Numerical_Digit_Recognition_on_MNIST_Dataset.ipynb

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      "name": "python",
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    },
    "colab": {
      "name": "Numerical Digit Recognition on MNIST Dataset",
      "provenance": []
    }
  },
  "nbformat_minor": 0,
  "nbformat": 4,
  "cells": [
    {
      "source": [
        "\n",
        "# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES\n",
        "# TO THE CORRECT LOCATION (/kaggle/input) IN YOUR NOTEBOOK,\n",
        "# THEN FEEL FREE TO DELETE THIS CELL.\n",
        "# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON\n",
        "# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR\n",
        "# NOTEBOOK.\n",
        "\n",
        "import os\n",
        "import sys\n",
        "from tempfile import NamedTemporaryFile\n",
        "from urllib.request import urlopen\n",
        "from urllib.parse import unquote, urlparse\n",
        "from urllib.error import HTTPError\n",
        "from zipfile import ZipFile\n",
        "import tarfile\n",
        "import shutil\n",
        "\n",
        "CHUNK_SIZE = 40960\n",
        "DATA_SOURCE_MAPPING = 'digit-recognizer:https%3A%2F%2Fstorage.googleapis.com%2Fkaggle-competitions-data%2Fkaggle-v2%2F3004%2F861823%2Fbundle%2Farchive.zip%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com%252F20240213%252Fauto%252Fstorage%252Fgoog4_request%26X-Goog-Date%3D20240213T085648Z%26X-Goog-Expires%3D259200%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%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'\n",
        "\n",
        "KAGGLE_INPUT_PATH='/kaggle/input'\n",
        "KAGGLE_WORKING_PATH='/kaggle/working'\n",
        "KAGGLE_SYMLINK='kaggle'\n",
        "\n",
        "!umount /kaggle/input/ 2> /dev/null\n",
        "shutil.rmtree('/kaggle/input', ignore_errors=True)\n",
        "os.makedirs(KAGGLE_INPUT_PATH, 0o777, exist_ok=True)\n",
        "os.makedirs(KAGGLE_WORKING_PATH, 0o777, exist_ok=True)\n",
        "\n",
        "try:\n",
        "  os.symlink(KAGGLE_INPUT_PATH, os.path.join(\"..\", 'input'), target_is_directory=True)\n",
        "except FileExistsError:\n",
        "  pass\n",
        "try:\n",
        "  os.symlink(KAGGLE_WORKING_PATH, os.path.join(\"..\", 'working'), target_is_directory=True)\n",
        "except FileExistsError:\n",
        "  pass\n",
        "\n",
        "for data_source_mapping in DATA_SOURCE_MAPPING.split(','):\n",
        "    directory, download_url_encoded = data_source_mapping.split(':')\n",
        "    download_url = unquote(download_url_encoded)\n",
        "    filename = urlparse(download_url).path\n",
        "    destination_path = os.path.join(KAGGLE_INPUT_PATH, directory)\n",
        "    try:\n",
        "        with urlopen(download_url) as fileres, NamedTemporaryFile() as tfile:\n",
        "            total_length = fileres.headers['content-length']\n",
        "            print(f'Downloading {directory}, {total_length} bytes compressed')\n",
        "            dl = 0\n",
        "            data = fileres.read(CHUNK_SIZE)\n",
        "            while len(data) > 0:\n",
        "                dl += len(data)\n",
        "                tfile.write(data)\n",
        "                done = int(50 * dl / int(total_length))\n",
        "                sys.stdout.write(f\"\\r[{'=' * done}{' ' * (50-done)}] {dl} bytes downloaded\")\n",
        "                sys.stdout.flush()\n",
        "                data = fileres.read(CHUNK_SIZE)\n",
        "            if filename.endswith('.zip'):\n",
        "              with ZipFile(tfile) as zfile:\n",
        "                zfile.extractall(destination_path)\n",
        "            else:\n",
        "              with tarfile.open(tfile.name) as tarfile:\n",
        "                tarfile.extractall(destination_path)\n",
        "            print(f'\\nDownloaded and uncompressed: {directory}')\n",
        "    except HTTPError as e:\n",
        "        print(f'Failed to load (likely expired) {download_url} to path {destination_path}')\n",
        "        continue\n",
        "    except OSError as e:\n",
        "        print(f'Failed to load {download_url} to path {destination_path}')\n",
        "        continue\n",
        "\n",
