DIGIT_CLASSIFICATION_USING_MNIST_DATASET.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "V28"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7z5awdwhWrEG",
        "outputId": "45da2a5c-679e-4034-a1ed-34037f8ea794"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.15.0)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.3.25)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.6.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.2.0)\n",
            "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.12.1)\n",
            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (18.1.1)\n",
            "Requirement already satisfied: ml-dtypes~=0.2.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.2.0)\n",
            "Requirement already satisfied: numpy<2.0.0,>=1.23.5 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.26.4)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.4.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.1)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.20.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow) (75.1.0)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.16.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.5.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (4.12.2)\n",
            "Requirement already satisfied: wrapt<1.15,>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.14.1)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.37.1)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.66.2)\n",
            "Requirement already satisfied: tensorboard<2.16,>=2.15 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.15.2)\n",
            "Requirement already satisfied: tensorflow-estimator<2.16,>=2.15.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.15.0)\n",
            "Requirement already satisfied: keras<2.16,>=2.15.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.15.0)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow) (0.44.0)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (2.27.0)\n",
            "Requirement already satisfied: google-auth-oauthlib<2,>=0.5 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (1.2.1)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (3.7)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (2.32.3)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (3.0.4)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (5.5.0)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (0.4.1)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (4.9)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow) (2.0.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow) (2024.8.30)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.16,>=2.15->tensorflow) (3.0.1)\n",
            "Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (0.6.1)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow) (3.2.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**IMPORT LIBRARIES**\n"
      ],
      "metadata": {
        "id": "GPj3GBYokrRX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n"
      ],
      "metadata": {
        "id": "kCz8jhMEkm65"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**LOADING MNIST DATASET**"
      ],
      "metadata": {
        "id": "ipX6AQT1mG0c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mnist=tf.keras.datasets.mnist"
      ],
      "metadata": {
        "id": "qlahY2NKl8hN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**UNPACKING DATASET INTO TRAIN AND TEST DATASETS**"
      ],
      "metadata": {
        "id": "macqMfKgmWmR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(x_train,y_train),(x_test,y_test)=mnist.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GBb6sAbNmSF7",
        "outputId": "c4d70930-92fd-4cef-cc3f-1360abf151c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"train dataset:\",x_train.shape)\n",
        "print(\"test dataset:\",x_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mFP88ySmmw4x",
        "outputId": "4e305fc3-1856-4ec6-f2c0-acb2c8a700cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train dataset: (60000, 28, 28)\n",
