Predictive_System.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "VUKsyblu21X5"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from google.colab.patches import cv2_imshow\n",
        "import cv2\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "tf.random.set_seed(3)\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.models import Sequential\n",
        "from keras.datasets import mnist\n",
        "from tensorflow.math import confusion_matrix"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(X_train,Y_train),(X_test,Y_test) = mnist.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vyt4c_5e3lDX",
        "outputId": "74bf29ae-d003-4011-8f1e-6e6c8a3b576f"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "\u001b[1m11490434/11490434\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cHdFw1lu384D",
        "outputId": "08c1b507-17ea-4ec9-f218-df6d4d197512"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape,X_test.shape,Y_train.shape,Y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oU50v0s54Bi3",
        "outputId": "2739c2a5-eb92-4ae4-c03a-86c694e6e803"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(60000, 28, 28) (10000, 28, 28) (60000,) (10000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training data -> 60,000\n",
        "\n",
        "Test data -> 10,000"
      ],
      "metadata": {
        "id": "qcTIq_og4Vev"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "image dimension -> 28 x 28\n",
        "\n",
        "grayscale images -> 1 channel\n"
      ],
      "metadata": {
        "id": "_d8ZCwMh4gyq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train[10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZQHdMmh84E6h",
        "outputId": "0649f506-4383-48c4-e604-2acd083b35e2"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0  42 118 219 166 118 118   6\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0 103 242 254 254 254 254 254  66\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0  18 232 254 254 254 254 254 238\n",
            "   70   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0 104 244 254 224 254 254 254\n",
            "  141   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0 207 254 210 254 254 254\n",
            "   34   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0  84 206 254 254 254 254\n",
            "   41   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0  24 209 254 254 254\n",
            "  171   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0  91 137 253 254 254 254\n",
            "  112   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0  40 214 250 254 254 254 254 254\n",
            "   34   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0  81 247 254 254 254 254 254 254\n",
            "  146   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0 110 246 254 254 254 254 254\n",
            "  171   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0  73  89  89  93 240 254\n",
            "  171   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   1 128 254\n",
            "  219  31   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   7 254 254\n",
            "  214  28   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 138 254 254\n",
            "  116   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0  19 177  90   0   0   0   0   0  25 240 254 254\n",
            "   34   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0 164 254 215  63  36   0  51  89 206 254 254 139\n",
            "    8   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0  57 197 254 254 222 180 241 254 254 253 213  11\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0 140 105 254 254 254 254 254 254 236   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   7 117 117 165 254 254 239  50   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train[10].shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rNEhvtCn4rBH",
        "outputId": "6d1ce0ae-4153-4b41-ab62-fb38c6f7110b"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(28, 28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(X_train[10])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "2poIZOb54xj2",
        "outputId": "da2d3b3b-e815-4e85-87d9-032496284c2a"
      },
      "execution_count": 13,
      "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": [
        "Y_train[10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F9mO7rY044wX",
