Project2_Object_Recognition.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Project2_Object_Recognition.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMf3lDrt+rJjAL8AI494cp/",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/snehasaran09/Object-Recognition-/blob/main/Project2_Object_Recognition.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importing the Required Libraries "
      ],
      "metadata": {
        "id": "mcIbXfJ2wvmv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf \n",
        "from keras.utils import np_utils\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np \n",
        "import keras \n",
        "from keras.models import Sequential \n",
        "from keras.layers import Dense , Conv2D , Dropout , MaxPool2D, Flatten"
      ],
      "metadata": {
        "id": "3wRhqeNqw2ky"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importing and Splitting the Dataset"
      ],
      "metadata": {
        "id": "GFnzniVPxmeI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(x_train , y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()"
      ],
      "metadata": {
        "id": "AlcBJWeFxp6l"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Training Images: {}'.format(x_train.shape))\n",
        "print('Testing Images: {}'.format(x_test.shape))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k_BqJZFwyTL4",
        "outputId": "33246d85-1c5a-4a54-f4f4-525db138a1fb"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Images: (50000, 32, 32, 3)\n",
            "Testing Images: (10000, 32, 32, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_train[0].shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pHVcMou-yk7y",
        "outputId": "a7d442e8-5b33-46bd-a3cb-10515e3ad377"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(32, 32, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XSBHZVayyxdk",
        "outputId": "9aa805f6-0cea-4149-d6f4-b79e25764d42"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[6]\n",
            " [9]\n",
            " [9]\n",
            " ...\n",
            " [9]\n",
            " [1]\n",
            " [1]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(132,139):\n",
        "  plt.subplots(figsize=(2,2))\n",
        "  img = x_train[i]\n",
        "  plt.imshow(img)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "tzej_10xzFM8",