        "print('Data source import complete.')\n"
      ],
      "metadata": {
        "id": "tkGc7Ec2rOFl",
        "outputId": "5f99c32f-c3e3-43a2-9dac-f2f39de799dd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading digit-recognizer, 16054568 bytes compressed\n",
            "[==================================================] 16054568 bytes downloaded\n",
            "Downloaded and uncompressed: digit-recognizer\n",
            "Data source import complete.\n"
          ]
        }
      ],
      "execution_count": 1
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "x3OCQTvPcjae"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Libraries for deep learning\n",
        "!pip install np_utils"
      ],
      "metadata": {
        "execution": {
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O2c70nUrckB3"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting np_utils\n",
            "  Downloading np_utils-0.6.0.tar.gz (61 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.0/62.0 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy>=1.0 in /usr/local/lib/python3.10/dist-packages (from np_utils) (1.25.2)\n",
            "Building wheels for collected packages: np_utils\n",
            "  Building wheel for np_utils (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for np_utils: filename=np_utils-0.6.0-py3-none-any.whl size=56439 sha256=c157865aedcf76267a3e88713d5c43b68e9bf95cda699773fcfcef9c5fad6243\n",
            "  Stored in directory: /root/.cache/pip/wheels/b6/c7/50/2307607f44366dd021209f660045f8d51cb976514d30be7cc7\n",
            "Successfully built np_utils\n",
            "Installing collected packages: np_utils\n",
            "Successfully installed np_utils-0.6.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Numerical Digit Recognition using CNN**"
      ],
      "metadata": {
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "id": "TPaRjNx8rOFq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hi Kaggle Fellows,\n",
        "This is my first try on Deep Learning model. Many more to come. I hope this will help all the beginners and I'm waiting to here from you all. Thanking you all in advance for the feedbacks and suggestions."
      ],
      "metadata": {
        "id": "lBr5z6S6rOFr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **STEP 1: Importing Required Libraries**"
      ],
      "metadata": {
        "id": "GJi1tsF4rOFs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Libraries for data preprocessing and visualization\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "import seaborn as sns\n",
        "sns.set(style='white', context='notebook', palette='deep')\n",
        "\n",
        "\n",
        "np.random.seed(2)"
      ],
      "metadata": {
        "execution": {
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        "id": "zOWowc3NrOFs"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\"np.random.seed(2)\" sets the seed of the NumPy random number generator to 2.\n",
        "The NumPy random number generator is a pseudorandom number generator that produces a sequence of numbers that appears random but is actually determined by an initial seed value. Setting the seed to a specific value ensures that the same sequence of random numbers is generated every time the code is run, which can be useful for debugging and testing purposes."
      ],
      "metadata": {
        "id": "r9oTGqu-rOFs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Libraries for data splitting and confusion matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "\n",
        "import itertools"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-24T09:27:34.660369Z",
          "iopub.execute_input": "2023-02-24T09:27:34.660972Z",
          "iopub.status.idle": "2023-02-24T09:27:34.676167Z",
          "shell.execute_reply.started": "2023-02-24T09:27:34.660935Z",
          "shell.execute_reply": "2023-02-24T09:27:34.675126Z"
        },
        "trusted": true,
        "id": "rGday2_9rOFt"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The itertools module is a standard library module in Python that provides various functions for creating iterators and generators for efficient looping. It contains a collection of tools that allow you to work with sequences, iterators, and functions that return iterators."
