ANN_on_MNIST_Number_Dataset.ipynb

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  "metadata": {
    "colab": {
      "name": "ANN on MNIST Number Dataset.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "oMSFB5SMwtST"
      },
      "source": [
        "#########################################\n",
        "# ANN on MNIST Number Dataset\n",
        "# By Giriraj Vaishnav\n",
        "#########################################"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "sJ_LmizjxAPv",
        "outputId": "7febeb28-75f5-49f3-97cd-801f5dfa0cd3"
      },
      "source": [
        "# Loading the MNIST Dataset\n",
        "\n",
        "from keras.datasets import mnist\n",
        "import matplotlib.pyplot as plt\n",
        "(X_train,y_train), (X_test, y_test)= mnist.load_data()\n",
        "plt.subplot()\n",
        "plt.imshow(X_train[9], cmap=plt.get_cmap('gray'))"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f990fb93d10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WLMetryLxKyq"
      },
      "source": [
        "# Importing necessary libraries\n",
        "\n",
        "import numpy as np\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Activation\n",
        "from keras.optimizers import SGD, Adam\n",
        "from keras.utils import np_utils"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S4fyWhiIxVSg"
      },
      "source": [
        "# Reshape the Data\n",
        "\n",
        "number_pix=X_train.shape[1]*X_train.shape[2] \n",
        "X_train=X_train.reshape(X_train.shape[0], number_pix).astype('float32')\n",
        "X_test=X_test.reshape(X_test.shape[0], number_pix).astype('float32')"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lEGadCiwxV6O",
        "outputId": "2a908608-ee41-459b-b028-dfc6c445efce"
      },
      "source": [
        "# Normalize the data\n",
        "\n",
        "X_train=X_train/255\n",
        "X_test=X_test/255\n",
        "y_train= np_utils.to_categorical(y_train)\n",
        "y_test= np_utils.to_categorical(y_test)\n",
        "num_classes=y_train.shape[1]\n",
        "print(num_classes)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dmyrxtsQxYif",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5e0e812e-293d-415d-b1ad-63e54af27935"
      },
      "source": [
        "# Training the ANN model\n",
        "\n",
        "model=Sequential()\n",
        "model.add(Dense(number_pix, input_dim=number_pix, activation='relu'))\n",
        "model.add(Dense(num_classes, activation='softmax'))\n",
        "model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])\n",
        "H = model.fit(X_train, y_train, validation_data=(X_test,y_test),epochs=10, batch_size=200, verbose=2)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "300/300 - 5s - loss: 0.2758 - accuracy: 0.9224 - val_loss: 0.1346 - val_accuracy: 0.9606\n",
            "Epoch 2/10\n",
