pothole_detection_model_prototype_4_sgd.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        },
        "id": "-GdFlDcHqCs9",
        "outputId": "c2464794-93b9-46c0-bbb3-51bc0ffda74b"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-4996ee3d8d09>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdrive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdrive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/gdrive'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36mmount\u001b[0;34m(mountpoint, force_remount, timeout_ms)\u001b[0m\n\u001b[1;32m    107\u001b[0m       \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mforce_remount\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m       \u001b[0mtimeout_ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout_ms\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 109\u001b[0;31m       ephemeral=True)\n\u001b[0m\u001b[1;32m    110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36m_mount\u001b[0;34m(mountpoint, force_remount, timeout_ms, ephemeral)\u001b[0m\n\u001b[1;32m    243\u001b[0m         \u001b[0mproblem_and_stopped\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    244\u001b[0m         \u001b[0mdrive_exited\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m         \u001b[0mdomain_disabled_drivefs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    246\u001b[0m     ])\n\u001b[1;32m    247\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcase\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pexpect/spawnbase.py\u001b[0m in \u001b[0;36mexpect\u001b[0;34m(self, pattern, timeout, searchwindowsize, async_, **kw)\u001b[0m\n\u001b[1;32m    342\u001b[0m         \u001b[0mcompiled_pattern_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile_pattern_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpattern\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m         return self.expect_list(compiled_pattern_list,\n\u001b[0;32m--> 344\u001b[0;31m                 timeout, searchwindowsize, async_)\n\u001b[0m\u001b[1;32m    345\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    346\u001b[0m     def expect_list(self, pattern_list, timeout=-1, searchwindowsize=-1,\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pexpect/spawnbase.py\u001b[0m in \u001b[0;36mexpect_list\u001b[0;34m(self, pattern_list, timeout, searchwindowsize, async_, **kw)\u001b[0m\n\u001b[1;32m    370\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mexpect_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    371\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 372\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mexp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpect_loop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    373\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    374\u001b[0m     def expect_exact(self, pattern_list, timeout=-1, searchwindowsize=-1,\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pexpect/expect.py\u001b[0m in \u001b[0;36mexpect_loop\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    169\u001b[0m                 \u001b[0mincoming\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mspawn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_nonblocking\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspawn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaxread\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspawn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelayafterread\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 171\u001b[0;31m                     \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspawn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelayafterread\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    172\u001b[0m                 \u001b[0midx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mincoming\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    173\u001b[0m                 \u001b[0;31m# Keep reading until exception or return.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OVNTgKpZr8LA",
