MNIST_Digit_Classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "**MNIST Handwritten Digit Classification Using Neural Networks**"
      ],
      "metadata": {
        "id": "MBBSwnMCuov2"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "sdJnEvAetm7a"
      },
      "outputs": [],
      "source": [
        "## Importing necessary Libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import cv2\n",
        "from google.colab.patches import cv2_imshow\n",
        "from PIL import Image\n",
        "\n",
        "import tensorflow as tf\n",
        "tf.random.set_seed(3)\n",
        "from tensorflow import keras\n",
        "from keras.datasets import mnist\n",
        "from tensorflow.math import confusion_matrix"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Loading the data\n",
        "\n",
        "(X_train, Y_train), (X_test,Y_test) = mnist.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E-h989hju3TP",
        "outputId": "470e2c6e-6390-47db-d2b9-a68cce278a4b"
      },
      "execution_count": 3,
      "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": [
        "## Shape of numpy arrays\n",
        "print((X_train.shape, Y_train.shape), (X_test.shape,Y_test.shape))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CpR4Iphqwx58",
        "outputId": "ab1c31ff-939a-4f9e-ad44-6a8f5b585996"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "((60000, 28, 28), (60000,)) ((10000, 28, 28), (10000,))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training Data = 60,000 Images\n",
        "\n",
        "Test Data = 10,000 Images\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Image Dimensions --> 28x28\n",
        "\n",
        "Grayscale Image --> 1 Channel"
      ],
      "metadata": {
        "id": "ssM8S7MAxj7I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Printing the 12th image\n",
        "print(X_train[13])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fOEtyAq1xEcU",
        "outputId": "90612813-e4ca-425c-9506-7dd13fb6de57"
      },
      "execution_count": 5,
      "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  38 222\n",
            "  225   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 147 234 252\n",
            "  176   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0  23 197 253 252 208\n",
            "   19   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0  38 178 252 253 117  65\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0  57 252 252 253  89   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0  38 222 253 253  79   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0 131 252 179  27   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0 198 246 220  37   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  79 253 252 135  28   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0  16 140 253 252 118   0   0   0   0 111\n",
            "  140 140   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0  13 191 255 253  56   0   0 114 113 222\n",
            "  253 253 255  27   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0  76 252 253 223  37   0  48 174 252 252\n",
            "  242 214 253 199  31   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0  13 109 252 228 130   0  38 165 253 233 164\n",
            "   49  63 253 214  31   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0  73 252 252 126   0  23 178 252 240 148   7\n",
            "   44 215 240 148   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0 119 252 252   0   0 197 252 252  63   0  57\n",
            "  252 252 140   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0 135 253 174   0  48 229 253 112   0  38 222\n",
            "  253 112   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0 135 252 173   0  48 227 252 158 226 234 201\n",
            "   27  12   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0  57 252 252  57 104 240 252 252 253 233  74\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0  51 242 252 253 252 252 252 252 240 148   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0  75 189 253 252 252 157 112  63   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   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": [
        "## Shape of hte image\n",
        "print(X_train[13].shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZmwQB2mlyYRs",
        "outputId": "1d62ddb3-9a2d-44f6-a959-0057021b050e"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(28, 28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Displaying Image\n",
        "\n",
        "plt.imshow(X_train[13])\n",
        "plt.show()\n",
        "\n",
        "## Print the corresponding label\n",
        "print(Y_train[13])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "8OJ1stFGyYOO",
        "outputId": "324119c5-43f7-4601-8f80-04b53e997544"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Image Labels**"
      ],
      "metadata": {
        "id": "h1jpgWXvy6uY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y_train.shape,Y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uQkf0bPqyYLw",
        "outputId": "390f73fd-f4ad-4d8f-b42f-22d0f7927409"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(60000,) (10000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Unique values in Y_train\n",
        "print(np.unique(Y_train))\n",
        "\n",
        "## Unique values in Y_test\n",
        "print(np.unique(Y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N1Rev--FyYJc",
        "outputId": "6e907287-b506-4e73-c41b-03bf1f877a91"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 2 3 4 5 6 7 8 9]\n",
            "[0 1 2 3 4 5 6 7 8 9]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**We can use these labels as such or we can also apply One Hot Encoding.**"
      ],
      "metadata": {
        "id": "AIqlDe3x0Lmc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "All the images have same dimensions in this dataset. If not, we have to resize all the images to a common dimension."