            "test dataset: (10000, 28, 28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(x_train[8])\n",
        "plt.show()\n",
        "plt.imshow(x_train[8],cmap=plt.cm.binary)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 860
        },
        "id": "RcALO2XNnNFk",
        "outputId": "7135fe62-f91f-4573-e926-f6114230d94b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7b6be6591b70>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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UXl6u8vLyXg8F4G4ejyfibRISEmIwCRAd3AsOAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJnr1G1EB9L1AIBDxNvv37+/Vay1atKhX2wGR4AwIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADDBzUgBA3v27Il4m2HDhkW8zdNPPx3xNkBf4QwIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADDBzUgBA88991zE23z55ZcRbzN8+PCItwH6CmdAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJbkYKGNi9e7f1CIA5zoAAACYIEADAREQBKisr07Rp05SYmKi0tDQtWLBAdXV1YevMnj1bHo8nbFm1alVUhwYAxL+IAlRdXa3i4mIdP35cR44c0Y0bNzR37lx1dnaGrbdixQq1tLSEls2bN0d1aABA/IvoIoTDhw+HfVxRUaG0tDTV1tZq1qxZocdHjBghv98fnQkBAAPSQ70HFAgEJEkpKSlhj3/44YdKTU3VpEmTVFpaqitXrtzzc3R1dSkYDIYtAICBr9eXYXd3d2vt2rWaMWOGJk2aFHr8xRdf1NixY5WZmakzZ87ojTfeUF1dnT7++OMeP09ZWZk2btzY2zEAAHHK45xzvdlw9erV+vvf/67PPvtMo0ePvud6R48e1Zw5c1RfX6/x48ff9XxXV5e6urpCHweDQWVlZSkQCCgpKak3owEADAWDQfl8vvt+He/VGdCaNWt06NAhHTt27FvjI0m5ubmSdM8Aeb1eeb3e3owBAIhjEQXIOadXX31V+/btU1VVlbKzs++7zenTpyVJGRkZvRoQADAwRRSg4uJi7dy5UwcOHFBiYqJaW1slST6fT8OHD1dDQ4N27typn/70pxo5cqTOnDmjdevWadasWZoyZUpM/gIAgPgU0XtAHo+nx8d37Nih5cuXq6mpST//+c919uxZdXZ2KisrSwsXLtSbb775wO/nPOj3DgEA/VNM3gO6X6uysrJUXV0dyacEADyiuBccAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMDEEOsB7uSckyQFg0HjSQAAvXH76/ftr+f30u8C1NHRIUnKysoyngQA8DA6Ojrk8/nu+bzH3S9Rfay7u1vNzc1KTEyUx+MJey4YDCorK0tNTU1KSkoymtAe++EW9sMt7Idb2A+39If94JxTR0eHMjMzNWjQvd/p6XdnQIMGDdLo0aO/dZ2kpKRH+gC7jf1wC/vhFvbDLeyHW6z3w7ed+dzGRQgAABMECABgIq4C5PV6tWHDBnm9XutRTLEfbmE/3MJ+uIX9cEs87Yd+dxECAODREFdnQACAgYMAAQBMECAAgAkCBAAwETcBKi8v1/e+9z0NGzZMubm5+te//mU9Up97++235fF4wpaJEydajxVzx44d07x585SZmSmPx6P9+/eHPe+c0/r165WRkaHhw4crPz9f586dsxk2hu63H5YvX37X8VFYWGgzbIyUlZVp2rRpSkxMVFpamhYsWKC6urqwda5du6bi4mKNHDlSjz/+uBYvXqy2tjajiWPjQfbD7Nmz7zoeVq1aZTRxz+IiQHv27FFJSYk2bNigL774Qjk5OSooKNDFixetR+tzzzzzjFpaWkLLZ599Zj1SzHV2dionJ0fl5eU9Pr9582a9++672r59u06cOKHHHntMBQUFunbtWh9PGlv32w+SVFhYGHZ87Nq1qw8njL3q6moVFxfr+PHjOnLkiG7cuKG5c+eqs7MztM66det08OBB7d27V9XV1WpubtaiRYsMp46+B9kPkrRixYqw42Hz5s1GE9+DiwPTp093xcXFoY9v3rzpMjMzXVlZmeFUfW/Dhg0uJyfHegxTkty+fftCH3d3dzu/3+/eeeed0GPt7e3O6/W6Xbt2GUzYN+7cD845t2zZMjd//nyTeaxcvHjRSXLV1dXOuVv/7YcOHer27t0bWufLL790klxNTY3VmDF3535wzrnnnnvO/fKXv7Qb6gH0+zOg69evq7a2Vvn5+aHHBg0apPz8fNXU1BhOZuPcuXPKzMzUuHHj9NJLL+n8+fPWI5lqbGxUa2tr2PHh8/mUm5v7SB4fVVVVSktL04QJE7R69WpdunTJeqSYCgQCkqSUlBRJUm1trW7cuBF2PEycOFFjxowZ0MfDnfvhtg8//FCpqamaNGmSSktLdeXKFYvx7qnf3Yz0Tt98841u3ryp9PT0sMfT09P11VdfGU1lIzc3VxUVFZowYYJaWlq0ceNGzZw5U2fPnlViYqL1eCZaW1slqcfj4/Zzj4rCwkItWrRI2dnZamho0G9+8xsVFRWppqZGgwcPth4v6rq7u7V27VrNmDFDkyZNknTreEhISFBycnLYugP5eOhpP0jSiy++qLFjxyozM1NnzpzRG2+8obq6On388ceG04br9wHC/xQVFYX+PGXKFOXm5mrs2LH66KOP9MorrxhOhv5g6dKloT9PnjxZU6ZM0fjx41VVVaU5c+YYThYbxcXFOnv27CPxPui3udd+WLlyZejPkydPVkZGhubMmaOGhgaNHz++r8fsUb//FlxqaqoGDx5811UsbW1t8vv9RlP1D8nJyXrqqadUX19vPYqZ28cAx8fdxo0bp9TU1AF5fKxZs0aHDh3Sp59+GvbrW/x+v65fv6729vaw9Qfq8XCv/dCT3NxcSepXx0O/D1BCQoKmTp2qysrK0GPd3d2qrKxUXl6e4WT2Ll++rIaGBmVkZFiPYiY7O1t+vz/s+AgGgzpx4sQjf3xcuHBBly5dGlDHh3NOa9as0b59+3T06FFlZ2eHPT916lQNHTo07Hioq6vT+fPnB9TxcL/90JPTp09LUv86HqyvgngQu3fvdl6v11VUVLh///vfbuXKlS45Odm1trZaj9anfvWrX7mqqirX2Njo/vnPf7r8/HyXmprqLl68aD1aTHV0dLhTp065U6dOOUluy5Yt7tSpU+7rr792zjn3+9//3iUnJ7sDBw64M2fOuPnz57vs7Gx39epV48mj69v2Q0dHh3vttddcTU2Na2xsdJ988on74Q9/6J588kl37do169GjZvXq1c7n87mqqirX0tISWq5cuRJaZ9WqVW7MmDHu6NGj7uTJky4vL8/l5eUZTh1999sP9fX1btOmTe7kyZOusbHRHThwwI0bN87NmjXLePJwcREg55x777333JgxY1xCQoKbPn26O378uPVIfW7JkiUuIyPDJSQkuO9+97tuyZIlrr6+3nqsmPv000+dpLuWZcuWOeduXYr91ltvufT0dOf1et2cOXNcXV2d7dAx8G374cqVK27u3Llu1KhRbujQoW7s2LFuxYoVA+4faT39/SW5HTt2hNa5evWq+8UvfuG+853vuBEjRriFCxe6lpYWu6Fj4H774fz5827WrFkuJSXFeb1e98QTT7hf//rXLhAI2A5+B34dAwDARL9/DwgAMDARIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACb+D11LKa8Rq5SBAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**CHECKING THE VALUE OF EACH PIXEL BEFORE NORMALIZATION**"
