Pothole_Detection_sgd_prototype_2.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": "2953f891-c5ca-4a60-812e-0638b4ba48ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OVNTgKpZr8LA",
        "outputId": "ea1ef345-c81b-4e17-9c47-2ca9b6f25e56"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Pothole-Detection'...\n",
            "remote: Enumerating objects: 29926, done.\u001b[K\n",
            "remote: Counting objects: 100% (33/33), done.\u001b[K\n",
            "remote: Compressing objects: 100% (28/28), done.\u001b[K\n",
            "remote: Total 29926 (delta 7), reused 22 (delta 4), pack-reused 29893\u001b[K\n",
            "Receiving objects: 100% (29926/29926), 1.14 GiB | 25.84 MiB/s, done.\n",
            "Resolving deltas: 100% (1426/1426), done.\n",
            "Checking out files: 100% (30004/30004), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone \"https://github.com/srajan-kiyotaka/Pothole-Detection.git\" "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "z9QvRrgEm7Fv",
        "outputId": "404a6f65-c1bc-4584-9efc-0d9c17e5fb11"
      },
      "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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              "      <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",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>pothole_image_1299</td>\n",
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              "      <td>3</td>\n",
              "      <td>B</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>1</th>\n",
              "      <td>pothole_image_10064</td>\n",
              "      <td>1</td>\n",
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              "      <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",
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              "    <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",
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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",
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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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DFVSPuZFm8Ex",
        "outputId": "5be7bb3c-0129-419f-8e8e-52d37277c76d"
      },
      "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": null,
      "metadata": {
        "id": "E-HnvxGFnFeC"
      },
      "outputs": [],
      "source": [
        "features = features.drop('Unnamed: 4', axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "2bb-uXLT2CSX",
        "outputId": "6b1d1354-6c4d-4eac-b2a3-f24a8e92e9a9"
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      "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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              "      <th>4</th>\n",
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              "      <th>5</th>\n",
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              "      <td>A</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>pothole_image_10119</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>A</td>\n",
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              "    <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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          "metadata": {},
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      "source": [
        "features.head(10)"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UXRkxcOl2EwW",
        "outputId": "800d3c80-3cbe-498d-94f1-ce2acea7b969"
      },
      "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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OhnJtc4F-IC2",
        "outputId": "ffb7a8b6-e472-4d6d-b21f-65feb5779b84"
      },
      "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": null,
      "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": null,