        "outputId": "b061e056-9bde-472f-d546-70c82df34889"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Pothole-Detection'...\n",
            "remote: Enumerating objects: 29963, done.\u001b[K\n",
            "remote: Counting objects: 100% (70/70), done.\u001b[K\n",
            "remote: Compressing objects: 100% (52/52), done.\u001b[K\n",
            "remote: Total 29963 (delta 19), reused 56 (delta 15), pack-reused 29893\u001b[K\n",
            "Receiving objects: 100% (29963/29963), 1.29 GiB | 16.98 MiB/s, done.\n",
            "Resolving deltas: 100% (1438/1438), done.\n",
            "Checking out files: 100% (30027/30027), 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": "c0a0a544-7c85-459f-c9e8-a11a281820a2"
      },
      "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"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-da8a5b97-fb39-4a74-80f3-afb313df8b8f\">\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",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<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",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-da8a5b97-fb39-4a74-80f3-afb313df8b8f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-da8a5b97-fb39-4a74-80f3-afb313df8b8f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-da8a5b97-fb39-4a74-80f3-afb313df8b8f');\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",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </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": "e96ce401-8c94-4c2b-81f6-cd35f269a7ee"
      },
      "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": "b53db603-0931-4a3a-8d03-c0718eb9bc53"
      },
      "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"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3d7dfad8-f650-4b80-a0a9-11111567f7df\">\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",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "features.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UXRkxcOl2EwW",
        "outputId": "173c6d5d-8cc0-4949-9ee7-4a7bbdf9561c"
      },
      "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": "4d35b22a-e02e-4215-95ef-881f3971b7f1"
      },
      "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",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gxBC_7wIuD7u",
        "outputId": "ad9e3e73-2f9b-48f5-e1d1-f8b3391a4738"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "type(y_train)"
      ]
    },
    {
      "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": "f7a20135-15c9-4435-dfb8-a841bfe38d86"
      },
      "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,(160, 160))\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 = 75\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": "e9de6de6-4349-4e84-a4d1-8897230dbdc5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " Input Layer (InputLayer)       [(None, 160, 160, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " rescaling (Rescaling)          (None, 160, 160, 3)  0           ['Input Layer[0][0]']            \n",
            "                                                                                                  \n",
            " conv_1 (Conv2D)                (None, 153, 153, 64  12352       ['rescaling[0][0]']              \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " act_1 (Activation)             (None, 153, 153, 64  0           ['conv_1[0][0]']                 \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " pool_1 (MaxPooling2D)          (None, 146, 146, 64  0           ['act_1[0][0]']                  \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv_2 (Conv2D)                (None, 145, 145, 32  8224        ['pool_1[0][0]']                 \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " act_2 (Activation)             (None, 145, 145, 32  0           ['conv_2[0][0]']                 \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " pool_2 (MaxPooling2D)          (None, 138, 138, 32  0           ['act_2[0][0]']                  \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv_3 (Conv2D)                (None, 135, 135, 16  8208        ['pool_2[0][0]']                 \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " act_3 (Activation)             (None, 135, 135, 16  0           ['conv_3[0][0]']                 \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " pool_3 (MaxPooling2D)          (None, 120, 120, 16  0           ['act_3[0][0]']                  \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv_4 (Conv2D)                (None, 117, 117, 8)  2056        ['pool_3[0][0]']                 \n",
