - pothole_accuracy: 0.9088 - pothole_level_accuracy: 0.8464 - val_loss: 1.2804 - val_pothole_loss: 0.3755 - val_pothole_level_loss: 0.9050 - val_pothole_accuracy: 0.8658 - val_pothole_level_accuracy: 0.7833\n",
            "Epoch 10/50\n",
            "233/233 [==============================] - 193s 826ms/step - loss: 0.6777 - pothole_loss: 0.2259 - pothole_level_loss: 0.4518 - pothole_accuracy: 0.9147 - pothole_level_accuracy: 0.8530 - val_loss: 0.7732 - val_pothole_loss: 0.2809 - val_pothole_level_loss: 0.4923 - val_pothole_accuracy: 0.9017 - val_pothole_level_accuracy: 0.8340\n",
            "Epoch 11/50\n",
            "233/233 [==============================] - 192s 824ms/step - loss: 0.6580 - pothole_loss: 0.2151 - pothole_level_loss: 0.4430 - pothole_accuracy: 0.9182 - pothole_level_accuracy: 0.8560 - val_loss: 1.1141 - val_pothole_loss: 0.4151 - val_pothole_level_loss: 0.6990 - val_pothole_accuracy: 0.8328 - val_pothole_level_accuracy: 0.7610\n",
            "Epoch 12/50\n",
            "233/233 [==============================] - 192s 825ms/step - loss: 0.6387 - pothole_loss: 0.2057 - pothole_level_loss: 0.4329 - pothole_accuracy: 0.9224 - pothole_level_accuracy: 0.8587 - val_loss: 1.0692 - val_pothole_loss: 0.5473 - val_pothole_level_loss: 0.5219 - val_pothole_accuracy: 0.8031 - val_pothole_level_accuracy: 0.8258\n",
            "Epoch 13/50\n",
            "233/233 [==============================] - 192s 824ms/step - loss: 0.6211 - pothole_loss: 0.1970 - pothole_level_loss: 0.4241 - pothole_accuracy: 0.9247 - pothole_level_accuracy: 0.8602 - val_loss: 1.4644 - val_pothole_loss: 0.6591 - val_pothole_level_loss: 0.8053 - val_pothole_accuracy: 0.7173 - val_pothole_level_accuracy: 0.6894\n",
            "Epoch 14/50\n",
            "233/233 [==============================] - 192s 824ms/step - loss: 0.6014 - pothole_loss: 0.1860 - pothole_level_loss: 0.4155 - pothole_accuracy: 0.9277 - pothole_level_accuracy: 0.8618 - val_loss: 1.1594 - val_pothole_loss: 0.4657 - val_pothole_level_loss: 0.6936 - val_pothole_accuracy: 0.8659 - val_pothole_level_accuracy: 0.8159\n",
            "Epoch 15/50\n",
            "233/233 [==============================] - 192s 824ms/step - loss: 0.5866 - pothole_loss: 0.1765 - pothole_level_loss: 0.4102 - pothole_accuracy: 0.9338 - pothole_level_accuracy: 0.8643 - val_loss: 1.0786 - val_pothole_loss: 0.4569 - val_pothole_level_loss: 0.6217 - val_pothole_accuracy: 0.8220 - val_pothole_level_accuracy: 0.7880\n",
            "Epoch 16/50\n",
            "233/233 [==============================] - 193s 826ms/step - loss: 0.5695 - pothole_loss: 0.1674 - pothole_level_loss: 0.4022 - pothole_accuracy: 0.9351 - pothole_level_accuracy: 0.8648 - val_loss: 3.2865 - val_pothole_loss: 0.5561 - val_pothole_level_loss: 2.7304 - val_pothole_accuracy: 0.8603 - val_pothole_level_accuracy: 0.7816\n",
            "Epoch 17/50\n",
            "233/233 [==============================] - 193s 828ms/step - loss: 0.5489 - pothole_loss: 0.1545 - pothole_level_loss: 0.3944 - pothole_accuracy: 0.9402 - pothole_level_accuracy: 0.8674 - val_loss: 0.9897 - val_pothole_loss: 0.4497 - val_pothole_level_loss: 0.5400 - val_pothole_accuracy: 0.8748 - val_pothole_level_accuracy: 0.8216\n",
            "Epoch 18/50\n",
            "233/233 [==============================] - 193s 826ms/step - loss: 0.5351 - pothole_loss: 0.1479 - pothole_level_loss: 0.3872 - pothole_accuracy: 0.9438 - pothole_level_accuracy: 0.8682 - val_loss: 0.7906 - val_pothole_loss: 0.3228 - val_pothole_level_loss: 0.4678 - val_pothole_accuracy: 0.8812 - val_pothole_level_accuracy: 0.8526\n",
            "Epoch 19/50\n",