      "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": "1c11fb31-db0c-4c00-d79c-f74e71ffb3a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vJnzrZS9dRSY",
        "outputId": "73c7009f-adb2-42bc-d87a-8c7577009062"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(21000, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "y_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": null,
      "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": null,
      "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": null,
      "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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j3oMzubsTJQU",
        "outputId": "d17b2b16-65bc-43a3-d872-87114f850526"
      },
      "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, 185, 185, 32  8224        ['pool_1[0][0]']                 \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " act_2 (Activation)             (None, 185, 185, 32  0           ['conv_2[0][0]']                 \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " pool_2 (MaxPooling2D)          (None, 178, 178, 32  0           ['act_2[0][0]']                  \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv_3 (Conv2D)                (None, 175, 175, 16  8208        ['pool_2[0][0]']                 \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " act_3 (Activation)             (None, 175, 175, 16  0           ['conv_3[0][0]']                 \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " pool_3 (MaxPooling2D)          (None, 160, 160, 16  0           ['act_3[0][0]']                  \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv_4 (Conv2D)                (None, 157, 157, 8)  2056        ['pool_3[0][0]']                 \n",
            "                                                                                                  \n",
            " act_4 (Activation)             (None, 157, 157, 8)  0           ['conv_4[0][0]']                 \n",
            "                                                                                                  \n",
            " pool_4 (MaxPooling2D)          (None, 150, 150, 8)  0           ['act_4[0][0]']                  \n",
            "                                                                                                  \n",
            " flat_1 (Flatten)               (None, 409600)       0           ['pool_3[0][0]']                 \n",
            "                                                                                                  \n",
            " flat_2 (Flatten)               (None, 180000)       0           ['pool_4[0][0]']                 \n",
            "                                                                                                  \n",
            " dense_1 (Dense)                (None, 128)          52428928    ['flat_1[0][0]']                 \n",
            "                                                                                                  \n",
            " dense_4 (Dense)                (None, 128)          23040128    ['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, 64)           8256        ['batch_6[0][0]']                \n",
            "                                                                                                  \n",
            " drop_2 (Dropout)               (None, 64)           0           ['dense_5[0][0]']                \n",
            "                                                                                                  \n",
            " dense_6 (Dense)                (None, 32)           2080        ['drop_2[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_7 (BatchNormalization)   (None, 32)           128         ['dense_6[0][0]']                \n",