      ],
      "metadata": {
        "id": "AijuBwFN0Wu5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Scaling the values\n",
        "\n",
        "X_train = X_train/255\n",
        "X_test = X_test/255"
      ],
      "metadata": {
        "id": "Z2y_vpUk0CeC"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train[13])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IHYK-PFiA_xU",
        "outputId": "92954f6c-7bcc-481c-c4fd-f7f0e9a659ea"
      },
      "execution_count": 13,
      "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",
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            "  0.         0.         0.         0.         0.         0.\n",
            "  0.14901961 0.69803922 0.98823529 0.99215686 0.45882353 0.25490196\n",
            "  0.         0.         0.         0.         0.         0.\n",
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            "  0.22352941 0.98823529 0.98823529 0.99215686 0.34901961 0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
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            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.14901961\n",
            "  0.87058824 0.99215686 0.99215686 0.30980392 0.         0.\n",
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            "  0.98823529 0.70196078 0.10588235 0.         0.         0.\n",
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            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.77647059 0.96470588\n",
            "  0.8627451  0.14509804 0.         0.         0.         0.\n",
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            "  0.         0.         0.05098039 0.74901961 1.         0.99215686\n",
            "  0.21960784 0.         0.         0.44705882 0.44313725 0.87058824\n",
            "  0.99215686 0.99215686 1.         0.10588235 0.         0.\n",
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            " [0.         0.         0.         0.         0.         0.\n",
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            " [0.         0.         0.         0.         0.         0.\n",
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            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.28627451 0.98823529 0.98823529 0.49411765 0.\n",
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            "  0.17254902 0.84313725 0.94117647 0.58039216 0.         0.\n",
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            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.46666667 0.98823529 0.98823529 0.         0.\n",
            "  0.77254902 0.98823529 0.98823529 0.24705882 0.         0.22352941\n",
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            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.52941176 0.99215686 0.68235294 0.         0.18823529\n",
            "  0.89803922 0.99215686 0.43921569 0.         0.14901961 0.87058824\n",
            "  0.99215686 0.43921569 0.         0.         0.         0.\n",
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            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.52941176 0.98823529 0.67843137 0.         0.18823529\n",
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            "  0.10588235 0.04705882 0.         0.         0.         0.\n",
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            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.22352941 0.98823529 0.98823529 0.22352941 0.40784314\n",
            "  0.94117647 0.98823529 0.98823529 0.99215686 0.91372549 0.29019608\n",
            "  0.         0.         0.         0.         0.         0.\n",