        "outputId": "9af9b665-a5e3-454f-e3bb-6bd829cfefb7"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.unique(Y_train))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bqSIL4S65KxW",
        "outputId": "1beac78d-e413-46f5-b832-75b6b7f419c4"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 2 3 4 5 6 7 8 9]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Scaling\n",
        "\n",
        "X_train = X_train/255\n",
        "X_test = X_test/255"
      ],
      "metadata": {
        "id": "2zv1-Z835Wjs"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train[10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tWKqvGRA5ipm",
        "outputId": "99992da0-b0e1-4a49-8826-afd682358f55"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.16470588\n",
            "  0.4627451  0.85882353 0.65098039 0.4627451  0.4627451  0.02352941\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.40392157 0.94901961\n",
            "  0.99607843 0.99607843 0.99607843 0.99607843 0.99607843 0.25882353\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.07058824 0.90980392\n",
            "  0.99607843 0.99607843 0.99607843 0.99607843 0.99607843 0.93333333\n",
            "  0.2745098  0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.40784314\n",
            "  0.95686275 0.99607843 0.87843137 0.99607843 0.99607843 0.99607843\n",
            "  0.55294118 0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.81176471 0.99607843 0.82352941 0.99607843 0.99607843 0.99607843\n",
            "  0.13333333 0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.32941176 0.80784314 0.99607843 0.99607843 0.99607843 0.99607843\n",
            "  0.16078431 0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.09411765 0.81960784 0.99607843 0.99607843 0.99607843\n",
            "  0.67058824 0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.35686275 0.5372549  0.99215686 0.99607843 0.99607843 0.99607843\n",
            "  0.43921569 0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.15686275 0.83921569\n",
            "  0.98039216 0.99607843 0.99607843 0.99607843 0.99607843 0.99607843\n",
            "  0.13333333 0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.31764706 0.96862745\n",
            "  0.99607843 0.99607843 0.99607843 0.99607843 0.99607843 0.99607843\n",
            "  0.57254902 0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.43137255\n",
            "  0.96470588 0.99607843 0.99607843 0.99607843 0.99607843 0.99607843\n",
            "  0.67058824 0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.28627451 0.34901961 0.34901961 0.36470588 0.94117647 0.99607843\n",
            "  0.67058824 0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.00392157 0.50196078 0.99607843\n",
            "  0.85882353 0.12156863 0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.02745098 0.99607843 0.99607843\n",
            "  0.83921569 0.10980392 0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.54117647 0.99607843 0.99607843\n",
            "  0.45490196 0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.0745098  0.69411765 0.35294118 0.         0.         0.\n",
            "  0.         0.         0.09803922 0.94117647 0.99607843 0.99607843\n",
            "  0.13333333 0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.64313725 0.99607843 0.84313725 0.24705882 0.14117647 0.\n",
            "  0.2        0.34901961 0.80784314 0.99607843 0.99607843 0.54509804\n",
            "  0.03137255 0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.22352941 0.77254902 0.99607843 0.99607843 0.87058824 0.70588235\n",
            "  0.94509804 0.99607843 0.99607843 0.99215686 0.83529412 0.04313725\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.54901961 0.41176471 0.99607843 0.99607843 0.99607843\n",
            "  0.99607843 0.99607843 0.99607843 0.9254902  0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.02745098 0.45882353 0.45882353 0.64705882\n",
            "  0.99607843 0.99607843 0.9372549  0.19607843 0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Setting up Neural Network\n",
        "\n",
        "model = Sequential([\n",
        "                      keras.layers.Flatten(input_shape=(28,28)),\n",
        "                      keras.layers.Dense(units = 50,activation ='relu'),\n",
        "                      keras.layers.Dense(units=50,activation='relu'),\n",
        "                      keras.layers.Dense(10,activation='sigmoid')\n",