      ],
      "metadata": {
        "id": "_1yt_Oo5rOFt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Libraries for deep learning\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D\n",
        "from keras.optimizers import RMSprop\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.callbacks import ReduceLROnPlateau"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-24T09:27:34.67919Z",
          "iopub.execute_input": "2023-02-24T09:27:34.679848Z",
          "iopub.status.idle": "2023-02-24T09:27:34.687754Z",
          "shell.execute_reply.started": "2023-02-24T09:27:34.679773Z",
          "shell.execute_reply": "2023-02-24T09:27:34.686853Z"
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        "trusted": true,
        "id": "G1D43i3OrOFt"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. \"from tensorflow.keras.utils import to_categorical\" - Import a function from Keras to convert numerical labels to one-hot encoded categorical labels.\n",
        "2. \"from keras.models import Sequential\" - Import the Sequential model from Keras for building a linear stack of layers.\n",
        "3. \"from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D\" - Import various layer types from Keras for constructing a neural network architecture.\n",
        "4. \"from keras.optimizers import RMSprop\" - Import the RMSprop optimizer from Keras for training a neural network.\n",
        "5. \"from keras.preprocessing.image import ImageDataGenerator\" - Import the ImageDataGenerator class from Keras for generating batches of image data with data augmentation.\n",
        "6. \"from keras.callbacks import ReduceLROnPlateau\" - Import the ReduceLROnPlateau callback from Keras for reducing the learning rate during training when the validation loss plateaus."
      ],
      "metadata": {
        "id": "mKAnzboFrOFu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **STEP 2: Data Preprocessing**"
      ],
      "metadata": {
        "id": "W3TJuFwyrOFu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Loading the dataset\n",
        "train = pd.read_csv(\"/kaggle/input/digit-recognizer/train.csv\")\n",
        "test = pd.read_csv(\"/kaggle/input/digit-recognizer/test.csv\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-24T09:27:34.689693Z",
          "iopub.execute_input": "2023-02-24T09:27:34.690152Z",
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        "id": "6zxwnTJZrOFy"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Viewing the dataset\n",
        "train.head()"
      ],
      "metadata": {
        "execution": {
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 785 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-20c99b43-5e43-4baf-881f-15119a08d907')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .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-20c99b43-5e43-4baf-881f-15119a08d907 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-20c99b43-5e43-4baf-881f-15119a08d907');\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-7d12936f-ee85-47c7-a1e4-7b96bd67da47\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7d12936f-ee85-47c7-a1e4-7b96bd67da47')\"\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-7d12936f-ee85-47c7-a1e4-7b96bd67da47 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Defining the features and output for training data\n",
        "X_train = train.drop(labels = [\"label\"],axis = 1)\n",
        "y_train = train[\"label\"]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-24T09:27:37.97432Z",
          "iopub.execute_input": "2023-02-24T09:27:37.974793Z",
          "iopub.status.idle": "2023-02-24T09:27:38.063013Z",
          "shell.execute_reply.started": "2023-02-24T09:27:37.974753Z",
          "shell.execute_reply": "2023-02-24T09:27:38.061778Z"
        },
        "trusted": true,
        "id": "1wbNhldzrOFz"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking for null values\n",
        "#Training data\n",
        "train.isnull().sum()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-24T09:27:38.064815Z",
          "iopub.execute_input": "2023-02-24T09:27:38.065399Z",
          "iopub.status.idle": "2023-02-24T09:27:38.112933Z",
          "shell.execute_reply.started": "2023-02-24T09:27:38.065359Z",
          "shell.execute_reply": "2023-02-24T09:27:38.111795Z"
        },
        "trusted": true,
        "id": "U_DA7yePrOF0",
        "outputId": "e7c029ac-86c4-4c8f-f812-ee1f8a7c01fb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "label       0\n",
              "pixel0      0\n",
              "pixel1      0\n",
              "pixel2      0\n",
              "pixel3      0\n",