            "233/233 [==============================] - 193s 826ms/step - loss: 0.5225 - pothole_loss: 0.1420 - pothole_level_loss: 0.3805 - pothole_accuracy: 0.9446 - pothole_level_accuracy: 0.8692 - val_loss: 1.5827 - val_pothole_loss: 0.6505 - val_pothole_level_loss: 0.9323 - val_pothole_accuracy: 0.8021 - val_pothole_level_accuracy: 0.7831\n",
            "Epoch 20/50\n",
            "233/233 [==============================] - 192s 825ms/step - loss: 0.5375 - pothole_loss: 0.1535 - pothole_level_loss: 0.3839 - pothole_accuracy: 0.9394 - pothole_level_accuracy: 0.8690 - val_loss: 2.7007 - val_pothole_loss: 1.3998 - val_pothole_level_loss: 1.3009 - val_pothole_accuracy: 0.7134 - val_pothole_level_accuracy: 0.7823\n",
            "Epoch 21/50\n",
            "233/233 [==============================] - 192s 823ms/step - loss: 0.5094 - pothole_loss: 0.1350 - pothole_level_loss: 0.3744 - pothole_accuracy: 0.9467 - pothole_level_accuracy: 0.8709 - val_loss: 2.4836 - val_pothole_loss: 1.0587 - val_pothole_level_loss: 1.4249 - val_pothole_accuracy: 0.7454 - val_pothole_level_accuracy: 0.7823\n",
            "Epoch 22/50\n",
            "233/233 [==============================] - 192s 825ms/step - loss: 0.4918 - pothole_loss: 0.1268 - pothole_level_loss: 0.3650 - pothole_accuracy: 0.9513 - pothole_level_accuracy: 0.8722 - val_loss: 1.0034 - val_pothole_loss: 0.5241 - val_pothole_level_loss: 0.4794 - val_pothole_accuracy: 0.8296 - val_pothole_level_accuracy: 0.8500\n",
            "Epoch 23/50\n",
            "233/233 [==============================] - 192s 824ms/step - loss: 0.4709 - pothole_loss: 0.1098 - pothole_level_loss: 0.3612 - pothole_accuracy: 0.9576 - pothole_level_accuracy: 0.8733 - val_loss: 1.0424 - val_pothole_loss: 0.5776 - val_pothole_level_loss: 0.4647 - val_pothole_accuracy: 0.8241 - val_pothole_level_accuracy: 0.8621\n",
            "Epoch 24/50\n",
            "233/233 [==============================] - 192s 822ms/step - loss: 0.4558 - pothole_loss: 0.1037 - pothole_level_loss: 0.3522 - pothole_accuracy: 0.9604 - pothole_level_accuracy: 0.8766 - val_loss: 1.1253 - val_pothole_loss: 0.5328 - val_pothole_level_loss: 0.5926 - val_pothole_accuracy: 0.7999 - val_pothole_level_accuracy: 0.8268\n",
            "Epoch 25/50\n",
            "233/233 [==============================] - 192s 822ms/step - loss: 0.4379 - pothole_loss: 0.0914 - pothole_level_loss: 0.3465 - pothole_accuracy: 0.9651 - pothole_level_accuracy: 0.8766 - val_loss: 1.4510 - val_pothole_loss: 0.7428 - val_pothole_level_loss: 0.7082 - val_pothole_accuracy: 0.8740 - val_pothole_level_accuracy: 0.8389\n",
            "Epoch 26/50\n",
            "233/233 [==============================] - 192s 822ms/step - loss: 0.4237 - pothole_loss: 0.0851 - pothole_level_loss: 0.3386 - pothole_accuracy: 0.9666 - pothole_level_accuracy: 0.8774 - val_loss: 1.2178 - val_pothole_loss: 0.5792 - val_pothole_level_loss: 0.6386 - val_pothole_accuracy: 0.8842 - val_pothole_level_accuracy: 0.8242\n",
            "Epoch 27/50\n",
            "233/233 [==============================] - 191s 821ms/step - loss: 0.4192 - pothole_loss: 0.0810 - pothole_level_loss: 0.3382 - pothole_accuracy: 0.9675 - pothole_level_accuracy: 0.8769 - val_loss: 2.4035 - val_pothole_loss: 1.3062 - val_pothole_level_loss: 1.0973 - val_pothole_accuracy: 0.7928 - val_pothole_level_accuracy: 0.7929\n",
            "Epoch 28/50\n",
            "233/233 [==============================] - 191s 820ms/step - loss: 0.3943 - pothole_loss: 0.0695 - pothole_level_loss: 0.3248 - pothole_accuracy: 0.9745 - pothole_level_accuracy: 0.8822 - val_loss: 2.2314 - val_pothole_loss: 1.4663 - val_pothole_level_loss: 0.7652 - val_pothole_accuracy: 0.8119 - val_pothole_level_accuracy: 0.8082\n",
