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": [
        "Importing the Dependencies."
      ],
      "metadata": {
        "id": "tZKZVC0Amn-9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2uJEr_fFlg5X"
      },
      "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 the MNIST data from the keras.datsets"
      ],
      "metadata": {
        "id": "iEauaaslnPq_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(X_train, Y_train), (X_test, Y_test) = mnist.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hBes7o82mtSd",
        "outputId": "bcd20778-41fa-46a7-9b1f-0162827a9431"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape, Y_train.shape, X_test.shape, Y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iyRkuk-QoRvM",
        "outputId": "59b8105d-d9be-4dd8-addf-afcef920f4cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(60000, 28, 28) (60000,) (10000, 28, 28) (10000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "X_train:\n",
        "  images: 60000,\n",
        "  image dimension: 28*28,\n",
        "  grayscale"
      ],
      "metadata": {
        "id": "tfrKYqaQrFPY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# dispaly a image\n",
        "plt.imshow(X_train[4507])\n",
        "plt.show()\n",
        "\n",
        "# corresponding label\n",
        "print(Y_train[4507])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "GOO2BWjQrD0T",
        "outputId": "b7ac5471-88ab-49b2-dbb3-16f24e4c9fe1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# scaling the values.\n",
        "\n",
        "X_train  = X_train/255\n",
        "X_test = X_test/255"
      ],
      "metadata": {
        "id": "VnHzSTX-rucf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Building the Neural Network"
      ],
      "metadata": {
        "id": "ZrHhAWxVcWr2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# setting up the layers of the neural network\n",
        "\n",
        "model = keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape=(28,28)),\n",
        "    keras.layers.Dense(50, activation = 'relu'),\n",
        "    keras.layers.Dense(50, activation = 'relu'),\n",
        "    keras.layers.Dense(10, activation = 'sigmoid')\n",
        "])"
      ],
      "metadata": {
        "id": "yCrj56D4cAjI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# compiling the Neural Network\n",
        "\n",
        "model.compile(optimizer = 'adam',\n",
        "              loss = 'sparse_categorical_crossentropy',\n",
        "              metrics = ['accuracy'])"
      ],
      "metadata": {
        "id": "0Nr6qhTadecQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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": "OKD6rElueEYK",
        "outputId": "2b56bb61-cff9-4924-faef-af3730266e9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2917 - accuracy: 0.9162\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1358 - accuracy: 0.9589\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1023 - accuracy: 0.9689\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0824 - accuracy: 0.9756\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0705 - accuracy: 0.9783\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0598 - accuracy: 0.9812\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0512 - accuracy: 0.9836\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0464 - accuracy: 0.9851\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0407 - accuracy: 0.9868\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0355 - accuracy: 0.9886\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f596e6f2d40>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training data accuracy = 98.86%"
      ],
      "metadata": {
        "id": "faej3D5Ueq8l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loss, accuracy = model.evaluate(X_test, Y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3CFofYAoeXab",
        "outputId": "e20a724a-7671-4811-c7b6-578b2d80ccbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.1023 - accuracy: 0.9731\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VRwGYJrme9Nk",
        "outputId": "e1b271b0-542c-4ae0-b9f4-35033b7e6a6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9731000065803528\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Test data accuracy = 97.31%"
      ],
      "metadata": {
        "id": "Orx2qAutfDKy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(X_test[0])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "EszmLI1de_fp",
        "outputId": "6ff6be83-8bb7-4ecf-fc1b-ec95edb95c2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y_test[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IDTesbM2fQcH",
        "outputId": "bb5fc38b-7561-4e9f-eb82-e25fec9aa07d"
      },
      "execution_count": null,