              "           ..\n",
              "pixel779    0\n",
              "pixel780    0\n",
              "pixel781    0\n",
              "pixel782    0\n",
              "pixel783    0\n",
              "Length: 785, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Test data\n",
        "test.isnull().sum()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-24T09:27:38.114528Z",
          "iopub.execute_input": "2023-02-24T09:27:38.115139Z",
          "iopub.status.idle": "2023-02-24T09:27:38.149292Z",
          "shell.execute_reply.started": "2023-02-24T09:27:38.115102Z",
          "shell.execute_reply": "2023-02-24T09:27:38.148215Z"
        },
        "trusted": true,
        "id": "3bUfrAgBrOF0",
        "outputId": "2a4bd766-3216-48d9-e71c-61fa41ff601f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pixel0      0\n",
              "pixel1      0\n",
              "pixel2      0\n",
              "pixel3      0\n",
              "pixel4      0\n",
              "           ..\n",
              "pixel779    0\n",
              "pixel780    0\n",
              "pixel781    0\n",
              "pixel782    0\n",
              "pixel783    0\n",
              "Length: 784, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are no null values"
      ],
      "metadata": {
        "id": "oCfeDC6ArOF0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Normalization\n",
        "X_train = X_train / 255.0\n",
        "test = test / 255.0"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-24T09:27:38.150983Z",
          "iopub.execute_input": "2023-02-24T09:27:38.151354Z",
          "iopub.status.idle": "2023-02-24T09:27:38.326923Z",
          "shell.execute_reply.started": "2023-02-24T09:27:38.151319Z",
          "shell.execute_reply": "2023-02-24T09:27:38.325833Z"
        },
        "trusted": true,
        "id": "rsCZ1ZC-rOF1"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Done the above step because, CNN converg faster on [0....1] data than on [0....255]. Also, performed a grayscale normalization to reduce the effect of illumination's differences."
      ],
      "metadata": {
        "id": "r54yCfzMrOF1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Reshape image in 3 dimensions (height = 28px, width = 28px , canal = 1)\n",
        "X_train = X_train.values.reshape(-1,28,28,1)\n",
        "test = test.values.reshape(-1,28,28,1)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-24T09:27:38.328562Z",
          "iopub.execute_input": "2023-02-24T09:27:38.328992Z",
          "iopub.status.idle": "2023-02-24T09:27:38.334565Z",
          "shell.execute_reply.started": "2023-02-24T09:27:38.328943Z",
          "shell.execute_reply": "2023-02-24T09:27:38.333369Z"
        },
        "trusted": true,
        "id": "Z31y3L6WrOF1"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note: Train and test images (28px x 28px) are as 1D vectors of 784 values. We reshape all data to 28x28x1 3D matrices. But Keras requires an extra dimension in the end which correspond to RGB channels. MNIST images are gray scaled so it use only one channel. For RGB images, there is 3 channels, we should reshape 784px vectors to 28x28x3 3D matrices."
      ],
      "metadata": {
        "id": "e6fChmGTrOF1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Label Encoding\n",
        "#Encode labels to one hot vectors (ex : 2 -> [0,0,1,0,0,0,0,0,0,0])\n",
        "y_train = to_categorical(y_train, num_classes = 10)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-24T09:27:38.335998Z",
          "iopub.execute_input": "2023-02-24T09:27:38.337042Z",
          "iopub.status.idle": "2023-02-24T09:27:38.346431Z",
          "shell.execute_reply.started": "2023-02-24T09:27:38.337006Z",
          "shell.execute_reply": "2023-02-24T09:27:38.345497Z"
        },
        "trusted": true,
        "id": "wKpdzjHsrOF1"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Splitting the trainng dataset as for training and the validation set\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size = 0.1, random_state=0)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-24T09:27:38.349577Z",
          "iopub.execute_input": "2023-02-24T09:27:38.350631Z",
          "iopub.status.idle": "2023-02-24T09:27:38.725459Z",
          "shell.execute_reply.started": "2023-02-24T09:27:38.350604Z",
          "shell.execute_reply": "2023-02-24T09:27:38.724395Z"
        },
        "trusted": true,
        "id": "kgUl7NlirOF1"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\"stratify = True\" ensures that the distribution of the target variable is approximately the same in both the training and test datasets."
