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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hammaad2002/PSL_Recognition/blob/main/PSL_(Version_3).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!pip install mediapipe"
      ],
      "metadata": {
        "id": "UOpmkaQuX3j7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "En0H_oJVXYua"
      },
      "outputs": [],
      "source": [
        "from PSL_Pipeline import DataPipeline\n",
        "import cv2\n",
        "from google.colab.patches import cv2_imshow\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from tqdm.notebook import tqdm\n",
        "import os\n",
        "import requests\n",
        "from bs4 import BeautifulSoup\n",
        "import zipfile\n",
        "import random\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(42) # set the random seed to 42\n",
        "random.seed(42)"
      ],
      "metadata": {
        "id": "7-PiPxFmbqtz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# URL of the website to scrape\n",
        "url = \"https://www.mediafire.com/file/nfwsrnpsyqjon46/PictureAlphabetsUpdatedV3.0.zip/file\"\n",
        "\n",
        "# File path to save the downloaded file\n",
        "download_path = \"/content/PictureAlphabetsUpdatedV3.0.zip\"\n",
        "\n",
        "# Directory path to extract the contents of the zip file\n",
        "extract_dir = \"/content/dataset/\"\n",
        "\n",
        "# Send a GET request to the website\n",
        "response = requests.get(url)\n",
        "\n",
        "# Create a BeautifulSoup object to parse the HTML content\n",
        "soup = BeautifulSoup(response.content, \"html.parser\")\n",
        "\n",
        "# Find the <a> tag with the specified class\n",
        "download_link = soup.find(\"a\", class_=\"input popsok\")\n",
        "\n",
        "# Extract the value of the 'href' attribute\n",
        "download_url = download_link[\"href\"]\n",
        "\n",
        "# Send a GET request to download the file\n",
        "response = requests.get(download_url)\n",
        "\n",
        "# Save the file to the specified path\n",
        "with open(download_path, \"wb\") as file:\n",
        "    file.write(response.content)\n",
        "\n",
        "# Extract the contents of the zip file\n",
        "with zipfile.ZipFile(download_path, \"r\") as zip_ref:\n",
        "    zip_ref.extractall(extract_dir)\n",
        "\n",
        "# Remove the downloaded zip file\n",
        "os.remove(download_path)\n",
        "\n",
        "print(\"File downloaded and extracted successfully.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5FGlYTo_YM3Z",
        "outputId": "907f755c-e196-4d63-beff-2d91975547fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "File downloaded and extracted successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "files = os.listdir('/content/dataset/Picture Alphabets/')\n",
        "classes = [i.split()[0] for i in files]\n",
        "print('Total classes or alphabets:', len(classes))\n",
        "print('Name of all classes are:')\n",
        "[print(i) for i in classes][-1] # -1 because it was printing [none, none, none, ...]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IT3MLu-RYOzq",
        "outputId": "92c88090-116a-4919-ccde-9a1c9f06b672"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total classes or alphabets: 38\n",
            "Name of all classes are:\n",
            "Kaaf\n",
            "Aeein\n",
            "Gaaf\n",
            "Khayy\n",
            "Laam\n",
            "Zhuaad\n",
            "Sayy\n",
            "Sheen\n",
            "Fayy\n",
            "Seen\n",
            "Bayy\n",
            "Do-chashmi\n",
            "Gho-eein\n",
            "Jeem\n",
            "Hamza\n",
            "Dal\n",
            "Qaaf\n",
            "Noon\n",
            "Chayy\n",
            "Tayy\n",
            "Wow\n",
            "Choti-yay\n",
            "Rayy\n",
            "Ghaeein\n",
            "Zhe\n",
            "To-eein\n",
            "Alif\n",
            "Bari-yay\n",
            "Rhayy\n",
            "Choti-hay\n",
            "Tee\n",
            "Zayy\n",
            "Suaad\n",
            "Zaal\n",
            "Daal\n",
            "Hayy\n",
            "Payy\n",
            "Meem\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#encoding classes by creating a dictionary\n",
        "encoded_classes = {}\n",
        "for i, data in enumerate(classes):\n",
        "    encoded_classes[data] = i\n",
        "encoded_classes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sgfVZY95YQuw",
        "outputId": "598e8ec4-8d3e-41a8-f096-ca38dd0d0181"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Kaaf': 0,\n",
              " 'Aeein': 1,\n",
              " 'Gaaf': 2,\n",
              " 'Khayy': 3,\n",
              " 'Laam': 4,\n",
              " 'Zhuaad': 5,\n",
              " 'Sayy': 6,\n",
              " 'Sheen': 7,\n",
              " 'Fayy': 8,\n",
              " 'Seen': 9,\n",
              " 'Bayy': 10,\n",
              " 'Do-chashmi': 11,\n",
              " 'Gho-eein': 12,\n",
              " 'Jeem': 13,\n",
              " 'Hamza': 14,\n",
              " 'Dal': 15,\n",
              " 'Qaaf': 16,\n",
              " 'Noon': 17,\n",
              " 'Chayy': 18,\n",
              " 'Tayy': 19,\n",
              " 'Wow': 20,\n",
              " 'Choti-yay': 21,\n",
              " 'Rayy': 22,\n",
              " 'Ghaeein': 23,\n",
              " 'Zhe': 24,\n",
              " 'To-eein': 25,\n",
              " 'Alif': 26,\n",
              " 'Bari-yay': 27,\n",
              " 'Rhayy': 28,\n",
              " 'Choti-hay': 29,\n",
              " 'Tee': 30,\n",
              " 'Zayy': 31,\n",
              " 'Suaad': 32,\n",
              " 'Zaal': 33,\n",
              " 'Daal': 34,\n",
              " 'Hayy': 35,\n",
              " 'Payy': 36,\n",
              " 'Meem': 37}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = DataPipeline(640, 480)\n",
        "dataset = []\n",
        "for i in tqdm(range(len(files))):\n",
        "    for folder in files:\n",
        "        class_features = []\n",
        "        if folder.split()[0] == classes[i]:\n",
        "            path = '/content/dataset/Picture Alphabets/' + folder\n",
        "            pictures = os.listdir(path)\n",