      "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": "L5bJHKzKfXxy",
        "outputId": "b0229fd9-2918-47bf-a62b-ebe04d324e9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y_pred[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M-ef8f2QfjVE",
        "outputId": "bc4cdcbd-ab7d-4c84-c36f-8f01f6c6b1ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[6.7168334e-03 3.1460633e-03 5.9623003e-02 9.1827399e-01 3.2863852e-06\n",
            " 1.3643037e-01 2.6262295e-08 9.9999720e-01 2.6697525e-01 2.2407021e-01]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "model.predict() gives the prediction probability of each class for that data point"
      ],
      "metadata": {
        "id": "09QtQuokf_8o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "label_for_first_imamge = np.argmax(Y_pred[0])\n",
        "print(label_for_first_imamge)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MTbkxaVufnqE",
        "outputId": "a5904e7f-a6b5-4065-d159-db212304c5fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred_label = [np.argmax(i) for i in Y_pred]\n",
        "print(Y_pred_label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zsx8ENNyhI7V",
        "outputId": "1649adc1-7d25-4544-8232-e3cdca471500"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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5, 0, 1, 9, 1, 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, 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, 9, 4, 7, 8, 9, 0, 1, 2, 3, 5, 6, 7, 8, 9, 0, 1, 7, 3, 4, 7, 8, 9, 1, 4, 5, 3, 8, 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, 6, 4, 7, 4, 2, 7, 4, 2, 9, 2, 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7, 3, 5, 0, 1, 1, 3, 8, 4, 9, 4, 5, 1, 8, 6, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 3, 5, 3, 2, 9, 3, 2, 1, 4, 5, 5, 2, 3, 2, 1, 3, 9, 7, 2, 1, 2, 8, 9, 1, 8, 8, 7, 8, 1, 0, 0, 6, 7, 7, 8, 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, 0, 2, 2, 3, 4, 5, 6, 2, 0, 1, 2, 2, 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, 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, 0, 1, 2, 3, 4, 5, 6, 7, 8, 8, 0, 6, 0, 1, 2, 3, 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, 3, 3, 8, 1, 4, 7, 5, 2, 0, 0, 1, 7, 8, 7, 6, 8, 8, 2, 3, 5, 1, 8, 9, 5, 2, 0, 1, 2, 3, 4, 5, 6, 7, 5, 9, 0, 1, 2, 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, 3, 7, 8, 2, 7, 5, 8, 5, 3, 2, 2, 0, 5, 5, 6, 0, 3, 8, 1, 0, 3, 0, 4, 7, 4, 9, 0, 9, 0, 7, 1, 7, 1, 6, 6, 0, 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, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 7, 8, 9, 0, 8, 3, 9, 5, 5, 2, 6, 8, 4, 1, 7, 1, 7, 3, 5, 6, 9, 1, 1, 1, 2, 1, 2, 0, 7, 7, 5, 8, 2, 9, 8, 8, 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, 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, 3, 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, 8, 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": [
        "Confusion Matrix"
      ],
      "metadata": {
        "id": "v9MuqQzkh_xJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "conf_mat = confusion_matrix(Y_test, Y_pred_label)\n",
        "print(conf_mat)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "exi_VH1bhWPH",
        "outputId": "889febce-24df-4abb-ad8c-84a178043fbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(\n",
            "[[ 968    1    2    0    2    1    2    1    3    0]\n",
            " [   0 1122    4    1    0    1    2    0    5    0]\n",
            " [   5    3 1003    0    4    0    3    5    9    0]\n",
            " [   1    0    5  968    1   15    0    4    8    8]\n",
            " [   1    1    4    1  963    1    3    1    0    7]\n",
            " [   4    0    0    7    1  868    3    1    6    2]\n",
            " [   4    3    1    1    3    6  936    0    4    0]\n",
            " [   1    3   13    3    2    0    0 1000    1    5]\n",
            " [   9    1    2    5    4    7    3    3  936    4]\n",
            " [   1    2    0    6   16    7    1    6    9  961]], shape=(10, 10), dtype=int32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "uV2hI6fTiUu-",
        "outputId": "abb82136-4408-45ac-9861-c261715fc8f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 47.7222222222222, 'Predicted Labels')"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Predictive System"
      ],
      "metadata": {
        "id": "9YEdNB5fnOgI"
      }
    },
    {
      "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_prediction = model.predict(image_reshaped)\n",
        "\n",
        "# input_pred_label = np.argmax(input_prediction)\n",
        "\n",
        "# print('The Handwritten Digit is recognised as ', input_pred_label)"
      ],
      "metadata": {
        "id": "uQB1R85qjdiR"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}