      ],
      "metadata": {
        "id": "fwMNwx1BrOF1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualizing an example for better understanding the images in our dataset\n",
        "g = plt.imshow(X_train[8][:,:,0])"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-24T09:27:38.726786Z",
          "iopub.execute_input": "2023-02-24T09:27:38.727789Z",
          "iopub.status.idle": "2023-02-24T09:27:38.994725Z",
          "shell.execute_reply.started": "2023-02-24T09:27:38.727746Z",
          "shell.execute_reply": "2023-02-24T09:27:38.993827Z"
        },
        "trusted": true,
        "id": "2NPhYitErOF1",
        "outputId": "26e38d2d-c600-492f-8484-d0a74207f3ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        }
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **STEP 3: CNN - Convolution Neural Networks**"
      ],
      "metadata": {
        "id": "8cDx7sd6rOF1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Setting the CNN model\n",
        "#CNN architechture is In -> [[Conv2D->relu]*2 -> MaxPool2D -> Dropout]*2 -> Flatten -> Dense -> Dropout -> Out\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', activation ='relu', input_shape = (28,28,1)))\n",
        "model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', activation ='relu'))\n",
        "model.add(MaxPool2D(pool_size=(2,2)))\n",
        "model.add(Dropout(0.25))\n",
        "model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', activation ='relu'))\n",
        "model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', activation ='relu'))\n",
        "model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))\n",
        "model.add(Dropout(0.25))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(256, activation = \"relu\"))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(10, activation = \"softmax\"))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-24T09:27:38.99611Z",
          "iopub.execute_input": "2023-02-24T09:27:38.996557Z",
          "iopub.status.idle": "2023-02-24T09:27:39.074169Z",
          "shell.execute_reply.started": "2023-02-24T09:27:38.996521Z",
          "shell.execute_reply": "2023-02-24T09:27:39.073164Z"
        },
        "trusted": true,
        "id": "voRIo27lrOF1"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Convolutional Neural Network (CNN) model using the Keras API with the following architecture:\n",
        "\n",
        "1. Input layer: a 2D convolutional layer with 32 filters, a kernel size of (5, 5), and a ReLU activation function. The input shape is (28, 28, 1), representing a grayscale image with dimensions 28x28 pixels.\n",
        "2. Hidden layer 1: a 2D convolutional layer with 32 filters, a kernel size of (5, 5), and a ReLU activation function. Padding is set to 'Same' to ensure the output size matches the input size.\n",
        "3. Max pooling layer 1: a 2D max pooling layer with a pool size of (2, 2) and a stride of (2, 2). This layer reduces the spatial dimensions of the output from the previous layer by a factor of 2, helping to reduce overfitting.\n",
        "4. Dropout layer 1: a dropout layer with a rate of 0.25, which randomly drops out 25% of the input units during training to further prevent overfitting.\n",
        "5. Hidden layer 2: a 2D convolutional layer with 64 filters, a kernel size of (3, 3), and a ReLU activation function.\n",
        "6. Hidden layer 3: a 2D convolutional layer with 64 filters, a kernel size of (3, 3), and a ReLU activation function. Padding is set to 'Same' to ensure the output size matches the input size.\n",
        "7. Max pooling layer 2: a 2D max pooling layer with a pool size of (2, 2) and a stride of (2, 2). This layer reduces the spatial dimensions of the output from the previous layer by a factor of 2.\n",
        "8. Dropout layer 2: a dropout layer with a rate of 0.25.\n",
        "9. Flatten layer: a layer that flattens the output from the previous layer into a 1D array.\n",
        "10. Fully connected layer: a fully connected layer with 256 units and a ReLU activation function.\n",
        "11. Dropout layer 3: a dropout layer with a rate of 0.5.\n",
        "12. Output layer: a fully connected layer with 10 units and a softmax activation function, which outputs a probability distribution over the 10 classes (digits 0-9)."
