pothole_detection_model_prototype_1_adam.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "thKSLTN82xIW",
        "outputId": "e7bfec7e-cb00-4669-d907-3ae39b4013e3"
      },
      "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": "4fbb635c-9c78-4580-dd19-145bd538b790"
      },
      "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 | 20.23 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": "979370c7-308e-4be7-806d-9f3435ddaae8"
      },
      "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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              "      <div>\n",
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              "  <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",
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              "              title=\"Convert this dataframe to an interactive table.\"\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": "7daad274-9fd9-433e-bee6-a1a613e8f233"
      },
      "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)"
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "2bb-uXLT2CSX",
        "outputId": "83056486-4c1c-4a3e-ebf3-15323d98fb72"
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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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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>pothole_image_1299</td>\n",
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              "      <th>4</th>\n",
              "      <td>pothole_image_10087</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>pothole_image_10110</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>pothole_image_10119</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>pothole_image_10120</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>pothole_image_10135</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>pothole_image_10139</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>A</td>\n",
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          "metadata": {},
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      ],
      "source": [
        "features.head(10)"
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    },
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          "base_uri": "https://localhost:8080/"
        },
        "id": "UXRkxcOl2EwW",
        "outputId": "be342b38-c9e4-490c-b2f7-a268b5db911a"
      },
      "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": "ff2dc391-e82e-4196-8a7d-8bf38ad2e79d"
      },
      "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": "9366ecd7-1805-431e-c3ff-f11f5ea9c4c9"
      },
      "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": "35046262-1bfe-4462-cdae-330563fed226"
      },
      "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 = 100\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": {
        "id": "j3oMzubsTJQU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "99558484-7df9-4d93-903e-031549b09758"
      },
      "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, 197, 197, 32  544         ['Input Layer[0][0]']            \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " act_1 (Activation)             (None, 197, 197, 32  0           ['conv_1[0][0]']                 \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " pool_1 (MaxPooling2D)          (None, 190, 190, 32  0           ['act_1[0][0]']                  \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv_2 (Conv2D)                (None, 183, 183, 64  131136      ['pool_1[0][0]']                 \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " act_2 (Activation)             (None, 183, 183, 64  0           ['conv_2[0][0]']                 \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " pool_2 (MaxPooling2D)          (None, 180, 180, 64  0           ['act_2[0][0]']                  \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv_3 (Conv2D)                (None, 177, 177, 32  32800       ['pool_2[0][0]']                 \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " act_3 (Activation)             (None, 177, 177, 32  0           ['conv_3[0][0]']                 \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " pool_3 (MaxPooling2D)          (None, 170, 170, 32  0           ['act_3[0][0]']                  \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv_4 (Conv2D)                (None, 167, 167, 16  8208        ['pool_3[0][0]']                 \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " act_4 (Activation)             (None, 167, 167, 16  0           ['conv_4[0][0]']                 \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " pool_4 (MaxPooling2D)          (None, 164, 164, 16  0           ['act_4[0][0]']                  \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv_5 (Conv2D)                (None, 157, 157, 16  16400       ['pool_4[0][0]']                 \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " act_5 (Activation)             (None, 157, 157, 16  0           ['conv_5[0][0]']                 \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " pool_5 (MaxPooling2D)          (None, 154, 154, 16  0           ['act_5[0][0]']                  \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " flat_1 (Flatten)               (None, 924800)       0           ['pool_3[0][0]']                 \n",
