STAGE_D_TRAINV2_KAGGLE.ipynb

{
  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/nafkem/Hamoye-Internship/blob/main/STAGE_D_TRAINV2_KAGGLE.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QY2HYw0DEXQ8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0a4bc768-89f5-4905-af74-6b23e7fc8bd5"
      },
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "258"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import gc\n",
        "gc.collect()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade tensorflow"
      ],
      "metadata": {
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        },
        "id": "dY8nDoIvIBBw",
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      },
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            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow) (3.2.0)\n"
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XeGVJu2BE7LO"
      },
      "outputs": [],
      "source": [
        "train_classes= pd.read_csv('/content/train_v2.csv')"
      ]
    },
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              "       image_name           tags\n",
              "count       40479          40479\n",
              "unique      40479            449\n",
              "top       train_0  clear primary\n",
              "freq            1          13636"
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          "metadata": {},
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        "train_classes.describe()"
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              "  image_name                                       tags\n",
              "0    train_0                               haze primary\n",
              "1    train_1            agriculture clear primary water\n",
              "2    train_2                              clear primary\n",
              "3    train_3                              clear primary\n",
              "4    train_4  agriculture clear habitation primary road"
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              "          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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ],
      "source": [
        "train_classes.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X27QQl3bKoWL",
        "outputId": "dce6ad16-15f6-408e-d65d-3af4e63fc2ac"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40479, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ],
      "source": [
        "train_classes.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qh-6cohwK0cP",
        "outputId": "4ddc03e2-f7f7-4b1f-d952-780bf8cc1607"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "image_name    0\n",
              "tags          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ],
      "source": [
        "train_classes.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ED9FBcIGK8jc",
        "outputId": "c1c0bfb7-dd36-4cbf-c0a9-50300bdc74db"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 40479 entries, 0 to 40478\n",
            "Data columns (total 2 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   image_name  40479 non-null  object\n",
            " 1   tags        40479 non-null  object\n",
            "dtypes: object(2)\n",
            "memory usage: 632.6+ KB\n"
          ]
        }
      ],
      "source": [
        "train_classes.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u67ZfqScLbtD"
      },
      "outputs": [],
      "source": [
        "#defining a function to add unique labels to a set\n",
        "labels = set ()\n",
        "def splitting_tags(tags):\n",
        "  for tag in tags.split():\n",
        "      labels.add(tag)   "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PIN3m-jgMLFX",
        "outputId": "084a2e09-8c32-4bc7-de16-752ee47431db"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['blow_down', 'road', 'slash_burn', 'habitation', 'primary', 'partly_cloudy', 'artisinal_mine', 'cloudy', 'conventional_mine', 'haze', 'cultivation', 'clear', 'blooming', 'water', 'selective_logging', 'agriculture', 'bare_ground']\n"
          ]
        }
      ],
      "source": [
        "#redefine the train_classes;create a copy to avoid overwritting the existing one.\n",
        "train_classes1=train_classes.copy()\n",
        "\n",
        "train_classes1['tags'].apply(splitting_tags)\n",
        "labels = list(labels)\n",
        "print(labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "OUcfclxrMduc",
        "outputId": "c543cbe9-7b90-46de-d70a-4b24408d3cb4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        image_name                                           tags\n",
              "0          train_0                                   haze primary\n",
              "1          train_1                agriculture clear primary water\n",
              "2          train_2                                  clear primary\n",
              "3          train_3                                  clear primary\n",
              "4          train_4      agriculture clear habitation primary road\n",
              "...            ...                                            ...\n",
              "40474  train_40474                                  clear primary\n",
              "40475  train_40475                                         cloudy\n",
              "40476  train_40476                      agriculture clear primary\n",