      ],
      "metadata": {
        "id": "XvhhQZ-NvmsE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print((x_train[8]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UATqduvfumTP",
        "outputId": "390f925d-0933-4eba-9d61-8a620521ef04"
      },
      "execution_count": null,
      "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   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   5  63 197   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  20 254 230  24   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  20 254 254  48   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  20 254 255  48   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  20 254 254  57   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  20 254 254 108   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  16 239 254 143   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   0 178 254 143   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   0 178 254 143   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   0 178 254 162   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   0 178 254 240   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   0 113 254 240   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  83 254 245  31   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  79 254 246  38   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   0 214 254 150   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   0 144 241   8   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   0 144 240   2   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   0 144 254  82   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   0 230 247  40   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   0 168 209  31   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": "markdown",
      "source": [
        "**AS IMAGES ARE IN GREY LEVEL(1 CHANNEL==>0 TO 255), NOT COLOURED(RGB)**\n",
        "**Normalizing the data|pre-processing step**"
      ],
      "metadata": {
        "id": "ApjiBCXSv0Iz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train=tf.keras.utils.normalize(x_train,axis=1)\n",
        "x_test=tf.keras.utils.normalize(x_test,axis=1)\n",
        "plt.imshow(x_train[8],cmap=plt.cm.binary)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "xFIVvtwWvh1I",
        "outputId": "081910de-2216-4930-b2fb-7576007ad03e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7b6be65e5420>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "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(x_train[8]) #normalized value"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ui9rnW43xV_F",
        "outputId": "1960ad68-c5a8-4e23-b93a-0dc0f0ef1e43"
      },
      "execution_count": null,
      "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.\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",
            "  0.10469052 0.08602697 0.19174731 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",
            "  0.41876208 0.34683889 0.22386742 0.02883349 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",
            "  0.41876208 0.34683889 0.2472275  0.05766699 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",
            "  0.41876208 0.34683889 0.24820084 0.05766699 0.         0.\n",
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            " [0.         0.         0.         0.         0.         0.\n",
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            "  0.         0.         0.         0.        ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_train[8]) #verifying the lables"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qx5HL65Lxbxk",
        "outputId": "0766c451-21a4-4673-ac2d-62e8bb44c262"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**RESIZEING IMAGE TO MAKE IT SUITABLE FOR APPLY CONVOLUTION OPERATION**"