            "Epoch 29/50\n",
            "233/233 [==============================] - 191s 821ms/step - loss: 0.3893 - pothole_loss: 0.0658 - pothole_level_loss: 0.3235 - pothole_accuracy: 0.9754 - pothole_level_accuracy: 0.8794 - val_loss: 2.4098 - val_pothole_loss: 1.3360 - val_pothole_level_loss: 1.0738 - val_pothole_accuracy: 0.7787 - val_pothole_level_accuracy: 0.8029\n",
            "Epoch 30/50\n",
            "233/233 [==============================] - 191s 821ms/step - loss: 0.3824 - pothole_loss: 0.0637 - pothole_level_loss: 0.3187 - pothole_accuracy: 0.9753 - pothole_level_accuracy: 0.8808 - val_loss: 4.8679 - val_pothole_loss: 2.6385 - val_pothole_level_loss: 2.2295 - val_pothole_accuracy: 0.7358 - val_pothole_level_accuracy: 0.7811\n",
            "Epoch 31/50\n",
            "233/233 [==============================] - 191s 819ms/step - loss: 0.3730 - pothole_loss: 0.0579 - pothole_level_loss: 0.3151 - pothole_accuracy: 0.9783 - pothole_level_accuracy: 0.8835 - val_loss: 1.1077 - val_pothole_loss: 0.4976 - val_pothole_level_loss: 0.6101 - val_pothole_accuracy: 0.8686 - val_pothole_level_accuracy: 0.8362\n",
            "Epoch 32/50\n",
            "233/233 [==============================] - 191s 819ms/step - loss: 0.3595 - pothole_loss: 0.0523 - pothole_level_loss: 0.3072 - pothole_accuracy: 0.9802 - pothole_level_accuracy: 0.8844 - val_loss: 1.3071 - val_pothole_loss: 0.6300 - val_pothole_level_loss: 0.6771 - val_pothole_accuracy: 0.8671 - val_pothole_level_accuracy: 0.8131\n",
            "Epoch 33/50\n",
            "233/233 [==============================] - 191s 821ms/step - loss: 0.3506 - pothole_loss: 0.0444 - pothole_level_loss: 0.3062 - pothole_accuracy: 0.9839 - pothole_level_accuracy: 0.8846 - val_loss: 2.6391 - val_pothole_loss: 0.4782 - val_pothole_level_loss: 2.1609 - val_pothole_accuracy: 0.8654 - val_pothole_level_accuracy: 0.8118\n",
            "Epoch 34/50\n",
            "233/233 [==============================] - 191s 818ms/step - loss: 0.3437 - pothole_loss: 0.0432 - pothole_level_loss: 0.3004 - pothole_accuracy: 0.9838 - pothole_level_accuracy: 0.8851 - val_loss: 1.4491 - val_pothole_loss: 0.7599 - val_pothole_level_loss: 0.6892 - val_pothole_accuracy: 0.8572 - val_pothole_level_accuracy: 0.7978\n",
            "Epoch 35/50\n",
            "233/233 [==============================] - 191s 818ms/step - loss: 0.3334 - pothole_loss: 0.0404 - pothole_level_loss: 0.2930 - pothole_accuracy: 0.9843 - pothole_level_accuracy: 0.8880 - val_loss: 1.9517 - val_pothole_loss: 1.0375 - val_pothole_level_loss: 0.9143 - val_pothole_accuracy: 0.7868 - val_pothole_level_accuracy: 0.8003\n",
            "Epoch 36/50\n",
            "233/233 [==============================] - 191s 818ms/step - loss: 0.3266 - pothole_loss: 0.0383 - pothole_level_loss: 0.2883 - pothole_accuracy: 0.9850 - pothole_level_accuracy: 0.8872 - val_loss: 2.6288 - val_pothole_loss: 1.5220 - val_pothole_level_loss: 1.1069 - val_pothole_accuracy: 0.7004 - val_pothole_level_accuracy: 0.7871\n",
            "Epoch 37/50\n",
            "233/233 [==============================] - 190s 814ms/step - loss: 0.3217 - pothole_loss: 0.0378 - pothole_level_loss: 0.2839 - pothole_accuracy: 0.9860 - pothole_level_accuracy: 0.8895 - val_loss: 35.5771 - val_pothole_loss: 0.5985 - val_pothole_level_loss: 34.9786 - val_pothole_accuracy: 0.8781 - val_pothole_level_accuracy: 0.7890\n",
            "Epoch 38/50\n",