            "                                                                                                  \n",
            " act_4 (Activation)             (None, 117, 117, 8)  0           ['conv_4[0][0]']                 \n",
            "                                                                                                  \n",
            " pool_4 (MaxPooling2D)          (None, 110, 110, 8)  0           ['act_4[0][0]']                  \n",
            "                                                                                                  \n",
            " flat_1 (Flatten)               (None, 230400)       0           ['pool_3[0][0]']                 \n",
            "                                                                                                  \n",
            " flat_2 (Flatten)               (None, 96800)        0           ['pool_4[0][0]']                 \n",
            "                                                                                                  \n",
            " dense_1 (Dense)                (None, 128)          29491328    ['flat_1[0][0]']                 \n",
            "                                                                                                  \n",
            " dense_4 (Dense)                (None, 128)          12390528    ['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: 41,936,054\n",
            "Trainable params: 41,935,094\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, Rescaling\n",
        "\n",
        "input_ = Input(shape = (160, 160, 3), name = \"Input Layer\")\n",
        "\n",
        "rescale = Rescaling(scale=1./255)(input_)\n",
        "\n",
        "conv_1 = Conv2D(64, kernel_size = (8, 8), name = \"conv_1\")(rescale)\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 = 0.001)\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": "2c36663b-8f0d-428b-f4f5-8a8dd35d9fb7"
      },
      "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": "95b730aa-daf0-442e-ab34-4f0ca60a620b"
      },
      "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 1/44\n",
            "280/280 [==============================] - 150s 476ms/step - loss: 1.9481 - pothole_loss: 0.3412 - pothole_level_loss: 1.6068 - pothole_accuracy: 0.8712 - pothole_level_accuracy: 0.2015 - val_loss: 1.6490 - val_pothole_loss: 0.4018 - val_pothole_level_loss: 1.2473 - val_pothole_accuracy: 0.8613 - val_pothole_level_accuracy: 0.7354\n",
            "Epoch 2/44\n",
            "280/280 [==============================] - 132s 472ms/step - loss: 1.5090 - pothole_loss: 0.2758 - pothole_level_loss: 1.2333 - pothole_accuracy: 0.9006 - pothole_level_accuracy: 0.5596 - val_loss: 1.3655 - val_pothole_loss: 0.3380 - val_pothole_level_loss: 1.0275 - val_pothole_accuracy: 0.8704 - val_pothole_level_accuracy: 0.8164\n",
            "Epoch 3/44\n",
            "280/280 [==============================] - 133s 473ms/step - loss: 1.2747 - pothole_loss: 0.2539 - pothole_level_loss: 1.0208 - pothole_accuracy: 0.9066 - pothole_level_accuracy: 0.7434 - val_loss: 1.2420 - val_pothole_loss: 0.3811 - val_pothole_level_loss: 0.8610 - val_pothole_accuracy: 0.8372 - val_pothole_level_accuracy: 0.7958\n",
            "Epoch 4/44\n",
            "280/280 [==============================] - 133s 475ms/step - loss: 1.1164 - pothole_loss: 0.2401 - pothole_level_loss: 0.8764 - pothole_accuracy: 0.9119 - pothole_level_accuracy: 0.8032 - val_loss: 1.2289 - val_pothole_loss: 0.4248 - val_pothole_level_loss: 0.8041 - val_pothole_accuracy: 0.8171 - val_pothole_level_accuracy: 0.7771\n",