            "                                                                                                  \n",
            " flat_2 (Flatten)               (None, 379456)       0           ['pool_5[0][0]']                 \n",
            "                                                                                                  \n",
            " dense_1 (Dense)                (None, 128)          118374528   ['flat_1[0][0]']                 \n",
            "                                                                                                  \n",
            " dense_4 (Dense)                (None, 128)          48570496    ['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: 167,157,470\n",
            "Trainable params: 167,156,510\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(32, kernel_size = (4, 4), 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(64, kernel_size = (8, 8), name = \"conv_2\")(pool_1)\n",
        "act_2 = Activation(\"relu\", name = \"act_2\")(conv_2)\n",
        "pool_2 = MaxPooling2D(pool_size = (4, 4), strides = (1, 1), padding = \"valid\", name = \"pool_2\")(act_2)\n",
        "\n",
        "conv_3 = Conv2D(32, 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 = (8, 8), 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(16, 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 = (4, 4), 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_5)\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.Adam(learning_rate = 0.001)\n",
        "\n",
        "model.compile(\n",
        "              loss = {\n",
        "                  'pothole' : 'categorical_crossentropy',\n",
        "                #   'pothole_number' : 'categorical_crossentropy'\n",
        "                  'pothole_level' : 'categorical_crossentropy'\n",
        "              },\n",
        "              optimizer = opt,\n",
        "              metrics = ['accuracy']\n",
        ")\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YIeo01tcvYBr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "19bf8cc6-bac7-42bd-cefa-ba467eee3156"
      },
      "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": "1855ee37-4c47-46e0-d3b0-bc18cc575870"
      },
      "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",
            "210/210 [==============================] - 280s 1s/step - loss: 1.2208 - pothole_loss: 0.3759 - pothole_level_loss: 0.8449 - pothole_accuracy: 0.8414 - pothole_level_accuracy: 0.7159 - val_loss: 1.9075 - val_pothole_loss: 1.2226 - val_pothole_level_loss: 0.6849 - val_pothole_accuracy: 0.7329 - val_pothole_level_accuracy: 0.7834\n",
            "Epoch 2/50\n",
            "210/210 [==============================] - 256s 1s/step - loss: 0.9330 - pothole_loss: 0.2876 - pothole_level_loss: 0.6454 - pothole_accuracy: 0.8908 - pothole_level_accuracy: 0.7707 - val_loss: 1.3048 - val_pothole_loss: 0.4226 - val_pothole_level_loss: 0.8822 - val_pothole_accuracy: 0.7702 - val_pothole_level_accuracy: 0.7834\n",
            "Epoch 3/50\n",
            "210/210 [==============================] - 254s 1s/step - loss: 0.8635 - pothole_loss: 0.2652 - pothole_level_loss: 0.5983 - pothole_accuracy: 0.9011 - pothole_level_accuracy: 0.7721 - val_loss: 0.8347 - val_pothole_loss: 0.2681 - val_pothole_level_loss: 0.5666 - val_pothole_accuracy: 0.9063 - val_pothole_level_accuracy: 0.7834\n",
            "Epoch 4/50\n",
            "210/210 [==============================] - 254s 1s/step - loss: 0.8086 - pothole_loss: 0.2466 - pothole_level_loss: 0.5620 - pothole_accuracy: 0.9074 - pothole_level_accuracy: 0.7849 - val_loss: 0.9121 - val_pothole_loss: 0.2766 - val_pothole_level_loss: 0.6355 - val_pothole_accuracy: 0.9022 - val_pothole_level_accuracy: 0.8342\n",
            "Epoch 5/50\n",
            "210/210 [==============================] - 254s 1s/step - loss: 0.7604 - pothole_loss: 0.2327 - pothole_level_loss: 0.5278 - pothole_accuracy: 0.9134 - pothole_level_accuracy: 0.8325 - val_loss: 5.6415 - val_pothole_loss: 3.7117 - val_pothole_level_loss: 1.9298 - val_pothole_accuracy: 0.7327 - val_pothole_level_accuracy: 0.7834\n",
            "Epoch 6/50\n",
            "210/210 [==============================] - 254s 1s/step - loss: 0.7173 - pothole_loss: 0.2191 - pothole_level_loss: 0.4982 - pothole_accuracy: 0.9178 - pothole_level_accuracy: 0.8455 - val_loss: 0.7857 - val_pothole_loss: 0.2713 - val_pothole_level_loss: 0.5144 - val_pothole_accuracy: 0.9067 - val_pothole_level_accuracy: 0.8174\n",
            "Epoch 7/50\n",
            "210/210 [==============================] - 254s 1s/step - loss: 0.6802 - pothole_loss: 0.2016 - pothole_level_loss: 0.4786 - pothole_accuracy: 0.9239 - pothole_level_accuracy: 0.8524 - val_loss: 1.3483 - val_pothole_loss: 0.3258 - val_pothole_level_loss: 1.0225 - val_pothole_accuracy: 0.8720 - val_pothole_level_accuracy: 0.7883\n",
            "Epoch 8/50\n",