        "outputId": "a121cf1b-2aff-42c7-e296-027760442f59"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 144x144 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 144x144 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 144x144 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 144x144 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 144x144 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 144x144 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 144x144 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAI4AAACOCAYAAADn/TAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAWzklEQVR4nO1da2ycZXZ+3rlfPOMZ24kvcS5ObHIhKaEQCAVVLZtwWVqxrapqabfaSiv1Tyu1an90tb96leiftuqfSqhFRdtqWSQq7XZLF1iaQGERJCQhQEISk+DEjh3fxuMZz33m7Q+Pv3POy9gevpCJHb+PhDie8873fTM58577eZXWGhYWXxae2/0AFusTVnAsXMEKjoUrWMGxcAUrOBauYAXHwhVuSnCUUk8opS4opYaVUt/9qh7KYu1DuY3jKKW8AC4COApgFMAJAM9orc99dY9nsVbhu4n3PgBgWGt9GQCUUi8CeBrAsoKTSCR0X08PAEDXDKYislwqCla+kGt4vVAwJP6ORKMO7fHIzbRWoxuWyyWH9np9y66rViqSV602fJ9SSqxTK+3j7IeqjGfU7EuR15TXr9WqjCV5fp/fofl3YCwTf1fY5wKAWpWe49yF4Wmt9SYYuBnB2QLgGvt7FMCDK72hr6cH//7cvwAAyiUpOR4v0ddGLwveufNnHZoL3O7du8W6++67z6HD4YjgFQp5hx4bG3Po9kRCrMvnSEjTqZTgLcxnHDoZb3fogN8v1vkC9GGUR/6LVSoktKFwUPBK7AfjZ9fUhiQuLCzQvXxewevu7nboSDTC1sl/av7IqVRa8ObT9DkP/vKvj6ABbrlxrJT6A6XUSaXUydTc3K2+nUWLcDM7zhiArezv/vprAlrr5wA8BwD9vX36tddeBQCEw1GxLhgMOPSHZ08KXnaBBI7vOOfPnxfrMhn6pTz22GOCl8/TjhMKkYozdnDxS9++fbvgLWSyDl0u0s7h88rf33xm1qGrhhrgO6HXI3cqL9udYm20o03NzIp1/JLxeJvgVSplhy4U6HlN1c1h7kbtifiya53rrbpieZwAMKSUGlBKBQB8E8CPb+J6FusIrnccrXVFKfVHAF4F4AXwvNb6k6/sySzWNG5GVUFr/QqAV76iZ7FYR7gpwfmy8Pq8SCQXvZif/Nd/C97g0C6H7uzqELxNm8nzyWXJ6ykUCmLdhU8pErDnriHBi8ViDj186YJDJzu6xLptW8lsyzLvBQByzOOqMpe4mi+LdYUi2RY+n7Rjxm9MOHSxLO2fDvYs6XmyybzegFgXjfB/NmmlRdvIhiqW6Hnn09IxqZQpLGDaPyuGE5bes/oSC4svwgqOhSu0VFXFYzEc+dqjAICBHQOC99rrrzr02Nio4J06dcqhB7b1O/TmTTKgmZqZdui33jwmeE8++aRDt8fJ3QyHZPS5UCI3u1SWKigaI9e3i907GJTqaHqKnj81K1VEdw+pws6OzYIXi5FKnp2h4GM0KoOZsXiYnpe53AAQjtKz3Jig5/D7ZaAwmUjS805PC161LCPmjWB3HAtXsIJj4QpWcCxcoaU2joZGpbqoPxOJdsE7cGC/Q585kxe8+++7x6GjQdLv6TmZnMuk5x36w9NnBO/hww859MD2bQ7tDxrJ0CIlGjv6+gQvFieXPshso6mpSbFuaoaea1OXtGMSCQo1BANhwfvsCiV3uf2WSMoUQLlcYHRJ8C5/fNGhL12ilExPb49YV+kgN75iVAG0tcl0UCPYHcfCFazgWLhCS1VVtVrF3Nxiptej5K337qXamtNn3hM8rWkrnZmecuh8Tqq0EMuw5xakmypqXVg2OJOZF+sUKwwqFmVBmcrR7yyTp6jstFG3EwqRSotEZL0PjwIXijLyXWOfc3aOXORCXqrkUonelzYiwlxtelj2nUeKAVkFAEie1mbNwBdhdxwLV7CCY+EKLVVVSimnYCtoeDM8+trV1Sl4Vy5TUjLKPBGzjpbX4paMuuWf//wdh/YH6F59W3eIdV6WlNRG2Sd/X42xAkb0WWtSR9WKVANFVgA2PS29Ma+PLqoUfRaznWB+PoXlsG0bFZ8lWHTYrOOqVEnNm16U3yc/TyPYHcfCFazgWLiCFRwLV2htIZfX60SM83lpg9RqpMn7+rYJ3nz6uEMH46R/B7YNinW8ID0SzAjeuY/ITpqZJPf26NefFOsOHaYOn2zWcOmzZJ/w5h5v1TC2mEGhqzIqm2PXjIRlxprbOBHWOhMMyH+mcHj5Qi6PYjYa6+EKhWQrDg8WF4vyGfMlGSZoBLvjWLiCFRwLV2ipqqrVak7d7vj4DcGLRina2h6X7vjBe6hDMzc349BdnXLd7Cy5qalZ6bJq1tp7dYSaE3/44g/EuukZikwffvAhwWtLUAiBF3nFAtJ9rfnpXnmj0KpSITXQnpA9UV4fqZZgUDFaqrSqpt97fkGqlUKBVDTvXZvPyHWTN+j7DwSkGgv4bZLT4hbBCo6FK1jBsXCFlto4ALmIZaMQnE91MLPSvFh7aBcVJE1NSjtpoUh2jQpIF7PCerC8IXaviuydeuV/fuTQ10avCN5jjz3u0HwqRMF43kKeue01+RyJJCsGC0rbIp8n+6RcpmsuGP1d82xqRjptVgHQ99q/hQrjzZEqHmaXGYND4PeuLhar7jhKqeeVUpNKqY/Zax1KqdeVUpfq/0+udA2LOw/NqKp/A/CE8dp3AbyhtR4C8Eb9b4sNhFX3JK31W0qpHcbLTwP4lTr9AoDjAP58tWsppZwCInMoUqVC++XFykXB2zVIEeLx8UsOPTohp6pw9ecJSBe2kC2ydaQ+Oju7xTo+qOj8p3KMyth1ut/evXsd+oEH5DyprVsoQ83HjgDA2NhVh+7okHXX4QiprhJ7xqqh7sos4x4yBkglkmyMio+uZ07dUl7iec3UuUe2HDeCW+O4W2s9XqcnAHSvtNjizsNNe1V60dpddgKlmMiVshO57hS49apuKKV6tdbjSqleAJPLLeQTuQ7cvU8vTX8yBy7yyRNlo12jkCdescyKnbxym56dokkQ5gSGSIyizAts4sXoNTnt6q4hUoudPTIyzef3vfP2+w792fBVse6pp8gkfOiwVGM+lsjM5WQilqtar5eSlW0xqdKiUVpXKUuXqMwmYHB1x00BAAiFKGp9Y3JK8Cq11fcTtzvOjwF8u05/G8CPVlhrcQeiGXf8BwDeBbBbKTWqlPoOgGcBHFVKXQJwpP63xQZCM17VM8uwvvYVP4vFOkJrW4C1dvS43y83OzH50jC1L10apnUikyttnEqF3Ei/MXsYoPdFI5T93bmjX6zirmlqTkZlPR7mBrN+qaIRen3z+HGHPvH+u4J35MgRh+412nJLooCKbCFzlrhmPWnlqmwB9jAXHKw4bj4re7NCYfqugkZ2PxySrcmNYHNVFq5gBcfCFVqb5NTaOSvBjKjyodXtxiSLiQlKZraxwdHmYOeOBLnP0TZZJOXzUkTY76ftPGsUQo2MfO7QiURM8C5/TlHr/v5eh+7tk4MqfT5yic3er2PH/tehk0mZ4tu//4BDD7KwgDbG7mey1LasjEmPTDshworjYLRc8zMfYlFZuJVKLd+3tQS741i4ghUcC1ewgmPhCi3vHV9yk32+5TOwvEgKkIVcFz51yoLgMQwIPsDaPMAtyvR4jfESHTKtEAwRb2r2muAN7KTpWt09ZJ+kMxNi3fhV+ntgh+wR4881NSWnfZ49S59tD8u+H3lS9n7xAjDzvK1IhOyaQoFcdbMgvcjGwPDxMADQ2S5tu0awO46FK1jBsXCFFg+PpPOb5ubkNu3303a5ebMcuHjoEPVVTY5/7tBzaek25hao5yocltHQIDuGkW/vVchjGzUoo9zTI4c2xuOkMtPzrN7ZiHRv20Guerki65H5mJOaERLmw7TfP/GBQ//szbfEum/97rccev/+XxC82Rm6RiRCz18xxr7Mz1BBg3nKXjB46wq5LDY4rOBYuELL22OWoLWsgc1kKAlXME79PXBgn0Nf+pTmF4+wSC4AdHaSOgobB6X6mRdXrpA3pj1yXQfzKMJB+bvigxn5kYkzhsrMsoGU8TbpocRi5I3pmvz6OzeRit7CJoV9oc03S39rI8HKJ5aV2fQOU/3wY5MWMrL9ZnZEFrc1gt1xLFzBCo6FK1jBsXCFlts4S0XqZqEVj6i2t8vseDBA+vk3vvGbDv3yy3JESb5AdpLHmBgaYMVK+QK53EO7pTvrZ9M+vUpm8Hews7JKVbItRifGxbp0moq/Q0FZFNWVpEh1X6+MKodCFN3mrb1zKTnE+8Y4Rabf+Nnrgse/uz17qHctEt5krKNCtKRXuuMdSRlNbwS741i4ghUcC1dobZIT1O9kqqpIhKKyZoKSD4Xc2k/b+6899bRYd/IDqu9NpWYELxSi67cn+NYs3dm2GK3zaKmqIlFSd7PsiKOebhnpTrJrzExL13ZhnlRQ205ZM93VSdfZ2keTJnIFWVfMC60uXZIhiYsXaUjmJ59Q0nRgQB5lOThIxWc9PbL2uY/deznYHcfCFazgWLiCFRwLV2itO66UY+N4DRewZqaKGfjaKjtLaffefWLdlm2UlT59+pTg5VjYfuIGucvXx+WolFhkO6OlHTY/T5lnXogWNYq9yzm6V0d7l+D19rLjGmvSliuwQrR4G2W222IyS59Mkss9OLhT8Obm6BnTaQpPzMzItMg7b8t+L47N3asPH2mmBXirUuqYUuqcUuoTpdQf11+3U7k2MJpRVRUAf6a13gfgMIA/VErtg53KtaHRTO/4OIDxOp1RSp0HsAUupnLxFuBGvEY0AARYEZaPFWHl8rJFl2/pRx9/XPCmp8g9v3yFaonPn/9ErONZb55FB4AQa431sYhwwmih3T90Nz1TVGbHedghnzfadz302eIsAlw0etAW2LGOlbIcCdPfv8Wht2whtRiNyj4zXtd18aKcgHblshya2Qhfyjiuj3S7F8B7sFO5NjSaFhylVBuAlwH8idZaJE9WmsolJ3Kt3iFosT7QlOAopfxYFJr/0Fr/Z/3lG/VpXFhpKpfW+jmt9f1a6/vNlleL9YtVbRy1mM7+VwDntdZ/z1hLU7meRZNTubSuoVRa1OvDw8OCN8IO5ti+fbvgDQ1ReJynKvzGZFHFxqSVq9IuaO+kbPA9CaKH7rpLrDv3Ibnxl4fPCd7WrZQdP/zwIw5dMs7d1KVlRyLi+vXrDr2QlZV3vKCe/6QDYZlh5zZgLi+rJfnxVRMTlEVvM2ycdhYm2LVLpiP27dvj0H/613+FRmgmjvMwgN8D8JFSaqlu83tYFJiX6hO6RgD8dhPXsrhD0IxX9TbMY9gIdirXBsVtm8g1MyOz11wFdRrnUC2nnipVWcSdYW2tgYCM+ubZ5FLu3eqKLOLes3u/Q0+MyahyqUjR7SwfuC3r7kXCPWBUAXRuIuezu3eF6Dn7qZrDrdvbKewQjUo1xvvT/H66/pkzZ8W6wV28jdh4fN04ZMJhc1UWrmAFx8IVWnsKsMeLeHxxmz169Ijg8WkKk5PSs0+lqBgq2UGRWHPghcfD9lxlnL7LoszJdiqY8moZ2b1+9XOHThvDI+NxCid4FN28puW9uJopGmqGD6csGIPAuWrhA779xrDvXI68selp2UrN1bysP5beYy5HqtbnTwheNisHdzeC3XEsXMEKjoUrWMGxcIWWT+QK1Qdjm+M/5nKU/ipXjV7mNOvZrtG6zZul2x6OUOS1bIz1iLGobK1IrvlsSkZe337rHYfOzEtePEa2gIcVlEX8Rl82mxRWNWwc7nLnczKcoNnk1VCQ/ml0TV4jwIZg93b3CZ6XjSwp5uk7iEZk5LjKot2pGVlQbx4Y0gh2x7FwBSs4Fq7Q4r4qDU/ddfV4pCsaCbNHMfzsZJVqeuMRFik11IAu0/abnpZtsx7mI/MTdk+/L49P3D1EbbN5Y9wKd8evj1KycjYlt3r2GFjIyWtwN3tqampZHo84lwy1y4dmmtPLcqwHjfej8TZqAOCHzl25MiJ4gYA9y8HiFsEKjoUrWMGxcIUWTx2lQ0D4GZwAkCuSPs4aRejJDnKDA6ygG1VZMJXLUvG33yfdz9kpKlt99533HPrqiBxREnmAisk/NgrZT5w86dCFEj2vaeMolvrQRkUtH25t9mNt3kyZc546iCU6xDr+PrOSoIfZRllWKGYerBJmvfQHZ+Uhu/z5/+n730cj2B3HwhWs4Fi4QktVVblcwfX6NKlKTfYUxVlba2+7HLvByw+nJiiKnJ6VxwV6QC7nZxflkc6pKXLPX/vpGw596P77xLpzH1Od8ZUR2V8UbCM3tS1JxVSP/OqjYl2SqYV4TGbfuariNAB0dJBKyrHCs6zRf9XZ2dXwPYCMTPMostmDUmPtx739OyTTPGSrAeyOY+EKVnAsXKGlqqqmayjUo7ZtbTI6GWKFXMrYVnmkNBknDytrJCh/+gqpoA/ekzW2O7ZSi83OHdT+MTgwKNaNXKco6r79+wVv30E6+vDue4nu7ZOqNco+y3xaqtMxVsccDMnW4QCL7vLjkEIx2Y8WYacYe7zyt5+eo/uVWXG12YqTYwnQLsMzy2RsIZfFLYIVHAtXsIJj4QottXF8Pj+66tncYEDeOs8KsM0DPKo1yqSPj1Gk9/ix42LdFCty7zD61IOsz2pwJ9k7kzM3xLot/VQYdeiRBwVviLXGai8ZYqWSjIIXcxRVrhhjXQLsHC1uqwDyOGzN7DptGH011rjFj4EGgE2baRB2vkDPEW2T9yoV6Ts1R89098iMeyM0M5ErpJR6Xyn1YX0i11/WXx9QSr2nlBpWSv1QKbX66VgWdwyaUVVFAI9qre8BcBDAE0qpwwD+DsA/aK0HAaQAfOfWPabFWkMzveMawFLW0V//TwN4FMDv1F9/AcBfAPjnFW/m86Kj7vq1RaUrmmMuYKUsC5d42LOPDV/8xXvvFauGBkjdqZrcwstFKvpKp+le+w2X+9DhQw4dT8qhjaUaPRefGBHwy69Rsf6okuEudzFVVTH6qhRTTz6mgsIhuZn72FSOWlVGlfOsX4qrIG1GjlkRXG5BFr1l5mV7diM0Ox/HW59UMQngdQCfAZjT2ulEG8XieDeLDYKmBEdrXdVaHwTQD+ABAHtWeYsDPpFrxkjfW6xffCl3XGs9B+AYgIcAJJRSS3t0P4CxZd7jTOTq7Eg0WmKxDtHMRK5NAMpa6zmlVBjAUSwaxscA/BaAF9HkRC6Px+u4hR4jXethszYyRqE5L9ZWmtbt2C4Pq6j2kZs6OS4Lwceujjr00G6arHXvL8nseDBC9kTJGKMyz84NDbBwQsQji8byJW5byM8ZY9nypelkS1hgIQme5Y55ZYa9xnrSTDtpIUtFcF5WkOX1GXaYpusbp0eL0MVyaCaO0wvgBaWUF4s71Eta658opc4BeFEp9TcATmNx3JvFBkEzXtVZLI6oNV+/jEV7x2IDQplb6S29mVJTWJwX2AVgepXlGwVr/bvYrrXeZL7YUsFxbqrUSa31/S2/8RrEev0ubJLTwhWs4Fi4wu0SnOdu033XItbld3FbbByL9Q+rqixcoaWCo5R6Qil1oV7Ds+EORruTThtsmaqqR54vYjFlMQrgBIBntNbnVnzjHYT6KTu9WutTSqkYgA8AfAPA7wOY1Vo/W/9BJbXWKx4ad7vRyh3nAQDDWuvLWusSFnNcT6/ynjsKWutxrfWpOp0BwE8bfKG+7AUsCtOaRisFZwuAa+zvDV3Ds95PG7TG8W2A29MG1xJaKThjAHgdxLI1PHcybua0wbWEVgrOCQBD9e6IAIBvYvGUvQ2DJk4bBJqsbbrdaHV2/OsA/hGAF8DzWuu/bdnN1wCUUo8A+D8AH4FOtfoeFu2clwBsQ/20Qa31bMOLrBHYyLGFK1jj2MIVrOBYuIIVHAtXsIJj4QpWcCxcwQqOhStYwbFwBSs4Fq7w/w9oiQMy5w2cAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pre-Processing the Dataset"
      ],
      "metadata": {
        "id": "SjlGAALnz6_b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = x_train.reshape(x_train.shape[0], 32, 32, 3)\n",
        "x_test = x_test.reshape(x_test.shape[0], 32, 32, 3)\n",
        "x_train = x_train.astype('float32')\n",
        "x_test = x_test.astype('float32')\n",
        "x_train /=255\n",
        "x_test /=255\n",
        "print(x_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pr52dNoiz-gW",
        "outputId": "93e51de4-b73e-4cb8-d2b0-95787d5150f8"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[[[0.23137255 0.24313726 0.24705882]\n",
            "   [0.16862746 0.18039216 0.1764706 ]\n",
            "   [0.19607843 0.1882353  0.16862746]\n",
            "   ...\n",
            "   [0.61960787 0.5176471  0.42352942]\n",
            "   [0.59607846 0.49019608 0.4       ]\n",
            "   [0.5803922  0.4862745  0.40392157]]\n",
            "\n",
            "  [[0.0627451  0.07843138 0.07843138]\n",
            "   [0.         0.         0.        ]\n",
            "   [0.07058824 0.03137255 0.        ]\n",
            "   ...\n",
            "   [0.48235294 0.34509805 0.21568628]\n",
            "   [0.46666667 0.3254902  0.19607843]\n",
            "   [0.47843137 0.34117648 0.22352941]]\n",
            "\n",
            "  [[0.09803922 0.09411765 0.08235294]\n",
            "   [0.0627451  0.02745098 0.        ]\n",
            "   [0.19215687 0.10588235 0.03137255]\n",
            "   ...\n",
            "   [0.4627451  0.32941177 0.19607843]\n",