            "Epoch 5/44\n",
            "280/280 [==============================] - 132s 473ms/step - loss: 1.0084 - pothole_loss: 0.2285 - pothole_level_loss: 0.7800 - pothole_accuracy: 0.9177 - pothole_level_accuracy: 0.8154 - val_loss: 1.0079 - val_pothole_loss: 0.2853 - val_pothole_level_loss: 0.7227 - val_pothole_accuracy: 0.8990 - val_pothole_level_accuracy: 0.8016\n",
            "Epoch 6/44\n",
            "280/280 [==============================] - 133s 475ms/step - loss: 0.9333 - pothole_loss: 0.2180 - pothole_level_loss: 0.7153 - pothole_accuracy: 0.9196 - pothole_level_accuracy: 0.8200 - val_loss: 0.8932 - val_pothole_loss: 0.3230 - val_pothole_level_loss: 0.5702 - val_pothole_accuracy: 0.8930 - val_pothole_level_accuracy: 0.8529\n",
            "Epoch 7/44\n",
            "280/280 [==============================] - 132s 472ms/step - loss: 0.8741 - pothole_loss: 0.2078 - pothole_level_loss: 0.6663 - pothole_accuracy: 0.9228 - pothole_level_accuracy: 0.8255 - val_loss: 1.0258 - val_pothole_loss: 0.4034 - val_pothole_level_loss: 0.6224 - val_pothole_accuracy: 0.8489 - val_pothole_level_accuracy: 0.8062\n",
            "Epoch 8/44\n",
            "280/280 [==============================] - 133s 476ms/step - loss: 0.8354 - pothole_loss: 0.2009 - pothole_level_loss: 0.6345 - pothole_accuracy: 0.9255 - pothole_level_accuracy: 0.8287 - val_loss: 0.7963 - val_pothole_loss: 0.2413 - val_pothole_level_loss: 0.5550 - val_pothole_accuracy: 0.9156 - val_pothole_level_accuracy: 0.8467\n",
            "Epoch 9/44\n",
            "280/280 [==============================] - 133s 474ms/step - loss: 0.8039 - pothole_loss: 0.1951 - pothole_level_loss: 0.6088 - pothole_accuracy: 0.9272 - pothole_level_accuracy: 0.8305 - val_loss: 0.9069 - val_pothole_loss: 0.3719 - val_pothole_level_loss: 0.5351 - val_pothole_accuracy: 0.8809 - val_pothole_level_accuracy: 0.8068\n",
            "Epoch 10/44\n",
            "280/280 [==============================] - 133s 476ms/step - loss: 0.7760 - pothole_loss: 0.1873 - pothole_level_loss: 0.5887 - pothole_accuracy: 0.9284 - pothole_level_accuracy: 0.8331 - val_loss: 0.7443 - val_pothole_loss: 0.2679 - val_pothole_level_loss: 0.4763 - val_pothole_accuracy: 0.9009 - val_pothole_level_accuracy: 0.8570\n",
            "Epoch 11/44\n",
            "280/280 [==============================] - 132s 473ms/step - loss: 0.7561 - pothole_loss: 0.1819 - pothole_level_loss: 0.5742 - pothole_accuracy: 0.9315 - pothole_level_accuracy: 0.8352 - val_loss: 0.8014 - val_pothole_loss: 0.3071 - val_pothole_level_loss: 0.4943 - val_pothole_accuracy: 0.8761 - val_pothole_level_accuracy: 0.8627\n",
            "Epoch 12/44\n",
            "280/280 [==============================] - 133s 476ms/step - loss: 0.7323 - pothole_loss: 0.1767 - pothole_level_loss: 0.5557 - pothole_accuracy: 0.9330 - pothole_level_accuracy: 0.8374 - val_loss: 1.0935 - val_pothole_loss: 0.4069 - val_pothole_level_loss: 0.6866 - val_pothole_accuracy: 0.8436 - val_pothole_level_accuracy: 0.7843\n",
            "Epoch 13/44\n",
            "280/280 [==============================] - 133s 474ms/step - loss: 0.7122 - pothole_loss: 0.1691 - pothole_level_loss: 0.5430 - pothole_accuracy: 0.9367 - pothole_level_accuracy: 0.8400 - val_loss: 0.7517 - val_pothole_loss: 0.2608 - val_pothole_level_loss: 0.4909 - val_pothole_accuracy: 0.9056 - val_pothole_level_accuracy: 0.8686\n",
            "Epoch 14/44\n",
            "280/280 [==============================] - 133s 474ms/step - loss: 0.6939 - pothole_loss: 0.1623 - pothole_level_loss: 0.5316 - pothole_accuracy: 0.9380 - pothole_level_accuracy: 0.8437 - val_loss: 0.9410 - val_pothole_loss: 0.3523 - val_pothole_level_loss: 0.5887 - val_pothole_accuracy: 0.8561 - val_pothole_level_accuracy: 0.7866\n",
            "Epoch 15/44\n",
            "280/280 [==============================] - 133s 476ms/step - loss: 0.6835 - pothole_loss: 0.1599 - pothole_level_loss: 0.5236 - pothole_accuracy: 0.9382 - pothole_level_accuracy: 0.8435 - val_loss: 0.8267 - val_pothole_loss: 0.2914 - val_pothole_level_loss: 0.5353 - val_pothole_accuracy: 0.8977 - val_pothole_level_accuracy: 0.8413\n",