            "210/210 [==============================] - 253s 1s/step - loss: 0.6471 - pothole_loss: 0.1871 - pothole_level_loss: 0.4601 - pothole_accuracy: 0.9293 - pothole_level_accuracy: 0.8557 - val_loss: 1.0119 - val_pothole_loss: 0.3288 - val_pothole_level_loss: 0.6831 - val_pothole_accuracy: 0.8824 - val_pothole_level_accuracy: 0.8224\n",
            "Epoch 9/50\n",
            "210/210 [==============================] - 252s 1s/step - loss: 0.6243 - pothole_loss: 0.1760 - pothole_level_loss: 0.4483 - pothole_accuracy: 0.9322 - pothole_level_accuracy: 0.8614 - val_loss: 1.1259 - val_pothole_loss: 0.6576 - val_pothole_level_loss: 0.4684 - val_pothole_accuracy: 0.7851 - val_pothole_level_accuracy: 0.8514\n",
            "Epoch 10/50\n",
            "210/210 [==============================] - 251s 1s/step - loss: 0.6043 - pothole_loss: 0.1671 - pothole_level_loss: 0.4373 - pothole_accuracy: 0.9357 - pothole_level_accuracy: 0.8630 - val_loss: 1.2216 - val_pothole_loss: 0.7299 - val_pothole_level_loss: 0.4917 - val_pothole_accuracy: 0.7910 - val_pothole_level_accuracy: 0.8356\n",
            "Epoch 11/50\n",
            "210/210 [==============================] - 250s 1s/step - loss: 0.6015 - pothole_loss: 0.1678 - pothole_level_loss: 0.4337 - pothole_accuracy: 0.9338 - pothole_level_accuracy: 0.8635 - val_loss: 1.5135 - val_pothole_loss: 0.6639 - val_pothole_level_loss: 0.8496 - val_pothole_accuracy: 0.8288 - val_pothole_level_accuracy: 0.7854\n",
            "Epoch 12/50\n",
            "210/210 [==============================] - 250s 1s/step - loss: 0.5411 - pothole_loss: 0.1312 - pothole_level_loss: 0.4099 - pothole_accuracy: 0.9474 - pothole_level_accuracy: 0.8680 - val_loss: 0.7600 - val_pothole_loss: 0.2667 - val_pothole_level_loss: 0.4933 - val_pothole_accuracy: 0.9088 - val_pothole_level_accuracy: 0.8478\n",
            "Epoch 13/50\n",
            "210/210 [==============================] - 250s 1s/step - loss: 0.5187 - pothole_loss: 0.1164 - pothole_level_loss: 0.4023 - pothole_accuracy: 0.9537 - pothole_level_accuracy: 0.8675 - val_loss: 1.5474 - val_pothole_loss: 0.4033 - val_pothole_level_loss: 1.1441 - val_pothole_accuracy: 0.8802 - val_pothole_level_accuracy: 0.7998\n",
            "Epoch 14/50\n",
            "210/210 [==============================] - 250s 1s/step - loss: 0.4891 - pothole_loss: 0.1006 - pothole_level_loss: 0.3885 - pothole_accuracy: 0.9603 - pothole_level_accuracy: 0.8692 - val_loss: 1.1414 - val_pothole_loss: 0.3426 - val_pothole_level_loss: 0.7988 - val_pothole_accuracy: 0.8890 - val_pothole_level_accuracy: 0.8191\n",
            "Epoch 15/50\n",
            "210/210 [==============================] - 250s 1s/step - loss: 0.4538 - pothole_loss: 0.0797 - pothole_level_loss: 0.3741 - pothole_accuracy: 0.9702 - pothole_level_accuracy: 0.8730 - val_loss: 1.0225 - val_pothole_loss: 0.4473 - val_pothole_level_loss: 0.5752 - val_pothole_accuracy: 0.9107 - val_pothole_level_accuracy: 0.8730\n",
            "Epoch 16/50\n",
            "210/210 [==============================] - 250s 1s/step - loss: 0.4307 - pothole_loss: 0.0677 - pothole_level_loss: 0.3630 - pothole_accuracy: 0.9733 - pothole_level_accuracy: 0.8729 - val_loss: 1.0264 - val_pothole_loss: 0.5274 - val_pothole_level_loss: 0.4990 - val_pothole_accuracy: 0.8093 - val_pothole_level_accuracy: 0.8642\n",
            "Epoch 17/50\n",
            "210/210 [==============================] - 249s 1s/step - loss: 0.4071 - pothole_loss: 0.0601 - pothole_level_loss: 0.3470 - pothole_accuracy: 0.9778 - pothole_level_accuracy: 0.8762 - val_loss: 1.5272 - val_pothole_loss: 0.7903 - val_pothole_level_loss: 0.7370 - val_pothole_accuracy: 0.8350 - val_pothole_level_accuracy: 0.8581\n",
            "Epoch 18/50\n",
            "210/210 [==============================] - 249s 1s/step - loss: 0.3894 - pothole_loss: 0.0514 - pothole_level_loss: 0.3380 - pothole_accuracy: 0.9803 - pothole_level_accuracy: 0.8760 - val_loss: 0.9391 - val_pothole_loss: 0.4062 - val_pothole_level_loss: 0.5330 - val_pothole_accuracy: 0.8949 - val_pothole_level_accuracy: 0.8671\n",
            "Epoch 19/50\n",
            "210/210 [==============================] - 249s 1s/step - loss: 0.4025 - pothole_loss: 0.0607 - pothole_level_loss: 0.3418 - pothole_accuracy: 0.9765 - pothole_level_accuracy: 0.8764 - val_loss: 2.2247 - val_pothole_loss: 1.1468 - val_pothole_level_loss: 1.0779 - val_pothole_accuracy: 0.7616 - val_pothole_level_accuracy: 0.8028\n",
            "Epoch 20/50\n",
            "210/210 [==============================] - 249s 1s/step - loss: 0.3678 - pothole_loss: 0.0428 - pothole_level_loss: 0.3250 - pothole_accuracy: 0.9835 - pothole_level_accuracy: 0.8783 - val_loss: 1.3875 - val_pothole_loss: 0.7175 - val_pothole_level_loss: 0.6700 - val_pothole_accuracy: 0.8813 - val_pothole_level_accuracy: 0.8712\n",
            "Epoch 21/50\n",
            "210/210 [==============================] - 250s 1s/step - loss: 0.3512 - pothole_loss: 0.0359 - pothole_level_loss: 0.3153 - pothole_accuracy: 0.9866 - pothole_level_accuracy: 0.8780 - val_loss: 1.4986 - val_pothole_loss: 0.6971 - val_pothole_level_loss: 0.8016 - val_pothole_accuracy: 0.8698 - val_pothole_level_accuracy: 0.8413\n",