      ],
      "metadata": {
        "id": "bRM3dCTArOF2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "'relu' is the rectifier (activation function max(0,x). The rectifier activation function is used to add non linearity to the network."
      ],
      "metadata": {
        "id": "AIZHRK4ErOF2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Setting the Optimizer\n",
        "optimizer = RMSprop(learning_rate=0.001, rho=0.9, epsilon=1e-08)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-24T09:27:39.077356Z",
          "iopub.execute_input": "2023-02-24T09:27:39.077635Z",
          "iopub.status.idle": "2023-02-24T09:27:39.084067Z",
          "shell.execute_reply.started": "2023-02-24T09:27:39.077609Z",
          "shell.execute_reply": "2023-02-24T09:27:39.082592Z"
        },
        "trusted": true,
        "id": "_B18NxA_rOF2"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The optimizer is responsible for adjusting the weights and biases of the network during training in order to minimize the error or loss function. The annealer, on the other hand, is responsible for adjusting the learning rate of the optimizer during training. The learning rate determines the size of the steps taken by the optimizer when updating the weights and biases of the network. A high learning rate can cause the optimizer to overshoot the minimum of the loss function, while a low learning rate can cause the optimizer to take too long to converge to the minimum."
      ],
      "metadata": {
        "id": "7L9c2QQGrOF2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Compiling the model\n",
        "model.compile(optimizer = optimizer , loss = \"categorical_crossentropy\", metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-24T09:27:39.085373Z",
          "iopub.execute_input": "2023-02-24T09:27:39.086999Z",
          "iopub.status.idle": "2023-02-24T09:27:39.097766Z",
          "shell.execute_reply.started": "2023-02-24T09:27:39.086961Z",
          "shell.execute_reply": "2023-02-24T09:27:39.096759Z"
        },
        "trusted": true,
        "id": "hAyQHKN8rOF2"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "To train our neural network model, we must set up a score function, a loss function, and an optimization algorithm. The loss function measures the discrepancy between the predicted and true labels for labeled images, and for categorical classifications with more than two classes, we use the \"categorical_crossentropy\" form of the loss function.\n",
        "The optimizer is a crucial component of the training process, as it iteratively updates the parameters of the model (e.g. filters, kernel values, weights, and biases of neurons) to minimize the loss function. For this particular model, we have selected RMSprop as our optimizer, with default values. RMSprop is an effective optimizer that adjusts the Adagrad method in a simple way to reduce its aggressive, monotonically decreasing learning rate. Alternatively, we could have used Stochastic Gradient Descent ('sgd') optimizer, but it is slower than RMSprop."
      ],
      "metadata": {
        "id": "oVJInCkbrOF2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Setting the Annealer\n",
        "learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', patience=3, verbose=1, factor=0.5, min_lr=0.00001)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-24T09:27:39.105237Z",
          "iopub.execute_input": "2023-02-24T09:27:39.105636Z",
          "iopub.status.idle": "2023-02-24T09:27:39.112043Z",
          "shell.execute_reply.started": "2023-02-24T09:27:39.105608Z",
          "shell.execute_reply": "2023-02-24T09:27:39.11101Z"
        },
        "trusted": true,
        "id": "db14zx2DrOF2"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "To improve the speed and accuracy of the optimizer in finding the global minimum of the loss function, an annealing method of the learning rate (LR) was used.\n",
        "The LR determines the step size that the optimizer takes while traversing the 'loss landscape'. A higher LR results in larger steps and faster convergence, but poor sampling and a higher likelihood of falling into a local minima.\n",
        "To efficiently reach the global minimum of the loss function, it is better to have a decreasing LR during training. However, to maintain fast computation time with a high LR, the LR was decreased dynamically every X steps (epochs) only if necessary, i.e., when accuracy did not improve.\n",
        "The Keras.callbacks ReduceLROnPlateau function was used to reduce the LR by half if the accuracy did not improve after 3 epochs."