        "            for picture in pictures:\n",
        "                new_path = path + '/' + picture\n",
        "                image = cv2.imread(new_path)\n",
        "                output = data.apply_landmarks(image.copy(), draw=True)\n",
        "                if output == 0:\n",
        "                    continue\n",
        "                else:\n",
        "                    image_marked, pointsX, pointsY = output\n",
        "                final_image, meta_data = data.hand_cropper(image_marked, pointsX, pointsY)\n",
        "                x_points, y_points = data.points_calculator(final_image) # image with marked landmarks\n",
        "                if len(x_points) != 21:\n",
        "                    x_points, y_points = data.points_approximator(image, meta_data, pointsX, pointsY)\n",
        "                features = x_points + y_points\n",
        "                label = encoded_classes[folder.split()[0]]\n",
        "                total_row = features + [label]\n",
        "                dataset.append(total_row)"
      ],
      "metadata": {
        "colab": {
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/38 [00:00<?, ?it/s]"
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      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(dataset)\n",
        "df.head()"
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          "height": 256
        },
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-4ee0e57c-5134-4a4e-89b9-f61660c42c65 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-4ee0e57c-5134-4a4e-89b9-f61660c42c65');\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",
              "\n",
              "\n",
              "<div id=\"df-db584329-da27-414d-adc2-d892453a4ec4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-db584329-da27-414d-adc2-d892453a4ec4')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-db584329-da27-414d-adc2-d892453a4ec4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Training"
      ],
      "metadata": {
        "id": "qhNRy0dsk2YB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ML MODEL PART I"
      ],
      "metadata": {
        "id": "CEMWFY98jCM4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!pip install catboost"
      ],
      "metadata": {
        "id": "wQzY304MlCRx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
        "import lightgbm as lgb\n",
        "import catboost as cb\n",
        "import xgboost as xgb\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.model_selection import RandomizedSearchCV"
      ],
      "metadata": {
        "id": "oW97fjZJknve"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape # dataset shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-oMq2mhIlmYQ",
        "outputId": "6d8701e1-23fa-4645-dbb4-a5920a79ba8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(950, 43)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target = np.array(df.iloc[:,-1])\n",
        "target.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-qHqtmMilrRK",
        "outputId": "e59de9bc-958a-40d4-a3f4-ba6c48c37e5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(950,)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features = np.array(df.iloc[:,:-1])\n",
        "features.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z7l59ct_mLU7",
        "outputId": "ddde5366-b03f-45c3-96c1-af412513d84e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(950, 42)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "# Load your dataset (replace this with your data)\n",
        "X = features\n",
        "y = target\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "\n",
        "#Linear Kernel: This is the simplest kernel function and is used when the data is linearly separable. The syntax for the argument is kernel='linear' 13.\n",
        "#Polynomial Kernel: This kernel function is used when the data is not linearly separable. The syntax for the argument is kernel='poly' 1.\n",
        "#Radial Basis Function (RBF) Kernel: This kernel function is used when the data is not linearly separable in a higher-dimensional space. The syntax for the argument is kernel='rbf' 124.\n",
        "#Sigmoid Kernel: This kernel function is used when the data is not linearly separable and the data points are not in a high-dimensional space. The syntax for the argument is kernel='sigmoid' 1.\n",
        "\n",
        "# Define a list of classifiers\n",
        "classifiers = [\n",
        "    SVC(kernel='linear', random_state=42),\n",
        "    SVC(kernel='poly', random_state=42),\n",
        "    SVC(kernel='rbf', random_state=42),\n",
        "    SVC(kernel='sigmoid', random_state=42),\n",
        "    LogisticRegression(random_state=42),\n",
        "    DecisionTreeClassifier(random_state=42),\n",
        "    RandomForestClassifier(random_state=42),\n",
        "    AdaBoostClassifier(random_state=42),\n",
        "    lgb.LGBMClassifier(random_state=42),\n",
        "    cb.CatBoostClassifier(random_state=42),\n",
        "    xgb.XGBClassifier(random_state=42)\n",
        "]\n",
        "\n",
        "# Define a list of classifiers\n",
        "classifiers_list = [\n",
        "    'SVC Linear',\n",
        "    'SVC Poly',\n",
        "    'SVC RBF',\n",
        "    'SVC Sigmoid',\n",
        "    'Logistic Regression',\n",
        "    'Decision Tree Classifier',\n",
        "    'Random Forest Classifier',\n",
        "    'AdaBoost Classifier',\n",
        "    'LGBM Classifier',\n",
        "    'CatBoost Classifier',\n",
        "    'XGB Classifier'\n",
        "]\n",
        "\n",
        "# Train and evaluate each classifier\n",
        "accuracy_list = []\n",
        "for clf in classifiers:\n",
        "    clf.fit(X_train, y_train)\n",
        "    accuracy = clf.score(X_test, y_test)\n",
        "    accuracy_list.append(accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TZz5ADjMk7xv",
        "outputId": "3bfe9310-c0f9-464f-969c-d249318e0b4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Learning rate set to 0.078027\n",
            "0:\tlearn: 3.5661938\ttotal: 501ms\tremaining: 8m 20s\n",
            "1:\tlearn: 3.4756540\ttotal: 1.01s\tremaining: 8m 24s\n",
            "2:\tlearn: 3.3852972\ttotal: 1.49s\tremaining: 8m 15s\n",