              "40477  train_40477                 agriculture clear primary road\n",
              "40478  train_40478  agriculture cultivation partly_cloudy primary\n",
              "\n",
              "[40479 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-34aafbe5-ef9c-46fa-ae19-345378f797e7\">\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_name</th>\n",
              "      <th>tags</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>train_0</td>\n",
              "      <td>haze primary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>train_1</td>\n",
              "      <td>agriculture clear primary water</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>train_2</td>\n",
              "      <td>clear primary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>train_3</td>\n",
              "      <td>clear primary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>train_4</td>\n",
              "      <td>agriculture clear habitation primary road</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40474</th>\n",
              "      <td>train_40474</td>\n",
              "      <td>clear primary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40475</th>\n",
              "      <td>train_40475</td>\n",
              "      <td>cloudy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40476</th>\n",
              "      <td>train_40476</td>\n",
              "      <td>agriculture clear primary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40477</th>\n",
              "      <td>train_40477</td>\n",
              "      <td>agriculture clear primary road</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40478</th>\n",
              "      <td>train_40478</td>\n",
              "      <td>agriculture cultivation partly_cloudy primary</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>40479 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-34aafbe5-ef9c-46fa-ae19-345378f797e7')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-34aafbe5-ef9c-46fa-ae19-345378f797e7 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-34aafbe5-ef9c-46fa-ae19-345378f797e7');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ],
      "source": [
        "train_classes1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nnSaSy_pMLVy"
      },
      "outputs": [],
      "source": [
        "#import tensorflow libraies for training the dataset\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense,BatchNormalization,Conv2D, MaxPooling2D\n",
        "from tensorflow.keras.layers import Dropout,Flatten\n",
        "from tensorflow.keras.optimizers import Adam,SGD\n",
        "from tensorflow.keras.callbacks import EarlyStopping,ModelCheckpoint,TensorBoard\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fjVWXk-aMLpM"
      },
      "outputs": [],
      "source": [
        "#assert that the length of the dataframe is the same as the shaoe\n",
        "assert len(train_classes1['image_name'].unique())==train_classes1.shape[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "cgXwzJeSNU15",
        "outputId": "b3348ac9-4522-4369-aead-1178f98a824e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  image_name                                       tags\n",
              "0    train_0                               haze primary\n",
              "1    train_1            agriculture clear primary water\n",
              "2    train_2                              clear primary\n",
              "3    train_3                              clear primary\n",
              "4    train_4  agriculture clear habitation primary road"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-52d54f98-b6b6-4e41-b7d1-74657a3cf823\">\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_name</th>\n",
              "      <th>tags</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>train_0</td>\n",
              "      <td>haze primary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>train_1</td>\n",
              "      <td>agriculture clear primary water</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>train_2</td>\n",
              "      <td>clear primary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>train_3</td>\n",
              "      <td>clear primary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>train_4</td>\n",
              "      <td>agriculture clear habitation primary road</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-52d54f98-b6b6-4e41-b7d1-74657a3cf823')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-52d54f98-b6b6-4e41-b7d1-74657a3cf823 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-52d54f98-b6b6-4e41-b7d1-74657a3cf823');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ],
      "source": [
        "#adding .jpg extension to the column image_name so as to have same name format as the image files\n",
        "train_classes1['image_name'].apply(lambda x:'{}.jpg'.format(x))\n",
        "train_classes1.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hUQXOj2XSRDQ"
      },
      "outputs": [],
      "source": [
        "##One hot encoding is performed on the labels in train classes\n",
        "for tag in labels:\n",
        "    train_classes1[tag] = train_classes1['tags'].apply(lambda x: 1 if tag in x.split() else 0)\n",
        "    \n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## adding .jpg extension to the column image_name so as to have same name format as the image files\n",
