DLHndsOn17.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DLHndsOn17.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "mount_file_id": "1pAP_PrkGOoxlGW9AQdH_i-Nuv0hVZXpv",
      "authorship_tag": "ABX9TyNmTPIDUQmDYo+PpD0ext/R",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/3TInfoTinker/Binary-classification-dataset/blob/master/DLHndsOn17.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TD82qiRcAc_C"
      },
      "source": [
        "# **DL HandsOn Excercise-17**\n",
        "**Image Classification with Keras and TensorFlow**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QgcsMz45Cw8i"
      },
      "source": [
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import Flatten"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qqj9EiTrMBY-"
      },
      "source": [
        "# Loading Keras MNIST dataset\n",
        "\n",
        "mnist = tf.keras.datasets.mnist\n",
        "\n",
        "(trnfeatures, trnlabels), (testfeatures, testlabels) = mnist.load_data()\n",
        "\n",
        "# Normalizing the Data\n",
        "\n",
        "trnfeatures, testfeatures = trnfeatures / 255.0, testfeatures / 255.0\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oSJlm4A3OUP0"
      },
      "source": [
        "# **Building a Sequential Model** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vgh4sRDGNFHD"
      },
      "source": [
        "model = Sequential()\n",
        "\n",
        "fltnmdl = model.add(Flatten(input_shape = (28, 28)))\n",
        "\n",
        "#Input Layers\n",
        "model.add(Dense(units = 50, activation = 'relu'))\n",
        "model.add(Dense(units = 30, activation = 'relu'))   \n",
        "\n",
        "# Output Layer\n",
        "model.add(Dense(units = 10, activation = 'softmax'))\n"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "px9h0HRlOxQl"
      },
      "source": [
        "## **Compiling the Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lo2JiilnOwsA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "84562e26-f545-405e-ebb1-1d8b356b7149"
      },
      "source": [
        "model.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])\n",
        "\n",
        "# A look at the Model Summary\n",
        "model.summary()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten_3 (Flatten)          (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 50)                39250     \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 30)                1530      \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 10)                310       \n",
            "=================================================================\n",
            "Total params: 41,090\n",
            "Trainable params: 41,090\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OdLmo7RfQOlW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3d1af24-a955-4043-93e3-577eb445d9e9"
      },
      "source": [
        "# Training / Fitting the Model\n",
        "\n",
        "model.fit(trnfeatures, trnlabels, epochs = 100)\n",
        "\n",
        "# Evaluating the Model\n",
        "\n",
        "lossV, accuV = model.evaluate(testfeatures, testlabels)\n",
        "\n",
        "#Print the Loss and Accuracy validation valuew separately.\n",
        "print(\"\\n\\nLoss Validation: \", lossV, \"\\n\\n\" , \"Accuracy Validation: \", accuV)\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "1875/1875 [==============================] - 6s 2ms/step - loss: 0.5347 - accuracy: 0.8418\n",
            "Epoch 2/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1549 - accuracy: 0.9537\n",
            "Epoch 3/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1150 - accuracy: 0.9659\n",
            "Epoch 4/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0931 - accuracy: 0.9714\n",
            "Epoch 5/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0760 - accuracy: 0.9764\n",
            "Epoch 6/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0674 - accuracy: 0.9790\n",
            "Epoch 7/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0560 - accuracy: 0.9833\n",
            "Epoch 8/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0510 - accuracy: 0.9836\n",
            "Epoch 9/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0418 - accuracy: 0.9867\n",
            "Epoch 10/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0378 - accuracy: 0.9872\n",
            "Epoch 11/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0362 - accuracy: 0.9883\n",
            "Epoch 12/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0311 - accuracy: 0.9898\n",
            "Epoch 13/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0256 - accuracy: 0.9921\n",
            "Epoch 14/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0267 - accuracy: 0.9913\n",
            "Epoch 15/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0223 - accuracy: 0.9929\n",
            "Epoch 16/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0239 - accuracy: 0.9922\n",
            "Epoch 17/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0197 - accuracy: 0.9928\n",
            "Epoch 18/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0168 - accuracy: 0.9944\n",
            "Epoch 19/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0164 - accuracy: 0.9948\n",
            "Epoch 20/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0159 - accuracy: 0.9947\n",
            "Epoch 21/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0147 - accuracy: 0.9951\n",
            "Epoch 22/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0127 - accuracy: 0.9958\n",
            "Epoch 23/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0133 - accuracy: 0.9956\n",
            "Epoch 24/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0109 - accuracy: 0.9960\n",
            "Epoch 25/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0142 - accuracy: 0.9949\n",