            "300/300 - 4s - loss: 0.1120 - accuracy: 0.9678 - val_loss: 0.0906 - val_accuracy: 0.9733\n",
            "Epoch 3/10\n",
            "300/300 - 4s - loss: 0.0706 - accuracy: 0.9793 - val_loss: 0.0839 - val_accuracy: 0.9749\n",
            "Epoch 4/10\n",
            "300/300 - 4s - loss: 0.0520 - accuracy: 0.9844 - val_loss: 0.0677 - val_accuracy: 0.9799\n",
            "Epoch 5/10\n",
            "300/300 - 4s - loss: 0.0364 - accuracy: 0.9890 - val_loss: 0.0671 - val_accuracy: 0.9790\n",
            "Epoch 6/10\n",
            "300/300 - 4s - loss: 0.0264 - accuracy: 0.9932 - val_loss: 0.0634 - val_accuracy: 0.9802\n",
            "Epoch 7/10\n",
            "300/300 - 4s - loss: 0.0200 - accuracy: 0.9950 - val_loss: 0.0670 - val_accuracy: 0.9804\n",
            "Epoch 8/10\n",
            "300/300 - 4s - loss: 0.0148 - accuracy: 0.9965 - val_loss: 0.0610 - val_accuracy: 0.9829\n",
            "Epoch 9/10\n",
            "300/300 - 4s - loss: 0.0120 - accuracy: 0.9972 - val_loss: 0.0631 - val_accuracy: 0.9799\n",
            "Epoch 10/10\n",
            "300/300 - 4s - loss: 0.0084 - accuracy: 0.9983 - val_loss: 0.0597 - val_accuracy: 0.9813\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fFWVQv8Sa1sp",
        "outputId": "783ed8fd-8a4d-40f9-a52b-e8a6214fc180"
      },
      "source": [
        "# Evaluating the model\n",
        "\n",
        "score= model.evaluate(X_test, y_test, verbose=0)\n",
        "print('The error is: %.2f%%'%(100-score[1]*100))"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The error is: 1.87%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "cPg_t8Cqcreh",
        "outputId": "f012f242-8a16-40cf-9393-8d677a0ae856"
      },
      "source": [
        "# Ploting the graph for Training and Validation Loss\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "plt.plot(range(0,10), H.history[\"loss\"], \"r\", label = \"Training Loss\")\n",
        "plt.plot(range(0,10), H.history[\"val_loss\"], \"b\", label = \"Validation Loss\")\n",
        "plt.title(\"Loss in ANN\")\n",
        "plt.legend(loc = \"upper right\")\n",
        "plt.xlabel(\"Number of Epochs\")\n",
        "plt.ylabel(\"Training and Validation Loss\")\n",
        "plt.show()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1AS5xRFd6Ojz3HCxaBCedFO1ojDGmQtlM6uIMHOjWp7bRTMaYBGQJojiNGkGPHtZRbUwIlWU1ykRRln8vSxAlyciAhQthx45oR2JMzEhJSWH79u2WJOKEqrJ9+3ZSUlJKdV5MzIOIaenpcM89MHs2nHdetKMxJia0aNGCrKwswjpB1URUSkpKgRFSfliCKEmPHtCggeuHsARhDABVq1YlNTU12mGYCLNbTCVJTnad1bNm2SpzxpiEYgnCj4wM2LwZvMJYxhiTCEpMECJyvYjUFecZEVkiIoMqIriYMcj7cW00kzEmgfhpQYxX1d3AIKABcAnwQESjijUtWrjSGzYfwhiTQPwkCPEezwJeVNUVQfsSR0YGzJ8Pv/wS7UiMMaZC+EkQi0XkPVyCmCUidXA1khJLejocPOiWIjXGmATgJ0FcCtwK9FDVfUBVYFxEo4pFffpAzZrWD2GMSRh+EsTJwGpV3ekt7HMHsCuyYcWglBS3FKn1QxhjEoSfBPFvYJ+IdAZ+D3wL/CeiUcWqjAxYswbWrYt2JMYYE3F+EkSOuoIrQ4F/quq/gDqRDStGpae7R7vNZIxJAH4SxB4RuQ03vPUdEamC64dIPG3bQmqqJQhjTELwkyBGAQdw8yG24NaW/ktEo4pVIq4VMWeOG9FkjDGVWIkJwksKk4F6IjIYyFbVxOyDANcPsXcvLFgQ7UiMMSai/JTaOA9YCIwEzgM+F5ERkQ4sZg0Y4Ar42W0mY0wl5+cW0x9wcyDGqOpooCdwZ2TDimF160Lv3jbc1RhT6flJEFVU9aeg19t9nld5pafD0qWwZUu0IzHGmIjx80U/U0RmichYERkLvAO8G9mwYlxGhnt8773oxmGMMRHkp5P6JuAJ4ERve1JVb450YDGtc2do2tT6IYwxlZqvJUdV9U3gzcBrEfleVVtFLKpYV6WKu8307ruQl+deG2NMJVPWb7bEK/ddWHo6bNsGS5ZEOxJjjImIsiYIW5x50CA3cc5GMxljKqkibzGJyO+KeguoHZlw4kiTJtCtm+uHuOOOaEdjjDFhV1wLok4RW23g75EPLQ5kZMCnn8KuxKt+boyp/IpsQajqPeW9uIhk4JJJEvC0qj5Q6P3fAb8FcoCtuHpP33nvjcGtPQFwn6q+UN54wi49He6/39VmGjYs2tEYY0xYRWz4jYgkAf8CzgQ6AheISMdCh30BpKnqicDrwJ+9cxsCdwO9cDO37xaRBpGKtcxOOsnNrLZ+CGNMJRTJ8Zk9gbWquk5VDwJTcGtKHKaqc71lTAE+w1WKBUgH3lfVHar6M/A+kBHBWMumalU4/XTXD6HWb2+MqVwimSCaAxuDXmd5+4pyKfkztH2dKyKXi0imiGRu3bq1nOGWUUYGfP89fP11dD7fGGMipMSJciJSHRgOtA4+XlUnhSsIb63rNODU0pynqk8CTwKkpaVF50/44FXmjjsuKiEYY0wk+GlBvIW7NZQD/BK0lWQT0DLodQtvXwEiMhBXMXaIqh4ozbkx4eijoUMH64cwxlQ6fkpttFDVstz/XwS0FZFU3Jf7+cCFwQeISFdcnaeMQhVjZwF/DOqYHgTcVoYYKkZ6OjzxBOzfDzVqRDsaY4wJCz8tiAUi0qm0F1bVHOAa3Jf9KmCqqq4QkUkiMsQ77C+4eRWvichSEZnunbsDuBeXZBYBk7x9sSkjA7Kz4aOPoh2JMcaEjWgJo29EZCVwLLAetza1AOoNTY0ZaWlpmpmZGZ0P378fGjaEq66Cv/0tOjEYY0wZiMhiVU0L9Z6fW0xnhjmeyqdGDejXz/VDWIIwxlQSftaD+A6oD5zjbfUDs51NkIwMWLXKDXk1xphKoMQEISLXA5OBpt72kohcG+nA4k7wcFdjjKkE/HRSXwr0UtW7VPUu4CTgssiGFYeOOw5atrQEYYypNPwkCAFyg17nYgsG/ZqIa0XMng05OdGOxhhjys1PgngO+FxEJorIRFzNpGciGlW8yshwpb8//zzakRhjTLn56aT+GzAO2OFt41T1kUgHFpdOPx2SkmxWtTGmUigyQYhIXe+xIbABeMnbvvP2mcLq13clwK0fwhhTCRTXgnjZe1wMZAZtgdcmlPR0yMyEbduiHYkxxpRLkQlCVQd7j6mq2iZoS1XVNhUXYpzJyHBrQ7z/frQjMcaYcvEzD2KOn33G060bNGpk/RDGmLhXZKkNEUkBagKNvaqqgaGtdSl+4Z/ElpQEgwa5foi8PKgSyTWZjDEmcor79roC19/QwXsMbG8B/4x8aHEsPR1+/BGWL492JMYYU2bF9UH8XVVTgRuD+h5SVbWzqlqCKM6gQe7RRjMZY+KYn3kQ/xCRE0TkPBEZHdgqIri4ddRR0Lmz9UMYY+Kan07qu4F/eNsA4M/AkGJPMm400yefwJ490Y7EGGPKxE8P6gjgdGCLqo4DOgP1IhpVZZCeDocOwdy50Y7EGGPKxE+C2K+qeUCON7v6J6BlZMOqBHr3hlq1rB/CGBO3/Kwolyki9YGncKOY9gKfRjSqyqBaNTjtNOuHMMbELT+d1P+nqjtV9XHgDGCMd6vJlCQjA9atg7Vrox2JMcaUWnHF+roV3oCGQLL33JQksMqctSKMMXGouFtMf/UeU4A0YBluNvWJuGJ9J0c2tErgmGPg2GNdP8Q110Q7GmOMKZXiJsoNUNUBwA9AN1VNU9XuQFdgU0UFGPfS0+GDD+DAgWhHYowxpeJnFFN7Vf0y8EJVvwKOi1xIlUxGBuzb5+ZEGGNMHPGTIJaLyNMi0t/bngKsyJBf/fu7EU3WD2GMiTN+EsQ4YAVwvbet9PYZP2rXhj59bD6EMSbu+Bnmmq2qD6vqud72sKpmV0RwlUZGhqvsunlztCMxxhjfihvmOtV7/FJElhfeKi7ESiAw3PW996IbhzHGlEJxLYjrvcfBwDkhthKJSIaIrBaRtSJya4j3+4nIEhHJEZERhd7LFZGl3jbd108Tqzp1chVerR/CGBNHipwHoao/eI/fleXCIpIE/As3+zoLWCQi01V1ZdBh3wNjgRtDXGK/qnYpy2fHHBHXipg+HXJz3apzxhgT44q7xbRHRHaH2PaIyG4f1+4JrFXVdap6EJgCDA0+QFU3qOpyIK9cP0U8yMiAHTsgMzPakRhjjC/FTZSro6p1Q2x1VLWuj2s3BzYGvc6idGtZp4hIpoh8JiK/CXWAiFzuHZO5devWUlw6CgYOdC0JG81kjIkTfoa5AiAiTUWkVWCLZFCeo1U1DbgQeEREjil8gKo+6c3wTmvSpEkFhFQOjRpBz57WD2GMiRt+VpQbIiJrgPXAh8AG4F0f195EwXUjWlCKEh2qusl7XAfMw5X4iG/p6fD55/Dzz9GOxBhjSuSnBXEvcBLwjaqm4laX+8zHeYuAtiKSKiLVgPMBX6ORRKSBiFT3njcGeuMm6MW3jAzIy4PZs6MdiTHGlMhPgjikqtuBKiJSRVXn4qq7FktVc4BrgFnAKmCqqq4QkUkiMgRARHqISBYwEnhCRFZ4px+HW6hoGTAXeKDQ6Kf41KMH1K9vt5mMMXHBz4pyO0WkNvARMFlEfgJ+8XNxVZ0BzCi0766g54twt54Kn7cA6OTnM+JKcjKccYbrqFZ1ndbGGBOjihvmOlJEUnBDU/cBE4CZwLf4nChnQkhPh02bYMWKko81xpgoKu4W04W4iWyPAxmAquoLqvqod8vJlEWg7IYNdzXGxLji5kGcCxwLzAauBbJE5HERObWigquUWrSA44+3fghjTMwrtpNaVXd7rYYzgROAL4BHRWRjceeZEmRkwEcfwS++unKMMSYqfE2UE5EGwDBgFNAQeD2SQVW05cvd6NMKk54OBw/Chx9W4IcaY0zpFNdJXVtELhGRGbg5CGm4ORGtVHVCRQUYaV9/7UafXnGFq6NXIfr2hRo1rB/CGBPTihvmugE3aukxYJaqHqqQiCpY+/Zw001w//2wfz88/7wbjRpRKSluKVLrhzDGxLDivgpbqur+CoskSkTgvvugZk34wx8gOxteftktIx1RGRlw/fWwfj2kpkb4w4wxpvSKG8VU6ZNDsNtvh4cfhjfegGHDXKKIKBvuaoyJcb6ruSaCG26Axx+Hd96Bc86J8CCjdu2gdWtLEMaYmGUJopArroAXXoAPPnB3gXb7WRqpLAKrzM2ZA4cqZfeOMSbOFdkHISJvA1rU+6o6JCIRxYDRo90gowsvdOv8zJwJDRtG4IMyMuCJJ+DTT6Ffvwh8gDHGlF1xLYiHgL/i1oHYDzzlbXtx9ZgqtZEj4c03YdkyOO00+OmnCHzIaae5IVM2mskYE4OK66T+UFU/BHqr6ihVfdvbLgT6VlyI0XPOOfC//8E337hRqZs3h/kD6taFU06xfghjTEzy0wdRS0TaBF6ISCpQK3IhxZYzzoB334WNG91doO++C/MHZGTAkiXw449hvrAxxpSPnwQxAZgnIvNE5EPcAj43RDas2HLqqfD++7B9u0sSa9eG8eKB4a7vvx/GixpjTPmVmCBUdSbQFrgeuA5or6oJd0/kpJPcyKZffnFJYtWqMF24Sxdo2tT6IYwxMcfvMNfuwPFAZ2CUiIyOXEixq2tXV18vL8+1KpYtC8NFq1SBQYPgvfcquGKgMcYUr8QEISIv4kY09QF6eFuJa1JXVscf7yp1V68OAwbAwoVhuGhGBmzdCl98EYaLGWNMePgpS5cGdFTVIudEJJp27WD+fDdKdeBAmDED+vQpxwXPOMM9zpoF3buHJUZjjCkvP7eYvgKOjHQg8aZ1a5ckmjXLnxBdZk2busRg/RDGmBjiJ0E0BlaKyCwRmR7YIh1YPGje3PVJHHMMnH22q+FUZunpbkb1zp1hi88YY8rDT4KYCPwG+CNuZnVgM8ARR8DcuXDCCXDuua4abJmce65bsWjoUEsSxpiY4GeY64ehtooILl40auRuMfXoAaNGweTJZbhIWhq88kp+XaawT9s2xpjS8TOK6SQRWSQie0XkoIjkikikapzGrXr1XB9zv35wySXw9NNluMioUa7He/16V4Jj9eqwx2mMMX75ucX0T+ACYA1QA/gt8K9IBhWvatd2/RDp6XDZZfCPf5ThIgMHuo6N/fuhd+8wjaM1xpjS8zVRTlXXAkmqmquqzwEZkQ0rftWoAdOmuS6F666DBx8sw0W6dYMFC1yzZMAAG91kjIkKPwlin4hUA5aKyJ9FZILP8xJW9erw6qtwwQVw661w991Q6lkkxxzjkkT79q6s7IsvRiRWY4wpip8v+ku8464BfgFaAsP9XFxEMkRktYisFZFbQ7zfT0SWiEiOiIwo9N4YEVnjbWP8fF4sqVrVfaePGweTJsEtt5QhSRxxBMyb5+p6jB4NDz0UiVCNMSakEmdSq2qgwHU2cI/fC4tIEq6v4gwgC1gkItNVdWXQYd8DY4EbC53bELgbN4tbgcXeuT/7/fxYkJTkOqtr1oS//AX27YNHH3Xll3yrW9d1bIweDTfdBFu2wJ//XMqLGGNM6fkptVFWPYG1qroOQESmAEOBwwlCVTd47xWuUpcOvK+qO7z338f1e7wSwXgjokoV11ldo4ZrAOzfD08+6ZKHb9WruyGwRxwBf/2rWzvi2WddM8UYYyIkkgmiObAx6HUW0Ksc5zYvfJCIXA5cDtCqVauyRVkBRNwf/TVruttN+/fDCy+U8vu9ShX4+9/hqKPg9ttdcb/XX3dDp4wxJgLi+j6Fqj6pqmmqmtakSZNoh1MsEbjnHnjgAdcYGDUKDhwow0Vuuw2eecYtMHTaaS5RGGNMBJTYghCRt3H9AMF2AZnAE6qaXcSpm3Ad2gEtvH1+bAL6Fzp3ns9zY9ott7iWxHXX5ZfmqFGjlBcZPx4aN3ZZpndvt5ZE69aRCNcYk8D8tCDWAXuBp7xtN7AHaOe9LsoioK2IpHrDZM8H/Bb5mwUMEpEGItIAGOTtqxSuvdb1Q8yc6Yr87d1bhosMGQKzZ8O2bW7W9fLlYY/TGJPY/CSIU1T1QlV929suBnqo6tVAt6JOUtUc3NDYWcAqYKqqrhCRSSIyBEBEeohIFjASeEJEVnjn7gDuxSWZRcCkQId1ZXHZZfCf/7jFh9LTYdeuMlykd29XczwpCfr2debfLoEAABdiSURBVDOwjTEmTKSkdYBEZBWQrqrfe69bAbNU9TgR+UJVu1ZAnCVKS0vTzMzMaIdRam+8AeefD507u1pOjRqV4SIbN7oss24dvPwyDBsW9jiNMZWTiCxW1ZCrhPppQfwe+FhE5orIPGA+cKOI1AJeCF+YiWn4cFea46uvXKG/P/7RDU5atgx++cXnRVq2hI8/diU6RoyAxx+PaMzGmMRQYgsCQESqAx28l6