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            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.2        0.94901961 0.98823529 0.99215686 0.98823529\n",
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            "  0.         0.         0.         0.         0.         0.\n",
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            " [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",
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            " [0.         0.         0.         0.         0.         0.\n",
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            "  0.         0.         0.         0.         0.         0.\n",
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            " [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",
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            " [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": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "**Building a Neural Network**"
      ],
      "metadata": {
        "id": "6tJDfKz0BP-b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Setting up the layers of NN\n",
        "\n",
        "model = keras.Sequential([\n",
        "                          keras.layers.Flatten(input_shape=(28,28)), #Grayscale Image\n",
        "                          keras.layers.Dense(50, activation='relu'), # Hidden layer\n",
        "                          keras.layers.Dense(50, activation='relu'),\n",
        "                          keras.layers.Dense(10, activation='sigmoid') #Output Layer/Neural\n",
        "])"
      ],
      "metadata": {
        "id": "HouaHMFtA_t6"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Compiling The Neural Network**"
      ],
      "metadata": {
        "id": "GAJv9x3YC3mu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss = 'sparse_categorical_crossentropy',\n",
        "              metrics = ['accuracy'])"
      ],
      "metadata": {
        "id": "i4tAOGs2A_lq"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Training The Neural Network**"
      ],
      "metadata": {
        "id": "YAV39b1qGmeY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train,Y_train, epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6mTVza-7A_b8",
        "outputId": "0b35c17d-603f-4cb5-dc8f-fe91b48ae21b"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 6s 2ms/step - loss: 0.3055 - accuracy: 0.9120\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1422 - accuracy: 0.9581\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1060 - accuracy: 0.9679\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0849 - accuracy: 0.9735\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0701 - accuracy: 0.9785\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0604 - accuracy: 0.9805\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0529 - accuracy: 0.9830\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0446 - accuracy: 0.9854\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0397 - accuracy: 0.9870\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0355 - accuracy: 0.9881\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7a30ac8b7520>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training Data Accuracy = **98.9%**"
      ],
      "metadata": {
        "id": "QfPiJFh3HUXn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Accuracy for Test Data\n",
        "\n",
        "loss, accuracy = model.evaluate(X_test,Y_test)\n",
        "print(accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4NqAoCpAHdjy",
        "outputId": "a16452d7-bd40-42d6-9fa2-73f652b81f79"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 2s 5ms/step - loss: 0.1166 - accuracy: 0.9712\n",