            "   [0.47058824 0.32941177 0.19607843]\n",
            "   [0.42745098 0.28627452 0.16470589]]\n",
            "\n",
            "  ...\n",
            "\n",
            "  [[0.8156863  0.6666667  0.3764706 ]\n",
            "   [0.7882353  0.6        0.13333334]\n",
            "   [0.7764706  0.6313726  0.10196079]\n",
            "   ...\n",
            "   [0.627451   0.52156866 0.27450982]\n",
            "   [0.21960784 0.12156863 0.02745098]\n",
            "   [0.20784314 0.13333334 0.07843138]]\n",
            "\n",
            "  [[0.7058824  0.54509807 0.3764706 ]\n",
            "   [0.6784314  0.48235294 0.16470589]\n",
            "   [0.7294118  0.5647059  0.11764706]\n",
            "   ...\n",
            "   [0.72156864 0.5803922  0.36862746]\n",
            "   [0.38039216 0.24313726 0.13333334]\n",
            "   [0.3254902  0.20784314 0.13333334]]\n",
            "\n",
            "  [[0.69411767 0.5647059  0.45490196]\n",
            "   [0.65882355 0.5058824  0.36862746]\n",
            "   [0.7019608  0.5568628  0.34117648]\n",
            "   ...\n",
            "   [0.84705883 0.72156864 0.54901963]\n",
            "   [0.5921569  0.4627451  0.32941177]\n",
            "   [0.48235294 0.36078432 0.28235295]]]\n",
            "\n",
            "\n",
            " [[[0.6039216  0.69411767 0.73333335]\n",
            "   [0.49411765 0.5372549  0.53333336]\n",
            "   [0.4117647  0.40784314 0.37254903]\n",
            "   ...\n",
            "   [0.35686275 0.37254903 0.2784314 ]\n",
            "   [0.34117648 0.3529412  0.2784314 ]\n",
            "   [0.30980393 0.31764707 0.27450982]]\n",
            "\n",
            "  [[0.54901963 0.627451   0.6627451 ]\n",
            "   [0.5686275  0.6        0.6039216 ]\n",
            "   [0.49019608 0.49019608 0.4627451 ]\n",
            "   ...\n",
            "   [0.3764706  0.3882353  0.30588236]\n",
            "   [0.3019608  0.3137255  0.24313726]\n",
            "   [0.2784314  0.28627452 0.23921569]]\n",
            "\n",
            "  [[0.54901963 0.60784316 0.6431373 ]\n",
            "   [0.54509807 0.57254905 0.58431375]\n",
            "   [0.4509804  0.4509804  0.4392157 ]\n",
            "   ...\n",
            "   [0.30980393 0.32156864 0.2509804 ]\n",
            "   [0.26666668 0.27450982 0.21568628]\n",
            "   [0.2627451  0.27058825 0.21568628]]\n",
            "\n",
            "  ...\n",
            "\n",
            "  [[0.6862745  0.654902   0.6509804 ]\n",