      ],
      "metadata": {
        "id": "RmROkzApx0RX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "IMG_SIZE=28\n",
        "x_trainr=np.array(x_train).reshape(-1,IMG_SIZE,IMG_SIZE,1) #increasing one dimension for kernal operation\n",
        "x_testr=np.array(x_test).reshape(-1,IMG_SIZE,IMG_SIZE,1) #increasing one dimension for kernal operation\n",
        "print(\"Training sample dimension\",x_trainr.shape)\n",
        "print(\"Testing sample dimension\",x_testr.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X_RqwcJrxy25",
        "outputId": "18c18531-ea32-42d4-b89d-11c3e5f50bb1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training sample dimension (60000, 28, 28, 1)\n",
            "Testing sample dimension (10000, 28, 28, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**CREATING DEEP NEURAL NETWORK**\n",
        "\n",
        "\n",
        "\n",
        "TRAINING ON 60000 SAMPLES OF MNIST HANDWRITTEN DATASET"
      ],
      "metadata": {
        "id": "WM0Y3r6ty9Gb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense,Dropout,Activation,Flatten,Conv2D,MaxPooling2D"
      ],
      "metadata": {
        "id": "_pFe_AK2zXlO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating neural network\n",
        "model=Sequential()\n",
        "\n",
        "#First convolution Layer 0 1 2 3 (60000,28,28,1)\n",
        "model.add(Conv2D(64,(3,3),input_shape=x_trainr.shape[1:])) #only for first convolution layer to mention input layer size.\n",
        "model.add(Activation(\"relu\")) #activation function to make it non-linear <0,remove>0\n",
        "model.add(MaxPooling2D(pool_size=(2,2))) #Maxpooling single maximum value of 2X2\n",
        "\n",
        "model.add(Conv2D(64,(3,3))) #2nd convolution Layer\n",
        "model.add(Activation(\"relu\")) #activation function\n",
        "model.add(MaxPooling2D(pool_size=(2,2))) #Maxpooling\n",
        "\n",
        "model.add(Conv2D(64,(3,3))) #3rd convolution Layer\n",
        "model.add(Activation(\"relu\")) #activation function\n",
        "\n",
        "#Fully Conected Layer #1 20x20=400\n",
        "model.add(Flatten()) #before using fully conected layer need, to be flatten so that 2D to 1D\n",
        "model.add(Dense(64))\n",
        "model.add(Activation(\"relu\"))\n",
        "\n",
        "#Fully Conected Layer #2 20x20=400\n",
        "model.add(Flatten())\n",
        "model.add(Dense(32))\n",
        "model.add(Activation(\"relu\"))\n",
        "\n",
        "#Last Fully Conected Layer, output must be equal to number of class, 10 (0-9)\n",
        "model.add(Dense(10)) #this last dense layer must be equal to 10\n",
        "model.add(Activation(\"softmax\")) #activation function is changed to softmax(class probabilities)"
      ],
      "metadata": {
        "id": "Flf_sJ_z1H-W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "StqBcTwC5MZO",
        "outputId": "87227b72-48e9-470c-882a-2a5d8303d0eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 26, 26, 64)        640       \n",
            "                                                                 \n",
            " activation (Activation)     (None, 26, 26, 64)        0         \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2  (None, 13, 13, 64)        0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 11, 11, 64)        36928     \n",
            "                                                                 \n",
            " activation_1 (Activation)   (None, 11, 11, 64)        0         \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPoolin  (None, 5, 5, 64)          0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 3, 3, 64)          36928     \n",
            "                                                                 \n",
            " activation_2 (Activation)   (None, 3, 3, 64)          0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 576)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                36928     \n",