            "                                                                                                  \n",
            " dense_2 (Dense)                (None, 64)           8256        ['batch_1[0][0]']                \n",
            "                                                                                                  \n",
            " dense_7 (Dense)                (None, 16)           528         ['batch_7[0][0]']                \n",
            "                                                                                                  \n",
            " batch_2 (BatchNormalization)   (None, 64)           256         ['dense_2[0][0]']                \n",
            "                                                                                                  \n",
            " drop_3 (Dropout)               (None, 16)           0           ['dense_7[0][0]']                \n",
            "                                                                                                  \n",
            " dense_3 (Dense)                (None, 32)           2080        ['batch_2[0][0]']                \n",
            "                                                                                                  \n",
            " dense_8 (Dense)                (None, 8)            136         ['drop_3[0][0]']                 \n",
            "                                                                                                  \n",
            " pothole (Dense)                (None, 2)            66          ['dense_3[0][0]']                \n",
            "                                                                                                  \n",
            " pothole_level (Dense)          (None, 4)            36          ['dense_8[0][0]']                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 75,515,062\n",
            "Trainable params: 75,514,102\n",
            "Non-trainable params: 960\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 = (2, 2), 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 = (4, 4), 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(64, activation = \"relu\", name = \"dense_2\")(batch_1)\n",
        "batch_2 = BatchNormalization(name = \"batch_2\")(dense_2)\n",
        "dense_3 = Dense(32, 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 = (8, 8), 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(64, 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(32, activation = \"relu\", name = \"dense_6\")(drop_2)\n",
        "batch_7 = BatchNormalization(name = \"batch_7\")(dense_6)\n",
        "dense_7 = Dense(16, activation = \"relu\", name = \"dense_7\")(batch_7)\n",
        "drop_3 = Dropout(rate = 0.4, name = \"drop_3\")(dense_7)\n",
        "dense_8 = Dense(8, 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.SGD(learning_rate = 0.002)\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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "YIeo01tcvYBr",
        "outputId": "a6c6d15d-83b2-453d-cfde-3cb44a761985"
      },
      "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": null,
      "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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P3BLNzCo3Za5",
        "outputId": "6d5a467c-3e30-4788-d7c0-2f18cd11460f"
      },
      "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 [==============================] - 203s 800ms/step - loss: 1.8692 - pothole_loss: 0.3759 - pothole_level_loss: 1.4933 - pothole_accuracy: 0.8459 - pothole_level_accuracy: 0.3253 - val_loss: 1.7826 - val_pothole_loss: 0.6581 - val_pothole_level_loss: 1.1245 - val_pothole_accuracy: 0.6416 - val_pothole_level_accuracy: 0.7713\n",
            "Epoch 2/50\n",