uL6ZiOmnhtQQTMmQOXXgrffVdwf/Pm0LatW+Y0+LFNGzc9ooB9+1zH9f/+5+p73H23G/lkjDFFKK4F4XceRHegtXd8ZxFBVf8TpvgMcPrpsGGD67Beuxa++QbWrHHbN9/Am2+6/uiAKlXg6KMLJ4+atH3ovxzd6EqS77kHfvgBHnuslLPyjDHG8dMH8SJwDLAUyPV2q6peF+HYSiXeWxB+/PxzfsIITh5r1sDuoBU6qlZV2tTZRtsdn9HuWKXtdWfS7viqtG3rWiRWpcMYE1BcC8JvJ3VH9XMvKooSIUEURRV++ilE8liwjbU/1GQ/NQ8fW6MGHHts6NtWTZvaHSljEk15bzF9BRwJ/BDWqEzYiLgyTUccAX36BL/TmLxXXmXT6NtY0/I0vrn8IdZsrc+aNbBiBbz9Nhw6lH903bouUXTq5Pq6Bw2yck/GJDI/LYi5QBdgIXC4OISqDolsaKWTyC2IEs2e7aZtN2rkxtK2bw9ATo7rFC/c8li40N3OatwYzjsPLrzQzcWz1oUxlU95bzGdGmq/qsbUrCxLECVYsgTOPBNyc1358F5F1008eNDlkcmTYfp0V1ywdWuXKC68EI4/vuLCNsZEVrkSRLywBOHDt9+6+0ZbtrjJFmeeWeIpe/a4eRqTJ7v6gHl5blLfRRe5FfNatKiAuI0xEVOmiXIi8rH3uEdEdgdte0Rkd1HnmRgWvIzpkCGu1kcJ6tSBSy5xdaM2b3YLHqWkwM03Q6tW0L8/PPWUuyVljKlcrAWRiHbvdn0SH3zglrq78caSzylk7VpXtnzyZFi9GqpVg7POcregBg8uQ4VaY0xUlLfUBiKSJCLNRKRVYAtviKZC1a0LM2a4HuibboLf/97dOyqFY4+FO++EVasgMxOuvho+/9xd8sgj3Vrcs2e7Lg9jTHzy00l9LW596B+BwLeIquqJEY6tVKwFUQZ5eXD99fDPf7o//Z97zjUFyig3F+bNc62KN95wDZUjj3TFCC+6CLp3t5FQxsSa8o5iWgv0UtXtkQguXCxBlJEq/OlP8Ic/uA7s1193HQ/llJ3tBktNnuweDx50E/IuvNAli2OPDUPsxphyK+8tpo24FeRMZSTi1rh++ml3T+i009y07HJKSXGVat980w2aeuopV+bjnnvcZLxevdwS21u2hOFnMMZEhJ8WxDNAe+AdCk6U+1tkQysda0GEwfTprhrsEUe4jutLLoF69cL6EVlZMGWKa1ksXerqQg0c6FoVv/mN6x4xxlSc8rYgvgfeB6oBdYI2U9kMGeJGNjVp4tZFbdYMfvtbWLw4bB/RooXLPV984cp93Habm709ZozLS6NGuTx18GDYPtIYU0Y2zNWEtnixW3jo5ZfdOhNpaXDlla7HuVatsH6UKnz2mWtVvPqqK2veoIFbRbVaNVetPLAlJxd8XRHv1a7tcmbTpq6Fk4gd7dnZsHUr7NzpBh40bpyYv4fKqEyd1CLyiKreICJvA786yGoxJYhdu+Cll+Df/3Z/8tetC6NHu2QRgZobhw65GdsvvwzLl7uRUbm5rm5U4HnwFmp/Tk7YwzqsalWXLApvTZuG3t+gQex9kaq6L/pt29yX/tatoZ8H7yu8umHt2m7RqjZtIDU1/3mbNq4sS0pKVH40UwZlTRDdVXWx1WIygPtW+eQT16p47TV3D6hvX5cohg8PsbxddOXl+U8oJe3fs8f12we+LIO3wP49e0LHkZzs/tr2m1AaNiz9eh0HD8L27cV/wQc/37696CRas6aLIxBzcOyNG7suqS1b3PLnwdv+/QWv06xZwaQRnESOPNLWJIklVovJhNe2bfD88y5ZfPut++YYNw4uvzxhx69mZxf8Ei4poewqYlxgUpIruls4cTRunH+bp/AXf1HXApdwgq9R+HnhfTVrFn2togTWIymcNNavd49ZWe6YgJQU18oITiCBJJKaGpZR1mGVm+vusu7d++stN9f9jhs1cluDBvG3gGN550G0Bf4EdAQONxxVtU04gywvSxBRkJfnFtN+/HF46y33f8ugQa5Vcc457s9nE9LBgwW/6EtKKj//7PpjSvqCD37esGFs/BMcOODKygcnjeBtd6HKbk2aFH37qkWL4r+ADx50t8NCfZmXddu3z//PKgL167vffyBpBLZQ+wJbNG/JlTdBfIybSf0wcA4wDqiiqneFO9DysAQRZZs3wzPPwJNPuj8ZAyOgfvtbaNky2tHFvdxcd1sm1vozykvVJb9QLY9161xiCS7Xkpzs1mJv1swlnsJf5qUZ/ZaS4vpSyrOJwI4d7rZdYNu2reDrwFa4HydYrVr+kknw/jp1wvPfQ3kTxGJV7S4iX6pqp+B95Q8tfCxBxIicHFfn6fHHXQlYEdeauPJK17qwm8+mFHJyYOPGX7c8fvjB3Q4r6xd7rVoV37rKzg6dOEIllMC+nTsL3p4LVrVq/u2tXr3g2WfLFld5lxw9ICJVgDUicg2wCahdtlBMpZec7OZTDBni/q9+6inXsnjrLXe/4PLLYfx410trTAmSk/P7Jk47LdrRlE9Kiqsm0Ly5/3Nyc10Lq6TWSZjnsx7mpwXRA1gF1AfuBeoCf1HVzyITUtlYCyKGHTwI//2va1XMm+f+9Bk+3LUq+vWrfPdNjIkjZZ5JLSJJwChV3auqWao6TlWHx1pyMDGuWjU3RXruXFcf/P/+z91+6t8fOnZ0RZlsxSFjYk5xK8olq2ou0KcC4zGVXYcO8MgjsGmTKy9erx7ccIPrdRw3DhYuLPqmqzGmQhXXgljoPX4hItNF5BIRGRbY/FxcRDJEZLWIrBWRW0O8X11EXvXe/1xEWnv7W4vIfhFZ6m2Pl/YHMzGuZk0YO9bV2FiyxM3Ofu0119vWvbsbDbV3b7