            "0.9711999893188477\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Test Data Accuracy = **97.11%**"
      ],
      "metadata": {
        "id": "_LOONhIfHx3n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qG9Sa6NyHtfa",
        "outputId": "c2489bac-b4a6-4dd8-a812-b5586554a518"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(10000, 28, 28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## First data point in X_test\n",
        "plt.imshow(X_test[0])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "yJhWqSSxH5vk",
        "outputId": "43b772d3-236a-4db8-923a-e7d787b267a5"
      },
      "execution_count": 25,
      "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])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "scOWhzJrIGId",
        "outputId": "3cf255b8-2d29-4743-bd45-d6ed9edcfcd5"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred = model.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qn6pP9hMIKZ7",
        "outputId": "6ca90e8c-a089-4f91-a89b-74d32a8b4b0d"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y_pred.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UGGeMRFeISb1",
        "outputId": "1bd98164-d923-4488-f450-074f463d5db0"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(10000, 10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## What's this 10? --> For each data point there are 10 values\n",
        "print(Y_pred[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KVPo6GoeIXTf",
        "outputId": "50006105-fdfa-4852-a334-492f780df213"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2.7049952e-03 1.6870105e-01 5.8127757e-02 1.9845936e-01 1.2782680e-08\n",
            " 8.5681928e-03 2.4104456e-06 9.9999881e-01 7.5858845e-03 4.5670655e-01]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*`model.predict()` gives the prediction probability of each class for that data point*"
      ],
      "metadata": {
        "id": "4rBlP00wI6Ax"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Converting the prediction probabilities to class labels.**"
      ],
      "metadata": {
        "id": "EIrVBj5NJPNQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "label_for_first_img = np.argmax(Y_pred[0])   ## np.argmax will tell the index of the max value\n",
        "print(label_for_first_img)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R1qGwNCBIsV-",
        "outputId": "a7b5b970-7cfc-4ece-ac51-50469031ac31"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label_for_fifth_img = np.argmax(Y_pred[4])   ## np.argmax will tell the index of the max value\n",
        "print(label_for_fifth_img)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lLQNauGkJlb5",
        "outputId": "74087983-f590-445c-d007-12427a4dd291"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Converting the prdiction probabbilities to class labels for all test data.**"
      ],
      "metadata": {
        "id": "lVZe6AjNKAgY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_labels = [np.argmax(i) for i in Y_pred]\n",
        "print(y_pred_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z244u5nVJ5o3",
        "outputId": "02383139-7dcc-4cf5-890a-5a603ca63030"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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2, 4, 5, 7, 2, 6, 4, 9, 4, 9, 4, 1, 2, 2, 5, 8, 1, 3, 2, 9, 4, 3, 8, 2, 2, 1, 2, 8, 6, 5, 1, 6, 7, 2, 1, 3, 9, 3, 8, 7, 5, 7, 0, 7, 4, 8, 8, 5, 0, 6, 6, 3, 7, 6, 9, 9, 4, 8, 4, 1, 6, 6, 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, 4, 5, 6, 7, 8, 9, 7, 4, 0, 4, 0, 1, 7, 9, 5, 1, 4, 2, 8, 9, 4, 3, 7, 8, 2, 4, 4, 3, 3, 6, 9, 7, 5, 8, 6, 7, 0, 6, 8, 2, 6, 3, 9, 3, 2, 8, 6, 1, 7, 4, 8, 8, 9, 0, 3, 3, 9, 0, 5, 2, 9, 4, 1, 0, 3, 7, 5, 8, 7, 7, 8, 2, 9, 