CNN_digit_identification_mnist.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Faizan96322/IdentifyHandwrittenNumbers/blob/main/CNN_digit_identification_mnist.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "03Si-nWBlbMM",
        "outputId": "91d0d46b-26c0-4321-c0a4-318d5ddce8a7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "\n",
        "mnist = tf.keras.datasets.mnist\n",
        "# print(mnist)\n",
        "(x_train,y_train),(x_test,y_test) = mnist.load_data()\n",
        "print(y_train[5])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "p_xg9onYmf-I",
        "outputId": "63b80116-ddaa-471a-d934-eb6ac717af79"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fe583642810>"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.imshow(x_train[5])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0wMESbaUmz2d",
        "outputId": "f60e7c8d-af6a-424f-eb44-5558855ec657"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 1.7884 - accuracy: 0.8746\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3629 - accuracy: 0.9267\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2312 - accuracy: 0.9434\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1689 - accuracy: 0.9556\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1441 - accuracy: 0.9602\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1295 - accuracy: 0.9639\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1226 - accuracy: 0.9665\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1100 - accuracy: 0.9700\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1055 - accuracy: 0.9727\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0996 - accuracy: 0.9735\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fe58359f050>"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
        "\n",
        "\n",
        "model = tf.keras.Sequential()\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation = 'relu'))\n",
        "model.add(Dense(128, activation = 'relu'))\n",
        "model.add(Dense(10, activation = 'softmax'))\n",
        "\n",
        "model.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])\n",
        "\n",
        "model.fit(x_train,y_train, epochs = 10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j3ECdxyNsN5K",
        "outputId": "60a10c6a-ff32-4417-8cad-a1ff6fa8f70c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 0s 1ms/step\n",
            "[9.9865079e-01 1.5385019e-08 9.6039388e-05 9.6738681e-08 1.5782688e-06\n",
            " 2.5765658e-07 1.2146694e-03 5.2841796e-07 7.0248333e-08 3.5854391e-05]\n"
          ]
        }
      ],
      "source": [
        "predictions = model.predict(x_test)\n",
        "print(predictions[3])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qzKFrZ53tApd",
        "outputId": "c7fabedb-aa4d-4404-d872-84a89f94bafb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0\n"
          ]
        }
      ],
      "source": [
        "print(np.argmax(predictions[3]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "1HbNyMqotLC_",
        "outputId": "878a6062-d589-4786-c0de-d6e4f5963734"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fe57ed876d0>"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.imshow(x_test[3])"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YLCY-w2Vl6su"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOezpJIDddZx1fMmkhT7q4q",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}