            "233/233 [==============================] - 191s 818ms/step - loss: 1.3490 - pothole_loss: 0.2974 - pothole_level_loss: 1.0515 - pothole_accuracy: 0.8888 - pothole_level_accuracy: 0.6760 - val_loss: 1.5473 - val_pothole_loss: 0.6361 - val_pothole_level_loss: 0.9112 - val_pothole_accuracy: 0.6258 - val_pothole_level_accuracy: 0.7713\n",
            "Epoch 3/50\n",
            "233/233 [==============================] - 190s 816ms/step - loss: 1.1159 - pothole_loss: 0.2736 - pothole_level_loss: 0.8423 - pothole_accuracy: 0.8992 - pothole_level_accuracy: 0.7705 - val_loss: 1.0002 - val_pothole_loss: 0.3357 - val_pothole_level_loss: 0.6644 - val_pothole_accuracy: 0.8774 - val_pothole_level_accuracy: 0.7736\n",
            "Epoch 4/50\n",
            "233/233 [==============================] - 191s 820ms/step - loss: 0.9815 - pothole_loss: 0.2575 - pothole_level_loss: 0.7240 - pothole_accuracy: 0.9049 - pothole_level_accuracy: 0.7980 - val_loss: 1.3359 - val_pothole_loss: 0.5163 - val_pothole_level_loss: 0.8196 - val_pothole_accuracy: 0.7448 - val_pothole_level_accuracy: 0.7720\n",
            "Epoch 5/50\n",
            "233/233 [==============================] - 191s 821ms/step - loss: 0.9074 - pothole_loss: 0.2489 - pothole_level_loss: 0.6585 - pothole_accuracy: 0.9094 - pothole_level_accuracy: 0.8076 - val_loss: 3.1426 - val_pothole_loss: 0.6410 - val_pothole_level_loss: 2.5016 - val_pothole_accuracy: 0.7623 - val_pothole_level_accuracy: 0.1334\n",
            "Epoch 6/50\n",
            "233/233 [==============================] - 192s 823ms/step - loss: 0.8775 - pothole_loss: 0.2546 - pothole_level_loss: 0.6229 - pothole_accuracy: 0.9051 - pothole_level_accuracy: 0.8114 - val_loss: 1.4531 - val_pothole_loss: 0.8791 - val_pothole_level_loss: 0.5740 - val_pothole_accuracy: 0.5384 - val_pothole_level_accuracy: 0.8306\n",
            "Epoch 7/50\n",
            "233/233 [==============================] - 192s 824ms/step - loss: 0.8166 - pothole_loss: 0.2336 - pothole_level_loss: 0.5830 - pothole_accuracy: 0.9141 - pothole_level_accuracy: 0.8223 - val_loss: 0.8979 - val_pothole_loss: 0.3241 - val_pothole_level_loss: 0.5738 - val_pothole_accuracy: 0.8926 - val_pothole_level_accuracy: 0.8186\n",
            "Epoch 8/50\n",
            "233/233 [==============================] - 192s 823ms/step - loss: 0.7896 - pothole_loss: 0.2225 - pothole_level_loss: 0.5671 - pothole_accuracy: 0.9169 - pothole_level_accuracy: 0.8253 - val_loss: 0.7517 - val_pothole_loss: 0.2492 - val_pothole_level_loss: 0.5025 - val_pothole_accuracy: 0.9066 - val_pothole_level_accuracy: 0.8471\n",
            "Epoch 9/50\n",
            "233/233 [==============================] - 192s 823ms/step - loss: 0.7675 - pothole_loss: 0.2161 - pothole_level_loss: 0.5515 - pothole_accuracy: 0.9193 - pothole_level_accuracy: 0.8291 - val_loss: 0.7968 - val_pothole_loss: 0.2793 - val_pothole_level_loss: 0.5175 - val_pothole_accuracy: 0.8957 - val_pothole_level_accuracy: 0.8431\n",
            "Epoch 10/50\n",
            "233/233 [==============================] - 192s 824ms/step - loss: 0.7513 - pothole_loss: 0.2128 - pothole_level_loss: 0.5385 - pothole_accuracy: 0.9215 - pothole_level_accuracy: 0.8309 - val_loss: 1.0516 - val_pothole_loss: 0.4974 - val_pothole_level_loss: 0.5541 - val_pothole_accuracy: 0.7536 - val_pothole_level_accuracy: 0.8443\n",
            "Epoch 11/50\n",
            "233/233 [==============================] - 192s 824ms/step - loss: 0.7326 - pothole_loss: 0.2045 - pothole_level_loss: 0.5281 - pothole_accuracy: 0.9240 - pothole_level_accuracy: 0.8356 - val_loss: 1.5996 - val_pothole_loss: 0.3959 - val_pothole_level_loss: 1.2036 - val_pothole_accuracy: 0.8760 - val_pothole_level_accuracy: 0.5901\n",
            "Epoch 12/50\n",
            "233/233 [==============================] - 194s 830ms/step - loss: 0.7113 - pothole_loss: 0.1955 - pothole_level_loss: 0.5158 - pothole_accuracy: 0.9277 - pothole_level_accuracy: 0.8357 - val_loss: 0.7738 - val_pothole_loss: 0.2923 - val_pothole_level_loss: 0.4815 - val_pothole_accuracy: 0.8883 - val_pothole_level_accuracy: 0.8517\n",