            "3:\tlearn: 3.3068808\ttotal: 1.95s\tremaining: 8m 6s\n",
            "4:\tlearn: 3.2209557\ttotal: 2.5s\tremaining: 8m 17s\n",
            "5:\tlearn: 3.1576203\ttotal: 3s\tremaining: 8m 16s\n",
            "6:\tlearn: 3.1189699\ttotal: 3.67s\tremaining: 8m 40s\n",
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            "8:\tlearn: 2.9909881\ttotal: 5.25s\tremaining: 9m 38s\n",
            "9:\tlearn: 2.9373003\ttotal: 6s\tremaining: 9m 54s\n",
            "10:\tlearn: 2.8776431\ttotal: 6.73s\tremaining: 10m 4s\n",
            "11:\tlearn: 2.8214956\ttotal: 7.12s\tremaining: 9m 45s\n",
            "12:\tlearn: 2.7651932\ttotal: 7.54s\tremaining: 9m 32s\n",
            "13:\tlearn: 2.7168098\ttotal: 7.99s\tremaining: 9m 22s\n",
            "14:\tlearn: 2.6609584\ttotal: 8.23s\tremaining: 9m\n",
            "15:\tlearn: 2.6025853\ttotal: 8.46s\tremaining: 8m 40s\n",
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            "20:\tlearn: 2.3621837\ttotal: 9.61s\tremaining: 7m 28s\n",
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            "996:\tlearn: 0.0715090\ttotal: 4m 30s\tremaining: 815ms\n",
            "997:\tlearn: 0.0714996\ttotal: 4m 31s\tremaining: 543ms\n",
            "998:\tlearn: 0.0713605\ttotal: 4m 31s\tremaining: 272ms\n",
            "999:\tlearn: 0.0712837\ttotal: 4m 31s\tremaining: 0us\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(accuracy_list)):\n",
        "    print(f\"{classifiers_list[i]} model's accuracy is {accuracy_list[i]}.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xn_bDyO3ohkG",
        "outputId": "663760be-ae56-451c-fe3f-5167c135b03c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVC Linear model's accuracy is 0.6736842105263158.\n",
            "SVC Poly model's accuracy is 0.14210526315789473.\n",
            "SVC RBF model's accuracy is 0.1.\n",
            "SVC Sigmoid model's accuracy is 0.031578947368421054.\n",
            "Logistic Regression model's accuracy is 0.5473684210526316.\n",
            "Decision Tree Classifier model's accuracy is 0.5894736842105263.\n",
            "Random Forest Classifier model's accuracy is 0.7736842105263158.\n",
            "AdaBoost Classifier model's accuracy is 0.021052631578947368.\n",
            "LGBM Classifier model's accuracy is 0.7631578947368421.\n",
            "CatBoost Classifier model's accuracy is 0.7684210526315789.\n",
            "XGB Classifier model's accuracy is 0.7052631578947368.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Best performing model is {classifiers_list[accuracy_list.index(max(accuracy_list))]} with an accuracy of {max(accuracy_list)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zfdntu_UrqhZ",
        "outputId": "3c2981c4-55b8-481b-b15a-911f448db959"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best performing model is Random Forest Classifier with an accuracy of 0.7736842105263158\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Random Forest Parameters:\n",
        "\n",
        "\n",
        "```\n",
        "n_estimators=100\n",
        "criterion='gini'\n",
        "max_depth=None\n",
        "min_samples_split=2\n",
        "min_samples_leaf=1\n",
        "min_weight_fraction_leaf=0.0\n",
        "max_features='sqrt'\n",
        "max_leaf_nodes=None\n",
        "min_impurity_decrease=0.0\n",
        "bootstrap=True\n",
        "oob_score=False\n",
        "n_jobs=None\n",
        "random_state=None\n",
        "verbose=0\n",
        "warm_start=False\n",
        "class_weight=None\n",
        "ccp_alpha=0.0\n",
        "max_samples=None\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "29GyYfj3h2vv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define parameter grid to search over\n",
        "grid = {\n",
        "    'n_estimators': [100, 200, 300],\n",
        "    'min_samples_split': [2, 4, 6, 8, 10],\n",
        "    'min_samples_leaf': [1, 2, 3, 4, 5],\n",
        "    'min_weight_fraction_leaf': [0.0, 0.1, 0.2, 0.3],\n",
        "    'max_leaf_nodes': [1, 2, 3, 4, 5]\n",
        "}"
      ],
      "metadata": {
        "id": "nQ61bZb6_wfK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Utilizing Grid Search"
      ],
      "metadata": {
        "id": "nUR7z0Dej3Ua"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestClassifier(random_state=42)\n",
        "grid_search = GridSearchCV(estimator=model, param_grid=grid, cv=3, verbose=2, n_jobs=-1)\n",
        "grid_search.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "DtgWQXzR5Mlg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "best_params_grid = grid_search.best_params_\n",
        "best_score_grid = grid_search.best_score_\n",
        "print('Best parameters are:')\n",
        "print(best_params_grid)\n",
        "print('Best score is ', best_score_grid)"
      ],
      "metadata": {
        "id": "KGVkQqNBnzeb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Utilizing Random Search"
      ],
      "metadata": {
        "id": "sKTFiJJYj0Dl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestClassifier(random_state=42)\n",
        "random_search = RandomizedSearchCV(estimator=model, param_distributions=grid, cv=3, verbose=2, n_jobs=-1, random_state=np.random.seed(42))\n",
        "random_search.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "U9BaCPZejqgO",
        "outputId": "1dd3ead8-2c36-4d75-a34a-8fe1761e646a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:378: FitFailedWarning: \n",
            "3 fits failed out of a total of 30.\n",
            "The score on these train-test partitions for these parameters will be set to nan.\n",
            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
            "\n",
            "Below are more details about the failures:\n",
            "--------------------------------------------------------------------------------\n",
            "3 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py\", line 340, in fit\n",
            "    self._validate_params()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 600, in _validate_params\n",
            "    validate_parameter_constraints(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 97, in validate_parameter_constraints\n",
            "    raise InvalidParameterError(\n",