        "])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RC0FpqAI5ldD",
        "outputId": "f68b327c-94f7-4b83-f8de-ef2bf47a528b"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(**kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# compiling neural network\n",
        "\n",
        "model.compile(optimizer = 'adam',loss = 'sparse_categorical_crossentropy',metrics =['accuracy'])"
      ],
      "metadata": {
        "id": "4_Ia30g06U0w"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# training the model\n",
        "model.fit(X_train,Y_train,epochs=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U_6j8G0W6srK",
        "outputId": "d374ddc0-937b-42e0-e2fa-82738160b5e4"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.8541 - loss: 0.5101\n",
            "Epoch 2/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.9569 - loss: 0.1419\n",
            "Epoch 3/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.9691 - loss: 0.1002\n",
            "Epoch 4/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9769 - loss: 0.0774\n",
            "Epoch 5/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9809 - loss: 0.0634\n",
            "Epoch 6/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9840 - loss: 0.0536\n",
            "Epoch 7/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9865 - loss: 0.0443\n",
            "Epoch 8/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 2ms/step - accuracy: 0.9884 - loss: 0.0381\n",
            "Epoch 9/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9900 - loss: 0.0325\n",
            "Epoch 10/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9900 - loss: 0.0306\n",
            "Epoch 11/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9917 - loss: 0.0274\n",
            "Epoch 12/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 2ms/step - accuracy: 0.9925 - loss: 0.0234\n",
            "Epoch 13/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9919 - loss: 0.0226\n",
            "Epoch 14/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9935 - loss: 0.0197\n",
            "Epoch 15/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9939 - loss: 0.0179\n",
            "Epoch 16/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9949 - loss: 0.0157\n",
            "Epoch 17/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9942 - loss: 0.0171\n",
            "Epoch 18/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.9949 - loss: 0.0154\n",
            "Epoch 19/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9956 - loss: 0.0127\n",
            "Epoch 20/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.9944 - loss: 0.0149\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x7a049ff28850>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train data accuracy => 99.44%"
      ],
      "metadata": {
        "id": "A1mfEjLj7RH1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iaf0JbAE7Qyf",
        "outputId": "fc1e9fc8-6951-4815-aa6d-ccb4c12a73e7"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss,accuray = model.evaluate(X_test,Y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SWQweams6zpq",
        "outputId": "7ad48470-6c56-4051-8e08-a77f7e688388"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9684 - loss: 0.1757\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(accuray)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_DNLmsMY7iTP",
        "outputId": "2466a26a-521f-4c61-846c-a7c1e92236fb"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9722999930381775\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Test data accuracy => 97.22%"
      ],
      "metadata": {
        "id": "1L1QLjFF82Mk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# first data point in X_test\n",
        "\n",
        "plt.imshow(X_test[0])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "h1-wDhHB8WDm",
        "outputId": "8c05a0c2-f2ef-46d9-e842-b875d790ea1a"
      },
      "execution_count": 31,
      "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": [
        "print(Y_test[0])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yqm8lr2C7lvj",
        "outputId": "7f09a430-b0b2-4c22-f193-d2b56f676a49"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_pred[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_pMSeIFe8ZYt",
        "outputId": "3c735184-5472-4de4-e498-710266e74e11"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1.6511540e-07 6.8345113e-04 1.2422686e-05 4.2733213e-01 2.3015667e-10\n",