            "                                                                 \n",
            " activation_3 (Activation)   (None, 64)                0         \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 32)                2080      \n",
            "                                                                 \n",
            " activation_4 (Activation)   (None, 32)                0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 10)                330       \n",
            "                                                                 \n",
            " activation_5 (Activation)   (None, 10)                0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 113834 (444.66 KB)\n",
            "Trainable params: 113834 (444.66 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"total training samples=\",len(x_trainr))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YIK0nMWa6Jjq",
        "outputId": "7addebff-4f7a-4db6-962f-4a6f7753aeac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total training samples= 60000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss=\"sparse_categorical_crossentropy\",optimizer=\"adam\",metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "LFbGpOe86XfN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_trainr,y_train,epochs=25,validation_split=0.3) #train the model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jde4cDfc7CnW",
        "outputId": "04562152-55ef-44cb-a230-9fb9f258acfd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "1313/1313 [==============================] - 16s 11ms/step - loss: 0.2463 - accuracy: 0.9215 - val_loss: 0.1064 - val_accuracy: 0.9680\n",
            "Epoch 2/25\n",
            "1313/1313 [==============================] - 14s 11ms/step - loss: 0.0695 - accuracy: 0.9788 - val_loss: 0.0624 - val_accuracy: 0.9816\n",
            "Epoch 3/25\n",
            "1313/1313 [==============================] - 14s 11ms/step - loss: 0.0469 - accuracy: 0.9852 - val_loss: 0.0574 - val_accuracy: 0.9841\n",
            "Epoch 4/25\n",
            "1313/1313 [==============================] - 14s 11ms/step - loss: 0.0365 - accuracy: 0.9892 - val_loss: 0.0581 - val_accuracy: 0.9833\n",
            "Epoch 5/25\n",
            "1313/1313 [==============================] - 14s 11ms/step - loss: 0.0304 - accuracy: 0.9900 - val_loss: 0.0468 - val_accuracy: 0.9865\n",
            "Epoch 6/25\n",
            "1313/1313 [==============================] - 14s 11ms/step - loss: 0.0237 - accuracy: 0.9925 - val_loss: 0.0466 - val_accuracy: 0.9874\n",
            "Epoch 7/25\n",
            "1313/1313 [==============================] - 14s 11ms/step - loss: 0.0204 - accuracy: 0.9933 - val_loss: 0.0444 - val_accuracy: 0.9880\n",
            "Epoch 8/25\n",
            "1313/1313 [==============================] - 14s 11ms/step - loss: 0.0148 - accuracy: 0.9951 - val_loss: 0.0449 - val_accuracy: 0.9879\n",
            "Epoch 9/25\n",
            "1313/1313 [==============================] - 14s 11ms/step - loss: 0.0146 - accuracy: 0.9954 - val_loss: 0.0499 - val_accuracy: 0.9874\n",
            "Epoch 10/25\n",
            "1313/1313 [==============================] - 14s 11ms/step - loss: 0.0113 - accuracy: 0.9960 - val_loss: 0.0621 - val_accuracy: 0.9858\n",
            "Epoch 11/25\n",
            "1313/1313 [==============================] - 14s 11ms/step - loss: 0.0123 - accuracy: 0.9957 - val_loss: 0.0493 - val_accuracy: 0.9873\n",
            "Epoch 12/25\n",
            "1313/1313 [==============================] - 14s 10ms/step - loss: 0.0092 - accuracy: 0.9971 - val_loss: 0.0585 - val_accuracy: 0.9864\n",
            "Epoch 13/25\n",
            "1313/1313 [==============================] - 14s 11ms/step - loss: 0.0086 - accuracy: 0.9973 - val_loss: 0.0537 - val_accuracy: 0.9883\n",
            "Epoch 14/25\n",
            "1313/1313 [==============================] - 14s 11ms/step - loss: 0.0091 - accuracy: 0.9971 - val_loss: 0.0528 - val_accuracy: 0.9880\n",
            "Epoch 15/25\n",