            "sklearn.utils._param_validation.InvalidParameterError: The 'max_leaf_nodes' parameter of RandomForestClassifier must be an int in the range [2, inf) or None. Got 1 instead.\n",
            "\n",
            "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py:952: UserWarning: One or more of the test scores are non-finite: [0.18420425 0.31444711 0.24869026 0.18420425 0.32362827 0.35130144\n",
            " 0.30787505        nan 0.12764931 0.37755335]\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=3, estimator=RandomForestClassifier(random_state=42),\n",
              "                   n_jobs=-1,\n",
              "                   param_distributions={'max_leaf_nodes': [1, 2, 3, 4, 5],\n",
              "                                        'min_samples_leaf': [1, 2, 3, 4, 5],\n",
              "                                        'min_samples_split': [2, 4, 6, 8, 10],\n",
              "                                        'min_weight_fraction_leaf': [0.0, 0.1,\n",
              "                                                                     0.2, 0.3],\n",
              "                                        'n_estimators': [100, 200, 300]},\n",
              "                   verbose=2)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=3, estimator=RandomForestClassifier(random_state=42),\n",
              "                   n_jobs=-1,\n",
              "                   param_distributions={&#x27;max_leaf_nodes&#x27;: [1, 2, 3, 4, 5],\n",
              "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 3, 4, 5],\n",
              "                                        &#x27;min_samples_split&#x27;: [2, 4, 6, 8, 10],\n",
              "                                        &#x27;min_weight_fraction_leaf&#x27;: [0.0, 0.1,\n",
              "                                                                     0.2, 0.3],\n",
              "                                        &#x27;n_estimators&#x27;: [100, 200, 300]},\n",
              "                   verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=3, estimator=RandomForestClassifier(random_state=42),\n",
              "                   n_jobs=-1,\n",
              "                   param_distributions={&#x27;max_leaf_nodes&#x27;: [1, 2, 3, 4, 5],\n",
              "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 3, 4, 5],\n",
              "                                        &#x27;min_samples_split&#x27;: [2, 4, 6, 8, 10],\n",
              "                                        &#x27;min_weight_fraction_leaf&#x27;: [0.0, 0.1,\n",
              "                                                                     0.2, 0.3],\n",
              "                                        &#x27;n_estimators&#x27;: [100, 200, 300]},\n",
              "                   verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_params_random = random_search.best_params_\n",
        "best_score_random = random_search.best_score_\n",
        "print('Best parameters are:')\n",
        "print(best_params_random)\n",
        "print('Best score is ', best_score_random)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JdAmLiOgCJJS",
        "outputId": "494a2209-a96e-4b1d-ee5a-4d5be364b09b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best parameters are:\n",
            "{'n_estimators': 300, 'min_weight_fraction_leaf': 0.0, 'min_samples_split': 8, 'min_samples_leaf': 1, 'max_leaf_nodes': 5}\n",
            "Best score is  0.3775533493095972\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = cb.CatBoostClassifier(**best_params_random, iterations = 2000, random_state=42, task_type='GPU')"
      ],
      "metadata": {
        "id": "IyGIWcN_eX-B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train,\n",
        "          eval_set = [(X_test, y_test)],\n",
        "          verbose = True)\n",
        "\n",
        "# Accuracy\n",
        "model.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OaYK-GHi5W0Q",
        "outputId": "1c30a48b-4f21-4ffd-d49e-0db207b5f771"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0:\tlearn: 3.5562667\ttest: 3.5797084\tbest: 3.5797084 (0)\ttotal: 101ms\tremaining: 3m 21s\n",
            "1:\tlearn: 3.4794026\ttest: 3.5203247\tbest: 3.5203247 (1)\ttotal: 186ms\tremaining: 3m 6s\n",
            "2:\tlearn: 3.4113185\ttest: 3.4704024\tbest: 3.4704024 (2)\ttotal: 242ms\tremaining: 2m 41s\n",
            "3:\tlearn: 3.3346211\ttest: 3.4166028\tbest: 3.4166028 (3)\ttotal: 305ms\tremaining: 2m 32s\n",
            "4:\tlearn: 3.2830688\ttest: 3.3767257\tbest: 3.3767257 (4)\ttotal: 364ms\tremaining: 2m 25s\n",
            "5:\tlearn: 3.2285400\ttest: 3.3373490\tbest: 3.3373490 (5)\ttotal: 421ms\tremaining: 2m 20s\n",
            "6:\tlearn: 3.1791167\ttest: 3.3004626\tbest: 3.3004626 (6)\ttotal: 463ms\tremaining: 2m 11s\n",
            "7:\tlearn: 3.1093242\ttest: 3.2461448\tbest: 3.2461448 (7)\ttotal: 519ms\tremaining: 2m 9s\n",
            "8:\tlearn: 3.0499839\ttest: 3.2089982\tbest: 3.2089982 (8)\ttotal: 565ms\tremaining: 2m 5s\n",
            "9:\tlearn: 2.9998545\ttest: 3.1699289\tbest: 3.1699289 (9)\ttotal: 607ms\tremaining: 2m\n",
            "10:\tlearn: 2.9462165\ttest: 3.1410850\tbest: 3.1410850 (10)\ttotal: 655ms\tremaining: 1m 58s\n",
            "11:\tlearn: 2.8919286\ttest: 3.1044283\tbest: 3.1044283 (11)\ttotal: 703ms\tremaining: 1m 56s\n",
            "12:\tlearn: 2.8264134\ttest: 3.0595334\tbest: 3.0595334 (12)\ttotal: 747ms\tremaining: 1m 54s\n",
            "13:\tlearn: 2.7744202\ttest: 3.0194066\tbest: 3.0194066 (13)\ttotal: 797ms\tremaining: 1m 53s\n",
            "14:\tlearn: 2.7398091\ttest: 2.9945608\tbest: 2.9945608 (14)\ttotal: 836ms\tremaining: 1m 50s\n",
            "15:\tlearn: 2.6883808\ttest: 2.9585491\tbest: 2.9585491 (15)\ttotal: 879ms\tremaining: 1m 49s\n",
            "16:\tlearn: 2.6412312\ttest: 2.9328147\tbest: 2.9328147 (16)\ttotal: 930ms\tremaining: 1m 48s\n",
            "17:\tlearn: 2.5861518\ttest: 2.8976726\tbest: 2.8976726 (17)\ttotal: 978ms\tremaining: 1m 47s\n",
            "18:\tlearn: 2.5262177\ttest: 2.8568019\tbest: 2.8568019 (18)\ttotal: 1.03s\tremaining: 1m 47s\n",
            "19:\tlearn: 2.4787023\ttest: 2.8293987\tbest: 2.8293987 (19)\ttotal: 1.09s\tremaining: 1m 47s\n",