SjNCah+RlSkgJsB04DBuOGug4u6STv9tS/gDNxcyguEJGOhQ67FPhZVY/FDaN9MOi9b1W1i7dd6SNOE6+6doUnnnBDZR97zA1dueKK/Op9b77566m6xpiIKy5BNBWR3wFfAV96jyu8x698XLsnsFZV16nqQWAKMLTQMUOBF7znrwOni1iPZcKqWxeuugqWLXNlPUaPdtVlhw93o54uvhjeftsNgDfGRFxxCSIJN5y1Nq68d+1CW0ma4xYbCsjy9oU8RlVzcAsTNfLeSxWRL0TkQxHpG+oDRORyEckUkcytW7f6CMnEBRE45RRXIPCHH+C991xLYsYMN3z2iCNcf8XMma66nzEmIoqbB/GDqk6qsEgKfTbQSlW3i0h3YJqIHK+qBSblq+qTwJPghrlGIU4TacnJcMYZbnvsMbfq3auvuttOzz/vZgkNG+YSSP/+8VcIx5gYVlwLory3ejYBwTUWWnj7Qh4jIslAPWC7qh4IrIGtqouBb4F25YzHxLtq1eCss+CFF+DHH2HaNDc7++WX3bJ0zZrB1VfD/PmuTpQxplyKSxCnl/Pai4C2IpIqItWA84HphY6ZDozxno8APlBVFZEmXic3ItIGaAusK2c8pjJJSYGhQ11y+OknNwKqXz9Xb6BfP2jVCiZMcKOkbNisMWVSZIJQ1R3lubDXp3ANMAs3E3uqqq4QkUkiElhs6BmgkYisBX4HBIbC9gOWi8hSXOf1leWNx1RiNWvCiBEuSWzd6pJGWpq7JXXyya5Ow803u1XyLFkY45utB2Eqr127XA2oKVPcMnU5OW69ivPOc30WnTpZmQ+T8MpcasOYuFavnhsqO2OGWwbtqafcSjUPPACdO7syHxMnuvIfxphfsQRhEkOjRm5tivffd0NnH3vMDZedNMklihNPhPvvh7Vrox2pMTHDEoRJPE2bugl58+a5xY3+/ne3+sodd0Dbtq7Ux5//DBs2RDtSY6LK+iCMCdi4EaZOdfMsFi1y+3r1gsGDXWd3jx5utrcxlUi5VpSLF5YgTFitW+eSxdSp8MUXbp8IHH88nHRS/nbccbZKnolrliCMKY+dO10Z8s8+y98C61fUrQs9e+YnjF693MLBxsQJSxDGhJMqrFlTMGEsX+7WhwQ3lDaQME4+2Q2nrVo1ujEbUwRLEMZE2i+/uIl4n30Gn37qHrdsce/VqOEm7gXfmmrWLLrxGuOxBGFMRVOF778v2MpYssStzw3QsmXBhNGtmysfYkwFKy5BFFfN1RhTViJw9NFuGzXK7TtwAJYuLZg0XnvNvVe1KnTpUjBppKbaTG8TVdaCMCaatmyBzz/PTxgLF8K+fe69pk0Ldn6fcAI0aWJJw4SV3WIyJl7k5MCKFQVbGV9/nf9+3bquEzzUduSRljxMqVmCMCae/fyza1msXu1KgQS29etdQgmoWbNgwmjbNv95s2Y2X8OEZH0QxsSzBg0gPd1twXJyXEf4mjUFE8fKlfC//+V3iIPrAD/mmNAtj5YtbSU+E5IlCGPiVXIytGnjtsLJIzfX1ZkKThyBbdYsyM7OP7ZaNdchHtziCGxHH+0+xyQk+5c3pjJKSsofRXV6ocUh8/JcRdvgpBFohcyd6+Z0BCQnuxLpwUmjVSvX6mjZ0nWa262rSssShDGJpkoVaN7cbaeeWvA9Vbfed6iWx4IFsHt3weOrVYMWLfITRqitQQPrPI9TliCMMflE3GioI4+EPn0KvqcK27e7qreB7fvv85/Pnw+bNhXsOAfXeV5cAmnZ0pVbNzHHEoQxxh8RV4iwcWPo2jX0Mbm5rgUSnESCt1mz3O2twqMn69UrPoG0aOFKlpgKZQnCGBM+SUluSG2zZm5yXyiHDsHmzUUnkcxM2Lr11+c1bpyfMJo3h4YNXWKpXz9/C35drx5Urx7Zn7eSswRhjKlYVavmd6AXJTvbjcIKlUDWr4dPPnFl2AMVdIuSkvLrxBEqkRT1Xs2aCd1/YgnCGBN7UlLyR00VRdWNuNq1yyWLwFbS6w0b8p8fOFB8HElJxSeWBg1cy6ZJk/zbb40buzXQK0GJd0sQxpj4JAK1a7utefOyXSM7u2ACKep58OvVq/Nf791b9LXr1y+YNAonkcL76tWLuSHDliCMMYkrJcVtRxxRtvMPHnQju7Zty9+2bv3166wsV8l369aiWy1JSa7lUTiRFJdYatYs+8/ugyUIY4wpq2rV4Kij3OaHqqvWGyqJFH69cqV7vn27m9wYSo0aLlH07g2vvBK+n8tjCcIYYyqKCNSq5bbWrf2dk5fnbmkVl1TKeoutBJYgjDEmllWp4ob0NmwI7dtX7EdX6KcZY4yJGxFNECKSISKrRWStiNwa4v3qIvKq9/7nItI66L3bvP2rRSS98LnGGGMiK2IJQkSSgH8BZwIdgQtEpGOhwy4FflbVY4GHgQe9czsC5wPHAxnAY971jDHGVJBItiB6AmtVdZ2qHgSmAEMLHTMUeMF7/jpwuoiIt3+Kqh5Q1fXAWu96xhhjKkgkE0RzYGPQ6yxvX8hjVDUH2AU08nkuInK5iGSKSObWULVbjDHGlFlcd1Kr6pOqmqaqaU2aNIl2OMYYU6lEMkFsAloGvW7h7Qt5jIgkA/WA7T7PNcYYE0GRTBCLgLYikioi1XCdztMLHTMdGOM9HwF8oKrq7T/fG+WUCrQFFkYwVmOMMYVEbKKcquaIyDXALCAJeFZVV4jIJCBTVacDzwAvishaYAcuieAdNxVYCeQAV6tqsXV9Fy9evE1EvitHyI2BbeU4vzKx30VB9vsoyH4f+SrD76LIuuuihVd2SlAikqmqadGOIxbY76Ig+30UZL+PfJX9dxHXndTGGGMixxKEMcaYkCxB5Hsy2gHEEPtdFGS/j4Ls95GvUv8urA/CGGNMSNaCMMYYE5IlCGOMMSElfIIoqSR5IhGRliIyV0RWisgKEbk+2jFFm4gkicgXIvK/aMcSbSJSX0ReF5GvRWSViJwc7ZiiSUQmeP+ffCUir4hISrRjCreEThA+S5Inkhzg96raETgJuDrBfx8A1wOroh1EjPg7MFNVOwCdSeDfi4g0B64D0lT1BNxk4POjG1X4JXSCwF9J8oShqj+o6hLv+R7cF0BkFruNAyLSAjgbeDrasUSbiNQD+uGqH6CqB1V1Z3SjirpkoIZXR64msDnK8YRdoicIX2XFE5G3ul9X4PPoRhJVjwA3A3nRDiQGpAJbgee8W25Pi0itaAcVLaq6CXgI+B74Adilqu9FN6rwS/QEYUIQkdrAG8ANqro72vFEg4gMBn5S1cXRjiVGJAPdgH+ralfgFyBh++xEpAHubkMq0AyoJSIXRzeq8Ev0BGFlxQsRkaq45DBZVd+MdjxR1BsYIiIbcLceTxORl6IbUlRlAVmqGmhRvo5LGIlqILBeVbeq6iHgTeCUKMcUdomeIPyUJE8Y3nKvzwCrVPVv0Y4nmlT1NlVtoaqtcf9dfKCqle4vRL9UdQuwUUTae7tOx1VbTlTfAyeJSE3v/5vTqYSd9hEr9x0PiipJHuWwoqk3cAnwpYgs9fbdrqozohiTiR3XApO9P6bWAeOiHE/UqOrnIvI6sAQ3+u8LKmHZDSu1YYwxJqREv8VkjDGmCJYgjDHGhGQJwhhjTEiWIIwxxoRkCcIYY0xIliBM3BERFZG/Br2+UUQmhunaz4vIiHBcq4TPGelVRJ1baH9rEdkvIkuDttFh/Nz+VpnW+JXQ8yBM3DoADBORP6nqtmgHEyAiyaqa4/PwS4HLVPXjEO99q6pdwhiaMWViLQgTj3Jwk5ImFH6jcAtARPZ6j/1F5EMReUtE1onIAyJykYgsFJEvReSYoMsMFJFMEfnGq8kUWBfiLyKySESWi8gVQdedLyLTCTGzWEQu8K7/lYg86O27C+gDPCMif/H7Q4vIXhF52FuDYI6INPH2dxGRz7y4/uvVCUJEjhWR2SKyTESWBP2MtYPWdZjszQTG+52s9K7zkN+4TCWmqrbZFlcbsBeoC2wA6gE3AhO9954HRgQf6z32B3YCRwHVcTW37vHeux54JOj8mbg/ntriahClAJcDd3jHVAcycYXa+uMK16WGiLMZriRDE1xr/QPgN95783BrCRQ+pzWwH1gatPX13lPgIu/5XcA/vefLgVO955OCfpbPgXO95ym4ktT9gV24umNVgE9xyaoRsJr8ybP1o/3vbFv0N2tBmLikrsrsf3CLtvi1SN2aFweAb4FAeeYvcV/MAVNVNU9V1+BKSnQABgGjvRIkn+O+UNt6xy9U1fUhPq8HME9dQbccYDJuTYWSfKuqXYK2+d7+POBV7/lLQB9vnYb6qvqht/8FoJ+I1AGaq+p/AVQ1W1X3BcWbpap5uATUGpc0snGtmmFA4FiTwCxBmHj2CO5efvC6BDl4/12LSBWgWtB7B4Ke5wW9zqNgf1zh+jMKCHBt0Jd2qubX//+lXD9F2ZW1Tk7w7yEXCPSd9MRVaR2Ma0WZBGcJwsQtVd0BTMUliYANQHfv+RCgahkuPVJEqnj37Nvgbr3MAq7yyqEjIu18LJizEDhVRBp7y9teAHxYwjnFqQIE+lcuBD5W1V3AzyLS19t/CfChuhUBs0TkN1681UWkZlEX9tYAqaeuMOME3JKiJsHZKCYT7/4KXBP0+ingLRFZhvsruCx/3X+P+3KvC1ypqtki8jTuVswSr1N3K/Cb4i6iqj+IyK3AXFwL5B1VfcvH5x8TVE0XXJXhR3E/S08RuQP4CRjlvT8GeNxLAMFVVi8BnhCRScAhYGQxn1kH93tL8WL9nY84TSVn1VyNiRMisldVa0c7DpM47BaTMcaYkKwFYYwxJiRrQRhjjAnJEoQxxpiQLEEYY4wJyRKEMcaYkCxBGGOMCen/ATMe71uVCHfqAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "QwN4ySMZc5rr",
        "outputId": "9b66ce72-98d0-444f-8efc-f6fb27f7ddc4"
      },
      "source": [
        "# Ploting the graph for Training and Validation Accuracy\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "plt.plot(range(0,10), H.history[\"accuracy\"], \"r\", label = \"Training Accuracy\")\n",
        "plt.plot(range(0,10), H.history[\"val_accuracy\"], \"b\", label = \"Validation Accuracy\")\n",
        "plt.title(\"Accuracy in ANN\")\n",
        "plt.legend(loc = \"lower right\")\n",
        "plt.xlabel(\"Number of Epochs\")\n",
        "plt.ylabel(\"Training and Validation Accuracy\")\n",
        "plt.show()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3L02gpD_x3H8",
        "outputId": "a8c8c2e2-9a4a-40cb-b628-6fdf0efefa38"
      },
      "source": [
        "# Predicting the Test Set\n",