7, 1, 2, 6, 4, 2, 5, 2, 3, 6, 6, 5, 0, 0, 2, 8, 1, 6, 1, 0, 4, 3, 1, 6, 1, 9, 0, 1, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 8, 4, 0, 0, 7, 2, 4, 3, 8, 6, 6, 3, 2, 6, 3, 3, 3, 1, 4, 7, 8, 0, 3, 1, 9, 0, 1, 9, 1, 2, 7, 0, 1, 3, 8, 2, 9, 2, 7, 6, 5, 5, 9, 9, 8, 2, 9, 1, 3, 2, 3, 4, 3, 1, 9, 0, 7, 3, 6, 8, 7, 0, 1, 0, 5, 8, 2, 7, 7, 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, 7, 4, 8, 1, 5, 6, 5, 7, 2, 8, 6, 3, 3, 8, 6, 5, 4, 0, 9, 1, 7, 2, 9, 1, 5, 1, 3, 2, 2, 3, 0, 6, 4, 3, 7, 6, 9, 0, 4, 8, 1, 4, 0, 6, 1, 2, 6, 9, 2, 2, 3, 5, 5, 1, 0, 7, 7, 9, 6, 2, 9, 4, 7, 0, 2, 3, 4, 0, 0, 8, 8, 8, 5, 1, 3, 7, 4, 9, 8, 8, 9, 0, 9, 8, 9, 0, 2, 6, 5, 6, 7, 4, 7, 5, 4, 1, 3, 5, 3, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 6, 0, 1, 2, 4, 5, 6, 7, 8, 1, 7, 2, 4, 1, 4, 1, 4, 9, 6, 8, 4, 5, 3, 7, 8, 4, 3, 3, 5, 6, 7, 0, 6, 1, 6, 8, 7, 6, 1, 5, 0, 8, 5, 0, 1, 5, 8, 4, 2, 3, 9, 7, 6, 9, 1, 9, 0, 6, 7, 1, 2, 3, 9, 2, 4, 5, 5, 3, 7, 5, 3, 1, 8, 2, 2, 3, 0, 2, 9, 4, 9, 7, 0, 2, 7, 4, 9, 9, 2, 5, 9, 8, 3, 8, 6, 7, 0, 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, 0, 0, 7, 2, 6, 5, 5, 3, 7, 3, 6, 6, 6, 6, 4, 3, 8, 8, 3, 0, 1, 9, 0, 5, 4, 1, 9, 1, 2, 7, 0, 1, 3, 8, 2, 9, 2, 7, 4, 2, 6, 5, 5, 9, 9, 1, 1, 5, 7, 6, 8, 2, 9, 4, 3, 1, 9, 0, 9, 3, 6, 8, 7, 0, 1, 0, 5, 8, 2, 7, 7, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 2, 1, 2, 1, 3, 9, 9, 8, 5, 3, 7, 0, 7, 7, 5, 7, 9, 9, 4, 7, 0, 3, 4, 1, 5, 8, 1, 4, 8, 4, 1, 8, 6, 6, 4, 6, 0, 5, 5, 3, 3, 5, 7, 2, 5, 9, 6, 9, 2, 6, 2, 1, 2, 0, 8, 3, 8, 3, 0, 8, 7, 4, 9, 5, 0, 9, 7, 0, 0, 4, 6, 0, 9, 1, 6, 2, 7, 6, 8, 3, 5, 2, 1, 8, 3, 8, 6, 1, 0, 2, 1, 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, 7, 6, 4, 7, 6, 2, 3, 4, 8, 7, 8, 6, 9, 8, 3, 2, 2, 8, 4, 8, 5, 6, 5, 0, 2, 0, 1, 1, 2, 9, 6, 8, 2, 1, 0, 6, 5, 2, 9, 7, 5, 3, 9, 3, 7, 1, 8, 3, 8, 1, 9, 5, 5, 0, 1, 1, 9, 8, 2, 6, 0, 4, 5, 0, 3, 1, 8, 6, 7, 5, 9, 9, 3, 0, 3, 1, 4, 4, 0, 4, 9, 0, 1, 2, 3, 5, 6, 7, 8, 0, 1, 2, 3, 5, 6, 7, 8, 9, 0, 1, 2, 3, 5, 6, 7, 8, 9, 9, 7, 0, 9, 0, 1, 5, 8, 8, 0, 9, 3, 2, 7, 8, 4, 6, 1, 0, 4, 9, 4, 4, 0, 5, 0, 1, 6, 9, 3, 2, 9, 1, 6, 0, 1, 1, 8, 7, 7, 6, 3, 6, 0, 7, 2, 4, 1, 7, 0, 6, 7, 1, 2, 5, 8, 1, 5, 2, 8, 7, 6, 8, 7, 1, 6, 2, 9, 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, 8, 9, 5, 7, 0, 3, 1, 6, 8, 4, 1, 5, 6, 4, 2, 7, 8, 1, 3, 4, 3, 4, 7, 2, 0, 5, 0, 1, 7, 2, 3, 2, 3, 5, 5, 7, 8, 4, 9, 9, 7, 1, 1, 9, 0, 7, 8, 3, 4, 8, 6, 3, 8, 0, 9, 6, 2, 1, 0, 1, 0, 6, 2, 3, 8, 9, 0, 7, 2, 3, 4, 5, 5, 2, 8, 5, 4, 6, 6, 6, 7, 9, 1, 8, 2, 1, 5, 3, 4, 7, 9, 4, 0, 0, 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, 9, 0, 1, 3, 1, 5, 1, 2, 4, 9, 1, 4, 6, 8, 0, 1, 1, 9, 2, 6, 6, 8, 7, 4, 2, 9, 7, 0, 2, 1, 0, 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, 4, 5, 6, 7, 8, 9, 8, 6, 5, 9, 7, 0, 2, 3, 4, 3, 8, 5, 1, 5, 2, 3, 0, 1, 2, 1, 3, 2, 6, 5, 3, 0, 7, 2, 7, 4, 6, 4, 0, 5, 9, 9, 5, 9, 5, 3, 1, 7, 4, 7, 6, 5, 4, 0, 0, 6, 6, 2, 0, 6, 3, 7, 7, 4, 4, 3, 9, 2, 8, 9, 6, 0, 9, 5, 3, 8, 8, 7, 1, 4, 0, 4, 8, 5, 2, 3, 9, 0, 1, 9, 1, 5, 1, 7, 4, 8, 6, 2, 1, 6, 8, 8, 0, 1, 2, 3, 4, 7, 8, 9, 0, 1, 2, 3, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 7, 8, 9, 1, 4, 5, 3, 3, 0, 9, 5, 4, 9, 0, 8, 4, 6, 7, 0, 7, 7, 1, 6, 9, 1, 3, 6, 2, 3, 8, 2, 3, 8, 9, 5, 8, 8, 7, 1, 7, 1, 1, 0, 3, 4, 2, 4, 4, 7, 4, 2, 7, 4, 2, 9, 2, 7, 9, 2, 1, 6, 6, 5, 3, 4, 8, 5, 9, 6, 9, 0, 6, 3, 0, 8, 1, 6, 0, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 7, 8, 9, 0, 1, 2, 3, 4, 7, 2, 5, 1, 6, 4, 3, 9, 9, 0, 9, 7, 1, 6, 4, 3, 6, 2, 0, 9, 8, 6, 5, 7, 0, 0, 1, 7, 4, 3, 2, 4, 1, 3, 7, 6, 4, 7, 7, 7, 9, 8, 4, 3, 5, 2, 8, 3, 5, 8, 0, 5, 4, 7, 1, 3, 1, 7, 9, 6, 2, 0, 9, 1, 7, 3, 3, 9, 1, 6, 4, 3, 9, 8, 2, 1, 8, 6, 4, 1, 5, 5, 6, 5, 0, 1, 2, 3, 4, 