            "Epoch 13/50\n",
            "233/233 [==============================] - 192s 825ms/step - loss: 0.6954 - pothole_loss: 0.1866 - pothole_level_loss: 0.5088 - pothole_accuracy: 0.9305 - pothole_level_accuracy: 0.8397 - val_loss: 1.0452 - val_pothole_loss: 0.5544 - val_pothole_level_loss: 0.4908 - val_pothole_accuracy: 0.8242 - val_pothole_level_accuracy: 0.8416\n",
            "Epoch 14/50\n",
            "233/233 [==============================] - 192s 826ms/step - loss: 0.6839 - pothole_loss: 0.1795 - pothole_level_loss: 0.5045 - pothole_accuracy: 0.9325 - pothole_level_accuracy: 0.8399 - val_loss: 0.7262 - val_pothole_loss: 0.2604 - val_pothole_level_loss: 0.4658 - val_pothole_accuracy: 0.9024 - val_pothole_level_accuracy: 0.8602\n",
            "Epoch 15/50\n",
            "233/233 [==============================] - 192s 826ms/step - loss: 0.6604 - pothole_loss: 0.1701 - pothole_level_loss: 0.4903 - pothole_accuracy: 0.9348 - pothole_level_accuracy: 0.8443 - val_loss: 0.7781 - val_pothole_loss: 0.3306 - val_pothole_level_loss: 0.4475 - val_pothole_accuracy: 0.8846 - val_pothole_level_accuracy: 0.8618\n",
            "Epoch 16/50\n",
            "233/233 [==============================] - 192s 825ms/step - loss: 0.6525 - pothole_loss: 0.1633 - pothole_level_loss: 0.4893 - pothole_accuracy: 0.9373 - pothole_level_accuracy: 0.8471 - val_loss: 0.7815 - val_pothole_loss: 0.3269 - val_pothole_level_loss: 0.4546 - val_pothole_accuracy: 0.8779 - val_pothole_level_accuracy: 0.8602\n",
            "Epoch 17/50\n",
            "233/233 [==============================] - 193s 826ms/step - loss: 0.6374 - pothole_loss: 0.1559 - pothole_level_loss: 0.4815 - pothole_accuracy: 0.9422 - pothole_level_accuracy: 0.8478 - val_loss: 0.8216 - val_pothole_loss: 0.3327 - val_pothole_level_loss: 0.4889 - val_pothole_accuracy: 0.8551 - val_pothole_level_accuracy: 0.8439\n",
            "Epoch 18/50\n",
            "233/233 [==============================] - 193s 828ms/step - loss: 0.6297 - pothole_loss: 0.1487 - pothole_level_loss: 0.4810 - pothole_accuracy: 0.9437 - pothole_level_accuracy: 0.8484 - val_loss: 1.2441 - val_pothole_loss: 0.4754 - val_pothole_level_loss: 0.7687 - val_pothole_accuracy: 0.7961 - val_pothole_level_accuracy: 0.7881\n",
            "Epoch 19/50\n",
            "233/233 [==============================] - 193s 827ms/step - loss: 0.6177 - pothole_loss: 0.1428 - pothole_level_loss: 0.4749 - pothole_accuracy: 0.9459 - pothole_level_accuracy: 0.8504 - val_loss: 1.9640 - val_pothole_loss: 0.9250 - val_pothole_level_loss: 1.0390 - val_pothole_accuracy: 0.7341 - val_pothole_level_accuracy: 0.7776\n",
            "Epoch 20/50\n",
            "233/233 [==============================] - 193s 828ms/step - loss: 0.6042 - pothole_loss: 0.1353 - pothole_level_loss: 0.4689 - pothole_accuracy: 0.9503 - pothole_level_accuracy: 0.8508 - val_loss: 0.8846 - val_pothole_loss: 0.3805 - val_pothole_level_loss: 0.5041 - val_pothole_accuracy: 0.8864 - val_pothole_level_accuracy: 0.8481\n",
            "Epoch 21/50\n",
            "233/233 [==============================] - 193s 827ms/step - loss: 0.6007 - pothole_loss: 0.1370 - pothole_level_loss: 0.4636 - pothole_accuracy: 0.9475 - pothole_level_accuracy: 0.8522 - val_loss: 1.2139 - val_pothole_loss: 0.4072 - val_pothole_level_loss: 0.8068 - val_pothole_accuracy: 0.8352 - val_pothole_level_accuracy: 0.7244\n",
            "Epoch 22/50\n",
            "233/233 [==============================] - 193s 827ms/step - loss: 0.5961 - pothole_loss: 0.1320 - pothole_level_loss: 0.4641 - pothole_accuracy: 0.9511 - pothole_level_accuracy: 0.8510 - val_loss: 1.5201 - val_pothole_loss: 0.9763 - val_pothole_level_loss: 0.5438 - val_pothole_accuracy: 0.7504 - val_pothole_level_accuracy: 0.8428\n",