            "1313/1313 [==============================] - 14s 11ms/step - loss: 0.0073 - accuracy: 0.9974 - val_loss: 0.0594 - val_accuracy: 0.9885\n",
            "Epoch 16/25\n",
            "1313/1313 [==============================] - 14s 11ms/step - loss: 0.0075 - accuracy: 0.9976 - val_loss: 0.0537 - val_accuracy: 0.9878\n",
            "Epoch 17/25\n",
            "1313/1313 [==============================] - 14s 11ms/step - loss: 0.0077 - accuracy: 0.9975 - val_loss: 0.0592 - val_accuracy: 0.9886\n",
            "Epoch 18/25\n",
            "1313/1313 [==============================] - 14s 11ms/step - loss: 0.0090 - accuracy: 0.9971 - val_loss: 0.0532 - val_accuracy: 0.9882\n",
            "Epoch 19/25\n",
            "1313/1313 [==============================] - 14s 10ms/step - loss: 0.0048 - accuracy: 0.9987 - val_loss: 0.0553 - val_accuracy: 0.9895\n",
            "Epoch 20/25\n",
            "1313/1313 [==============================] - 14s 11ms/step - loss: 0.0062 - accuracy: 0.9983 - val_loss: 0.0696 - val_accuracy: 0.9872\n",
            "Epoch 21/25\n",
            "1313/1313 [==============================] - 14s 10ms/step - loss: 0.0062 - accuracy: 0.9982 - val_loss: 0.0671 - val_accuracy: 0.9882\n",
            "Epoch 22/25\n",
            "1313/1313 [==============================] - 14s 10ms/step - loss: 0.0051 - accuracy: 0.9985 - val_loss: 0.0683 - val_accuracy: 0.9889\n",
            "Epoch 23/25\n",
            "1313/1313 [==============================] - 14s 10ms/step - loss: 0.0062 - accuracy: 0.9981 - val_loss: 0.0699 - val_accuracy: 0.9875\n",
            "Epoch 24/25\n",
            "1313/1313 [==============================] - 14s 10ms/step - loss: 0.0048 - accuracy: 0.9985 - val_loss: 0.0882 - val_accuracy: 0.9865\n",
            "Epoch 25/25\n",
            "1313/1313 [==============================] - 14s 11ms/step - loss: 0.0078 - accuracy: 0.9979 - val_loss: 0.0580 - val_accuracy: 0.9894\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7b6be6565510>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Evaulating on testing dataset\n",
        "test_loss,test_acc=model.evaluate(x_testr,y_test)\n",
        "print(\"test loss on 10,000 samples\",test_loss)\n",
        "print(\"Validation accuracy on 10,000 test samples\",test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZVBHcTvL8CXG",
        "outputId": "9c213162-754c-4304-8726-a4f7317d6775"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 4ms/step - loss: 0.0633 - accuracy: 0.9894\n",
            "test loss on 10,000 samples 0.0633358284831047\n",
            "Validation accuracy on 10,000 test samples 0.9894000291824341\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions=model.predict([x_testr])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7mJayhRD-Nt2",
        "outputId": "59af7d67-3879-4612-aa45-64c54755434a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 4ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D6xnwVGK-YTB",
        "outputId": "e457383b-ce53-422b-911d-0c06cd6abeee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[4.0908076e-12 4.3732415e-10 2.5320376e-10 ... 9.9999994e-01\n",
            "  6.4669494e-12 2.1349351e-11]\n",
            " [2.9549359e-11 5.5454592e-09 9.9999994e-01 ... 1.5299305e-11\n",
            "  5.8706707e-13 1.3598117e-15]\n",
            " [1.8294646e-14 9.9999994e-01 9.7585687e-13 ... 2.5031824e-14\n",
            "  5.4837900e-13 7.0744246e-14]\n",
            " ...\n",
            " [2.6294719e-12 1.0564730e-10 9.3517538e-10 ... 3.4607528e-10\n",
            "  7.0845635e-11 3.1251921e-11]\n",
            " [1.6985680e-14 4.9016314e-13 3.0961829e-20 ... 1.8922635e-18\n",
            "  8.4992462e-13 7.4544704e-11]\n",
            " [2.2788978e-08 3.0466078e-11 7.0776141e-09 ... 8.5868016e-15\n",
            "  2.4011081e-08 9.8634066e-11]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.argmax(predictions[8]))\n",
        "plt.imshow(x_test[8])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "_nQQwgQF-8z2",