            "Epoch 16/44\n",
            "280/280 [==============================] - 133s 474ms/step - loss: 0.6665 - pothole_loss: 0.1534 - pothole_level_loss: 0.5131 - pothole_accuracy: 0.9409 - pothole_level_accuracy: 0.8455 - val_loss: 1.9632 - val_pothole_loss: 1.0488 - val_pothole_level_loss: 0.9144 - val_pothole_accuracy: 0.5579 - val_pothole_level_accuracy: 0.6608\n",
            "Epoch 17/44\n",
            "280/280 [==============================] - 133s 474ms/step - loss: 0.6516 - pothole_loss: 0.1488 - pothole_level_loss: 0.5028 - pothole_accuracy: 0.9417 - pothole_level_accuracy: 0.8470 - val_loss: 1.8577 - val_pothole_loss: 0.7809 - val_pothole_level_loss: 1.0768 - val_pothole_accuracy: 0.6758 - val_pothole_level_accuracy: 0.5637\n",
            "Epoch 18/44\n",
            "280/280 [==============================] - 133s 474ms/step - loss: 0.6405 - pothole_loss: 0.1438 - pothole_level_loss: 0.4967 - pothole_accuracy: 0.9459 - pothole_level_accuracy: 0.8486 - val_loss: 0.7408 - val_pothole_loss: 0.2502 - val_pothole_level_loss: 0.4906 - val_pothole_accuracy: 0.9087 - val_pothole_level_accuracy: 0.8323\n",
            "Epoch 19/44\n",
            "280/280 [==============================] - 133s 475ms/step - loss: 0.6236 - pothole_loss: 0.1371 - pothole_level_loss: 0.4865 - pothole_accuracy: 0.9475 - pothole_level_accuracy: 0.8520 - val_loss: 0.6909 - val_pothole_loss: 0.2587 - val_pothole_level_loss: 0.4323 - val_pothole_accuracy: 0.9122 - val_pothole_level_accuracy: 0.8632\n",
            "Epoch 20/44\n",
            "280/280 [==============================] - 133s 474ms/step - loss: 0.6121 - pothole_loss: 0.1312 - pothole_level_loss: 0.4810 - pothole_accuracy: 0.9511 - pothole_level_accuracy: 0.8524 - val_loss: 1.0016 - val_pothole_loss: 0.4523 - val_pothole_level_loss: 0.5493 - val_pothole_accuracy: 0.8238 - val_pothole_level_accuracy: 0.8193\n",
            "Epoch 21/44\n",
            "280/280 [==============================] - 134s 477ms/step - loss: 0.5970 - pothole_loss: 0.1258 - pothole_level_loss: 0.4712 - pothole_accuracy: 0.9528 - pothole_level_accuracy: 0.8550 - val_loss: 0.6889 - val_pothole_loss: 0.2566 - val_pothole_level_loss: 0.4323 - val_pothole_accuracy: 0.9118 - val_pothole_level_accuracy: 0.8697\n",
            "Epoch 22/44\n",
            "280/280 [==============================] - 133s 474ms/step - loss: 0.5934 - pothole_loss: 0.1201 - pothole_level_loss: 0.4732 - pothole_accuracy: 0.9547 - pothole_level_accuracy: 0.8532 - val_loss: 0.6921 - val_pothole_loss: 0.2625 - val_pothole_level_loss: 0.4297 - val_pothole_accuracy: 0.9116 - val_pothole_level_accuracy: 0.8713\n",
            "Epoch 23/44\n",
            "280/280 [==============================] - 133s 475ms/step - loss: 0.5757 - pothole_loss: 0.1136 - pothole_level_loss: 0.4621 - pothole_accuracy: 0.9589 - pothole_level_accuracy: 0.8559 - val_loss: 0.7616 - val_pothole_loss: 0.2655 - val_pothole_level_loss: 0.4961 - val_pothole_accuracy: 0.9073 - val_pothole_level_accuracy: 0.8332\n",
            "Epoch 24/44\n",
            "280/280 [==============================] - 133s 474ms/step - loss: 0.5678 - pothole_loss: 0.1084 - pothole_level_loss: 0.4594 - pothole_accuracy: 0.9611 - pothole_level_accuracy: 0.8575 - val_loss: 1.2287 - val_pothole_loss: 0.3112 - val_pothole_level_loss: 0.9176 - val_pothole_accuracy: 0.8993 - val_pothole_level_accuracy: 0.6479\n",
            "Epoch 25/44\n",
            "280/280 [==============================] - 133s 475ms/step - loss: 0.5572 - pothole_loss: 0.1053 - pothole_level_loss: 0.4519 - pothole_accuracy: 0.9606 - pothole_level_accuracy: 0.8572 - val_loss: 0.8119 - val_pothole_loss: 0.3486 - val_pothole_level_loss: 0.4632 - val_pothole_accuracy: 0.8811 - val_pothole_level_accuracy: 0.8494\n",
            "Epoch 26/44\n",
            "280/280 [==============================] - 134s 478ms/step - loss: 0.5480 - pothole_loss: 0.1018 - pothole_level_loss: 0.4463 - pothole_accuracy: 0.9637 - pothole_level_accuracy: 0.8598 - val_loss: 0.7181 - val_pothole_loss: 0.2698 - val_pothole_level_loss: 0.4483 - val_pothole_accuracy: 0.9159 - val_pothole_level_accuracy: 0.8579\n",