        "train_classes1['image_name'] = train_classes1['image_name'].apply(lambda x: '{}.jpg'.format(x))\n",
        "train_classes1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "1ImtxngHGi1i",
        "outputId": "72af5294-9550-4bd8-b58c-340effff845e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    image_name                                       tags  blow_down  road  \\\n",
              "0  train_0.jpg                               haze primary          0     0   \n",
              "1  train_1.jpg            agriculture clear primary water          0     0   \n",
              "2  train_2.jpg                              clear primary          0     0   \n",
              "3  train_3.jpg                              clear primary          0     0   \n",
              "4  train_4.jpg  agriculture clear habitation primary road          0     1   \n",
              "\n",
              "   slash_burn  habitation  primary  partly_cloudy  artisinal_mine  cloudy  \\\n",
              "0           0           0        1              0               0       0   \n",
              "1           0           0        1              0               0       0   \n",
              "2           0           0        1              0               0       0   \n",
              "3           0           0        1              0               0       0   \n",
              "4           0           1        1              0               0       0   \n",
              "\n",
              "   conventional_mine  haze  cultivation  clear  blooming  water  \\\n",
              "0                  0     1            0      0         0      0   \n",
              "1                  0     0            0      1         0      1   \n",
              "2                  0     0            0      1         0      0   \n",
              "3                  0     0            0      1         0      0   \n",
              "4                  0     0            0      1         0      0   \n",
              "\n",
              "   selective_logging  agriculture  bare_ground  \n",
              "0                  0            0            0  \n",
              "1                  0            1            0  \n",
              "2                  0            0            0  \n",
              "3                  0            0            0  \n",
              "4                  0            1            0  "
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xt1Hi0u3NUh7"
      },
      "outputs": [],
      "source": [
        "#define a function for accuracy for multi_label classification\n",
        "def multi_label_acc(y_true, y_pred, epsilon = 1e-4):\n",
        "    \n",
        "    y_true = tf.cast(y_true, tf.float32)\n",
        "    y_pred = tf.cast(tf.greater(tf.cast(y_pred, tf.float32), tf.constant(0.5)), tf.float32)\n",
        "    \n",
        "    tp = tf.reduce_sum(y_true * y_pred, axis = 1)\n",
        "    fp = tf.reduce_sum(y_pred, axis = 1) - tp\n",
        "    fn = tf.reduce_sum(y_true, axis = 1) - tp\n",
        "    \n",
        "    y_true = tf.cast(y_true, tf.bool)\n",
        "    y_pred = tf.cast(y_pred, tf.bool)\n",
        "        \n",
        "    tn = tf.reduce_sum(tf.cast(tf.logical_not(y_true), tf.float32) * tf.cast(tf.logical_not(y_pred), tf.float32), \n",
        "                       axis = 1)\n",
        "    return (tp+tn)/(tp+tn+fp+fn+epsilon)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def fbeta(y_true, y_pred, beta = 2, epsilon = 1e-4):\n",
        "    '''\n",
        "    Set y_true and y_pred\n",
        "\n",
        "    Args:\n",
        "        y_true: correct target values\n",
        "        Y_pred: predicted values returned by the classifer\n",
        "        beta = 2\n",
        "        epsilon= 1e-4\n",
        "        \n",
        "    Returns:\n",
        "        fbeta score\n",
        "    '''\n",
        "    \n",
        "    beta_squared = beta**2\n",
        "    \n",
        "    y_true = tf.cast(y_true, tf.float32)\n",
        "    y_pred = tf.cast(tf.greater(tf.cast(y_pred, tf.float32), tf.constant(0.5)), tf.float32)\n",
        "    \n",
        "    tp = tf.reduce_sum(y_true * y_pred, axis = 1)\n",
        "    fp = tf.reduce_sum(y_pred, axis = 1) - tp\n",
        "    fn = tf.reduce_sum(y_true, axis = 1) - tp\n",
        "    \n",
        "    precision = tp/(tp+fp+epsilon)\n",
        "    recall = tp/(tp+fn+epsilon)\n",
        "    \n",
        "    fb = (1+beta_squared)*precision*recall / (beta_squared*precision+recall+epsilon)\n",
        "    return fb\n"
      ],
      "metadata": {
        "id": "mpXURjNUy0uk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#modelcheckpoint is set to monitor the model using validation fbeta score and save the best only\n",
        "save_best_check_point = ModelCheckpoint(filepath = 'best_model.hdf5', monitor = 'val_fbeta', mode = 'max',save_best_only = True, save_weights_only = True)"
      ],
      "metadata": {
        "id": "uuZnSdAwHX-y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#initializing imagedatagenerator with a validation split of 0.2\n",
        "train_image_gen = ImageDataGenerator(rescale = 1/255, validation_split = 0.2)\n"
      ],
      "metadata": {