            "Epoch 23/50\n",
            "233/233 [==============================] - 193s 829ms/step - loss: 0.5811 - pothole_loss: 0.1255 - pothole_level_loss: 0.4556 - pothole_accuracy: 0.9549 - pothole_level_accuracy: 0.8539 - val_loss: 0.8753 - val_pothole_loss: 0.4269 - val_pothole_level_loss: 0.4483 - val_pothole_accuracy: 0.8322 - val_pothole_level_accuracy: 0.8637\n",
            "Epoch 24/50\n",
            "233/233 [==============================] - 193s 828ms/step - loss: 0.5730 - pothole_loss: 0.1174 - pothole_level_loss: 0.4555 - pothole_accuracy: 0.9571 - pothole_level_accuracy: 0.8544 - val_loss: 0.7645 - val_pothole_loss: 0.3089 - val_pothole_level_loss: 0.4556 - val_pothole_accuracy: 0.8726 - val_pothole_level_accuracy: 0.8541\n",
            "Epoch 25/50\n",
            "233/233 [==============================] - 193s 827ms/step - loss: 0.5618 - pothole_loss: 0.1144 - pothole_level_loss: 0.4474 - pothole_accuracy: 0.9588 - pothole_level_accuracy: 0.8550 - val_loss: 0.8046 - val_pothole_loss: 0.3479 - val_pothole_level_loss: 0.4568 - val_pothole_accuracy: 0.8871 - val_pothole_level_accuracy: 0.8569\n",
            "Epoch 26/50\n",
            "233/233 [==============================] - 193s 829ms/step - loss: 0.5537 - pothole_loss: 0.1046 - pothole_level_loss: 0.4491 - pothole_accuracy: 0.9636 - pothole_level_accuracy: 0.8567 - val_loss: 1.6517 - val_pothole_loss: 0.4759 - val_pothole_level_loss: 1.1757 - val_pothole_accuracy: 0.8024 - val_pothole_level_accuracy: 0.5407\n",
            "Epoch 27/50\n",
            "233/233 [==============================] - 193s 829ms/step - loss: 0.5568 - pothole_loss: 0.1086 - pothole_level_loss: 0.4481 - pothole_accuracy: 0.9617 - pothole_level_accuracy: 0.8560 - val_loss: 0.7169 - val_pothole_loss: 0.2874 - val_pothole_level_loss: 0.4295 - val_pothole_accuracy: 0.8901 - val_pothole_level_accuracy: 0.8659\n",
            "Epoch 28/50\n",
            "233/233 [==============================] - 193s 828ms/step - loss: 0.5454 - pothole_loss: 0.1011 - pothole_level_loss: 0.4443 - pothole_accuracy: 0.9644 - pothole_level_accuracy: 0.8567 - val_loss: 2.2040 - val_pothole_loss: 0.3328 - val_pothole_level_loss: 1.8712 - val_pothole_accuracy: 0.8900 - val_pothole_level_accuracy: 0.4076\n",
            "Epoch 29/50\n",
            "233/233 [==============================] - 193s 829ms/step - loss: 0.5299 - pothole_loss: 0.0931 - pothole_level_loss: 0.4368 - pothole_accuracy: 0.9685 - pothole_level_accuracy: 0.8596 - val_loss: 0.9746 - val_pothole_loss: 0.5251 - val_pothole_level_loss: 0.4495 - val_pothole_accuracy: 0.8453 - val_pothole_level_accuracy: 0.8597\n",
            "Epoch 30/50\n",
            "233/233 [==============================] - 193s 829ms/step - loss: 0.5209 - pothole_loss: 0.0868 - pothole_level_loss: 0.4340 - pothole_accuracy: 0.9720 - pothole_level_accuracy: 0.8601 - val_loss: 1.1899 - val_pothole_loss: 0.4666 - val_pothole_level_loss: 0.7233 - val_pothole_accuracy: 0.8156 - val_pothole_level_accuracy: 0.7940\n",
            "Epoch 31/50\n",
            "233/233 [==============================] - 193s 826ms/step - loss: 0.5125 - pothole_loss: 0.0829 - pothole_level_loss: 0.4296 - pothole_accuracy: 0.9734 - pothole_level_accuracy: 0.8600 - val_loss: 0.8394 - val_pothole_loss: 0.2920 - val_pothole_level_loss: 0.5474 - val_pothole_accuracy: 0.8872 - val_pothole_level_accuracy: 0.8271\n",
            "Epoch 32/50\n",
            "233/233 [==============================] - 193s 829ms/step - loss: 0.5023 - pothole_loss: 0.0766 - pothole_level_loss: 0.4257 - pothole_accuracy: 0.9762 - pothole_level_accuracy: 0.8615 - val_loss: 0.6969 - val_pothole_loss: 0.2657 - val_pothole_level_loss: 0.4312 - val_pothole_accuracy: 0.9044 - val_pothole_level_accuracy: 0.8680\n",