      ],
      "metadata": {
        "id": "iiTDtfoErOF2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = 30\n",
        "batch_size = 86"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-24T09:27:39.113353Z",
          "iopub.execute_input": "2023-02-24T09:27:39.114441Z",
          "iopub.status.idle": "2023-02-24T09:27:39.121746Z",
          "shell.execute_reply.started": "2023-02-24T09:27:39.114404Z",
          "shell.execute_reply": "2023-02-24T09:27:39.120722Z"
        },
        "trusted": true,
        "id": "aWavsoOBrOF2"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The model will train for 1 epoch with a batch size of 86. To achieve a higher accuracy of 0.9967, the number of epochs can be increased to 30."
      ],
      "metadata": {
        "id": "Dj3XTwGxrOF3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Fitting the model to the training set\n",
        "history = model.fit(X_train, y_train, batch_size = batch_size, epochs = epochs, validation_data = (X_val, y_val), verbose = 2)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-24T09:27:39.124377Z",
          "iopub.execute_input": "2023-02-24T09:27:39.124672Z",
          "iopub.status.idle": "2023-02-24T09:27:45.300765Z",
          "shell.execute_reply.started": "2023-02-24T09:27:39.124647Z",
          "shell.execute_reply": "2023-02-24T09:27:45.299656Z"
        },
        "trusted": true,
        "id": "6ziDIA5orOF3",
        "outputId": "7be945de-7bd8-4909-ecfb-1b717e65b940",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "440/440 - 133s - loss: 0.0210 - accuracy: 0.9942 - val_loss: 0.0201 - val_accuracy: 0.9938 - 133s/epoch - 302ms/step\n",
            "Epoch 2/30\n",
            "440/440 - 133s - loss: 0.0200 - accuracy: 0.9940 - val_loss: 0.0339 - val_accuracy: 0.9943 - 133s/epoch - 302ms/step\n",
            "Epoch 3/30\n",
            "440/440 - 131s - loss: 0.0182 - accuracy: 0.9944 - val_loss: 0.0273 - val_accuracy: 0.9940 - 131s/epoch - 298ms/step\n",
            "Epoch 4/30\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **STEP 4: Evaluation**"
      ],
      "metadata": {
        "id": "xC29DoIarOF3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#CONFUSION MATRIX\n",
        "\n",
        "#Defining a function for confusion matrix\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, cm[i, j],\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "\n",
        "#Predicting the values from the validation dataset\n",
        "y_pred = model.predict(X_val)\n",
        "#Converting predictions classes to one hot vectors\n",
        "y_pred_classes = np.argmax(y_pred,axis = 1)\n",
        "#Converting validation observations to one hot vectors\n",
        "y_true = np.argmax(y_val,axis = 1)\n",
        "#Computing the confusion matrix\n",
        "confusion_mtx = confusion_matrix(y_true, y_pred_classes)\n",
        "#Plotting the confusion matrix\n",
        "plot_confusion_matrix(confusion_mtx, classes = range(10))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-24T09:27:45.30471Z",
          "iopub.execute_input": "2023-02-24T09:27:45.305043Z",
          "iopub.status.idle": "2023-02-24T09:27:46.897367Z",
          "shell.execute_reply.started": "2023-02-24T09:27:45.305013Z",
          "shell.execute_reply": "2023-02-24T09:27:46.896345Z"
        },
        "trusted": true,
        "id": "TIy4U4ZFrOF6",
        "outputId": "5ed33957-0ea6-444e-9cb0-b49aaff0f3da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        }
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "132/132 [==============================] - 4s 31ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#To display some error results\n",
        "\n",
        "errors = (y_pred_classes - y_true != 0)\n",
        "\n",
        "y_pred_classes_errors = y_pred_classes[errors]\n",
        "y_pred_errors = y_pred[errors]\n",
        "y_true_errors = y_true[errors]\n",
        "X_val_errors = X_val[errors]\n",
        "\n",
        "#Defining a function for plotting the erros results\n",
        "def display_errors(errors_index, img_errors, pred_errors, obs_errors):\n",