        "id": "OMBiYt81H0Ml"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "columns = list(train_classes1. columns [2:])"
      ],
      "metadata": {
        "id": "NF6DPpZbMoRq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "YXCyKed5DfLm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5kCAOSizM-2X",
        "outputId": "8fd249d5-b6c8-4ed0-ce6b-4911b767fc30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['blow_down',\n",
              " 'road',\n",
              " 'slash_burn',\n",
              " 'habitation',\n",
              " 'primary',\n",
              " 'partly_cloudy',\n",
              " 'artisinal_mine',\n",
              " 'cloudy',\n",
              " 'conventional_mine',\n",
              " 'haze',\n",
              " 'cultivation',\n",
              " 'clear',\n",
              " 'blooming',\n",
              " 'water',\n",
              " 'selective_logging',\n",
              " 'agriculture',\n",
              " 'bare_ground']"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#defining our model using a function build_model()\n",
        "def build_model():\n",
        "    model = keras.Sequential()\n",
        "    model.add(BatchNormalization(input_shape=(128, 128, 3)))\n",
        "    model.add(Conv2D(32, kernel_size=(3, 3), padding='same', activation='relu'))\n",
        "    model.add(Conv2D(32, kernel_size=(3, 3), activation='relu'))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "    model.add(Dropout(0.2))\n",
        "\n",
        "    model.add(Conv2D(64, kernel_size=(3, 3), padding='same', activation='relu'))\n",
        "    model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "    model.add(Dropout(0.2))\n",
        "\n",
        "    model.add(Conv2D(128, kernel_size=(3, 3), padding='same', activation='relu'))\n",
        "    model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "    model.add(Dropout(0.2))\n",
        "\n",
        "    model.add(Conv2D(256, kernel_size=(3, 3), padding='same', activation='relu'))\n",
        "    model.add(Conv2D(256, kernel_size=(3, 3), activation='relu'))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "    model.add(Dropout(0.2))\n",
        "\n",
        "    model.add(Flatten())\n",
        "    model.add(Dense(512, activation='relu'))\n",
        "    model.add(Dropout(0.5))\n",
        "    model.add(Dense(17, activation='sigmoid'))\n",
        "\n",
        "    opt = Adam(lr=1e-4)\n",
        "\n",
        "    model.compile(loss='binary_crossentropy',\n",
        "              # We NEED binary here, since categorical_crossentropy l1 norms the output before calculating loss.\n",
        "              optimizer=opt,metrics=[multi_label_acc, fbeta])\n",
        "\n",
        "    return model\n",
        "\n",
        "    "
      ],
      "metadata": {
        "id": "HznAeQObdMNF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#setting up step size for training and validation image data\n",
        "step_train_size = int(np.ceil(train_generator.samples / train_generator.batch_size))\n",
        "step_val_size = int(np.ceil(val_generator.samples / val_generator.batch_size))"
      ],
      "metadata": {
        "id": "NzWJZBf9Mitk"
      },
      "execution_count": 149,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#initializing the model\n",
        "model1 = build_model()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-nAYqYCDMtWl",
        "outputId": "420237ac-d0aa-4061-acfe-fbe0b89fa5c6"
      },
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#this shows the summary of the model, simply put as the model architecture\n",
        "model1.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MuijJBhnM-Mx",
        "outputId": "31d9c68c-603c-43a4-b65f-fd85eeef8e6b"
      },
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " batch_normalization_2 (Batc  (None, 128, 128, 3)      12        \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " conv2d_16 (Conv2D)          (None, 128, 128, 32)      896       \n",
            "                                                                 \n",
            " conv2d_17 (Conv2D)          (None, 126, 126, 32)      9248      \n",
            "                                                                 \n",
            " max_pooling2d_8 (MaxPooling  (None, 63, 63, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_10 (Dropout)        (None, 63, 63, 32)        0         \n",
            "                                                                 \n",
            " conv2d_18 (Conv2D)          (None, 63, 63, 64)        18496     \n",
            "                                                                 \n",
            " conv2d_19 (Conv2D)          (None, 61, 61, 64)        36928     \n",
            "                                                                 \n",
            " max_pooling2d_9 (MaxPooling  (None, 30, 30, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_11 (Dropout)        (None, 30, 30, 64)        0         \n",
            "                                                                 \n",
            " conv2d_20 (Conv2D)          (None, 30, 30, 128)       73856     \n",
            "                                                                 \n",
            " conv2d_21 (Conv2D)          (None, 28, 28, 128)       147584    \n",
            "                                                                 \n",