            "Epoch 26/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0099 - accuracy: 0.9970\n",
            "Epoch 27/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0085 - accuracy: 0.9969\n",
            "Epoch 28/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0099 - accuracy: 0.9967\n",
            "Epoch 29/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0117 - accuracy: 0.9956\n",
            "Epoch 30/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0087 - accuracy: 0.9971\n",
            "Epoch 31/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0121 - accuracy: 0.9959\n",
            "Epoch 32/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0080 - accuracy: 0.9973\n",
            "Epoch 33/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0099 - accuracy: 0.9971\n",
            "Epoch 34/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0094 - accuracy: 0.9966\n",
            "Epoch 35/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0055 - accuracy: 0.9984\n",
            "Epoch 36/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0066 - accuracy: 0.9978\n",
            "Epoch 37/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0105 - accuracy: 0.9963\n",
            "Epoch 38/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0050 - accuracy: 0.9982\n",
            "Epoch 39/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0089 - accuracy: 0.9968\n",
            "Epoch 40/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0081 - accuracy: 0.9973\n",
            "Epoch 41/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0085 - accuracy: 0.9973\n",
            "Epoch 42/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0065 - accuracy: 0.9977\n",
            "Epoch 43/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0077 - accuracy: 0.9974\n",
            "Epoch 44/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0087 - accuracy: 0.9971\n",
            "Epoch 45/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0085 - accuracy: 0.9972\n",
            "Epoch 46/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0056 - accuracy: 0.9978\n",
            "Epoch 47/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0081 - accuracy: 0.9973\n",
            "Epoch 48/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0075 - accuracy: 0.9974\n",
            "Epoch 49/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0070 - accuracy: 0.9977\n",
            "Epoch 50/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0087 - accuracy: 0.9972\n",
            "Epoch 51/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0079 - accuracy: 0.9972\n",
            "Epoch 52/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0046 - accuracy: 0.9984\n",
            "Epoch 53/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0063 - accuracy: 0.9979\n",
            "Epoch 54/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0075 - accuracy: 0.9975\n",
            "Epoch 55/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0067 - accuracy: 0.9976\n",
            "Epoch 56/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0085 - accuracy: 0.9977\n",
            "Epoch 57/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0041 - accuracy: 0.9986\n",
            "Epoch 58/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0085 - accuracy: 0.9974\n",
            "Epoch 59/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0100 - accuracy: 0.9966\n",
            "Epoch 60/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0039 - accuracy: 0.9988\n",
            "Epoch 61/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0067 - accuracy: 0.9979\n",
            "Epoch 62/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0085 - accuracy: 0.9976\n",
            "Epoch 63/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0069 - accuracy: 0.9978\n",
            "Epoch 64/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0091 - accuracy: 0.9976\n",
            "Epoch 65/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0037 - accuracy: 0.9986\n",
            "Epoch 66/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0070 - accuracy: 0.9980\n",
            "Epoch 67/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0071 - accuracy: 0.9980\n",
            "Epoch 68/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0053 - accuracy: 0.9985\n",
            "Epoch 69/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0089 - accuracy: 0.9974\n",
            "Epoch 70/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0048 - accuracy: 0.9986\n",
            "Epoch 71/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0098 - accuracy: 0.9975\n",
            "Epoch 72/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0030 - accuracy: 0.9991\n",
            "Epoch 73/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0067 - accuracy: 0.9973\n",
            "Epoch 74/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0046 - accuracy: 0.9986\n",
            "Epoch 75/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0059 - accuracy: 0.9982\n",
            "Epoch 76/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0052 - accuracy: 0.9982\n",
            "Epoch 77/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0052 - accuracy: 0.9983\n",
            "Epoch 78/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0060 - accuracy: 0.9983\n",
            "Epoch 79/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0030 - accuracy: 0.9990\n",
            "Epoch 80/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0068 - accuracy: 0.9983\n",
            "Epoch 81/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0066 - accuracy: 0.9981\n",
            "Epoch 82/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0047 - accuracy: 0.9985\n",
            "Epoch 83/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0079 - accuracy: 0.9980\n",