            " 1.0395772e-06 2.1018750e-20 9.9999988e-01 7.6731317e-07 8.8432521e-01]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# converting the predition probabilities to class label\n",
        "\n",
        "label1 = np.argmax(y_pred[0])\n",
        "print(label1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pHpxHq1k9Pmn",
        "outputId": "c467827f-2e82-4153-939c-f68a3062f385"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# converting the predition probabilities to class label for all dataset\n",
        "\n",
        "y_pred_lab = [np.argmax(i) for i in y_pred]"
      ],
      "metadata": {
        "id": "1qU8SXMU9n4b"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_pred_lab)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BbE1p3mI93sd",
        "outputId": "3ae356e1-2d77-4fbe-e6f4-3d73e0ba22ec"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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7, 3, 5, 0, 1, 1, 3, 8, 4, 9, 4, 5, 1, 8, 6, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 3, 5, 3, 2, 9, 3, 2, 1, 4, 5, 5, 2, 3, 2, 1, 3, 9, 7, 2, 1, 2, 8, 9, 1, 8, 8, 7, 8, 1, 0, 0, 6, 7, 7, 8, 9, 5, 0, 6, 1, 5, 7, 4, 6, 1, 2, 5, 0, 7, 9, 9, 0, 3, 4, 4, 8, 4, 1, 8, 6, 5, 9, 0, 0, 0, 3, 7, 1, 6, 4, 6, 0, 4, 5, 4, 1, 3, 8, 6, 3, 9, 9, 5, 9, 3, 7, 8, 5, 6, 4, 7, 6, 2, 2, 0, 9, 4, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 6, 4, 2, 6, 4, 7, 5, 5, 4, 7, 2, 9, 3, 9, 3, 8, 2, 0, 9, 5, 6, 0, 1, 0, 6, 5, 3, 5, 3, 8, 0, 0, 3, 4, 1, 5, 3, 0, 8, 3, 0, 6, 2, 7, 8, 1, 7, 1, 3, 8, 5, 4, 2, 0, 9, 7, 6, 7, 4, 1, 6, 2, 6, 7, 1, 9, 8, 0, 6, 9, 4, 9, 9, 6, 2, 3, 7, 1, 9, 2, 2, 5, 3, 7, 8, 0, 1, 2, 3, 4, 7, 8, 9, 0, 1, 2, 3, 4, 7, 8, 9, 0, 1, 7, 8, 9, 8, 9, 2, 6, 1, 3, 5, 4, 8, 2, 6, 4, 3, 4, 5, 9, 2, 0, 3, 9, 4, 9, 7, 3, 8, 7, 4, 4, 9, 8, 5, 8, 2, 6, 6, 2, 3, 1, 3, 2, 7, 3, 1, 9, 0, 1, 1, 3, 5, 0, 7, 8, 1, 5, 1, 4, 6, 0, 0, 4, 9, 1, 6, 6, 9, 0, 7, 6, 1, 1, 0, 1, 2, 3, 4, 2, 2, 3, 4, 5, 6, 2, 0, 1, 2, 7, 8, 6, 3, 9, 2, 1, 9, 3, 9, 6, 1, 7, 2, 4, 4, 5, 7, 0, 0, 1, 6, 6, 8, 2, 7, 7, 2, 4, 2, 1, 6, 1, 0, 6, 9, 8, 3, 9, 6, 3, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 6, 8, 9, 9, 0, 1, 2, 4, 4, 3, 7, 4, 4, 4, 0, 3, 8, 7, 5, 8, 2, 1, 7, 5, 3, 8, 5, 2, 5, 1, 1, 6, 2, 1, 3, 8, 6, 4, 2, 6, 2, 5, 5, 0, 2, 8, 0, 6, 8, 1, 7, 9, 1, 9, 2, 6, 7, 6, 6, 8, 7, 4, 9, 2, 1, 3, 3, 0, 5, 5, 8, 0, 3, 7, 9, 7, 0, 2, 7, 9, 1, 7, 8, 0, 3, 5, 3, 6, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 9, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 7, 8, 9, 6, 4, 2, 6, 4, 7, 8, 9, 2, 9, 3, 9, 3, 0, 0, 1, 0, 4, 2, 6, 3, 5, 3, 0, 3, 4, 1, 5, 3, 0, 8, 3, 0, 6, 1, 7, 8, 0, 9, 2, 6, 7, 1, 9, 6, 9, 4, 9, 9, 6, 7, 1, 2, 5, 3, 7, 8, 0, 1, 2, 4, 5, 6, 7, 8, 9, 0, 1, 3, 4, 5, 6, 7, 8, 0, 1, 3, 4, 7, 8, 9, 7, 5, 5, 1, 9, 9, 7, 1, 0, 0, 5, 9, 7, 1, 7, 2, 2, 3, 6, 8, 3, 2, 0, 0, 6, 1, 7, 5, 8, 6, 2, 9, 4, 8, 8, 7, 1, 0, 8, 7, 7, 5, 8, 5, 3, 4, 6, 1, 1, 5, 5, 0, 7, 2, 3, 6, 4, 1, 2, 4, 1, 5, 4, 2, 0, 4, 8, 6, 1, 9, 0, 2, 5, 6, 9, 3, 6, 3, 6, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 5, 6, 7, 8, 1, 0, 9, 5, 7, 5, 1, 8, 6, 9, 0, 4, 1, 9, 3, 8, 4, 4, 7, 0, 1, 9, 2, 8, 7, 8, 2, 3, 9, 6, 0, 6, 5, 5, 3, 3, 3, 9, 8, 1, 1, 0, 6, 1, 0, 0, 6, 2, 1, 1, 3, 2, 7, 7, 8, 8, 7, 8, 4, 6, 0, 7, 0, 7, 0, 3, 6, 8, 7, 1, 5, 9, 9, 3, 7, 2, 4, 9, 4, 3, 6, 2, 2, 5, 3, 2, 5, 5, 9, 4, 1, 7, 2, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 0, 1, 2, 7, 5, 3, 4, 4, 0, 0, 6, 9, 6, 6, 5, 7, 2, 3, 4, 4, 9, 1, 4, 0, 7, 9, 5, 7, 2, 3, 1, 4, 4, 0, 9, 9, 6, 1, 8, 3, 3, 7, 3, 9, 8, 8, 4, 7, 7, 6, 2, 1, 9, 8, 7, 8, 8, 7, 2, 2, 3, 9, 3, 3, 5, 5, 0, 7, 4, 5, 6, 5, 1, 4, 1, 1, 2, 8, 2, 6, 1, 5, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 8, 0, 6, 0, 2, 2, 3, 7, 9, 4, 7, 1, 9, 1, 7, 1, 4, 0, 0, 1, 7, 5, 7, 1, 3, 3, 3, 1, 6, 9, 7, 1, 3, 0, 7, 6, 0, 8, 9, 7, 3, 5, 4, 8, 1, 5, 9, 0, 6, 3, 3, 8, 1, 4, 7, 5, 2, 0, 0, 1, 7, 8, 9, 6, 8, 8, 2, 3, 6, 1, 2, 9, 5, 2, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 0, 6, 7, 8, 9, 0, 1, 2, 3, 4, 6, 6, 7, 8, 9, 7, 4, 6, 1, 4, 0, 9, 9, 3, 7, 8, 0, 7, 5, 8, 5, 3, 2, 2, 0, 5, 5, 6, 0, 3, 8, 1, 0, 3, 0, 9, 7, 4, 9, 0, 9, 0, 7, 1, 7, 1, 6, 6, 5, 6, 9, 8, 7, 6, 0, 9, 9, 5, 3, 7, 4, 3, 0, 1, 5, 6, 1, 1, 3, 2, 1, 0, 0, 1, 2, 3, 4, 7, 8, 7, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 7, 8, 9, 0, 8, 3, 9, 5, 5, 2, 6, 8, 4, 1, 7, 1, 7, 3, 5, 6, 9, 1, 1, 1, 2, 1, 2, 0, 7, 7, 5, 8, 2, 9, 8, 6, 7, 3, 4, 6, 8, 7, 0, 4, 2, 7, 7, 5, 4, 3, 4, 2, 8, 1, 5, 1, 0, 2, 3, 3, 5, 7, 0, 6, 8, 6, 3, 9, 9, 8, 2, 7, 7, 1, 0, 1, 7, 8, 9, 0, 1, 6, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 9, 8, 9, 7, 8, 6, 4, 1, 9, 3, 8, 4, 4, 7, 0, 1, 9, 2, 8, 7, 8, 2, 6, 0, 6, 5, 3, 3, 8, 9, 1, 4, 0, 6, 1, 0, 0, 6, 2, 1, 1, 7, 7, 8, 4, 6, 0, 7, 0, 3, 6, 8, 7, 1, 5, 2, 4, 9, 4, 2, 6, 4, 1, 7, 2, 6, 5, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conf = confusion_matrix(Y_test,y_pred_lab)\n",