            "Epoch 27/44\n",
            "280/280 [==============================] - 133s 475ms/step - loss: 0.5416 - pothole_loss: 0.0977 - pothole_level_loss: 0.4439 - pothole_accuracy: 0.9661 - pothole_level_accuracy: 0.8599 - val_loss: 0.7640 - val_pothole_loss: 0.2732 - val_pothole_level_loss: 0.4908 - val_pothole_accuracy: 0.9054 - val_pothole_level_accuracy: 0.8523\n",
            "Epoch 28/44\n",
            "280/280 [==============================] - 133s 475ms/step - loss: 0.5355 - pothole_loss: 0.0944 - pothole_level_loss: 0.4411 - pothole_accuracy: 0.9671 - pothole_level_accuracy: 0.8598 - val_loss: 0.8658 - val_pothole_loss: 0.2918 - val_pothole_level_loss: 0.5740 - val_pothole_accuracy: 0.8950 - val_pothole_level_accuracy: 0.8049\n",
            "Epoch 29/44\n",
            "280/280 [==============================] - 133s 475ms/step - loss: 0.5301 - pothole_loss: 0.0908 - pothole_level_loss: 0.4393 - pothole_accuracy: 0.9690 - pothole_level_accuracy: 0.8623 - val_loss: 0.7152 - val_pothole_loss: 0.2991 - val_pothole_level_loss: 0.4161 - val_pothole_accuracy: 0.8920 - val_pothole_level_accuracy: 0.8726\n",
            "Epoch 30/44\n",
            "280/280 [==============================] - 133s 474ms/step - loss: 0.5191 - pothole_loss: 0.0882 - pothole_level_loss: 0.4309 - pothole_accuracy: 0.9718 - pothole_level_accuracy: 0.8615 - val_loss: 1.0637 - val_pothole_loss: 0.3017 - val_pothole_level_loss: 0.7620 - val_pothole_accuracy: 0.8859 - val_pothole_level_accuracy: 0.7173\n",
            "Epoch 31/44\n",
            "280/280 [==============================] - 133s 475ms/step - loss: 0.5205 - pothole_loss: 0.0862 - pothole_level_loss: 0.4344 - pothole_accuracy: 0.9718 - pothole_level_accuracy: 0.8610 - val_loss: 0.7420 - val_pothole_loss: 0.2738 - val_pothole_level_loss: 0.4682 - val_pothole_accuracy: 0.9082 - val_pothole_level_accuracy: 0.8500\n",
            "Epoch 32/44\n",
            "280/280 [==============================] - 134s 478ms/step - loss: 0.5084 - pothole_loss: 0.0803 - pothole_level_loss: 0.4281 - pothole_accuracy: 0.9744 - pothole_level_accuracy: 0.8629 - val_loss: 0.7193 - val_pothole_loss: 0.2765 - val_pothole_level_loss: 0.4428 - val_pothole_accuracy: 0.9157 - val_pothole_level_accuracy: 0.8614\n",
            "Epoch 33/44\n",
            "280/280 [==============================] - 134s 478ms/step - loss: 0.5006 - pothole_loss: 0.0774 - pothole_level_loss: 0.4232 - pothole_accuracy: 0.9757 - pothole_level_accuracy: 0.8643 - val_loss: 0.6983 - val_pothole_loss: 0.2780 - val_pothole_level_loss: 0.4203 - val_pothole_accuracy: 0.8969 - val_pothole_level_accuracy: 0.8751\n",
            "Epoch 34/44\n",
            "280/280 [==============================] - 133s 475ms/step - loss: 0.4915 - pothole_loss: 0.0720 - pothole_level_loss: 0.4195 - pothole_accuracy: 0.9790 - pothole_level_accuracy: 0.8644 - val_loss: 0.7672 - val_pothole_loss: 0.3216 - val_pothole_level_loss: 0.4456 - val_pothole_accuracy: 0.8892 - val_pothole_level_accuracy: 0.8600\n",
            "Epoch 35/44\n",
            "280/280 [==============================] - 133s 475ms/step - loss: 0.4876 - pothole_loss: 0.0732 - pothole_level_loss: 0.4144 - pothole_accuracy: 0.9786 - pothole_level_accuracy: 0.8660 - val_loss: 0.6904 - val_pothole_loss: 0.2885 - val_pothole_level_loss: 0.4019 - val_pothole_accuracy: 0.9022 - val_pothole_level_accuracy: 0.8732\n",
            "Epoch 36/44\n",
            "280/280 [==============================] - 133s 475ms/step - loss: 0.4816 - pothole_loss: 0.0673 - pothole_level_loss: 0.4143 - pothole_accuracy: 0.9801 - pothole_level_accuracy: 0.8644 - val_loss: 0.6985 - val_pothole_loss: 0.2662 - val_pothole_level_loss: 0.4324 - val_pothole_accuracy: 0.9080 - val_pothole_level_accuracy: 0.8693\n",
            "Epoch 37/44\n",