            "Epoch 22/50\n",
            "210/210 [==============================] - 249s 1s/step - loss: 0.3398 - pothole_loss: 0.0318 - pothole_level_loss: 0.3080 - pothole_accuracy: 0.9880 - pothole_level_accuracy: 0.8807 - val_loss: 3.4791 - val_pothole_loss: 0.5762 - val_pothole_level_loss: 2.9030 - val_pothole_accuracy: 0.8997 - val_pothole_level_accuracy: 0.7983\n",
            "Epoch 23/50\n",
            "210/210 [==============================] - 249s 1s/step - loss: 0.3291 - pothole_loss: 0.0297 - pothole_level_loss: 0.2994 - pothole_accuracy: 0.9903 - pothole_level_accuracy: 0.8815 - val_loss: 1.0857 - val_pothole_loss: 0.5100 - val_pothole_level_loss: 0.5757 - val_pothole_accuracy: 0.8964 - val_pothole_level_accuracy: 0.8659\n",
            "Epoch 24/50\n",
            "210/210 [==============================] - 248s 1s/step - loss: 0.3133 - pothole_loss: 0.0224 - pothole_level_loss: 0.2909 - pothole_accuracy: 0.9917 - pothole_level_accuracy: 0.8835 - val_loss: 1.0856 - val_pothole_loss: 0.5237 - val_pothole_level_loss: 0.5619 - val_pothole_accuracy: 0.9050 - val_pothole_level_accuracy: 0.8681\n",
            "Epoch 25/50\n",
            "210/210 [==============================] - 249s 1s/step - loss: 0.3026 - pothole_loss: 0.0203 - pothole_level_loss: 0.2823 - pothole_accuracy: 0.9925 - pothole_level_accuracy: 0.8839 - val_loss: 1.1617 - val_pothole_loss: 0.5589 - val_pothole_level_loss: 0.6027 - val_pothole_accuracy: 0.8986 - val_pothole_level_accuracy: 0.8587\n",
            "Epoch 26/50\n",
            "210/210 [==============================] - 249s 1s/step - loss: 0.2924 - pothole_loss: 0.0186 - pothole_level_loss: 0.2738 - pothole_accuracy: 0.9931 - pothole_level_accuracy: 0.8872 - val_loss: 1.3524 - val_pothole_loss: 0.6971 - val_pothole_level_loss: 0.6553 - val_pothole_accuracy: 0.9132 - val_pothole_level_accuracy: 0.8737\n",
            "Epoch 27/50\n",
            "210/210 [==============================] - 249s 1s/step - loss: 0.2924 - pothole_loss: 0.0215 - pothole_level_loss: 0.2709 - pothole_accuracy: 0.9927 - pothole_level_accuracy: 0.8892 - val_loss: 1.3403 - val_pothole_loss: 0.7835 - val_pothole_level_loss: 0.5568 - val_pothole_accuracy: 0.8420 - val_pothole_level_accuracy: 0.7937\n",
            "Epoch 28/50\n",
            "210/210 [==============================] - 248s 1s/step - loss: 0.2842 - pothole_loss: 0.0156 - pothole_level_loss: 0.2687 - pothole_accuracy: 0.9948 - pothole_level_accuracy: 0.8905 - val_loss: 1.2984 - val_pothole_loss: 0.6365 - val_pothole_level_loss: 0.6619 - val_pothole_accuracy: 0.8867 - val_pothole_level_accuracy: 0.8641\n",
            "Epoch 29/50\n",
            "210/210 [==============================] - 248s 1s/step - loss: 0.2717 - pothole_loss: 0.0155 - pothole_level_loss: 0.2562 - pothole_accuracy: 0.9946 - pothole_level_accuracy: 0.8947 - val_loss: 1.6977 - val_pothole_loss: 0.8973 - val_pothole_level_loss: 0.8004 - val_pothole_accuracy: 0.9072 - val_pothole_level_accuracy: 0.8487\n",
            "Epoch 30/50\n",
            "210/210 [==============================] - 248s 1s/step - loss: 0.2642 - pothole_loss: 0.0146 - pothole_level_loss: 0.2496 - pothole_accuracy: 0.9951 - pothole_level_accuracy: 0.8966 - val_loss: 1.2392 - val_pothole_loss: 0.5961 - val_pothole_level_loss: 0.6431 - val_pothole_accuracy: 0.8891 - val_pothole_level_accuracy: 0.8342\n",
            "Epoch 31/50\n",
            "210/210 [==============================] - 247s 1s/step - loss: 0.2693 - pothole_loss: 0.0185 - pothole_level_loss: 0.2508 - pothole_accuracy: 0.9936 - pothole_level_accuracy: 0.8969 - val_loss: 1.8700 - val_pothole_loss: 0.7743 - val_pothole_level_loss: 1.0957 - val_pothole_accuracy: 0.8664 - val_pothole_level_accuracy: 0.8108\n",
            "Epoch 32/50\n",
            "210/210 [==============================] - 247s 1s/step - loss: 0.2532 - pothole_loss: 0.0126 - pothole_level_loss: 0.2406 - pothole_accuracy: 0.9959 - pothole_level_accuracy: 0.9007 - val_loss: 2.1919 - val_pothole_loss: 1.1807 - val_pothole_level_loss: 1.0113 - val_pothole_accuracy: 0.8666 - val_pothole_level_accuracy: 0.8716\n",
            "Epoch 33/50\n",
            "210/210 [==============================] - 247s 1s/step - loss: 0.2466 - pothole_loss: 0.0148 - pothole_level_loss: 0.2318 - pothole_accuracy: 0.9950 - pothole_level_accuracy: 0.9042 - val_loss: 1.3090 - val_pothole_loss: 0.6849 - val_pothole_level_loss: 0.6241 - val_pothole_accuracy: 0.9110 - val_pothole_level_accuracy: 0.8563\n",
            "Epoch 34/50\n",
            "210/210 [==============================] - 249s 1s/step - loss: 0.2392 - pothole_loss: 0.0121 - pothole_level_loss: 0.2272 - pothole_accuracy: 0.9959 - pothole_level_accuracy: 0.9047 - val_loss: 1.6961 - val_pothole_loss: 0.7479 - val_pothole_level_loss: 0.9482 - val_pothole_accuracy: 0.8719 - val_pothole_level_accuracy: 0.8180\n",
            "Epoch 35/50\n",
            "210/210 [==============================] - 247s 1s/step - loss: 0.2382 - pothole_loss: 0.0106 - pothole_level_loss: 0.2276 - pothole_accuracy: 0.9965 - pothole_level_accuracy: 0.9081 - val_loss: 1.4941 - val_pothole_loss: 0.8481 - val_pothole_level_loss: 0.6459 - val_pothole_accuracy: 0.8582 - val_pothole_level_accuracy: 0.8071\n",