            "Epoch 33/50\n",
            "233/233 [==============================] - 193s 826ms/step - loss: 0.4950 - pothole_loss: 0.0724 - pothole_level_loss: 0.4226 - pothole_accuracy: 0.9780 - pothole_level_accuracy: 0.8631 - val_loss: 0.8486 - val_pothole_loss: 0.3615 - val_pothole_level_loss: 0.4871 - val_pothole_accuracy: 0.8583 - val_pothole_level_accuracy: 0.8423\n",
            "Epoch 34/50\n",
            "233/233 [==============================] - 193s 826ms/step - loss: 0.4888 - pothole_loss: 0.0709 - pothole_level_loss: 0.4180 - pothole_accuracy: 0.9776 - pothole_level_accuracy: 0.8640 - val_loss: 0.7617 - val_pothole_loss: 0.2802 - val_pothole_level_loss: 0.4815 - val_pothole_accuracy: 0.9029 - val_pothole_level_accuracy: 0.8438\n",
            "Epoch 35/50\n",
            "233/233 [==============================] - 193s 827ms/step - loss: 0.4777 - pothole_loss: 0.0642 - pothole_level_loss: 0.4135 - pothole_accuracy: 0.9825 - pothole_level_accuracy: 0.8650 - val_loss: 0.7243 - val_pothole_loss: 0.2891 - val_pothole_level_loss: 0.4351 - val_pothole_accuracy: 0.9069 - val_pothole_level_accuracy: 0.8620\n",
            "Epoch 36/50\n",
            "233/233 [==============================] - 193s 829ms/step - loss: 0.4692 - pothole_loss: 0.0604 - pothole_level_loss: 0.4088 - pothole_accuracy: 0.9834 - pothole_level_accuracy: 0.8659 - val_loss: 0.7774 - val_pothole_loss: 0.3336 - val_pothole_level_loss: 0.4439 - val_pothole_accuracy: 0.8951 - val_pothole_level_accuracy: 0.8649\n",
            "Epoch 37/50\n",
            "233/233 [==============================] - 193s 827ms/step - loss: 0.4730 - pothole_loss: 0.0602 - pothole_level_loss: 0.4128 - pothole_accuracy: 0.9820 - pothole_level_accuracy: 0.8659 - val_loss: 0.8386 - val_pothole_loss: 0.3783 - val_pothole_level_loss: 0.4603 - val_pothole_accuracy: 0.8677 - val_pothole_level_accuracy: 0.8564\n",
            "Epoch 38/50\n",
            "233/233 [==============================] - 193s 828ms/step - loss: 0.4698 - pothole_loss: 0.0611 - pothole_level_loss: 0.4087 - pothole_accuracy: 0.9817 - pothole_level_accuracy: 0.8662 - val_loss: 0.8863 - val_pothole_loss: 0.4346 - val_pothole_level_loss: 0.4517 - val_pothole_accuracy: 0.8454 - val_pothole_level_accuracy: 0.8578\n",
            "Epoch 39/50\n",
            "233/233 [==============================] - 193s 829ms/step - loss: 0.4672 - pothole_loss: 0.0631 - pothole_level_loss: 0.4041 - pothole_accuracy: 0.9811 - pothole_level_accuracy: 0.8667 - val_loss: 0.7850 - val_pothole_loss: 0.3157 - val_pothole_level_loss: 0.4693 - val_pothole_accuracy: 0.8841 - val_pothole_level_accuracy: 0.8561\n",
            "Epoch 40/50\n",
            "233/233 [==============================] - 194s 832ms/step - loss: 0.4621 - pothole_loss: 0.0600 - pothole_level_loss: 0.4021 - pothole_accuracy: 0.9823 - pothole_level_accuracy: 0.8674 - val_loss: 0.9246 - val_pothole_loss: 0.3312 - val_pothole_level_loss: 0.5934 - val_pothole_accuracy: 0.8891 - val_pothole_level_accuracy: 0.8170\n",
            "Epoch 41/50\n",
            "233/233 [==============================] - 193s 828ms/step - loss: 0.4598 - pothole_loss: 0.0585 - pothole_level_loss: 0.4013 - pothole_accuracy: 0.9830 - pothole_level_accuracy: 0.8663 - val_loss: 0.8188 - val_pothole_loss: 0.3545 - val_pothole_level_loss: 0.4643 - val_pothole_accuracy: 0.8827 - val_pothole_level_accuracy: 0.8648\n",
            "Epoch 42/50\n",
            "233/233 [==============================] - 193s 827ms/step - loss: 0.4497 - pothole_loss: 0.0527 - pothole_level_loss: 0.3971 - pothole_accuracy: 0.9849 - pothole_level_accuracy: 0.8683 - val_loss: 1.0364 - val_pothole_loss: 0.4211 - val_pothole_level_loss: 0.6152 - val_pothole_accuracy: 0.8742 - val_pothole_level_accuracy: 0.8013\n",
            "Epoch 43/50\n",
