MNIST_Handwritten_Digit_Classification_using_NN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MNIST Handwritten Digit Classification using NN.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Importing the libraries"
      ],
      "metadata": {
        "id": "GsgVz8fr2GCV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "DstUq9_DxnPe"
      },
      "outputs": [],
      "source": [
        "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",
        "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": "markdown",
      "source": [
        "Loading MNIST dataset from keras.datasets\n",
        "\n",
        "The dataset is already preprocessed and splitted into training and testing data. The dataset returns 4 values which are taken in (X_train,Y_train) , (X_test,Y_test)"
      ],
      "metadata": {
        "id": "OMxuikHv5hx9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(X_train,Y_train), (X_test,Y_test)=mnist.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y6jriSLz3J8G",
        "outputId": "15ab82c8-7ba1-4a55-8def-a3446145e699"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "11501568/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking the datatype\n",
        "\n",
        "type(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k058W5Z464Pb",
        "outputId": "e4396095-1b62-4937-c7fb-2f222051bca8"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape,Y_train.shape,X_test.shape,Y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Apq9U47Z6_pK",
        "outputId": "e66c0362-3a59-41f5-dc20-6b8ea35eaa79"
      },
      "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",
        "Testing data=10,000 Images\n",
        "\n",
        "Image dimension=28 x 28\n",
        "\n",
        "Grayscale Images=1 channel"
      ],
      "metadata": {
        "id": "j0wmxcKB76wz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#printing 10th image\n",
        "\n",
        "print(X_train[10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wuOT_Lty7P2-",
        "outputId": "c40bd51e-ab37-4df5-a0e4-a9d3c3767872"
      },
      "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   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0  42 118 219 166 118 118   6\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0 103 242 254 254 254 254 254  66\n",
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            " [  0   0   0   0   0   0   0   0   0   0  18 232 254 254 254 254 254 238\n",
            "   70   0   0   0   0   0   0   0   0   0]\n",
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            "  141   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0 207 254 210 254 254 254\n",
            "   34   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0  84 206 254 254 254 254\n",
            "   41   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0  24 209 254 254 254\n",
            "  171   0   0   0   0   0   0   0   0   0]\n",
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            " [  0   0   0   0   0   0   0   0   0   0  40 214 250 254 254 254 254 254\n",
            "   34   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0  81 247 254 254 254 254 254 254\n",
            "  146   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0 110 246 254 254 254 254 254\n",
            "  171   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0  73  89  89  93 240 254\n",
            "  171   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   1 128 254\n",
            "  219  31   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   7 254 254\n",
            "  214  28   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 138 254 254\n",