            "Epoch 36/50\n",
            "210/210 [==============================] - 247s 1s/step - loss: 0.2343 - pothole_loss: 0.0112 - pothole_level_loss: 0.2231 - pothole_accuracy: 0.9962 - pothole_level_accuracy: 0.9114 - val_loss: 1.5919 - val_pothole_loss: 0.8549 - val_pothole_level_loss: 0.7370 - val_pothole_accuracy: 0.8983 - val_pothole_level_accuracy: 0.8656\n",
            "Epoch 37/50\n",
            "210/210 [==============================] - 247s 1s/step - loss: 0.2181 - pothole_loss: 0.0079 - pothole_level_loss: 0.2102 - pothole_accuracy: 0.9974 - pothole_level_accuracy: 0.9139 - val_loss: 1.5863 - val_pothole_loss: 0.8322 - val_pothole_level_loss: 0.7540 - val_pothole_accuracy: 0.9076 - val_pothole_level_accuracy: 0.8181\n",
            "Epoch 38/50\n",
            "210/210 [==============================] - 247s 1s/step - loss: 0.2140 - pothole_loss: 0.0109 - pothole_level_loss: 0.2031 - pothole_accuracy: 0.9962 - pothole_level_accuracy: 0.9164 - val_loss: 2.2060 - val_pothole_loss: 0.7003 - val_pothole_level_loss: 1.5057 - val_pothole_accuracy: 0.8861 - val_pothole_level_accuracy: 0.7840\n",
            "Epoch 39/50\n",
            "210/210 [==============================] - 247s 1s/step - loss: 0.2102 - pothole_loss: 0.0094 - pothole_level_loss: 0.2007 - pothole_accuracy: 0.9970 - pothole_level_accuracy: 0.9149 - val_loss: 1.3228 - val_pothole_loss: 0.7057 - val_pothole_level_loss: 0.6171 - val_pothole_accuracy: 0.8976 - val_pothole_level_accuracy: 0.8062\n",
            "Epoch 40/50\n",
            "210/210 [==============================] - 247s 1s/step - loss: 0.1955 - pothole_loss: 0.0063 - pothole_level_loss: 0.1892 - pothole_accuracy: 0.9979 - pothole_level_accuracy: 0.9218 - val_loss: 1.5603 - val_pothole_loss: 0.8309 - val_pothole_level_loss: 0.7295 - val_pothole_accuracy: 0.9110 - val_pothole_level_accuracy: 0.8541\n",
            "Epoch 41/50\n",
            "210/210 [==============================] - 247s 1s/step - loss: 0.1931 - pothole_loss: 0.0055 - pothole_level_loss: 0.1876 - pothole_accuracy: 0.9981 - pothole_level_accuracy: 0.9222 - val_loss: 2.0667 - val_pothole_loss: 0.8322 - val_pothole_level_loss: 1.2346 - val_pothole_accuracy: 0.9088 - val_pothole_level_accuracy: 0.7971\n",
            "Epoch 42/50\n",
            "210/210 [==============================] - 246s 1s/step - loss: 0.1996 - pothole_loss: 0.0105 - pothole_level_loss: 0.1891 - pothole_accuracy: 0.9964 - pothole_level_accuracy: 0.9208 - val_loss: 1.4695 - val_pothole_loss: 0.7592 - val_pothole_level_loss: 0.7103 - val_pothole_accuracy: 0.8970 - val_pothole_level_accuracy: 0.8533\n",
            "Epoch 43/50\n",
            "210/210 [==============================] - 246s 1s/step - loss: 0.1943 - pothole_loss: 0.0089 - pothole_level_loss: 0.1854 - pothole_accuracy: 0.9966 - pothole_level_accuracy: 0.9246 - val_loss: 2.6424 - val_pothole_loss: 1.0304 - val_pothole_level_loss: 1.6120 - val_pothole_accuracy: 0.9044 - val_pothole_level_accuracy: 0.8397\n",
            "Epoch 44/50\n",
            "210/210 [==============================] - 246s 1s/step - loss: 0.1935 - pothole_loss: 0.0137 - pothole_level_loss: 0.1797 - pothole_accuracy: 0.9953 - pothole_level_accuracy: 0.9267 - val_loss: 1.5190 - val_pothole_loss: 0.8455 - val_pothole_level_loss: 0.6735 - val_pothole_accuracy: 0.8790 - val_pothole_level_accuracy: 0.8260\n",
            "Epoch 45/50\n",
            "210/210 [==============================] - 246s 1s/step - loss: 0.1843 - pothole_loss: 0.0086 - pothole_level_loss: 0.1757 - pothole_accuracy: 0.9974 - pothole_level_accuracy: 0.9273 - val_loss: 1.7539 - val_pothole_loss: 0.8166 - val_pothole_level_loss: 0.9373 - val_pothole_accuracy: 0.9029 - val_pothole_level_accuracy: 0.8699\n",
            "Epoch 46/50\n",
            "210/210 [==============================] - 246s 1s/step - loss: 0.1764 - pothole_loss: 0.0082 - pothole_level_loss: 0.1683 - pothole_accuracy: 0.9974 - pothole_level_accuracy: 0.9309 - val_loss: 1.7099 - val_pothole_loss: 0.9564 - val_pothole_level_loss: 0.7535 - val_pothole_accuracy: 0.8683 - val_pothole_level_accuracy: 0.8437\n",
            "Epoch 47/50\n",
            "210/210 [==============================] - 246s 1s/step - loss: 0.1741 - pothole_loss: 0.0076 - pothole_level_loss: 0.1666 - pothole_accuracy: 0.9973 - pothole_level_accuracy: 0.9320 - val_loss: 1.8051 - val_pothole_loss: 0.8896 - val_pothole_level_loss: 0.9154 - val_pothole_accuracy: 0.9219 - val_pothole_level_accuracy: 0.8503\n",
            "Epoch 48/50\n",
            "210/210 [==============================] - 246s 1s/step - loss: 0.1707 - pothole_loss: 0.0067 - pothole_level_loss: 0.1641 - pothole_accuracy: 0.9983 - pothole_level_accuracy: 0.9363 - val_loss: 1.8978 - val_pothole_loss: 0.8882 - val_pothole_level_loss: 1.0097 - val_pothole_accuracy: 0.9000 - val_pothole_level_accuracy: 0.8731\n",
            "Epoch 49/50\n",
            "210/210 [==============================] - 246s 1s/step - loss: 0.1752 - pothole_loss: 0.0081 - pothole_level_loss: 0.1670 - pothole_accuracy: 0.9972 - pothole_level_accuracy: 0.9333 - val_loss: 1.5779 - val_pothole_loss: 0.8836 - val_pothole_level_loss: 0.6944 - val_pothole_accuracy: 0.8743 - val_pothole_level_accuracy: 0.7987\n",