        "outputId": "217a2bae-6e85-4441-c742-8b11d636b081"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7b62980be3e0>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "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(np.argmax(predictions[360]))\n",
        "plt.imshow(x_test[360])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "SwgqDyHC_pDJ",
        "outputId": "47f50421-afed-4ab5-a28f-d17198959440"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7b627870d4b0>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "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(np.argmax(predictions[72]))\n",
        "plt.imshow(x_test[72])"
      ],
      "metadata": {
        "id": "72H4WeqbiNyC",
        "outputId": "f8996e71-6f59-4209-888b-52658332b59d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7b6278780550>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "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": [
        "from tensorflow.keras.models import load_model\n",
        "model.save('/content/drive/MyDrive/NN_DATA_MODEL/DIGIT_CLASSIFICATION.h5')"
      ],
      "metadata": {
        "id": "kYDAeQVyDmzQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "859e69d8-fb17-4f5b-c0ab-bdbcb380f541"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import cv2\n",
        "import numpy as np\n",
        "from google.colab import files\n",
        "from IPython.display import Image,display\n",
        "\n",
        "# Load a pre-trained digit classification model (assuming you have one)\n",
        "model = keras.models.load_model(\"/content/drive/MyDrive/NN_DATA_MODEL/DIGIT_CLASSIFICATION.h5\")\n",
        "\n",
        "# Function to predict the numerical value in an image\n",
        "def predict_digit(image_path, true_label=None):\n",
        "    img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n",
        "    img = cv2.resize(img, (28, 28))  # Resize the image to match the MNIST dataset\n",
        "    img = img / 255.0  # Normalize the pixel values to be between 0 and 1\n",
        "    img = img.reshape(1, 28, 28)  # Reshape to match the model's input shape\n",
        "\n",
        "    # Make the prediction\n",
        "    prediction = model.predict(img)\n",
        "    predicted_digit = np.argmax(prediction)\n",
        "    confidence = np.max(prediction)  # Confidence corresponds to the highest predicted probability\n",
        "\n",
        "    # Calculate accuracy\n",
        "    accuracy = None\n",
        "    if true_label is not None:\n",
        "        accuracy = 1 if true_label == predicted_digit else 0\n",
        "\n",
        "    return predicted_digit, confidence, accuracy\n",
        "\n",
        "# Allow the user to upload an image\n",
        "uploaded = files.upload()\n",
        "\n",
        "# Display the uploaded image\n",
        "image_path = list(uploaded.keys())[0]\n",
        "display(Image(image_path))\n",
        "\n",
        "# True label of the uploaded image (if available)\n",
        "true_label = None  # Replace None with the true label if known\n",
        "\n",
        "# Predict the digit in the uploaded image\n",
        "predicted_digit, confidence, accuracy = predict_digit(image_path, true_label)\n",
        "print(\"Predicted Digit: 6\", predicted_digit)\n",
        "print(\"Confidence:\", confidence)\n",
        "if accuracy is not None:\n",
        "    print(\"Accuracy:\", accuracy)\n"
      ],
      "metadata": {
        "id": "0qfoaGC40nqE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "outputId": "5ce10e1c-1539-445f-b8fe-a971eb5f8934"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-ce952f42-c7b6-4672-afe5-99a40e96b19c\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-ce952f42-c7b6-4672-afe5-99a40e96b19c\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving Untitled.png to Untitled (2).png\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 84ms/step\n",
            "Predicted Digit: 6\n",
            "Confidence: 0.64126647\n"
          ]
        }
      ]
    }
  ]
}