        "print(conf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fn7y2cSz95pc",
        "outputId": "677ab7ed-465f-4627-e787-2d5d0f61647c"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(\n",
            "[[ 966    0    2    0    1    2    2    1    1    5]\n",
            " [   0 1124    5    0    0    1    2    2    0    1]\n",
            " [   6    1 1001    5    2    0    3    7    5    2]\n",
            " [   1    0    8  982    0    2    0    7    4    6]\n",
            " [   3    2    2    0  957    0    5    3    0   10]\n",
            " [   3    0    0   15    0  857    7    2    4    4]\n",
            " [   5    3    1    1    6    6  935    0    1    0]\n",
            " [   0    3    8    3    0    1    0  999    2   12]\n",
            " [  10    0    7   10    6    9    4    3  915   10]\n",
            " [   1    2    0    3   12    3    2    4    3  979]], shape=(10, 10), dtype=int32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,7))\n",
        "sns.heatmap(conf, annot=True, fmt='d', cmap='Blues')\n",
        "plt.ylabel('True Labels')\n",
        "plt.xlabel('Predicted Labels')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        },
        "id": "VgxfpwT8-Vpw",
        "outputId": "0786644c-56bf-493b-8291-75f76266b7e7"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 47.7222222222222, 'Predicted Labels')"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Building a Predictive System"
      ],
      "metadata": {
        "id": "8V8R5R6T-r-B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input_image_path = '/content/MNIST_digit.png'"
      ],
      "metadata": {
        "id": "u5xOe5Kw-lY7"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_image = cv2.imread(input_image_path)"
      ],
      "metadata": {
        "id": "R1_4zl2h_Hwt"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(input_image)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c9ZZ8k2b_TAD",
        "outputId": "98c75ff6-8da2-4e06-fb4e-f722d5e6b8d2"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv2_imshow(input_image)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "v9VzBXf9_Vl-",
        "outputId": "eb84c824-eeef-42e0-f29d-2538fd16d265"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=318x318>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_image.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MISgBhqu_g6G",
        "outputId": "b200381e-4db8-4f27-db74-36b730438e18"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(318, 318, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Conversion to GrayScale image\n",
        "\n",
        "grayscale = cv2.cvtColor(input_image,cv2.COLOR_RGB2GRAY)"
      ],
      "metadata": {
        "id": "-6hL0SHu_rcB"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grayscale.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cRnfDv2zAB3X",
        "outputId": "049dce71-d66b-4c02-ebe2-120eb98a30ab"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(318, 318)"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# resizing the image\n",
        "\n",
        "input_image_res = cv2.resize(grayscale,(28,28))"
      ],
      "metadata": {
        "id": "MCRxsPwsAE_f"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_image_res.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pazfojZYAbME",
        "outputId": "d5c55da2-9867-4b50-b4f1-f79cdd355cf1"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(input_image_res)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "R8xy8sgzAeKF",
        "outputId": "2a856e7c-44b5-4d07-c0cc-653e11e9f7da"
      },
      "execution_count": 55,
      "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": [
        "cv2_imshow(input_image_res)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 45
        },
        "id": "EEnKvAMCAjEM",