            "Epoch 50/50\n",
            "210/210 [==============================] - 246s 1s/step - loss: 0.1614 - pothole_loss: 0.0047 - pothole_level_loss: 0.1567 - pothole_accuracy: 0.9984 - pothole_level_accuracy: 0.9363 - val_loss: 1.5803 - val_pothole_loss: 0.8683 - val_pothole_level_loss: 0.7121 - val_pothole_accuracy: 0.8820 - val_pothole_level_accuracy: 0.8443\n"
          ]
        }
      ],
      "source": [
        "_ = model.fit_generator(generator=my_training_batch_generator,\n",
        "                   steps_per_epoch = int(21000 // batch_size),\n",
        "                   epochs = 1,\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": "M5cf0v82VOMi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.save(\"pothole_detection_model_prototype_1_adam.h5\")"
      ],
      "metadata": {
        "id": "xJmE5pn_Pxpd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "path = f\"/content/gdrive/My Drive/pothole_detection_model_prototype_1_adam/1/\"\n",
        "\n",
        "tf.saved_model.save(model, path, signatures = model.concat.get_concrete_function(\n",
        "    tf.TensorSpec(shape=[], dtype=tf.string, name=\"Input_Layer\")))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "6gpI3NOhN2MU",
        "outputId": "e30e8c43-9f9f-4f44-d44a-6acccd855459"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-28-115c404bb10e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"/content/gdrive/My Drive/pothole_detection_model_prototype_1_adam/1/\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m tf.saved_model.save(model, path, signatures = model.concat.get_concrete_function(\n\u001b[0m\u001b[1;32m      4\u001b[0m     tf.TensorSpec(shape=[], dtype=tf.string, name=\"Input_Layer\")))\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'Functional' object has no attribute 'concat'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GTXri0-6QHQI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "outputId": "44e86d56-a590-44bf-ce94-8a7ff5a41536"
      },
      "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"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-22-e98f539d6b07>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"/content/gdrive/My Drive/{model_save_name}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'wb'\u001b[0m\u001b[0;34m)\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/keras/engine/training.py\u001b[0m in \u001b[0;36m__reduce__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    313\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuilt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    314\u001b[0m       return (pickle_utils.deserialize_model_from_bytecode,\n\u001b[0;32m--> 315\u001b[0;31m               pickle_utils.serialize_model_as_bytecode(self))\n\u001b[0m\u001b[1;32m    316\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[1;32m    317\u001b[0m       \u001b[0;31m# SavedModel (and hence serialize_model_as_bytecode) only support\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/keras/saving/pickle_utils.py\u001b[0m in \u001b[0;36mserialize_model_as_bytecode\u001b[0;34m(model)\u001b[0m\n\u001b[1;32m     62\u001b[0m   \"\"\"\n\u001b[1;32m     63\u001b[0m   \u001b[0mtemp_dir\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"ram://{uuid.uuid4()}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m   \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp_dir\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     65\u001b[0m   \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m   \u001b[0;32mwith\u001b[0m \u001b[0mtarfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"w\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0marchive\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/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m       \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\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/tensorflow/python/saved_model/save.py\u001b[0m in \u001b[0;36m_map_function_arguments_to_created_inputs\u001b[0;34m(function_arguments, signature_key, function_name)\u001b[0m\n\u001b[1;32m    608\u001b[0m       \u001b[0;31m# generated with non-unique argument names.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    609\u001b[0m       raise ValueError(\n\u001b[0;32m--> 610\u001b[0;31m           \u001b[0;34m\"Got non-flat/non-unique argument names for SavedModel signature \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    611\u001b[0m           \u001b[0;34mf\"'{signature_key}': more than one argument to \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    612\u001b[0m           \u001b[0;34mf\"'{compat.as_str_any(function_name)}' was named \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Got non-flat/non-unique argument names for SavedModel signature 'serving_default': more than one argument to '__inference_signature_wrapper_113562' was named 'Input Layer'. Signatures have one Tensor per named input, so to have predictable names Python functions used to generate these signatures should avoid *args and Tensors in nested structures unless unique names are specified for each. Use tf.TensorSpec(..., name=...) to provide a name for a Tensor input."