            " max_pooling2d_10 (MaxPoolin  (None, 14, 14, 128)      0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_12 (Dropout)        (None, 14, 14, 128)       0         \n",
            "                                                                 \n",
            " conv2d_22 (Conv2D)          (None, 14, 14, 256)       295168    \n",
            "                                                                 \n",
            " conv2d_23 (Conv2D)          (None, 12, 12, 256)       590080    \n",
            "                                                                 \n",
            " max_pooling2d_11 (MaxPoolin  (None, 6, 6, 256)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_13 (Dropout)        (None, 6, 6, 256)         0         \n",
            "                                                                 \n",
            " flatten_2 (Flatten)         (None, 9216)              0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 512)               4719104   \n",
            "                                                                 \n",
            " dropout_14 (Dropout)        (None, 512)               0         \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 17)                8721      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 5,900,093\n",
            "Trainable params: 5,900,087\n",
            "Non-trainable params: 6\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#fitting our model using the parameters already defined \n",
        "model1.fit(x = train_generator, steps_per_epoch = step_train_size, validation_data = val_generator, validation_steps = step_val_size,epochs = 25, \n",
        "callbacks = [save_best_check_point])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZZYpJIjsNIzQ",
        "outputId": "641dd6c1-6a7b-4d6c-c19c-4b5065c16695"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            " 314/2024 [===>..........................] - ETA: 2:12:22 - loss: 0.2694 - multi_label_acc: 0.9043 - fbeta: 0.6416"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#initializing imagedatagenerator with a validation split of 0.2\n",
        "train_image_gen = ImageDataGenerator(rescale = 1/255, validation_split = 0.2)"
      ],
      "metadata": {
        "id": "eP6CBZmqdqrn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#generating train data generator which is 80% of the train dataset\n",
        "#note that a generator contains both features and target of the data\n",
        "train_generator = train_image_gen.flow_from_dataframe(dataframe=train_classes1,\n",
        "                                                directory =\"/content/drive/MyDrive/train-jpg\",  \n",
        "                                                x_col=\"image_name\", y_col=columns, subset=\"training\", \n",
        "                                                batch_size=16,seed=2021, shuffle=True, \n",
        "                                                class_mode=\"raw\", target_size=(128,128))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IvibCpJkd0sZ",
        "outputId": "6f562fa7-3012-4cc6-fc4c-7bc1596ac2d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 32384 validated image filenames.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#generating validation data which is expected to be 20% of the train dataset since validation split is 0.2\n",
        "val_generator = train_image_gen.flow_from_dataframe(dataframe=train_classes1,\n",
        "                                                directory =\"/content/drive/MyDrive/train-jpg\",  \n",
        "                                                x_col=\"image_name\", y_col=columns, subset=\"validation\", \n",
        "                                                batch_size=16,seed=2021, shuffle=True, \n",
        "                                                class_mode=\"raw\", target_size=(128,128))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oon36u4gfD2M",
        "outputId": "a5b01655-4673-4969-e498-21d82f1f4292"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 8095 validated image filenames.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#fitting our model using the parameters already defined \n",
        "build_model.fit(x = train_generator, steps_per_epoch = step_train_size, validation_data = val_generator, \n",
        "           validation_steps = step_val_size,epochs = 25, callbacks = [save_best_check_point])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "id": "UIgsszt3onpQ",
        "outputId": "9733113b-778d-4340-874e-7bcb817f1b8f"
      },
      "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-146-89c72b5afb26>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#fitting our model using the parameters already defined\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m build_model.fit(x = train_generator, steps_per_epoch = step_train_size, validation_data = val_generator, \n\u001b[0m\u001b[1;32m      3\u001b[0m            validation_steps = step_val_size,epochs = 25, callbacks = [save_best_check_point])\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'function' object has no attribute 'fit'"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "STAGE D TRAINV2  KAGGLE.ipynb",
      "provenance": [],
      "mount_file_id": "1QJz3WXajP48oWSdmCkuDzEidEOB4NsOM",
      "authorship_tag": "ABX9TyMGBjhXI2OjonKSLACPSS+y",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}