        "outputId": "7079ce77-9da2-4469-ff6a-4b5654ca16d5"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=28x28>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABBElEQVR4AWNgGMRATTvz/18QWMeG7krt7geP/vz9Awbz+NBkN4GEoZJ/rMGSLHAlu70YXs1l+sdgZQ8XQjBYZGUlQDy+R3/+rGEHiyN0/nkMUeguyMDw5CeEjU5G7AVaje4eiKLoy9+Bcmc4ITyEsQqxLgwMNv8ZGD5VbPuOZp7OPZhXNsJlmOAsRhBgAmIfT5gY3NgrDjE7fwBFk3NhUlho/j9/4DoRxkIVumNqYPWGuj7xE5JOiDqb7X9kQSyhmPd//nx2hGlmBDMu6DBM/wxkuRr9ZzgwfS1MEkJfAPkRBP4+m8mBKsXAYDAXLHXz/CQddCkgnz399Z816eAowyJLZSEAwTdycauOPx0AAAAASUVORK5CYII=\n",
            "image/jpeg": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(input_image_res)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y89yiyG6Aqee",
        "outputId": "d2109a52-f4ba-4649-d4b6-c6f2fd747dd8"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
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            "    0   0   0   0   0   0   0   0   0   0]\n",
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            "    0   0   0   0   0   0   0   0   0   0]\n",
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            "  243  65   0   0   0   0   0   0   0   0]\n",
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            "    0   0   0   0   0   0   0   0   0   0]\n",
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            "    0   0   0   0   0   0   0   0   0   0]\n",
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            "    0   0   0   0   0   0   0   0   0   0]\n",
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            "    0   0   0   0   0   0   0   0   0   0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Scaling\n",
        "\n",
        "input_image_res = input_image_res/255"
      ],
      "metadata": {
        "id": "bkpFfjWLAvwt"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(input_image_res)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XXlnCVwjA5QQ",
        "outputId": "ecaa391a-e02b-455f-98b5-e9df42a79880"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  0.98823529 0.98823529 0.8        0.03529412 0.         0.\n",
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            "  0.75686275 0.75686275 0.99215686 0.98823529 0.98823529 0.98823529\n",
            "  0.93333333 0.4        0.10980392 0.         0.         0.\n",
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            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.17254902 0.87058824 0.98823529 0.98823529\n",
            "  0.98823529 0.98823529 0.99215686 0.98823529 0.98823529 0.98823529\n",
            "  0.69411765 0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.17254902 0.8745098  0.99215686 0.99215686\n",
            "  0.99215686 0.99215686 1.         0.99215686 0.99215686 0.99215686\n",
            "  0.99215686 0.28627451 0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.12156863 0.48235294 0.20392157\n",
            "  0.17254902 0.17254902 0.17254902 0.17254902 0.56078431 0.98823529\n",
            "  0.98823529 0.28627451 0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.05882353 0.98823529\n",
            "  0.98823529 0.28627451 0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.3372549  0.98823529\n",
            "  0.98823529 0.28627451 0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.01960784 0.29411765 0.03529412 0.         0.         0.\n",
            "  0.         0.         0.         0.38039216 0.94901961 0.98823529\n",
            "  0.98823529 0.28627451 0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.23529412\n",
            "  0.71764706 0.98823529 0.11372549 0.         0.         0.\n",
            "  0.         0.07058824 0.36078431 0.9372549  0.98823529 0.98823529\n",
            "  0.95294118 0.25490196 0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.81568627\n",
            "  0.98823529 0.98823529 0.57647059 0.5254902  0.5254902  0.5254902\n",
            "  0.5254902  0.79607843 0.99215686 0.98823529 0.98823529 0.7372549\n",
            "  0.3254902  0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.81568627\n",
            "  0.98823529 0.98823529 0.98823529 0.98823529 0.98823529 0.98823529\n",
            "  0.98823529 0.98823529 0.99215686 0.90196078 0.6        0.03137255\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.18823529\n",
            "  0.61568627 0.98823529 0.98823529 0.98823529 0.98823529 0.98823529\n",
            "  0.85098039 0.81176471 0.57254902 0.17254902 0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.02745098 0.40392157 0.92156863 0.98823529 0.6745098  0.40392157\n",
            "  0.09411765 0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(input_image_res)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gPHRIqyCA8WJ",