            "20:\tlearn: 2.4517922\ttest: 2.8099288\tbest: 2.8099288 (20)\ttotal: 1.13s\tremaining: 1m 46s\n",
            "21:\tlearn: 2.4006495\ttest: 2.7761285\tbest: 2.7761285 (21)\ttotal: 1.18s\tremaining: 1m 45s\n",
            "22:\tlearn: 2.3560071\ttest: 2.7406215\tbest: 2.7406215 (22)\ttotal: 1.22s\tremaining: 1m 45s\n",
            "23:\tlearn: 2.3149568\ttest: 2.7176215\tbest: 2.7176215 (23)\ttotal: 1.28s\tremaining: 1m 45s\n",
            "24:\tlearn: 2.2786149\ttest: 2.7016345\tbest: 2.7016345 (24)\ttotal: 1.33s\tremaining: 1m 45s\n",
            "25:\tlearn: 2.2434040\ttest: 2.6779873\tbest: 2.6779873 (25)\ttotal: 1.39s\tremaining: 1m 45s\n",
            "26:\tlearn: 2.2003092\ttest: 2.6533303\tbest: 2.6533303 (26)\ttotal: 1.45s\tremaining: 1m 46s\n",
            "27:\tlearn: 2.1635835\ttest: 2.6288948\tbest: 2.6288948 (27)\ttotal: 1.5s\tremaining: 1m 45s\n",
            "28:\tlearn: 2.1384538\ttest: 2.6105027\tbest: 2.6105027 (28)\ttotal: 1.53s\tremaining: 1m 44s\n",
            "29:\tlearn: 2.0963061\ttest: 2.5861145\tbest: 2.5861145 (29)\ttotal: 1.59s\tremaining: 1m 44s\n",
            "30:\tlearn: 2.0545918\ttest: 2.5506287\tbest: 2.5506287 (30)\ttotal: 1.65s\tremaining: 1m 45s\n",
            "31:\tlearn: 2.0177744\ttest: 2.5270649\tbest: 2.5270649 (31)\ttotal: 1.71s\tremaining: 1m 45s\n",
            "32:\tlearn: 1.9746248\ttest: 2.4908740\tbest: 2.4908740 (32)\ttotal: 1.75s\tremaining: 1m 44s\n",
            "33:\tlearn: 1.9382989\ttest: 2.4659654\tbest: 2.4659654 (33)\ttotal: 1.8s\tremaining: 1m 44s\n",
            "34:\tlearn: 1.9008288\ttest: 2.4420870\tbest: 2.4420870 (34)\ttotal: 1.85s\tremaining: 1m 43s\n",
            "35:\tlearn: 1.8717002\ttest: 2.4248325\tbest: 2.4248325 (35)\ttotal: 1.9s\tremaining: 1m 43s\n",
            "36:\tlearn: 1.8385371\ttest: 2.3993537\tbest: 2.3993537 (36)\ttotal: 1.95s\tremaining: 1m 43s\n",
            "37:\tlearn: 1.8130190\ttest: 2.3790180\tbest: 2.3790180 (37)\ttotal: 1.99s\tremaining: 1m 42s\n",
            "38:\tlearn: 1.7838437\ttest: 2.3605909\tbest: 2.3605909 (38)\ttotal: 2.03s\tremaining: 1m 42s\n",
            "39:\tlearn: 1.7521015\ttest: 2.3391500\tbest: 2.3391500 (39)\ttotal: 2.08s\tremaining: 1m 42s\n",
            "40:\tlearn: 1.7305107\ttest: 2.3267454\tbest: 2.3267454 (40)\ttotal: 2.12s\tremaining: 1m 41s\n",
            "41:\tlearn: 1.7062089\ttest: 2.3113040\tbest: 2.3113040 (41)\ttotal: 2.18s\tremaining: 1m 41s\n",
            "42:\tlearn: 1.6744430\ttest: 2.2869533\tbest: 2.2869533 (42)\ttotal: 2.22s\tremaining: 1m 41s\n",
            "43:\tlearn: 1.6428203\ttest: 2.2641322\tbest: 2.2641322 (43)\ttotal: 2.27s\tremaining: 1m 41s\n",
            "44:\tlearn: 1.6216734\ttest: 2.2446198\tbest: 2.2446198 (44)\ttotal: 2.31s\tremaining: 1m 40s\n",
            "45:\tlearn: 1.6009491\ttest: 2.2322706\tbest: 2.2322706 (45)\ttotal: 2.37s\tremaining: 1m 40s\n",
            "46:\tlearn: 1.5806234\ttest: 2.2163123\tbest: 2.2163123 (46)\ttotal: 2.41s\tremaining: 1m 40s\n",
            "47:\tlearn: 1.5594619\ttest: 2.2068612\tbest: 2.2068612 (47)\ttotal: 2.48s\tremaining: 1m 40s\n",
            "48:\tlearn: 1.5363790\ttest: 2.1904207\tbest: 2.1904207 (48)\ttotal: 2.53s\tremaining: 1m 40s\n",
            "49:\tlearn: 1.5076911\ttest: 2.1694212\tbest: 2.1694212 (49)\ttotal: 2.58s\tremaining: 1m 40s\n",
            "50:\tlearn: 1.4859895\ttest: 2.1575150\tbest: 2.1575150 (50)\ttotal: 2.62s\tremaining: 1m 40s\n",
            "51:\tlearn: 1.4624841\ttest: 2.1397443\tbest: 2.1397443 (51)\ttotal: 2.67s\tremaining: 1m 39s\n",
            "52:\tlearn: 1.4517790\ttest: 2.1348055\tbest: 2.1348055 (52)\ttotal: 2.71s\tremaining: 1m 39s\n",
            "53:\tlearn: 1.4382483\ttest: 2.1271717\tbest: 2.1271717 (53)\ttotal: 2.76s\tremaining: 1m 39s\n",
            "54:\tlearn: 1.4226836\ttest: 2.1159228\tbest: 2.1159228 (54)\ttotal: 2.79s\tremaining: 1m 38s\n",
            "55:\tlearn: 1.4040865\ttest: 2.1051395\tbest: 2.1051395 (55)\ttotal: 2.84s\tremaining: 1m 38s\n",
            "56:\tlearn: 1.3837969\ttest: 2.0939334\tbest: 2.0939334 (56)\ttotal: 2.88s\tremaining: 1m 38s\n",
            "57:\tlearn: 1.3618346\ttest: 2.0759364\tbest: 2.0759364 (57)\ttotal: 2.93s\tremaining: 1m 38s\n",
            "58:\tlearn: 1.3410188\ttest: 2.0597687\tbest: 2.0597687 (58)\ttotal: 2.98s\tremaining: 1m 37s\n",
            "59:\tlearn: 1.3175187\ttest: 2.0425158\tbest: 2.0425158 (59)\ttotal: 3.03s\tremaining: 1m 37s\n",
            "60:\tlearn: 1.2955150\ttest: 2.0308314\tbest: 2.0308314 (60)\ttotal: 3.08s\tremaining: 1m 37s\n",
            "61:\tlearn: 1.2722979\ttest: 2.0154260\tbest: 2.0154260 (61)\ttotal: 3.13s\tremaining: 1m 38s\n",
            "62:\tlearn: 1.2521647\ttest: 2.0058963\tbest: 2.0058963 (62)\ttotal: 3.2s\tremaining: 1m 38s\n",
            "63:\tlearn: 1.2334526\ttest: 1.9950782\tbest: 1.9950782 (63)\ttotal: 3.25s\tremaining: 1m 38s\n",
            "64:\tlearn: 1.2187622\ttest: 1.9866026\tbest: 1.9866026 (64)\ttotal: 3.29s\tremaining: 1m 38s\n",
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            "1910:\tlearn: 0.0157698\ttest: 0.9015142\tbest: 0.9015142 (1910)\ttotal: 1m 40s\tremaining: 4.66s\n",
            "1911:\tlearn: 0.0157599\ttest: 0.9015146\tbest: 0.9015142 (1910)\ttotal: 1m 40s\tremaining: 4.61s\n",
            "1912:\tlearn: 0.0157515\ttest: 0.9014823\tbest: 0.9014823 (1912)\ttotal: 1m 40s\tremaining: 4.55s\n",
            "1913:\tlearn: 0.0157402\ttest: 0.9014387\tbest: 0.9014387 (1913)\ttotal: 1m 40s\tremaining: 4.5s\n",
            "1914:\tlearn: 0.0157318\ttest: 0.9014313\tbest: 0.9014313 (1914)\ttotal: 1m 40s\tremaining: 4.45s\n",
            "1915:\tlearn: 0.0157249\ttest: 0.9014303\tbest: 0.9014303 (1915)\ttotal: 1m 40s\tremaining: 4.39s\n",
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            "1917:\tlearn: 0.0157040\ttest: 0.9014084\tbest: 0.9014084 (1917)\ttotal: 1m 40s\tremaining: 4.29s\n",
            "1918:\tlearn: 0.0156979\ttest: 0.9013588\tbest: 0.9013588 (1918)\ttotal: 1m 40s\tremaining: 4.24s\n",
            "1919:\tlearn: 0.0156902\ttest: 0.9013838\tbest: 0.9013588 (1918)\ttotal: 1m 40s\tremaining: 4.18s\n",
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            "1926:\tlearn: 0.0156309\ttest: 0.9011770\tbest: 0.9011770 (1926)\ttotal: 1m 40s\tremaining: 3.82s\n",
            "1927:\tlearn: 0.0156211\ttest: 0.9012095\tbest: 0.9011770 (1926)\ttotal: 1m 40s\tremaining: 3.76s\n",