            "Epoch 84/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0015 - accuracy: 0.9995\n",
            "Epoch 85/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0065 - accuracy: 0.9980\n",
            "Epoch 86/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0062 - accuracy: 0.9982\n",
            "Epoch 87/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0047 - accuracy: 0.9988\n",
            "Epoch 88/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0070 - accuracy: 0.9982\n",
            "Epoch 89/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0033 - accuracy: 0.9987\n",
            "Epoch 90/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0057 - accuracy: 0.9984\n",
            "Epoch 91/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0055 - accuracy: 0.9987\n",
            "Epoch 92/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0056 - accuracy: 0.9984\n",
            "Epoch 93/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0056 - accuracy: 0.9985\n",
            "Epoch 94/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0060 - accuracy: 0.9985\n",
            "Epoch 95/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0059 - accuracy: 0.9982\n",
            "Epoch 96/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0028 - accuracy: 0.9990\n",
            "Epoch 97/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0060 - accuracy: 0.9979\n",
            "Epoch 98/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0050 - accuracy: 0.9983\n",
            "Epoch 99/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0042 - accuracy: 0.9986\n",
            "Epoch 100/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0040 - accuracy: 0.9990\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3272 - accuracy: 0.9717\n",
            "\n",
            "\n",
            "Loss Validation:  0.3272090554237366 \n",
            "\n",
            " Accuracy Validation:  0.9717000126838684\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p8WUxaqMFSKY"
      },
      "source": [
        "# **Looking at the binary values of the Digit Image - 3**\n",
        "\n",
        "**Flatten /changed into grascale and size of  28 x 28 pixels**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kGRqQt6lDs-Q",
        "outputId": "c683b32c-a860-4af8-b48d-10dcd9c48c2f"
      },
      "source": [
        "print(trnfeatures[7])"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "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.         0.14901961\n",
            "  0.16862745 0.41176471 1.         0.99215686 0.99215686 0.99215686\n",
            "  0.99215686 0.99215686 0.68235294 0.02352941 0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.16862745 0.54509804 0.87843137\n",
            "  0.88627451 0.98823529 0.99215686 0.98823529 0.98823529 0.98823529\n",
            "  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",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.42745098 0.98823529 0.98823529\n",
            "  0.90196078 0.51764706 0.52156863 0.51764706 0.51764706 0.74117647\n",
            "  0.98823529 0.98823529 0.98823529 0.98823529 0.23137255 0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.01568627 0.11372549 0.11372549\n",
            "  0.09411765 0.         0.         0.         0.         0.05490196\n",
            "  0.88627451 0.98823529 0.98823529 0.6745098  0.02745098 0.\n",
            "  0.         0.         0.         0.        ]\n",
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            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.33333333\n",
            "  0.95294118 0.98823529 0.98823529 0.56470588 0.         0.\n",
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            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.34509804 0.74117647\n",
            "  0.98823529 0.98823529 0.98823529 0.05490196 0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.35686275 0.83137255 0.96862745 0.98823529\n",
            "  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.         0.17647059 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.         0.17647059 0.8745098  0.99215686 0.99215686\n",
            "  0.99215686 0.99215686 1.         0.99215686 0.99215686 0.99215686\n",
            "  0.99215686 0.29019608 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 0.29019608 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 0.29019608 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 0.29019608 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.         0.38431373 0.94901961 0.98823529\n",
            "  0.98823529 0.29019608 0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.23921569\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.         0.19215686\n",
            "  0.61568627 0.98823529 0.98823529 0.98823529 0.98823529 0.98823529\n",
            "  0.85098039 0.81176471 0.57254902 0.17647059 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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BmjXOpXlFAcv"
      },
      "source": [
        "# **At Index location-7, it is digit - 3.**\n",
        "**Showing the flatten image**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "_v4oQnWu-tYd",
        "outputId": "5562f743-1dc9-4f72-b045-c9ce055683b3"
      },
      "source": [
        "\n",
        "\n",
        "plt.imshow(trnfeatures[7], cmap = plt.cm.binary)  \n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "INWc-s_ODiX0"
      },
      "source": [
        "**Prediction reconfirmed with the original dataset.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "NV8EIu0wDMOL",
        "outputId": "fd729505-070e-4bde-dd18-7fd9ee5a1185"
      },
      "source": [
        "\n",
        "plt.imshow(trnfeatures[7]) \n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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J8AYdkARhB5Ig7EAShB1IgrADSRB2IAnCDiTxf2YjLzDBs2ChAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}