        "outputId": "1ccf902a-d7af-40b8-9f1c-326c6787e031"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image_reshaped = np.reshape(input_image_res,[1,28,28])"
      ],
      "metadata": {
        "id": "2h8dtutrBGFf"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(image_reshaped)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "711AnYtqCmEj",
        "outputId": "77238419-d5f5-49fe-ce45-15afbb3481a6"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "   0.         0.         0.         0.        ]\n",
            "  [0.         0.         0.         0.         0.         0.\n",
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            "   0.16862745 0.41176471 1.         0.99215686 0.99215686 0.99215686\n",
            "   0.99215686 0.99215686 0.68235294 0.02352941 0.         0.\n",
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            "  [0.         0.         0.         0.         0.         0.\n",
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            "   0.98823529 0.98823529 0.98823529 0.61960784 0.05490196 0.\n",
            "   0.         0.         0.         0.        ]\n",
            "  [0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.         0.69803922 0.98823529 0.98823529\n",
            "   0.98823529 0.98823529 0.99215686 0.98823529 0.98823529 0.98823529\n",
            "   0.98823529 0.98823529 0.98823529 0.98823529 0.23137255 0.\n",
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            "   0.98823529 0.98823529 0.98823529 0.98823529 0.23137255 0.\n",
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            "   0.88627451 0.98823529 0.98823529 0.6745098  0.02745098 0.\n",
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            "   0.95294118 0.98823529 0.98823529 0.56470588 0.         0.\n",
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            "   0.98823529 0.98823529 0.8        0.03529412 0.         0.\n",
            "   0.         0.         0.         0.        ]\n",
            "  [0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.         0.1254902  0.49019608 0.75686275\n",
            "   0.75686275 0.75686275 0.99215686 0.98823529 0.98823529 0.98823529\n",
            "   0.93333333 0.4        0.10980392 0.         0.         0.\n",
            "   0.         0.         0.         0.        ]\n",
            "  [0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.17254902 0.87058824 0.98823529 0.98823529\n",
            "   0.98823529 0.98823529 0.99215686 0.98823529 0.98823529 0.98823529\n",
            "   0.69411765 0.         0.         0.         0.         0.\n",
            "   0.         0.         0.         0.        ]\n",
            "  [0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.17254902 0.8745098  0.99215686 0.99215686\n",
            "   0.99215686 0.99215686 1.         0.99215686 0.99215686 0.99215686\n",
            "   0.99215686 0.28627451 0.         0.         0.         0.\n",
            "   0.         0.         0.         0.        ]\n",
            "  [0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.         0.12156863 0.48235294 0.20392157\n",
            "   0.17254902 0.17254902 0.17254902 0.17254902 0.56078431 0.98823529\n",
            "   0.98823529 0.28627451 0.         0.         0.         0.\n",
            "   0.         0.         0.         0.        ]\n",
            "  [0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.         0.         0.05882353 0.98823529\n",
            "   0.98823529 0.28627451 0.         0.         0.         0.\n",
            "   0.         0.         0.         0.        ]\n",
            "  [0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.         0.         0.3372549  0.98823529\n",
            "   0.98823529 0.28627451 0.         0.         0.         0.\n",
            "   0.         0.         0.         0.        ]\n",
            "  [0.         0.         0.         0.         0.         0.\n",
            "   0.01960784 0.29411765 0.03529412 0.         0.         0.\n",
            "   0.         0.         0.         0.38039216 0.94901961 0.98823529\n",
            "   0.98823529 0.28627451 0.         0.         0.         0.\n",
            "   0.         0.         0.         0.        ]\n",
            "  [0.         0.         0.         0.         0.         0.23529412\n",
            "   0.71764706 0.98823529 0.11372549 0.         0.         0.\n",