            "  116   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0  19 177  90   0   0   0   0   0  25 240 254 254\n",
            "   34   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0 164 254 215  63  36   0  51  89 206 254 254 139\n",
            "    8   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0  57 197 254 254 222 180 241 254 254 253 213  11\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0 140 105 254 254 254 254 254 254 236   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   7 117 117 165 254 254 239  50   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#displaying the image\n",
        "\n",
        "plt.imshow(X_train[25])\n",
        "plt.show()\n",
        "\n",
        "#printing the corresponding label\n",
        "\n",
        "print(Y_train[25])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "xJif2Ojy8ZOf",
        "outputId": "ca4c5bed-940e-4a77-becc-6b04f4a84aff"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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+pMabinqdpKXZ7aWSniiwl7dolGm8S00zroJfu8KnP3f3ul8kLVD/GfmXJf1tET2U6Gu6pGezy66ie5O0Wv1v686o/9zGnZIukbRR0kuS/lPShAbq7fuSdkjarv5gTSqot7nqf4u+XdK27LKg6Ncu0VddXjc+LgsEwQk6IAjCDgRB2IEgCDsQBGEHgiDsQBCEHQji/wDdL2q5+ijR0AAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Image Labels"
      ],
      "metadata": {
        "id": "yq2snHU19O-d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y_train.shape,Y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qSwS4ckt8t1C",
        "outputId": "37699fc6-3b4e-4ea5-e501-59840b73d29d"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(60000,) (10000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#printing unique values in Y_train\n",
        "\n",
        "print(np.unique(Y_train))\n",
        "\n",
        "#printing unique values in Y_test\n",
        "\n",
        "print(np.unique(Y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I7RXo3lA9WH3",
        "outputId": "323bbeca-4081-4014-d8a9-212585709b83"
      },
      "execution_count": 15,
      "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": [
        "Minimum value=0--> 0/255\n",
        "\n",
        "Maximum value=1--> 255/255\n",
        "\n",
        "All the other values will be between 0 & 1"
      ],
      "metadata": {
        "id": "E2FconoqAhaD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#scaling the images\n",
        "\n",
        "X_train=X_train/255\n",
        "X_test=X_test/255"
      ],
      "metadata": {
        "id": "YcV_X70z_EkE"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Building the Neural Network"
      ],
      "metadata": {
        "id": "PdjyzcZjAx75"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#setting up the layers in Neural Network\n",
        "\n",
        "model=keras.Sequential([\n",
        "                        keras.layers.Flatten(input_shape=(28,28)),     #one input layer\n",
        "                        keras.layers.Dense(50, activation='relu'),     #two hidden layer\n",
        "                        keras.layers.Dense(50, activation='relu'),\n",
        "                        keras.layers.Dense(10, activation='sigmoid')\n",
        "])"
      ],
      "metadata": {
        "id": "9_m4XxIdAU9M"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#compiling the model\n",
        "\n",
        "model.compile(\n",
        "    optimizer='adam',\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")"
      ],
      "metadata": {
        "id": "QTpKxlUYC16_"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Loss = True value- Predicted value\n",
        "\n",
        "If loss function is high, then model is not working properly"
      ],