        "\n",
        "pred_labels = model.predict_classes(X_test)\n",
        "print(pred_labels)\n",
        "test_label = np.argmax(y_test, axis=1)\n",
        "print(test_label)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[7 2 1 ... 4 5 6]\n",
            "[7 2 1 ... 4 5 6]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rd0z4a21yKEY",
        "outputId": "337c3af2-67b3-4fb0-e70e-26268b98f685"
      },
      "source": [
        "# Confusion matrix\n",
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "matrix = confusion_matrix(test_label, pred_labels)\n",
        "print('Confusion matrix : \\n',matrix)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix : \n",
            " [[ 972    1    0    1    1    0    1    1    3    0]\n",
            " [   0 1129    1    2    0    1    2    0    0    0]\n",
            " [   5    3 1004    2    2    0    2    5    8    1]\n",
            " [   0    0    2  997    0    1    0    2    3    5]\n",
            " [   1    2    2    1  967    0    3    1    1    4]\n",
            " [   2    0    0   11    1  866    1    1    6    4]\n",
            " [   5    2    3    1    3    3  941    0    0    0]\n",
            " [   1    7    7    2    1    0    0 1000    2    8]\n",
            " [   2    1    3    3    0    2    1    3  956    3]\n",
            " [   3    4    0    4    7    2    0    5    3  981]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yb6dzXN8yQco",
        "outputId": "4a6188dc-a1aa-442e-ed53-3b90e08910cd"
      },
      "source": [
        "# Classification report for precision, recall f1-score and accuracy\n",
        "\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "report = classification_report(test_label,pred_labels)\n",
        "print('Classification report : \\n',report)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Classification report : \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.99      0.99       980\n",
            "           1       0.98      0.99      0.99      1135\n",
            "           2       0.98      0.97      0.98      1032\n",
            "           3       0.97      0.99      0.98      1010\n",
            "           4       0.98      0.98      0.98       982\n",
            "           5       0.99      0.97      0.98       892\n",
            "           6       0.99      0.98      0.99       958\n",
            "           7       0.98      0.97      0.98      1028\n",
            "           8       0.97      0.98      0.98       974\n",
            "           9       0.98      0.97      0.97      1009\n",
            "\n",
            "    accuracy                           0.98     10000\n",
            "   macro avg       0.98      0.98      0.98     10000\n",
            "weighted avg       0.98      0.98      0.98     10000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}