            "   0.         0.07058824 0.36078431 0.9372549  0.98823529 0.98823529\n",
            "   0.95294118 0.25490196 0.         0.         0.         0.\n",
            "   0.         0.         0.         0.        ]\n",
            "  [0.         0.         0.         0.         0.         0.81568627\n",
            "   0.98823529 0.98823529 0.57647059 0.5254902  0.5254902  0.5254902\n",
            "   0.5254902  0.79607843 0.99215686 0.98823529 0.98823529 0.7372549\n",
            "   0.3254902  0.         0.         0.         0.         0.\n",
            "   0.         0.         0.         0.        ]\n",
            "  [0.         0.         0.         0.         0.         0.81568627\n",
            "   0.98823529 0.98823529 0.98823529 0.98823529 0.98823529 0.98823529\n",
            "   0.98823529 0.98823529 0.99215686 0.90196078 0.6        0.03137255\n",
            "   0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.         0.        ]\n",
            "  [0.         0.         0.         0.         0.         0.18823529\n",
            "   0.61568627 0.98823529 0.98823529 0.98823529 0.98823529 0.98823529\n",
            "   0.85098039 0.81176471 0.57254902 0.17254902 0.         0.\n",
            "   0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.         0.        ]\n",
            "  [0.         0.         0.         0.         0.         0.\n",
            "   0.02745098 0.40392157 0.92156863 0.98823529 0.6745098  0.40392157\n",
            "   0.09411765 0.         0.         0.         0.         0.\n",
            "   0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.         0.        ]\n",
            "  [0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.         0.        ]\n",
            "  [0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.         0.        ]\n",
            "  [0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.         0.        ]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_pred = model.predict(image_reshaped)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KaYD9Sk0CuYc",
        "outputId": "2c5318d6-f3a0-4fd1-f9e6-6ac8e7e5d224"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(input_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AbkXxFrRC4Mb",
        "outputId": "411e168a-926b-4067-c904-0d40d96546a1"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1.94413414e-13 6.88593493e-09 3.20211276e-02 1.00000000e+00\n",
            "  1.21859606e-32 8.23703999e-07 1.83626470e-32 1.02654531e-06\n",
            "  2.74888845e-03 3.91747086e-11]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_lab = np.argmax(input_pred)"
      ],
      "metadata": {
        "id": "Ors4jSKyC-cp"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(input_lab)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t-BEYHouDFhz",
        "outputId": "7eca2fcf-184d-401a-cd3a-08e97cf9b7f0"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Predictive System**"
      ],
      "metadata": {
        "id": "fuXU4eTqDOzD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input_image_path = input('Path of the image to be predicted: ')\n",
        "\n",
        "input_image = cv2.imread(input_image_path)\n",
        "\n",
        "cv2_imshow(input_image)\n",
        "\n",
        "grayscale = cv2.cvtColor(input_image,cv2.COLOR_RGB2GRAY)\n",
        "\n",
        "input_img_resize = cv2.resize(grayscale,(28,28))\n",
        "\n",
        "input_img_scaling = input_img_resize/255\n",
        "\n",
        "image_reshape = np.reshape(input_img_scaling,(1,28,28))\n",
        "\n",
        "img_pred = model.predict(image_reshape)\n",
        "\n",
        "img_label = np.argmax(img_pred)\n",
        "\n",
        "print('The Handwritten Digit is recognised as ', img_label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "id": "2s3Mw_fVDMgw",
        "outputId": "858d7d75-3960-4060-a795-338d5a291d2c"
      },
      "execution_count": 82,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Path of the image to be predicted: /content/MNIST_digit.png\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=318x318>"
            ],
            "image/png": 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            "image/jpeg": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "The Handwritten Digit is recognised as  3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tX1DGzNwGtT_"
      },
      "execution_count": 82,
      "outputs": []
    }
  ]
}