      "metadata": {
        "id": "1YQMyX7qEZiD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#training the Neural Network\n",
        "\n",
        "model.fit(X_train,Y_train,epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AsjEbksCDLXE",
        "outputId": "58c4f87a-1675-4035-d0a2-ffc291c9d4b5"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.2983 - accuracy: 0.9138\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1368 - accuracy: 0.9591\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1025 - accuracy: 0.9682\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0835 - accuracy: 0.9744\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0685 - accuracy: 0.9787\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0609 - accuracy: 0.9806\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0513 - accuracy: 0.9838\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0458 - accuracy: 0.9855\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0402 - accuracy: 0.9873\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0346 - accuracy: 0.9890\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fdbc81bef90>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training data accuracy= 98.9%"
      ],
      "metadata": {
        "id": "xHxUo2AoEmLg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Accuracy of Test data"
      ],
      "metadata": {
        "id": "KMyoHwg7ErdW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loss,accuracy=model.evaluate(X_test,Y_test)\n",
        "print(accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QVd1VXGQDaSL",
        "outputId": "a18ed268-637b-4fc9-bc0b-f34e73c2b4f3"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.1040 - accuracy: 0.9710\n",
            "0.9710000157356262\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Test data accuracy= 97.1%"
      ],
      "metadata": {
        "id": "iBnyUNq6E9LE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#displaying first data point from Test data\n",
        "\n",
        "plt.imshow(X_test[0])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "4zWE0yCME5WK",
        "outputId": "62accdc9-3059-4fab-edb1-de0052c6d85f"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAANiklEQVR4nO3df4wc9XnH8c8n/kV8QGtDcF3j4ISQqE4aSHWBRNDKESUFImSiJBRLtVyJ5lALElRRW0QVBalVSlEIok0aySluHESgaQBhJTSNa6W1UKljg4yxgdaEmsau8QFOaxPAP/DTP24cHXD7vWNndmft5/2SVrs7z87Oo/F9PLMzO/t1RAjA8e9tbTcAoD8IO5AEYQeSIOxAEoQdSGJ6Pxc207PiBA31c5FAKq/qZzoYBzxRrVbYbV8s6XZJ0yT9bUTcXHr9CRrSeb6wziIBFGyIdR1rXe/G254m6auSLpG0WNIy24u7fT8AvVXnM/u5kp6OiGci4qCkeyQtbaYtAE2rE/YFkn4y7vnOatrr2B6xvcn2pkM6UGNxAOro+dH4iFgZEcMRMTxDs3q9OAAd1An7LkkLxz0/vZoGYADVCftGSWfZfpftmZKulLSmmbYANK3rU28Rcdj2tZL+SWOn3lZFxLbGOgPQqFrn2SPiQUkPNtQLgB7i67JAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJGoN2Wx7h6T9kl6TdDgihptoCkDzaoW98rGIeKGB9wHQQ+zGA0nUDXtI+oHtR2yPTPQC2yO2N9nedEgHai4OQLfq7sZfEBG7bJ8maa3tpyJi/fgXRMRKSSsl6WTPjZrLA9ClWlv2iNhV3Y9Kul/SuU00BaB5XYfd9pDtk44+lvRxSVubagxAs+rsxs+TdL/to+/zrYj4fiNdAWhc12GPiGcknd1gLwB6iFNvQBKEHUiCsANJEHYgCcIOJNHEhTApvPjZj3asvXP508V5nxqdV6wfPDCjWF9wd7k+e+dLHWtHNj9RnBd5sGUHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQ4zz5Ff/xH3+pY+9TQT8szn1lz4UvK5R2HX+5Yu/35j9Vc+LHrR6NndKwN3foLxXmnr3uk6XZax5YdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5JwRP8GaTnZc+M8X9i35TXpZ58+r2PthQ+W/8+c82R5Hf/0V1ysz/zg/xbrt3zgvo61i97+SnHe7718YrH+idmdr5Wv65U4WKxvODBUrC854VDXy37P964u1t87srHr927ThlinfbF3wj8otuxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kATXs0/R0Hc2FGr13vvkerPrr39pScfan5+/qLzsfy3/5v0tS97TRUdTM/2VI8X60Jbdxfop6+8t1n91Zuff25+9o/xb/MejSbfstlfZHrW9ddy0ubbX2t5e3c/pbZsA6prKbvw3JF38hmk3SFoXEWdJWlc9BzDAJg17RKyXtPcNk5dKWl09Xi3p8ob7AtCwbj+zz4uIox+onpPUcTAz2yOSRiTpBM3ucnEA6qp9ND7GrqTpeKVHRKyMiOGIGJ6hWXUXB6BL3YZ9j+35klTdjzbXEoBe6DbsayStqB6vkPRAM+0A6JVJP7Pbvltjv1x+qu2dkr4g6WZJ37Z9laRnJV3RyyZRdvi5PR1rQ/d2rknSa5O899B3Xuyio2bs+b2PFuvvn1n+8/3S3vd1rC36u2eK8x4uVo9Nk4Y9IpZ1KB2bv0IBJMXXZYEkCDuQBGEHkiDsQBKEHUiCS1zRmulnLCzWv3LjV4r1GZ5WrP/D7b/ZsXbK7oeL8x6P2LIDSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKcZ0drnvrDBcX6h2eVh7LedrA8HPXcJ15+yz0dz9iyA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EASnGdHTx34xIc71h799G2TzF0eQej3r7uuWH/7v/1okvfPhS07kARhB5Ig7EAShB1IgrADSRB2IAnCDiTBeXb01H9f0nl7cqLL59GX/ddFxfrs7z9WrEexms+kW3bbq2yP2t46btpNtnfZ3lzdLu1tmwDqmspu/DckXTzB9Nsi4pzq9mCzbQFo2qRhj4j1kvb2oRcAPVTnAN21trdUu/lzOr3I9ojtTbY3HdKBGosDUEe3Yf+apDMlnSNpt6RbO70wIlZGxHBEDM+Y5MIGAL3TVdgjYk9EvBYRRyR9XdK5zbYFoGldhd32/HFPPylpa6fXAhgMk55nt323pCWSTrW9U9IXJC2xfY7GTmXukHR1D3vEAHvbSScV68t//aGOtX1HXi3OO/rFdxfrsw5sLNbxepOGPSKWTTD5jh70AqCH+LoskARhB5Ig7EAShB1IgrADSXCJK2rZftP7i/Xvnvo3HWtLt3+qOO+sBzm11iS27EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBOfZUfR/v/ORYn3Lb/9Vsf7jw4c61l76y9OL887S7mIdbw1bdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgvPsyU1f8MvF+vWf//tifZbLf0JXPra8Y+0d/8j16v3Elh1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkuA8+3HO08v/xGd/d2ex/pkTXyzW79p/WrE+7/OdtydHinOiaZNu2W0vtP1D20/Y3mb7umr6XNtrbW+v7uf0vl0A3ZrKbvxhSZ+LiMWSPiLpGtuLJd0gaV1EnCVpXfUcwICaNOwRsTsiHq0e75f0pKQFkpZKWl29bLWky3vVJID63tJndtuLJH1I0gZJ8yLi6I+EPSdpXod5RiSNSNIJmt1tnwBqmvLReNsnSrpX0vURsW98LSJCUkw0X0SsjIjhiBieoVm1mgXQvSmF3fYMjQX9roi4r5q8x/b8qj5f0mhvWgTQhEl3421b0h2SnoyIL48rrZG0QtLN1f0DPekQ9Zz9vmL5z067s9bbf/WLnynWf/Gxh2u9P5ozlc/s50taLulx25uraTdqLOTftn2VpGclXdGbFgE0YdKwR8RDktyhfGGz7QDoFb4uCyRB2IEkCDuQBGEHkiDsQBJc4nocmLb4vR1rI/fU+/rD4lXXFOuL7vz3Wu+P/mHLDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJcJ79OPDUH3T+Yd/LZu/rWJuK0//lYPkFMeEPFGEAsWUHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQ4z34MePWyc4v1dZfdWqgy5BbGsGUHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSSmMj77QknflDRPUkhaGRG3275J0mclPV+99MaIeLBXjWb2P+dPK9bfOb37c+l37T+tWJ+xr3w9O1ezHzum8qWaw5I+FxGP2j5J0iO211a12yLiS71rD0BTpjI++25Ju6vH+20/KWlBrxsD0Ky39Jnd9iJJH5K0oZp0re0ttlfZnvC3kWyP2N5ke9MhHajVLIDuTTnstk+UdK+k6yNin6SvSTpT0jka2/JP+AXtiFgZEcMRMTxDsxpoGUA3phR22zM0FvS7IuI+SYqIPRHxWkQckfR1SeWrNQC0atKw27akOyQ9GRFfHjd9/riXfVLS1ubbA9CUqRyNP1/SckmP295cTbtR0jLb52js7MsOSVf3pEPU8hcvLi7WH/6tRcV67H68wW7QpqkcjX9IkicocU4dOIbwDTogCcIOJEHYgSQIO5AEYQeSIOxAEo4+Drl7sufGeb6wb8sDstkQ67Qv9k50qpwtO5AFYQeSIOxAEoQdSIKwA0kQdiAJwg4k0dfz7Lafl/TsuEmnSnqhbw28NYPa26D2JdFbt5rs7YyIeMdEhb6G/U0LtzdFxHBrDRQMam+D2pdEb93qV2/sxgNJEHYgibbDvrLl5ZcMam+D2pdEb93qS2+tfmYH0D9tb9kB9AlhB5JoJey2L7b9H7aftn1DGz10YnuH7cdtb7a9qeVeVtketb113LS5ttfa3l7dTzjGXku93WR7V7XuNtu+tKXeFtr+oe0nbG+zfV01vdV1V+irL+ut75/ZbU+T9J+SLpK0U9JGScsi4om+NtKB7R2ShiOi9S9g2P4NSS9J+mZEfKCadoukvRFxc/Uf5ZyI+JMB6e0mSS+1PYx3NVrR/PHDjEu6XNLvqsV1V+jrCvVhvbWxZT9X0tMR8UxEHJR0j6SlLfQx8CJivaS9b5i8VNLq6vFqjf2x9F2H3gZCROyOiEerx/slHR1mvNV1V+irL9oI+wJJPxn3fKcGa7z3kPQD24/YHmm7mQnMi4jd1ePnJM1rs5kJTDqMdz+9YZjxgVl33Qx/XhcH6N7sgoj4NUmXSLqm2l0dSDH2GWyQzp1OaRjvfplgmPGfa3PddTv8eV1thH2XpIXjnp9eTRsIEbGruh+VdL8GbyjqPUdH0K3uR1vu5+cGaRjviYYZ1wCsuzaHP28j7BslnWX7XbZnSrpS0poW+ngT20PVgRPZHpL0cQ3eUNRrJK2oHq+Q9ECLvbzOoAzj3WmYcbW87lof/jwi+n6TdKnGjsj/WNKfttFDh77eLemx6rat7d4k3a2x3bpDGju2cZWkUyStk7Rd0j9LmjtAvd0p6XFJWzQWrPkt9XaBxnbRt0jaXN0ubXvdFfrqy3rj67JAEhygA5Ig7EAShB1IgrADSRB2IAnCDiRB2IEk/h9BCfQTVPflJQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y_test[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vTSKGqqgFPhk",
        "outputId": "e5fe7f9f-bafa-4398-85e6-49c3e83f8700"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#all the 10,000 labels has to be stored\n",
        "\n",
        "Y_pred=model.predict(X_test)\n",