            "1928:\tlearn: 0.0156105\ttest: 0.9011689\tbest: 0.9011689 (1928)\ttotal: 1m 40s\tremaining: 3.71s\n",
            "1929:\tlearn: 0.0156019\ttest: 0.9011626\tbest: 0.9011626 (1929)\ttotal: 1m 40s\tremaining: 3.66s\n",
            "1930:\tlearn: 0.0155920\ttest: 0.9011388\tbest: 0.9011388 (1930)\ttotal: 1m 40s\tremaining: 3.61s\n",
            "1931:\tlearn: 0.0155824\ttest: 0.9010714\tbest: 0.9010714 (1931)\ttotal: 1m 40s\tremaining: 3.55s\n",
            "1932:\tlearn: 0.0155733\ttest: 0.9010461\tbest: 0.9010461 (1932)\ttotal: 1m 41s\tremaining: 3.5s\n",
            "1933:\tlearn: 0.0155650\ttest: 0.9010574\tbest: 0.9010461 (1932)\ttotal: 1m 41s\tremaining: 3.45s\n",
            "1934:\tlearn: 0.0155542\ttest: 0.9010563\tbest: 0.9010461 (1932)\ttotal: 1m 41s\tremaining: 3.4s\n",
            "1935:\tlearn: 0.0155512\ttest: 0.9010288\tbest: 0.9010288 (1935)\ttotal: 1m 41s\tremaining: 3.34s\n",
            "1936:\tlearn: 0.0155447\ttest: 0.9009986\tbest: 0.9009986 (1936)\ttotal: 1m 41s\tremaining: 3.29s\n",
            "1937:\tlearn: 0.0155346\ttest: 0.9009636\tbest: 0.9009636 (1937)\ttotal: 1m 41s\tremaining: 3.24s\n",
            "1938:\tlearn: 0.0155246\ttest: 0.9009228\tbest: 0.9009228 (1938)\ttotal: 1m 41s\tremaining: 3.19s\n",
            "1939:\tlearn: 0.0155129\ttest: 0.9008914\tbest: 0.9008914 (1939)\ttotal: 1m 41s\tremaining: 3.13s\n",
            "1940:\tlearn: 0.0155075\ttest: 0.9008710\tbest: 0.9008710 (1940)\ttotal: 1m 41s\tremaining: 3.08s\n",
            "1941:\tlearn: 0.0154974\ttest: 0.9008235\tbest: 0.9008235 (1941)\ttotal: 1m 41s\tremaining: 3.03s\n",
            "1942:\tlearn: 0.0154865\ttest: 0.9007504\tbest: 0.9007504 (1942)\ttotal: 1m 41s\tremaining: 2.98s\n",
            "1943:\tlearn: 0.0154772\ttest: 0.9007907\tbest: 0.9007504 (1942)\ttotal: 1m 41s\tremaining: 2.92s\n",
            "1944:\tlearn: 0.0154679\ttest: 0.9007864\tbest: 0.9007504 (1942)\ttotal: 1m 41s\tremaining: 2.87s\n",
            "1945:\tlearn: 0.0154581\ttest: 0.9007957\tbest: 0.9007504 (1942)\ttotal: 1m 41s\tremaining: 2.82s\n",
            "1946:\tlearn: 0.0154508\ttest: 0.9007641\tbest: 0.9007504 (1942)\ttotal: 1m 41s\tremaining: 2.77s\n",
            "1947:\tlearn: 0.0154408\ttest: 0.9007673\tbest: 0.9007504 (1942)\ttotal: 1m 41s\tremaining: 2.71s\n",
            "1948:\tlearn: 0.0154325\ttest: 0.9007157\tbest: 0.9007157 (1948)\ttotal: 1m 41s\tremaining: 2.66s\n",
            "1949:\tlearn: 0.0154246\ttest: 0.9006935\tbest: 0.9006935 (1949)\ttotal: 1m 41s\tremaining: 2.61s\n",
            "1950:\tlearn: 0.0154150\ttest: 0.9006481\tbest: 0.9006481 (1950)\ttotal: 1m 41s\tremaining: 2.56s\n",
            "1951:\tlearn: 0.0154071\ttest: 0.9006468\tbest: 0.9006468 (1951)\ttotal: 1m 41s\tremaining: 2.5s\n",
            "1952:\tlearn: 0.0154000\ttest: 0.9006438\tbest: 0.9006438 (1952)\ttotal: 1m 41s\tremaining: 2.45s\n",
            "1953:\tlearn: 0.0153918\ttest: 0.9006259\tbest: 0.9006259 (1953)\ttotal: 1m 41s\tremaining: 2.4s\n",
            "1954:\tlearn: 0.0153819\ttest: 0.9005989\tbest: 0.9005989 (1954)\ttotal: 1m 41s\tremaining: 2.35s\n",
            "1955:\tlearn: 0.0153704\ttest: 0.9005345\tbest: 0.9005345 (1955)\ttotal: 1m 42s\tremaining: 2.29s\n",
            "1956:\tlearn: 0.0153617\ttest: 0.9005035\tbest: 0.9005035 (1956)\ttotal: 1m 42s\tremaining: 2.24s\n",
            "1957:\tlearn: 0.0153513\ttest: 0.9005346\tbest: 0.9005035 (1956)\ttotal: 1m 42s\tremaining: 2.19s\n",
            "1958:\tlearn: 0.0153438\ttest: 0.9005243\tbest: 0.9005035 (1956)\ttotal: 1m 42s\tremaining: 2.14s\n",
            "1959:\tlearn: 0.0153364\ttest: 0.9004666\tbest: 0.9004666 (1959)\ttotal: 1m 42s\tremaining: 2.08s\n",
            "1960:\tlearn: 0.0153281\ttest: 0.9004920\tbest: 0.9004666 (1959)\ttotal: 1m 42s\tremaining: 2.03s\n",
            "1961:\tlearn: 0.0153196\ttest: 0.9003732\tbest: 0.9003732 (1961)\ttotal: 1m 42s\tremaining: 1.98s\n",
            "1962:\tlearn: 0.0153126\ttest: 0.9003930\tbest: 0.9003732 (1961)\ttotal: 1m 42s\tremaining: 1.93s\n",
            "1963:\tlearn: 0.0153035\ttest: 0.9004674\tbest: 0.9003732 (1961)\ttotal: 1m 42s\tremaining: 1.88s\n",
            "1964:\tlearn: 0.0152934\ttest: 0.9004187\tbest: 0.9003732 (1961)\ttotal: 1m 42s\tremaining: 1.82s\n",
            "1965:\tlearn: 0.0152866\ttest: 0.9004017\tbest: 0.9003732 (1961)\ttotal: 1m 42s\tremaining: 1.77s\n",
            "1966:\tlearn: 0.0152773\ttest: 0.9004036\tbest: 0.9003732 (1961)\ttotal: 1m 42s\tremaining: 1.72s\n",
            "1967:\tlearn: 0.0152673\ttest: 0.9003720\tbest: 0.9003720 (1967)\ttotal: 1m 42s\tremaining: 1.67s\n",
            "1968:\tlearn: 0.0152578\ttest: 0.9003297\tbest: 0.9003297 (1968)\ttotal: 1m 42s\tremaining: 1.61s\n",
            "1969:\tlearn: 0.0152481\ttest: 0.9002889\tbest: 0.9002889 (1969)\ttotal: 1m 42s\tremaining: 1.56s\n",
            "1970:\tlearn: 0.0152391\ttest: 0.9002862\tbest: 0.9002862 (1970)\ttotal: 1m 42s\tremaining: 1.51s\n",
            "1971:\tlearn: 0.0152302\ttest: 0.9003096\tbest: 0.9002862 (1970)\ttotal: 1m 42s\tremaining: 1.46s\n",
            "1972:\tlearn: 0.0152211\ttest: 0.9003212\tbest: 0.9002862 (1970)\ttotal: 1m 42s\tremaining: 1.41s\n",
            "1973:\tlearn: 0.0152146\ttest: 0.9003081\tbest: 0.9002862 (1970)\ttotal: 1m 42s\tremaining: 1.35s\n",
            "1974:\tlearn: 0.0152055\ttest: 0.9002824\tbest: 0.9002824 (1974)\ttotal: 1m 42s\tremaining: 1.3s\n",
            "1975:\tlearn: 0.0151990\ttest: 0.9002885\tbest: 0.9002824 (1974)\ttotal: 1m 42s\tremaining: 1.25s\n",
            "1976:\tlearn: 0.0151896\ttest: 0.9002584\tbest: 0.9002584 (1976)\ttotal: 1m 42s\tremaining: 1.2s\n",
            "1977:\tlearn: 0.0151795\ttest: 0.9002750\tbest: 0.9002584 (1976)\ttotal: 1m 43s\tremaining: 1.15s\n",
            "1978:\tlearn: 0.0151706\ttest: 0.9002184\tbest: 0.9002184 (1978)\ttotal: 1m 43s\tremaining: 1.09s\n",
            "1979:\tlearn: 0.0151604\ttest: 0.9002056\tbest: 0.9002056 (1979)\ttotal: 1m 43s\tremaining: 1.04s\n",
            "1980:\tlearn: 0.0151534\ttest: 0.9001200\tbest: 0.9001200 (1980)\ttotal: 1m 43s\tremaining: 989ms\n",
            "1981:\tlearn: 0.0151429\ttest: 0.9000659\tbest: 0.9000659 (1981)\ttotal: 1m 43s\tremaining: 937ms\n",
            "1982:\tlearn: 0.0151359\ttest: 0.9000765\tbest: 0.9000659 (1981)\ttotal: 1m 43s\tremaining: 885ms\n",
