digit_recognition_mnist.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "digit_recognition_mnist.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNWWzAi7DkQtHtzlGExD2Ac",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/priyadarshini-silpa/neural_networks/blob/main/digit_recognition_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": 51,
      "metadata": {
        "id": "uaTr_zLELVeo"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import datasets, layers, models\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
        "image_index = 100\n",
        "print(y_train[image_index]) \n",
        "plt.imshow(x_train[image_index], cmap='Greys')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "RDuiztZDPoax",
        "outputId": "4c13d38d-3615-4bee-c8fa-5651da3d915f"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f23c4189d50>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)\n",
        "x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)"
      ],
      "metadata": {
        "id": "EBV3pY5RQrZ-"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = x_train.astype('float32')/255\n",
        "x_test = x_test.astype('float32')/255"
      ],
      "metadata": {
        "id": "niQWNuzQQvPK"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cnn = models.Sequential([\n",
        "    layers.Conv2D(filters=32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)),\n",
        "    layers.MaxPooling2D((2, 2)),\n",
        "    \n",
        "    layers.Conv2D(filters=64, kernel_size=(3, 3), activation='relu'),\n",
        "    layers.MaxPooling2D((2, 2)),\n",
        "    \n",
        "    layers.Flatten(),\n",
        "    layers.Dense(64, activation='relu'),\n",
        "    layers.Dense(10, activation='softmax')\n",
        "])"
      ],
      "metadata": {
        "id": "OF_1_dShHhFt"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cnn.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "cnn.fit(x_train, y_train, epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zofDUcImFKxx",
        "outputId": "6c0bea47-22b7-404a-ffb9-0de7da6f4668"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1431 - accuracy: 0.9561\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0460 - accuracy: 0.9854\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0323 - accuracy: 0.9893\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0231 - accuracy: 0.9924\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0176 - accuracy: 0.9944\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0134 - accuracy: 0.9956\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0106 - accuracy: 0.9967\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0086 - accuracy: 0.9972\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0072 - accuracy: 0.9976\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0073 - accuracy: 0.9976\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f23c424bf50>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cnn.evaluate(x_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NW1ejrhBITzs",
        "outputId": "6efabf9b-8212-4b30-d459-96892ee46d8e"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0352 - accuracy: 0.9900\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.03521151840686798, 0.9900000095367432]"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    }
  ]
}