        "print(Y_pred[0])                     "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UfIyY2cLFW0R",
        "outputId": "fc749498-9dac-4147-c492-ab8abf36bd30"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[3.5130084e-03 6.7106444e-01 6.6638464e-01 8.5868883e-01 6.2501094e-05\n",
            " 9.0813637e-04 4.0907306e-08 9.9999774e-01 1.3417095e-02 6.5949798e-02]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The probability of all numbers from 0-9 are displayed and we need to consider that probability which is highest, here in this case for number 7 the value is highest, which is 9.9999774e-01"
      ],
      "metadata": {
        "id": "vdAufB9oF8ir"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#converting the prediction to class labels\n",
        "\n",
        "Y_pred_labels=[np.argmax(i) for i in Y_pred]\n",
        "print(Y_pred_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LoJE2XU_Fnvw",
        "outputId": "d1e60933-bd88-440e-b94d-9f1dfb3a9eae"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[7, 2, 1, 0, 4, 1, 4, 9, 6, 9, 0, 6, 9, 0, 1, 5, 9, 7, 3, 4, 9, 6, 6, 5, 4, 0, 7, 4, 0, 1, 3, 1, 3, 4, 7, 2, 7, 1, 2, 1, 1, 7, 4, 2, 3, 5, 1, 2, 4, 4, 6, 3, 5, 5, 6, 0, 4, 1, 9, 5, 7, 8, 9, 3, 7, 4, 6, 4, 3, 0, 7, 0, 2, 9, 1, 7, 3, 2, 9, 7, 7, 6, 2, 7, 8, 4, 7, 3, 6, 1, 3, 6, 9, 3, 1, 4, 1, 7, 6, 9, 6, 0, 5, 4, 5, 9, 2, 1, 9, 4, 8, 7, 3, 9, 7, 4, 4, 4, 9, 2, 5, 4, 7, 6, 7, 9, 0, 5, 8, 5, 6, 6, 5, 7, 8, 1, 0, 1, 6, 4, 6, 7, 3, 1, 7, 1, 8, 2, 0, 4, 9, 8, 5, 5, 1, 5, 6, 0, 3, 4, 4, 6, 5, 4, 6, 5, 4, 5, 1, 4, 4, 7, 2, 3, 2, 7, 1, 8, 1, 8, 1, 8, 5, 0, 8, 9, 2, 5, 0, 1, 1, 1, 0, 9, 0, 3, 1, 6, 4, 2, 3, 6, 1, 1, 1, 3, 9, 5, 2, 9, 4, 5, 9, 3, 9, 0, 3, 6, 5, 5, 7, 2, 2, 7, 1, 2, 8, 4, 1, 7, 3, 3, 8, 8, 7, 9, 2, 2, 4, 1, 5, 8, 8, 7, 2, 3, 0, 6, 4, 2, 4, 1, 9, 5, 7, 7, 2, 8, 2, 6, 8, 5, 7, 7, 9, 1, 8, 1, 8, 0, 3, 0, 1, 9, 9, 4, 1, 8, 2, 1, 2, 9, 7, 5, 9, 2, 6, 4, 1, 5, 4, 2, 9, 2, 0, 4, 0, 0, 2, 8, 4, 7, 1, 2, 4, 0, 2, 7, 4, 3, 3, 0, 0, 3, 1, 9, 6, 5, 2, 5, 9, 7, 9, 3, 0, 4, 2, 0, 7, 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5, 0, 1, 9, 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, 8, 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, 8, 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, 4, 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, 4, 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, 5, 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, 4, 3, 4, 7, 8, 9, 0, 1, 2, 5, 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, 5, 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, 8, 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, 5, 8, 9, 5, 3, 1, 7, 4, 7, 0, 0, 6, 6, 6, 3, 7, 4, 2, 8, 8, 8, 7, 1, 9, 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, 0, 1, 2, 7, 8, 6, 3, 9, 2, 1, 9, 3, 9, 6, 1, 7, 2, 4, 4, 5, 7, 0, 0, 1, 6, 6, 8, 2, 7, 7, 2, 4, 2, 1, 6, 1, 0, 6, 9, 8, 3, 9, 6, 3, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 6, 8, 9, 9, 0, 1, 2, 4, 4, 3, 7, 4, 4, 4, 0, 3, 8, 7, 5, 8, 2, 1, 7, 5, 3, 8, 5, 2, 5, 1, 1, 6, 2, 1, 3, 8, 6, 4, 2, 6, 2, 5, 5, 0, 2, 8, 0, 6, 8, 1, 7, 9, 1, 9, 2, 6, 7, 6, 6, 8, 7, 4, 9, 2, 1, 3, 3, 0, 5, 5, 8, 0, 3, 7, 9, 7, 0, 2, 7, 9, 1, 7, 8, 0, 3, 5, 3, 6, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 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, 5, 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, 0, 1, 2, 3, 4, 5, 6, 7, 8, 8, 0, 6, 0, 1, 2, 5, 7, 9, 4, 7, 1, 9, 1, 7, 1, 4, 0, 0, 1, 7, 5, 7, 1, 3, 3, 3, 1, 6, 9, 7, 1, 3, 0, 2, 6, 0, 8, 9, 4, 3, 5, 4, 8, 1, 5, 9, 0, 6, 5, 3, 8, 1, 4, 7, 5, 2, 0, 0, 1, 7, 8, 7, 6, 8, 8, 2, 3, 6, 1, 8, 9, 5, 2, 0, 1, 2, 3, 4, 5, 6, 7, 5, 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, 9, 9, 8, 7, 8, 2, 7, 5, 8, 5, 3, 2, 2, 0, 5, 8, 6, 0, 3, 8, 1, 0, 3, 0, 4, 7, 4, 9, 0, 9, 0, 7, 1, 7, 1, 6, 6, 5, 6, 2, 8, 7, 5, 4, 9, 9, 5, 3, 7, 4, 3, 0, 9, 5, 6, 1, 1, 3, 2, 1, 0, 0, 1, 2, 3, 4, 7, 8, 9, 0, 1, 8, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 7, 8, 9, 0, 8, 3, 9, 5, 5, 2, 6, 8, 4, 1, 7, 1, 8, 3, 5, 6, 9, 1, 1, 1, 2, 1, 2, 0, 7, 7, 5, 8, 2, 9, 8, 8, 7, 3, 4, 6, 8, 7, 0, 4, 2, 7, 7, 5, 4, 3, 4, 2, 8, 1, 5, 1, 0, 2, 3, 5, 5, 7, 0, 6, 8, 0, 3, 9, 9, 5, 2, 7, 7, 1, 0, 1, 7, 8, 9, 0, 1, 2, 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": [