            "1983:\tlearn: 0.0151274\ttest: 0.9000702\tbest: 0.9000659 (1981)\ttotal: 1m 43s\tremaining: 833ms\n",
            "1984:\tlearn: 0.0151177\ttest: 0.9000739\tbest: 0.9000659 (1981)\ttotal: 1m 43s\tremaining: 781ms\n",
            "1985:\tlearn: 0.0151074\ttest: 0.9000169\tbest: 0.9000169 (1985)\ttotal: 1m 43s\tremaining: 729ms\n",
            "1986:\tlearn: 0.0151010\ttest: 0.8999482\tbest: 0.8999482 (1986)\ttotal: 1m 43s\tremaining: 677ms\n",
            "1987:\tlearn: 0.0150916\ttest: 0.8998458\tbest: 0.8998458 (1987)\ttotal: 1m 43s\tremaining: 624ms\n",
            "1988:\tlearn: 0.0150839\ttest: 0.8998625\tbest: 0.8998458 (1987)\ttotal: 1m 43s\tremaining: 572ms\n",
            "1989:\tlearn: 0.0150757\ttest: 0.8999618\tbest: 0.8998458 (1987)\ttotal: 1m 43s\tremaining: 520ms\n",
            "1990:\tlearn: 0.0150661\ttest: 0.8998301\tbest: 0.8998301 (1990)\ttotal: 1m 43s\tremaining: 468ms\n",
            "1991:\tlearn: 0.0150584\ttest: 0.8997584\tbest: 0.8997584 (1991)\ttotal: 1m 43s\tremaining: 416ms\n",
            "1992:\tlearn: 0.0150499\ttest: 0.8997130\tbest: 0.8997130 (1992)\ttotal: 1m 43s\tremaining: 364ms\n",
            "1993:\tlearn: 0.0150408\ttest: 0.8997308\tbest: 0.8997130 (1992)\ttotal: 1m 43s\tremaining: 312ms\n",
            "1994:\tlearn: 0.0150316\ttest: 0.8996940\tbest: 0.8996940 (1994)\ttotal: 1m 43s\tremaining: 260ms\n",
            "1995:\tlearn: 0.0150205\ttest: 0.8996470\tbest: 0.8996470 (1995)\ttotal: 1m 43s\tremaining: 208ms\n",
            "1996:\tlearn: 0.0150110\ttest: 0.8995580\tbest: 0.8995580 (1996)\ttotal: 1m 43s\tremaining: 156ms\n",
            "1997:\tlearn: 0.0150020\ttest: 0.8994847\tbest: 0.8994847 (1997)\ttotal: 1m 43s\tremaining: 104ms\n",
            "1998:\tlearn: 0.0149923\ttest: 0.8995077\tbest: 0.8994847 (1997)\ttotal: 1m 43s\tremaining: 52ms\n",
            "1999:\tlearn: 0.0149821\ttest: 0.8994256\tbest: 0.8994256 (1999)\ttotal: 1m 44s\tremaining: 0us\n",
            "bestTest = 0.8994256271\n",
            "bestIteration = 1999\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7789473684210526"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "# Assuming you have a trained model object called 'model'\n",
        "# Save the model to a file called 'model.pkl'\n",
        "pickle.dump(model, open('89.9model.pkl', 'wb'))"
      ],
      "metadata": {
        "id": "UckM1cJOfvt1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# DL MODEL PART II"
      ],
      "metadata": {
        "id": "FaN3tt-8jcMH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Machine Learning approach's accuracy is 77.8 %"
      ],
      "metadata": {
        "id": "N-aTDDC1jntt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "```\n",
        "Use the Keras backend function clear_session(). This function clears the Keras session and releases all the resources3.\n",
        "Use del model to immediately remove the model variable from memory4.\n",
        "Use K.get_session().close() to close the current Keras session6.\n",
        "Use gc.collect() to force garbage collection of non-reference memory4.\n",
        "Use numba.cuda.close() to flush the GPU memory, but this will not allow the GPU to be used again2.\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "V32Z0ig3Gn_h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import keras\n",
        "import tensorflow as tf\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "-pRdTKKwEIDE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Data Preparation for the DL models**"
      ],
      "metadata": {
        "id": "N5oCCGFmEKKQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "keras_X = []\n",
        "keras_Y = []"
      ],
      "metadata": {
        "id": "y2cEHMUxxCfo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = DataPipeline(224, 224)\n",
        "for i in tqdm(range(len(files))):\n",
        "    for folder in files:\n",
        "        if folder.split()[0] == classes[i]:\n",
        "            path = '/content/dataset/Picture Alphabets/' + folder\n",
        "            pictures = os.listdir(path)\n",
        "            for picture in pictures:\n",
        "                new_path = path + '/' + picture\n",
        "                image = cv2.imread(new_path)\n",
        "                output = data.apply_landmarks(image.copy(), draw=False)\n",
        "                if output == 0:\n",
        "                    continue\n",
        "                else:\n",
        "                    image_marked, pointsX, pointsY = output\n",
        "                final_image, meta_data = data.hand_cropper(image_marked, pointsX, pointsY)\n",
        "                keras_X.append(final_image)\n",
        "                label = encoded_classes[folder.split()[0]]\n",
        "                keras_Y.append(label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "3eb23d7a54494a93a1c7d66113fc7951",
            "bbd4c609e32d44a3895b3f518c71df60",
            "06cd0deb8c4b4c2db65ae15d07b974eb",
            "9b358642c4fd4ee8ae927fe845d41520",
            "e0c29c0c20fc4dd086686926eaacbf6f",
            "05db3ce60e5d495f9a9f08422b5880f9",
            "a15b0c4d9fe1438495d8bfcd36944aea",
            "97183521eb0d47b59aba2eae79a97a85",
            "4580c0b0e4d3435d9553dbfd7d165d60",
            "5ace2735391a4dc2a6776008943626f1",
            "ab4a6970602640389d2a1b82499c8440"
          ]
        },
        "id": "YCAYXXjewnBI",
        "outputId": "44dd7dfb-1cac-42fd-f1ee-f49167270860"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/38 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3eb23d7a54494a93a1c7d66113fc7951"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess_data(X, Y):\n",
        "    \"\"\"\n",
        "    Does stuff :)\n",
        "    \"\"\"\n",
        "    x_p = keras.applications.resnet50.preprocess_input(X)\n",
        "    y_p = keras.utils.to_categorical(Y, 38)\n",
        "    return x_p, y_p"
      ],
      "metadata": {
        "id": "WkOjOV3L35jD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Stack the images along a new first axis to create a new tensor\n",
        "keras_X = np.stack(keras_X, axis=0)\n",
        "keras_Y = np.array(keras_Y)"
      ],
      "metadata": {
        "id": "eWgGX1D_2xZT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X, y = preprocess_data(keras_X, keras_Y)"