            "233/233 [==============================] - 193s 829ms/step - loss: 0.4344 - pothole_loss: 0.0463 - pothole_level_loss: 0.3882 - pothole_accuracy: 0.9881 - pothole_level_accuracy: 0.8700 - val_loss: 0.7622 - val_pothole_loss: 0.3263 - val_pothole_level_loss: 0.4359 - val_pothole_accuracy: 0.8834 - val_pothole_level_accuracy: 0.8633\n",
            "Epoch 44/50\n",
            "233/233 [==============================] - 193s 828ms/step - loss: 0.4309 - pothole_loss: 0.0475 - pothole_level_loss: 0.3835 - pothole_accuracy: 0.9870 - pothole_level_accuracy: 0.8701 - val_loss: 1.0562 - val_pothole_loss: 0.5760 - val_pothole_level_loss: 0.4802 - val_pothole_accuracy: 0.8050 - val_pothole_level_accuracy: 0.8640\n",
            "Epoch 45/50\n",
            "233/233 [==============================] - 193s 828ms/step - loss: 0.4270 - pothole_loss: 0.0426 - pothole_level_loss: 0.3844 - pothole_accuracy: 0.9894 - pothole_level_accuracy: 0.8713 - val_loss: 0.9643 - val_pothole_loss: 0.3284 - val_pothole_level_loss: 0.6359 - val_pothole_accuracy: 0.8823 - val_pothole_level_accuracy: 0.7980\n",
            "233/233 [==============================] - 193s 828ms/step - loss: 0.4405 - pothole_loss: 0.0512 - pothole_level_loss: 0.3893 - pothole_accuracy: 0.9844 - pothole_level_accuracy: 0.8689 - val_loss: 0.9361 - val_pothole_loss: 0.3916 - val_pothole_level_loss: 0.5445 - val_pothole_accuracy: 0.8789 - val_pothole_level_accuracy: 0.8359\n",
            "Epoch 47/50\n",
            "233/233 [==============================] - 193s 827ms/step - loss: 0.4380 - pothole_loss: 0.0523 - pothole_level_loss: 0.3857 - pothole_accuracy: 0.9843 - pothole_level_accuracy: 0.8699 - val_loss: 1.1366 - val_pothole_loss: 0.6265 - val_pothole_level_loss: 0.5101 - val_pothole_accuracy: 0.8232 - val_pothole_level_accuracy: 0.8510\n",
            "Epoch 48/50\n",
            "233/233 [==============================] - 193s 830ms/step - loss: 0.4351 - pothole_loss: 0.0524 - pothole_level_loss: 0.3827 - pothole_accuracy: 0.9843 - pothole_level_accuracy: 0.8693 - val_loss: 0.9888 - val_pothole_loss: 0.5162 - val_pothole_level_loss: 0.4727 - val_pothole_accuracy: 0.8503 - val_pothole_level_accuracy: 0.8418\n",
            "Epoch 49/50\n",
            "233/233 [==============================] - 193s 827ms/step - loss: 0.4230 - pothole_loss: 0.0477 - pothole_level_loss: 0.3753 - pothole_accuracy: 0.9867 - pothole_level_accuracy: 0.8717 - val_loss: 0.7546 - val_pothole_loss: 0.3236 - val_pothole_level_loss: 0.4309 - val_pothole_accuracy: 0.9054 - val_pothole_level_accuracy: 0.8654\n",
            "Epoch 50/50\n",
            "233/233 [==============================] - 193s 827ms/step - loss: 0.4243 - pothole_loss: 0.0471 - pothole_level_loss: 0.3772 - pothole_accuracy: 0.9870 - pothole_level_accuracy: 0.8726 - val_loss: 1.8072 - val_pothole_loss: 0.5701 - val_pothole_level_loss: 1.2371 - val_pothole_accuracy: 0.8111 - val_pothole_level_accuracy: 0.7753\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",
      "execution_count": null,
      "metadata": {
        "id": "GTXri0-6QHQI"
      },
      "outputs": [],
      "source": [
        "model.save(\"pothole_detection_model_prototype_2_sgd.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(\"pothole_detection_model_prototype_2_sgd_lite.tflite\" , \"wb\").write(tflite_model_quant)"
      ],
      "metadata": {
        "id": "5TOB4PXo7InP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1466b70c-6215-4c78-b10a-6ab669ad8d09"
      },
      "execution_count": null,
      "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": [
              "75531888"
            ]
          },
          "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "PxUQg_rz4G-1"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "Pothole_Detection_sgd.ipynb",
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}