          ]
        }
      ],
      "source": [
        "import pickle\n",
        "\n",
        "model_save_name = 'pothole_detection_model_prototype_1_adam.pkl'\n",
        "path = f\"/content/gdrive/My Drive/{model_save_name}\" \n",
        "\n",
        "pickle.dump(model, open(path, 'wb'))\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path = f\"/content/gdrive/My Drive/pothole_detection_model_prototype_1_adam\"\n",
        "model.save(path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "HdBkNCnu_ngo",
        "outputId": "b1ca22c9-d764-43f8-89b2-f2bfbefbd17e"
      },
      "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"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-24-3182dbf114fd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"/content/gdrive/My Drive/pothole_detection_model_prototype_1_adam\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\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/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m       \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\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/tensorflow/python/saved_model/save.py\u001b[0m in \u001b[0;36m_map_function_arguments_to_created_inputs\u001b[0;34m(function_arguments, signature_key, function_name)\u001b[0m\n\u001b[1;32m    608\u001b[0m       \u001b[0;31m# generated with non-unique argument names.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    609\u001b[0m       raise ValueError(\n\u001b[0;32m--> 610\u001b[0;31m           \u001b[0;34m\"Got non-flat/non-unique argument names for SavedModel signature \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    611\u001b[0m           \u001b[0;34mf\"'{signature_key}': more than one argument to \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    612\u001b[0m           \u001b[0;34mf\"'{compat.as_str_any(function_name)}' was named \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Got non-flat/non-unique argument names for SavedModel signature 'serving_default': more than one argument to '__inference_signature_wrapper_121838' was named 'Input Layer'. Signatures have one Tensor per named input, so to have predictable names Python functions used to generate these signatures should avoid *args and Tensors in nested structures unless unique names are specified for each. Use tf.TensorSpec(..., name=...) to provide a name for a Tensor input."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path = f\"/content/gdrive/My Drive/pothole_detection_model_prototype_1_adam/1/\"\n",
        "\n",
        "tf.saved_model.save(model, path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "id": "fDqxzdXY_neH",
        "outputId": "bafbb4a9-9e2b-42f2-d8c7-9ba5ab843d53"
      },
      "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"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-27-1da1248f4725>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"/content/gdrive/My Drive/pothole_detection_model_prototype_1_adam/1/\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msaved_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\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/tensorflow/python/saved_model/save.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(obj, export_dir, signatures, options)\u001b[0m\n\u001b[1;32m   1332\u001b[0m   \u001b[0;31m# pylint: enable=line-too-long\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1333\u001b[0m   \u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIncrementWriteApi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_SAVE_V2_LABEL\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1334\u001b[0;31m   \u001b[0msave_and_return_nodes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexport_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msignatures\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\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   1335\u001b[0m   \u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIncrementWrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrite_version\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"2\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1336\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/saved_model/save.py\u001b[0m in \u001b[0;36msave_and_return_nodes\u001b[0;34m(obj, export_dir, signatures, options, experimental_skip_checkpoint)\u001b[0m\n\u001b[1;32m   1367\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1368\u001b[0m   _, exported_graph, object_saver, asset_info, saved_nodes, node_paths = (\n\u001b[0;32m-> 1369\u001b[0;31m       _build_meta_graph(obj, signatures, options, meta_graph_def))\n\u001b[0m\u001b[1;32m   1370\u001b[0m   saved_model.saved_model_schema_version = (\n\u001b[1;32m   1371\u001b[0m       constants.SAVED_MODEL_SCHEMA_VERSION)\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/saved_model/save.py\u001b[0m in \u001b[0;36m_build_meta_graph\u001b[0;34m(obj, signatures, options, meta_graph_def)\u001b[0m\n\u001b[1;32m   1534\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1535\u001b[0m   \u001b[0;32mwith\u001b[0m \u001b[0msave_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1536\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_build_meta_graph_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msignatures\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeta_graph_def\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/tensorflow/python/saved_model/save.py\u001b[0m in \u001b[0;36m_build_meta_graph_impl\u001b[0;34m(obj, signatures, options, meta_graph_def)\u001b[0m\n\u001b[1;32m   1490\u001b[0m   asset_info, exported_graph = _fill_meta_graph_def(\n\u001b[1;32m   1491\u001b[0m       \u001b[0mmeta_graph_def\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaveable_view\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msignatures\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1492\u001b[0;31m       options.namespace_whitelist, options.experimental_custom_gradients)\n\u001b[0m\u001b[1;32m   1493\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction_aliases\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1494\u001b[0m     \u001b[0mfunction_aliases\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmeta_graph_def\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmeta_info_def\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction_aliases\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/tensorflow/python/saved_model/save.py\u001b[0m in \u001b[0;36m_fill_meta_graph_def\u001b[0;34m(meta_graph_def, saveable_view, signature_functions, namespace_whitelist, save_custom_gradients)\u001b[0m\n\u001b[1;32m    938\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    939\u001b[0m   \u001b[0;32mwith\u001b[0m \u001b[0mexported_graph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_default\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 940\u001b[0;31m     \u001b[0msignatures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_generate_signatures\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msignature_functions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresource_map\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    941\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mconcrete_function\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msaveable_view\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcrete_functions\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    942\u001b[0m       \u001b[0mconcrete_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_to_graph\u001b[0m\u001b[0;34m(\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/tensorflow/python/saved_model/save.py\u001b[0m in \u001b[0;36m_generate_signatures\u001b[0;34m(signature_functions, resource_map)\u001b[0m\n\u001b[1;32m    676\u001b[0m     mapped_inputs, exterior_argument_placeholders = (\n\u001b[1;32m    677\u001b[0m         _map_function_arguments_to_created_inputs(argument_inputs,\n\u001b[0;32m--> 678\u001b[0;31m                                                   signature_key, function.name))\n\u001b[0m\u001b[1;32m    679\u001b[0m     outputs = _call_function_with_mapped_captures(\n\u001b[1;32m    680\u001b[0m         function, mapped_inputs, resource_map)\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/saved_model/save.py\u001b[0m in \u001b[0;36m_map_function_arguments_to_created_inputs\u001b[0;34m(function_arguments, signature_key, function_name)\u001b[0m\n\u001b[1;32m    608\u001b[0m       \u001b[0;31m# generated with non-unique argument names.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    609\u001b[0m       raise ValueError(\n\u001b[0;32m--> 610\u001b[0;31m           \u001b[0;34m\"Got non-flat/non-unique argument names for SavedModel signature \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    611\u001b[0m           \u001b[0;34mf\"'{signature_key}': more than one argument to \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    612\u001b[0m           \u001b[0;34mf\"'{compat.as_str_any(function_name)}' was named \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Got non-flat/non-unique argument names for SavedModel signature 'serving_default': more than one argument to '__inference_signature_wrapper_134252' was named 'Input Layer'. Signatures have one Tensor per named input, so to have predictable names Python functions used to generate these signatures should avoid *args and Tensors in nested structures unless unique names are specified for each. Use tf.TensorSpec(..., name=...) to provide a name for a Tensor input."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "bW-vW7Nh_na-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "model = tf.keras.models.load_model('/content/gdrive/MyDrive/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/pothole_detection_model_prototype_1_adam.tflite\", \"wb\").write(tflite_model)"
      ],
      "metadata": {
        "id": "E_IQam9V5hQp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6dcd3cd3-a441-43aa-b637-c686d232b964"
      },
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "L0FhlFB1vOWl"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "Pothole_Detection.ipynb",
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}