      ],
      "metadata": {
        "id": "IQGiJIi33eN8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_shape = X[-1].shape\n",
        "num_classes = len(classes)"
      ],
      "metadata": {
        "id": "aHUqnq7bkqGF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# resnet 50 popular model\n",
        "input_tensor = keras.Input(shape=(input_shape))\n",
        "model = tf.keras.applications.resnet50.ResNet50(\n",
        "    include_top=False,\n",
        "    weights='imagenet',\n",
        "    input_tensor=input_tensor\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qu1JHhJPjwlH",
        "outputId": "74d65f8e-4077-4a3c-f8b4-d54a1ad73717"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94765736/94765736 [==============================] - 1s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for layers in model.layers[:143]:\n",
        "    layers.trainable = False"
      ],
      "metadata": {
        "id": "0ht_q9Wd4ouP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_model = keras.Sequential()\n",
        "new_model.add(keras.layers.Lambda(lambda image: tf.image.resize(image, (224, 224))))\n",
        "new_model.add(model)\n",
        "new_model.add(keras.layers.Flatten())\n",
        "new_model.add(keras.layers.Dense(num_classes, activation='softmax'))"
      ],
      "metadata": {
        "id": "acmW0AkD9bBQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# First, split into training (60%) and temp (40%)\n",
        "x_train, x_temp, y_train, y_temp = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)\n",
        "\n",
        "# Then, split the temp set into validation (20%) and test (20%)\n",
        "x_val, x_test, y_val, y_test = train_test_split(x_temp, y_temp, test_size=0.5, stratify=y_temp, random_state=42)"
      ],
      "metadata": {
        "id": "70cowniy8-YM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "check_point = keras.callbacks.ModelCheckpoint(filepath='/content/resNet.h5',\n",
        "                                              monitor='val_accuracy',\n",
        "                                              mode='max', # max means it should maximum accuracy in this case which is 'val_acc'.\n",
        "                                              save_best_only=True)"
      ],
      "metadata": {
        "id": "i0I-f10m8VXH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loss_fn = keras.losses.CategoricalCrossentropy(from_logits=False)\n",
        "new_model.compile(loss= loss_fn,\n",
        "                  optimizer=keras.optimizers.Adam(learning_rate=2e-5),\n",
        "                  metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "J6mXF5Q1_RJq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = new_model.fit(x_train, y_train, batch_size=32, epochs=15, verbose=2,\n",
        "                        validation_data=(x_val, y_val), callbacks=[check_point])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PxIfBDrv_PmJ",
        "outputId": "3b309aaf-1ae6-4b88-967c-849c173d3cef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "24/24 - 21s - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.4250 - val_accuracy: 0.8737 - 21s/epoch - 857ms/step\n",
            "Epoch 2/15\n",
            "24/24 - 5s - loss: 1.7982e-04 - accuracy: 1.0000 - val_loss: 0.4079 - val_accuracy: 0.8947 - 5s/epoch - 217ms/step\n",
            "Epoch 3/15\n",
            "24/24 - 5s - loss: 5.3758e-05 - accuracy: 1.0000 - val_loss: 0.3194 - val_accuracy: 0.9263 - 5s/epoch - 193ms/step\n",
            "Epoch 4/15\n",
            "24/24 - 3s - loss: 2.6337e-05 - accuracy: 1.0000 - val_loss: 0.3021 - val_accuracy: 0.9158 - 3s/epoch - 136ms/step\n",
            "Epoch 5/15\n",
            "24/24 - 3s - loss: 1.6032e-05 - accuracy: 1.0000 - val_loss: 0.3001 - val_accuracy: 0.9158 - 3s/epoch - 138ms/step\n",
            "Epoch 6/15\n",
            "24/24 - 3s - loss: 1.3006e-05 - accuracy: 1.0000 - val_loss: 0.2962 - val_accuracy: 0.9158 - 3s/epoch - 136ms/step\n",
            "Epoch 7/15\n",
            "24/24 - 3s - loss: 1.1642e-05 - accuracy: 1.0000 - val_loss: 0.2938 - val_accuracy: 0.9158 - 3s/epoch - 139ms/step\n",
            "Epoch 8/15\n",
            "24/24 - 3s - loss: 9.3616e-06 - accuracy: 1.0000 - val_loss: 0.2881 - val_accuracy: 0.9158 - 3s/epoch - 145ms/step\n",
            "Epoch 9/15\n",
            "24/24 - 3s - loss: 1.0252e-05 - accuracy: 1.0000 - val_loss: 0.2848 - val_accuracy: 0.9158 - 3s/epoch - 137ms/step\n",
            "Epoch 10/15\n",
            "24/24 - 3s - loss: 7.2540e-06 - accuracy: 1.0000 - val_loss: 0.2833 - val_accuracy: 0.9158 - 3s/epoch - 140ms/step\n",
            "Epoch 11/15\n",
            "24/24 - 3s - loss: 7.2975e-06 - accuracy: 1.0000 - val_loss: 0.2822 - val_accuracy: 0.9158 - 3s/epoch - 136ms/step\n",
            "Epoch 12/15\n",
            "24/24 - 3s - loss: 6.4195e-06 - accuracy: 1.0000 - val_loss: 0.2818 - val_accuracy: 0.9158 - 3s/epoch - 138ms/step\n",
            "Epoch 13/15\n",
            "24/24 - 3s - loss: 5.5435e-06 - accuracy: 1.0000 - val_loss: 0.2809 - val_accuracy: 0.9158 - 3s/epoch - 141ms/step\n",
            "Epoch 14/15\n",
            "24/24 - 3s - loss: 5.8601e-06 - accuracy: 1.0000 - val_loss: 0.2803 - val_accuracy: 0.9158 - 3s/epoch - 139ms/step\n",
            "Epoch 15/15\n",
            "24/24 - 3s - loss: 4.8532e-06 - accuracy: 1.0000 - val_loss: 0.2782 - val_accuracy: 0.9158 - 3s/epoch - 138ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Test'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "YSI6DNsYInWq",
        "outputId": "35ac4ebf-3ca2-41da-ac50-272232ceaa65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score = new_model.evaluate(x_test, y_test, verbose=0)\n",
        "print('Test loss:', score[0])\n",
        "print('Test accuracy:', score[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kshltCoGIjzy",
        "outputId": "507b4691-88d3-4f4d-8d48-199cb9fc73a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test loss: 0.23388147354125977\n",
            "Test accuracy: 0.9368420839309692\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()\n",
        "model.save('TrainedResNet50.h5')"
      ],
      "metadata": {
        "id": "sv9r0U9B_MuT"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}