        "    \"\"\" This function shows 12 images with their predicted and real labels\"\"\"\n",
        "    nrows = 4\n",
        "    ncols = 3\n",
        "    fig, ax = plt.subplots(nrows, ncols, figsize=(10, 10))\n",
        "    fig.subplots_adjust(hspace=0.5)\n",
        "    for i, axi in enumerate(ax.flat):\n",
        "        if i < len(errors_index):\n",
        "            error = errors_index[i]\n",
        "            axi.imshow((img_errors[error]).reshape((28,28)))\n",
        "            axi.set_title(\"Predicted label: {}\\nTrue label: {}\".format(pred_errors[error], obs_errors[error]))\n",
        "        axi.set_xticks([])\n",
        "        axi.set_yticks([])\n",
        "\n",
        "# Probabilities of the wrong predicted numbers\n",
        "y_pred_errors_prob = np.max(y_pred_errors, axis=1)\n",
        "\n",
        "# Predicted probabilities of the true values in the error set\n",
        "true_prob_errors = np.diagonal(np.take(y_pred_errors, y_true_errors, axis=1))\n",
        "\n",
        "# Difference between the probability of the predicted label and the true label\n",
        "delta_pred_true_errors = y_pred_errors_prob - true_prob_errors\n",
        "\n",
        "# Sorted list of the delta prob errors\n",
        "sorted_delta_errors = np.argsort(delta_pred_true_errors)\n",
        "\n",
        "# Top 12 errors\n",
        "most_important_errors = sorted_delta_errors[-12:]\n",
        "\n",
        "# Displaying the top 12 errors\n",
        "display_errors(most_important_errors, X_val_errors, y_pred_classes_errors, y_true_errors)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-24T09:27:46.901371Z",
          "iopub.execute_input": "2023-02-24T09:27:46.904386Z",
          "iopub.status.idle": "2023-02-24T09:27:47.638911Z",
          "shell.execute_reply.started": "2023-02-24T09:27:46.90434Z",
          "shell.execute_reply": "2023-02-24T09:27:47.637909Z"
        },
        "trusted": true,
        "id": "Hw6feyL_rOF6",
        "outputId": "eb6c1009-7889-47bd-e27d-23481f72f37e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        }
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# predicting the results\n",
        "results = model.predict(test)\n",
        "\n",
        "# selecting the indix with the maximum probability\n",
        "results = np.argmax(results,axis = 1)\n",
        "\n",
        "results = pd.Series(results,name=\"Label\")\n",
        "results"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-24T09:27:47.640191Z",
          "iopub.execute_input": "2023-02-24T09:27:47.640839Z",
          "iopub.status.idle": "2023-02-24T09:27:49.964276Z",
          "shell.execute_reply.started": "2023-02-24T09:27:47.640801Z",
          "shell.execute_reply": "2023-02-24T09:27:49.963289Z"
        },
        "trusted": true,
        "id": "405SPxj9rOF6",
        "outputId": "3061f90d-d22e-4a1d-e23a-a67b06fa0861",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "875/875 [==============================] - 33s 37ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        2\n",
              "1        0\n",
              "2        9\n",
              "3        9\n",
              "4        3\n",
              "        ..\n",
              "27995    9\n",
              "27996    7\n",
              "27997    3\n",
              "27998    9\n",
              "27999    2\n",
              "Name: Label, Length: 28000, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submission = pd.concat([pd.Series(range(1,28001),name = \"ImageId\"),results],axis = 1)\n",
        "submission.to_csv(\"submission.csv\",index=False)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-02-24T09:32:39.0983Z",
          "iopub.execute_input": "2023-02-24T09:32:39.098705Z",
          "iopub.status.idle": "2023-02-24T09:32:39.136574Z",
          "shell.execute_reply.started": "2023-02-24T09:32:39.098671Z",
          "shell.execute_reply": "2023-02-24T09:32:39.13567Z"
        },
        "trusted": true,
        "id": "m2gpO5sorOF6"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pqQaNDlfrOF7"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}