5, 6, 7, 0, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 6, 9, 7, 0, 2, 3, 4, 3, 8, 5, 1, 3, 0, 1, 2, 1, 3, 2, 0, 7, 2, 6, 4, 0, 5, 9, 9, 8, 9, 5, 3, 1, 7, 4, 7, 0, 0, 6, 6, 6, 3, 7, 4, 2, 6, 9, 8, 7, 1, 4, 0, 4, 8, 5, 2, 3, 9, 0, 1, 9, 1, 5, 1, 7, 6, 1, 2, 1, 6, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 5, 6, 7, 8, 1, 0, 4, 5, 6, 6, 3, 4, 4, 2, 8, 1, 0, 6, 4, 9, 7, 2, 9, 2, 0, 9, 3, 3, 9, 1, 5, 2, 3, 1, 6, 7, 3, 7, 8, 4, 0, 2, 4, 0, 2, 4, 7, 8, 0, 7, 0, 6, 9, 3, 2, 4, 8, 6, 0, 5, 7, 5, 1, 0, 8, 1, 6, 7, 2, 9, 7, 9, 5, 6, 5, 2, 6, 2, 8, 1, 7, 5, 5, 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, 7, 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, 5, 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, 7, 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, 3, 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, 5, 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, 5, 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, 2, 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, 7, 1, 2, 3, 4, 5, 6, 7, 8, 8, 0, 6, 0, 8, 2, 5, 7, 9, 4, 7, 1, 7, 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, 6, 3, 8, 1, 4, 7, 5, 2, 0, 0, 1, 7, 8, 7, 6, 8, 8, 2, 3, 5, 1, 2, 9, 5, 2, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 5, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 6, 6, 7, 8, 9, 7, 4, 6, 1, 4, 0, 4, 9, 8, 7, 8, 2, 7, 5, 8, 6, 3, 2, 2, 0, 5, 5, 6, 0, 3, 8, 1, 0, 8, 0, 4, 7, 4, 9, 0, 9, 6, 7, 1, 7, 1, 6, 6, 5, 6, 0, 8, 7, 6, 4, 9, 9, 5, 3, 7, 4, 3, 0, 1, 6, 6, 1, 1, 3, 2, 1, 0, 0, 1, 2, 3, 4, 7, 8, 4, 0, 1, 8, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 7, 8, 9, 0, 8, 5, 9, 5, 5, 2, 6, 8, 4, 1, 7, 1, 7, 3, 5, 6, 9, 1, 1, 1, 2, 1, 2, 5, 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, 0, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 7, 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, 3, 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": "markdown",
      "source": [
        "---\n",
        "**Building The Confusion Matrix**\n"
      ],
      "metadata": {
        "id": "rZJm_GQAKuzi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "conf_matrix = confusion_matrix(Y_test,y_pred_labels)"
      ],
      "metadata": {
        "id": "WAvxAHq5KUN5"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(conf_matrix)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4MFhJU29LEha",
        "outputId": "b2a94e8e-ada9-41c4-dd5f-2dee42993ecb"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(\n",
            "[[ 941    0    1    2    2   13    9    4    4    4]\n",
            " [   0 1123    4    0    0    3    2    2    1    0]\n",
            " [   5    3  998    3    4    2    2    9    5    1]\n",
            " [   0    1    5  971    0    9    0   12    7    5]\n",
            " [   0    1    3    1  962    1    4    3    0    7]\n",
            " [   0    0    0    7    2  875    3    1    1    3]\n",
            " [   0    1    1    0    5    6  944    1    0    0]\n",
            " [   1    0    9    2    0    0    0 1007    2    7]\n",
            " [   2    0    3    5    4   13    1   10  929    7]\n",
            " [   0    2    0    2   21    5    2   14    2  961]], shape=(10, 10), dtype=int32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,8))\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')\n",
        "plt.ylabel('True Labels')\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "cTMYFqNMLHZa",
        "outputId": "f86115eb-008e-4fbb-91b4-df035990b4e3"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*   `941` Images of `0` have been predicted correctly.\n",
        "*   `1123` Images of `1` have been predicted correctly.\n",
        "*    So on..."