            "233/233 [==============================] - 189s 812ms/step - loss: 0.3829 - pothole_loss: 0.0778 - pothole_level_loss: 0.3051 - pothole_accuracy: 0.9751 - pothole_level_accuracy: 0.8846 - val_loss: 9.4744 - val_pothole_loss: 4.3047 - val_pothole_level_loss: 5.1697 - val_pothole_accuracy: 0.7740 - val_pothole_level_accuracy: 0.7816\n",
            "Epoch 39/50\n",
            "233/233 [==============================] - 187s 803ms/step - loss: 0.3826 - pothole_loss: 0.0782 - pothole_level_loss: 0.3044 - pothole_accuracy: 0.9704 - pothole_level_accuracy: 0.8844 - val_loss: 1.2583 - val_pothole_loss: 0.7119 - val_pothole_level_loss: 0.5464 - val_pothole_accuracy: 0.8993 - val_pothole_level_accuracy: 0.8419\n",
            "Epoch 40/50\n",
            "233/233 [==============================] - 188s 804ms/step - loss: 0.3154 - pothole_loss: 0.0326 - pothole_level_loss: 0.2829 - pothole_accuracy: 0.9883 - pothole_level_accuracy: 0.8908 - val_loss: 1.3722 - val_pothole_loss: 0.7570 - val_pothole_level_loss: 0.6152 - val_pothole_accuracy: 0.8704 - val_pothole_level_accuracy: 0.8601\n",
            "Epoch 41/50\n",
            "233/233 [==============================] - 188s 805ms/step - loss: 0.2959 - pothole_loss: 0.0257 - pothole_level_loss: 0.2703 - pothole_accuracy: 0.9907 - pothole_level_accuracy: 0.8950 - val_loss: 1.3140 - val_pothole_loss: 0.5957 - val_pothole_level_loss: 0.7184 - val_pothole_accuracy: 0.8973 - val_pothole_level_accuracy: 0.8476\n",
            "Epoch 42/50\n",
            "233/233 [==============================] - 187s 804ms/step - loss: 0.2955 - pothole_loss: 0.0275 - pothole_level_loss: 0.2681 - pothole_accuracy: 0.9901 - pothole_level_accuracy: 0.8940 - val_loss: 1.1992 - val_pothole_loss: 0.6147 - val_pothole_level_loss: 0.5845 - val_pothole_accuracy: 0.9043 - val_pothole_level_accuracy: 0.8511\n",
            "Epoch 43/50\n",
            "233/233 [==============================] - 187s 803ms/step - loss: 0.2864 - pothole_loss: 0.0244 - pothole_level_loss: 0.2620 - pothole_accuracy: 0.9913 - pothole_level_accuracy: 0.8961 - val_loss: 1.1857 - val_pothole_loss: 0.6452 - val_pothole_level_loss: 0.5405 - val_pothole_accuracy: 0.8868 - val_pothole_level_accuracy: 0.8394\n",
            "Epoch 44/50\n",
            "233/233 [==============================] - 187s 804ms/step - loss: 0.2875 - pothole_loss: 0.0267 - pothole_level_loss: 0.2608 - pothole_accuracy: 0.9909 - pothole_level_accuracy: 0.8965 - val_loss: 3.1637 - val_pothole_loss: 1.5582 - val_pothole_level_loss: 1.6055 - val_pothole_accuracy: 0.8092 - val_pothole_level_accuracy: 0.7958\n",
            "Epoch 45/50\n",
            "233/233 [==============================] - 188s 805ms/step - loss: 0.2751 - pothole_loss: 0.0232 - pothole_level_loss: 0.2519 - pothole_accuracy: 0.9915 - pothole_level_accuracy: 0.8999 - val_loss: 1.4068 - val_pothole_loss: 0.7167 - val_pothole_level_loss: 0.6901 - val_pothole_accuracy: 0.8783 - val_pothole_level_accuracy: 0.8193\n",
            "Epoch 46/50\n",
            "233/233 [==============================] - 187s 803ms/step - loss: 0.2876 - pothole_loss: 0.0321 - pothole_level_loss: 0.2554 - pothole_accuracy: 0.9883 - pothole_level_accuracy: 0.8975 - val_loss: 1.5068 - val_pothole_loss: 0.8843 - val_pothole_level_loss: 0.6225 - val_pothole_accuracy: 0.8357 - val_pothole_level_accuracy: 0.7883\n",
            "Epoch 47/50\n",
            "233/233 [==============================] - 188s 804ms/step - loss: 0.3022 - pothole_loss: 0.0450 - pothole_level_loss: 0.2572 - pothole_accuracy: 0.9838 - pothole_level_accuracy: 0.8985 - val_loss: 1.4715 - val_pothole_loss: 0.8426 - val_pothole_level_loss: 0.6289 - val_pothole_accuracy: 0.8558 - val_pothole_level_accuracy: 0.8161\n",
            "Epoch 48/50\n",