            "280/280 [==============================] - 133s 475ms/step - loss: 0.4709 - pothole_loss: 0.0638 - pothole_level_loss: 0.4071 - pothole_accuracy: 0.9824 - pothole_level_accuracy: 0.8671 - val_loss: 0.7043 - val_pothole_loss: 0.3025 - val_pothole_level_loss: 0.4018 - val_pothole_accuracy: 0.9128 - val_pothole_level_accuracy: 0.8731\n",
            "Epoch 38/44\n",
            "280/280 [==============================] - 133s 475ms/step - loss: 0.4679 - pothole_loss: 0.0614 - pothole_level_loss: 0.4065 - pothole_accuracy: 0.9833 - pothole_level_accuracy: 0.8670 - val_loss: 0.7883 - val_pothole_loss: 0.2832 - val_pothole_level_loss: 0.5050 - val_pothole_accuracy: 0.9016 - val_pothole_level_accuracy: 0.8413\n",
            "Epoch 39/44\n",
            "280/280 [==============================] - 133s 475ms/step - loss: 0.4666 - pothole_loss: 0.0607 - pothole_level_loss: 0.4058 - pothole_accuracy: 0.9836 - pothole_level_accuracy: 0.8679 - val_loss: 0.7617 - val_pothole_loss: 0.3183 - val_pothole_level_loss: 0.4434 - val_pothole_accuracy: 0.9114 - val_pothole_level_accuracy: 0.8717\n",
            "Epoch 40/44\n",
            "280/280 [==============================] - 133s 475ms/step - loss: 0.4607 - pothole_loss: 0.0568 - pothole_level_loss: 0.4039 - pothole_accuracy: 0.9866 - pothole_level_accuracy: 0.8680 - val_loss: 0.7321 - val_pothole_loss: 0.2764 - val_pothole_level_loss: 0.4557 - val_pothole_accuracy: 0.9088 - val_pothole_level_accuracy: 0.8471\n",
            "Epoch 41/44\n",
            "280/280 [==============================] - 133s 475ms/step - loss: 0.4511 - pothole_loss: 0.0550 - pothole_level_loss: 0.3960 - pothole_accuracy: 0.9868 - pothole_level_accuracy: 0.8687 - val_loss: 0.8795 - val_pothole_loss: 0.4365 - val_pothole_level_loss: 0.4430 - val_pothole_accuracy: 0.8847 - val_pothole_level_accuracy: 0.8557\n",
            "Epoch 42/44\n",
            "280/280 [==============================] - 133s 475ms/step - loss: 0.4526 - pothole_loss: 0.0569 - pothole_level_loss: 0.3957 - pothole_accuracy: 0.9851 - pothole_level_accuracy: 0.8699 - val_loss: 0.8103 - val_pothole_loss: 0.4014 - val_pothole_level_loss: 0.4089 - val_pothole_accuracy: 0.8598 - val_pothole_level_accuracy: 0.8751\n",
            "Epoch 43/44\n",
            "280/280 [==============================] - 133s 476ms/step - loss: 0.4453 - pothole_loss: 0.0522 - pothole_level_loss: 0.3931 - pothole_accuracy: 0.9884 - pothole_level_accuracy: 0.8704 - val_loss: 0.8981 - val_pothole_loss: 0.4178 - val_pothole_level_loss: 0.4802 - val_pothole_accuracy: 0.8771 - val_pothole_level_accuracy: 0.8470\n",
            "Epoch 44/44\n",
            "280/280 [==============================] - 133s 475ms/step - loss: 0.4455 - pothole_loss: 0.0534 - pothole_level_loss: 0.3922 - pothole_accuracy: 0.9871 - pothole_level_accuracy: 0.8703 - val_loss: 0.6842 - val_pothole_loss: 0.2728 - val_pothole_level_loss: 0.4114 - val_pothole_accuracy: 0.9052 - val_pothole_level_accuracy: 0.8720\n"
          ]
        }
      ],
      "source": [
        "_ = model.fit_generator(generator=my_training_batch_generator,\n",
        "                   steps_per_epoch = int(21000 // batch_size),\n",
        "                   epochs = 44,\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": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Eje6gIAhczbV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "GTXri0-6QHQI"
      },
      "outputs": [],
      "source": [
        "# model.save(\"/content/gdrive/MyDrive/pothole_detection_model_prototype_3_sgd.h5\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "5TOB4PXo7InP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "36ae57cc-b8cc-4d67-e447-4aca622cf87a"
      },
      "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": [
              "41952976"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "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_sgd.tflite\" , \"wb\").write(tflite_model_quant)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "sfm6EsUsqvt4"
      },
      "outputs": [],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "PxUQg_rz4G-1"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "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
}