      ],
      "metadata": {
        "id": "wpuSnxfdL-RE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "**Building a Predictive System**\n"
      ],
      "metadata": {
        "id": "n9jr43NyMgjb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input_image_path = '/content/MNIST_digit.png'\n",
        "\n",
        "input_image = cv2.imread(input_image_path)"
      ],
      "metadata": {
        "id": "9V7tsHquLiOr"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(input_image)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SUOvkO6INKSo",
        "outputId": "75a98888-1d0e-407e-834f-c8d2a2f4e3a4"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(input_image)"
      ],
      "metadata": {
        "id": "jo_DWeZ0NMr7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv2_imshow(input_image)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "jGov6bAANQAz",
        "outputId": "7cd05760-b218-4072-9867-f70a220fb113"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=318x318>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_image.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FxuV8I58NYUH",
        "outputId": "7bff18aa-22ae-4116-b341-1c465c4a65d5"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(318, 318, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Convert the image from RGB to Grayscale and reduce the dimension\n",
        "grayscale = cv2.cvtColor(input_image, cv2.COLOR_RGB2GRAY)"
      ],
      "metadata": {
        "id": "2AOuyePcNbwF"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grayscale.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3NFjeOfMN3AP",
        "outputId": "abcb197e-2a44-4482-a736-88e6107c4f7a"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(318, 318)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Image Resize\n",
        "input_image_resize = cv2.resize(grayscale, (28,28))"
      ],
      "metadata": {
        "id": "-SQvIMBcN5XH"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_image_resize.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uy2QY70ROJg1",
        "outputId": "299c9db5-9569-46f7-82ba-c30b6b2b67fb"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv2_imshow(input_image_resize)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 45
        },
        "id": "BzykDYg3OKbH",
        "outputId": "e0c4a4f3-deeb-47f1-8ce7-a7b4993bc571"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=28x28>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABE0lEQVR4nM2RvUoDURSEv7tEo01EqzQLVjbZQiw1oIIS/HkAwViIRbCw9x3sbIJFfIIoaKGICD6AYCA2NiJRLLQQkiIKc12LzS56XTsLp7mHO2fOmeHAP8ZYYTO01lp72O9ShZ37lqwkSfs5hzyWFJOaAiCTkOeLPNe8DyanUzZmfD8PkGtJ9ayj1EP0lobh8T3d8sqFJNdPhNVmV9LVIM7Y0bU5KIbQ3j7pOprgLo5ylPx5SWWMMcYzxiwvJAHi4mamfPYGbGylGwVgSEqUnkuWfvb3LfXcr7e/KCMUT+UDjJRfpc5s4hGARkC1A8xPhFxWD74rG71LyT7tDbgrx2uSpNvr3SAlQLbyonol/0u8P8YnwTdycRbZK2kAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_image_resize = input_image_resize/255 ## Scaling down the value"
      ],
      "metadata": {
        "id": "ca0V5rDnOSBP"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "image_reshaped = np.reshape(input_image_resize, [1,28,28])"
      ],
      "metadata": {
        "id": "zvYy9OZeOd6P"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_pred = model.predict(image_reshaped)\n",
        "print(input_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DGpGLB-IOzqV",
        "outputId": "70572081-657e-40ef-e9b6-fe1567b2c412"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 38ms/step\n",
            "[[1.5385941e-13 2.4850029e-04 7.7551122e-05 1.0000000e+00 2.9750181e-11\n",
            "  1.4346364e-01 1.9845078e-12 1.3575967e-02 1.2132330e-03 1.9112809e-01]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_pred_label = np.argmax(input_pred)"
      ],
      "metadata": {
        "id": "EyTP1KJrPJNC"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(input_pred_label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tZrAcMRKPQMy",
        "outputId": "c6d589da-2156-4df6-dc1d-f36198d9c1ab"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Predictive System**"
      ],
      "metadata": {
        "id": "MBnbYNLnPbCS"
      }
    },
    {
      "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_image_resize = cv2.resize(grayscale, (28,28))\n",
        "\n",
        "input_image_resize = input_image_resize/255\n",
        "\n",
        "image_reshaped = np.reshape(input_image_resize, [1,28,28])\n",
        "\n",
        "input_pred = model.predict(image_reshaped)\n",
        "\n",
        "input_pred_label = np.argmax(input_pred)\n",
        "\n",
        "print('The Handwritten Digit is recognised as', input_pred_label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "id": "qlQisSBlPXTb",
        "outputId": "0f1f9e7e-1df9-409d-eae2-88c354432b97"
      },
      "execution_count": 60,
      "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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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 22ms/step\n",
            "The Handwritten Digit is recognised as 3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "g776QwlOQvoS"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}