            "233/233 [==============================] - 187s 804ms/step - loss: 0.2793 - pothole_loss: 0.0326 - pothole_level_loss: 0.2468 - pothole_accuracy: 0.9880 - pothole_level_accuracy: 0.9002 - val_loss: 1.3489 - val_pothole_loss: 0.6786 - val_pothole_level_loss: 0.6703 - val_pothole_accuracy: 0.8833 - val_pothole_level_accuracy: 0.8146\n",
            "Epoch 49/50\n",
            "233/233 [==============================] - 188s 804ms/step - loss: 0.2636 - pothole_loss: 0.0204 - pothole_level_loss: 0.2432 - pothole_accuracy: 0.9928 - pothole_level_accuracy: 0.9032 - val_loss: 1.4927 - val_pothole_loss: 0.7740 - val_pothole_level_loss: 0.7188 - val_pothole_accuracy: 0.8601 - val_pothole_level_accuracy: 0.8224\n",
            "Epoch 50/50\n",
            "233/233 [==============================] - 189s 809ms/step - loss: 0.2559 - pothole_loss: 0.0208 - pothole_level_loss: 0.2351 - pothole_accuracy: 0.9926 - pothole_level_accuracy: 0.9057 - val_loss: 1.7482 - val_pothole_loss: 0.7354 - val_pothole_level_loss: 1.0128 - val_pothole_accuracy: 0.8993 - val_pothole_level_accuracy: 0.8170\n"
          ]
        }
      ],
      "source": [
        "_ = model.fit_generator(generator=my_training_batch_generator,\n",
        "                   steps_per_epoch = int(21000 // batch_size),\n",
        "                   epochs = 50,\n",
        "                #    verbose = False,\n",
        "                   validation_data = my_validation_batch_generator,\n",
        "                #    callbacks = [\n",
        "                #                 Logger(),\n",
        "                #                 keras.callbacks.TensorBoard(log_dir = './logs')\n",
        "                #                 ],\n",
        "                   validation_steps = int(9000 // batch_size))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "MInT7A9T4Y5w"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "GTXri0-6QHQI"
      },
      "outputs": [],
      "source": [
        "model.save(\"/content/gdrive/MyDrive/pothole_detection_model_prototype_4_adam.h5\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
        "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
        "\n",
        "tflite_model_quant = converter.convert()\n",
        "open(\"/content/gdrive/MyDrive/pothole_detection_model_prototype_4_adam_lite.tflite\" , \"wb\").write(tflite_model_quant)"
      ],
      "metadata": {
        "id": "5TOB4PXo7InP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "082b7e5f-32b8-428f-f722-4ba2708068a3"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Function `_wrapped_model` contains input name(s) Input Layer with unsupported characters which will be renamed to placeholder in the SavedModel.\n",
            "WARNING:absl:Buffer deduplication procedure will be skipped when flatbuffer library is not properly loaded\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "67709296"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import tensorflow as tf\n",
        "\n",
        "# model = tf.keras.models.load_model('/content/gdrive/MyDrive/A_I/pothole_detection_model_prototype_1_adam.h5')\n",
        "# converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
        "# tflite_model = converter.convert()\n",
        "# open(\"/content/gdrive/MyDrive/A_I/pothole_detection_model_prototype_1_adam.tflite\", \"wb\").write(tflite_model)"
      ],
      "metadata": {
        "id": "sfm6EsUsqvt4"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "PxUQg_rz4G-1"
      },
      "execution_count": 24,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "Pothole_Detection_adam.ipynb",
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}