        "Y_test= True Labels\n",
        "\n",
        "Y_test_pred= Predicted Labels"
      ],
      "metadata": {
        "id": "ZRABFljnGuXA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Confusion Matrix"
      ],
      "metadata": {
        "id": "wSHwfeQiG6Za"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "conf_mat=confusion_matrix(Y_test,Y_pred_labels)"
      ],
      "metadata": {
        "id": "_jVh9S6DGlPh"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(conf_mat)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lLPQQiWuHOzX",
        "outputId": "7b2d500d-fc25-4bae-be3e-a4d6f390e235"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(\n",
            "[[ 969    1    1    0    1    1    2    2    3    0]\n",
            " [   0 1122    1    2    0    4    2    0    4    0]\n",
            " [   5    0  999    1    7    1    2    6   11    0]\n",
            " [   0    0    5  966    0   23    0    3    7    6]\n",
            " [   0    0    2    0  968    1    4    2    1    4]\n",
            " [   2    0    0    4    2  878    4    1    1    0]\n",
            " [   6    1    0    0    8   11  929    0    3    0]\n",
            " [   1    5    9    0    5    0    0  994    8    6]\n",
            " [   2    0    3    6    6   10    2    2  943    0]\n",
            " [   3    4    0    8   24   10    1    7   10  942]], shape=(10, 10), dtype=int32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#constructing heatmap for confusion matrix\n",
        "\n",
        "plt.figure(figsize=(15,7))\n",
        "sns.heatmap(conf_mat, annot=True, fmt='d', cmap='Blues')\n",
        "plt.ylabel('True Labels')\n",
        "plt.xlabel('Predicted Labels')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "9OBgixhFHTK_",
        "outputId": "95db5d8a-a99e-458d-bc87-78e50992fd03"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x504 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "If we consider y=0 and x=3 , it gives result  as 0 which means that no True label as 0 is Predicted as 3 , similarly for others"
      ],
      "metadata": {
        "id": "zh2LlGsFINah"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Making a Predictive System"
      ],
      "metadata": {
        "id": "DvYS9cmvIo_V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input_image_path='/content/MNIST_digit.png'"
      ],
      "metadata": {
        "id": "VyHlrRILH0vi"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_image=cv2.imread(input_image_path)"
      ],
      "metadata": {
        "id": "J1jiKHfnIvLq"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(input_image)               #converted into numpy array"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vyzy_Q21I26x",
        "outputId": "801c2d46-3527-419f-c131-d81f2add7f90"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[[0 0 0]\n",
            "  [0 0 0]\n",
            "  [0 0 0]\n",
            "  ...\n",
            "  [0 0 0]\n",
            "  [0 0 0]\n",
            "  [0 0 0]]\n",
            "\n",
            " [[0 0 0]\n",
            "  [0 0 0]\n",
            "  [0 0 0]\n",
            "  ...\n",
            "  [0 0 0]\n",
            "  [0 0 0]\n",
            "  [0 0 0]]\n",
            "\n",
            " [[0 0 0]\n",
            "  [0 0 0]\n",
            "  [0 0 0]\n",
            "  ...\n",
            "  [0 0 0]\n",
            "  [0 0 0]\n",
            "  [0 0 0]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[0 0 0]\n",
            "  [0 0 0]\n",
            "  [0 0 0]\n",
            "  ...\n",
            "  [0 0 0]\n",
            "  [0 0 0]\n",
            "  [0 0 0]]\n",
            "\n",
            " [[0 0 0]\n",
            "  [0 0 0]\n",
            "  [0 0 0]\n",
            "  ...\n",
            "  [0 0 0]\n",
            "  [0 0 0]\n",
            "  [0 0 0]]\n",
            "\n",
            " [[0 0 0]\n",
            "  [0 0 0]\n",
            "  [0 0 0]\n",
            "  ...\n",
            "  [0 0 0]\n",
            "  [0 0 0]\n",
            "  [0 0 0]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv2_imshow(input_image)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "KGYqh6oyI493",
        "outputId": "2175fa29-090c-490a-9c97-dbd9cb718ec4"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=318x318 at 0x7FDBC41207D0>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_image.shape                 #RGB image and not Grayscale image"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jpax4YLrJEGH",
        "outputId": "bcc40599-65dd-4dd1-b2f7-eda47f44d691"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(318, 318, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#converting RGB image to Grayscale image\n",
        "\n",
        "grayscale=cv2.cvtColor(input_image,cv2.COLOR_RGB2GRAY)"
      ],
      "metadata": {
        "id": "cb_FgV2KJK6K"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grayscale.shape                   #resizing of image is required"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ia6oywxLJlHT",
        "outputId": "b3c8c857-06ef-48b4-b167-c59385ff9798"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(318, 318)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_image_resized=cv2.resize(grayscale,(28,28))"
      ],
      "metadata": {
        "id": "kI2wl8SFJoyd"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_image_resized.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r6ZfEDEsKAuf",
        "outputId": "76b79754-16bc-4e4c-dd8d-9c42317417a6"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_image_resized=input_image_resized/255"
      ],
      "metadata": {
        "id": "yZn6UI43KEXq"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#reshaping to tell the model that only one image is used\n",
        "\n",
        "input_reshaped=np.reshape(input_image_resized, [1,28,28])       #one image therefore [1,28,28]. For two RGB images [2,28,28,3]"
      ],
      "metadata": {
        "id": "vbR7JNHuKOUV"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_prediction=model.predict(input_reshaped)\n",
        "print(input_prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oai_nqU1KkxE",
        "outputId": "e319fb0d-8886-4c81-911e-560b1fb9041e"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[5.9389083e-10 9.4131142e-02 1.9494593e-03 1.0000000e+00 4.7541544e-08\n",
            "  6.3185507e-01 2.8182675e-13 2.7394295e-04 9.5208365e-01 4.5282638e-01]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_prediction_label=np.argmax(input_prediction)\n",
        "print('The Handwritten Digit is recognised as:' , input_prediction_label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8LsWUTX6K8u1",
        "outputId": "f1ee057c-ed27-4ce6-a378-438ad7a9acc7"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Handwritten Digit is recognised as: 3\n"
          ]
        }
      ]
    }
  ]
}