            "   [0.6117647  0.6039216  0.627451  ]\n",
            "   [0.6039216  0.627451   0.6666667 ]\n",
            "   ...\n",
            "   [0.16470589 0.13333334 0.14117648]\n",
            "   [0.23921569 0.20784314 0.22352941]\n",
            "   [0.3647059  0.3254902  0.35686275]]\n",
            "\n",
            "  [[0.64705884 0.6039216  0.5019608 ]\n",
            "   [0.6117647  0.59607846 0.50980395]\n",
            "   [0.62352943 0.6313726  0.5568628 ]\n",
            "   ...\n",
            "   [0.40392157 0.3647059  0.3764706 ]\n",
            "   [0.48235294 0.44705883 0.47058824]\n",
            "   [0.5137255  0.4745098  0.5137255 ]]\n",
            "\n",
            "  [[0.6392157  0.5803922  0.47058824]\n",
            "   [0.61960787 0.5803922  0.47843137]\n",
            "   [0.6392157  0.6117647  0.52156866]\n",
            "   ...\n",
            "   [0.56078434 0.52156866 0.54509807]\n",
            "   [0.56078434 0.5254902  0.5568628 ]\n",
            "   [0.56078434 0.52156866 0.5647059 ]]]\n",
            "\n",
            "\n",
            " [[[1.         1.         1.        ]\n",
            "   [0.99215686 0.99215686 0.99215686]\n",
            "   [0.99215686 0.99215686 0.99215686]\n",
            "   ...\n",
            "   [0.99215686 0.99215686 0.99215686]\n",
            "   [0.99215686 0.99215686 0.99215686]\n",
            "   [0.99215686 0.99215686 0.99215686]]\n",
            "\n",
            "  [[1.         1.         1.        ]\n",
            "   [1.         1.         1.        ]\n",
            "   [1.         1.         1.        ]\n",
            "   ...\n",
            "   [1.         1.         1.        ]\n",
            "   [1.         1.         1.        ]\n",
            "   [1.         1.         1.        ]]\n",
            "\n",
            "  [[1.         1.         1.        ]\n",
            "   [0.99607843 0.99607843 0.99607843]\n",
            "   [0.99607843 0.99607843 0.99607843]\n",
            "   ...\n",
            "   [0.99607843 0.99607843 0.99607843]\n",
            "   [0.99607843 0.99607843 0.99607843]\n",
            "   [0.99607843 0.99607843 0.99607843]]\n",
            "\n",
            "  ...\n",
            "\n",
            "  [[0.44313726 0.47058824 0.4392157 ]\n",
            "   [0.43529412 0.4627451  0.43529412]\n",
            "   [0.4117647  0.4392157  0.41568628]\n",
            "   ...\n",
            "   [0.28235295 0.31764707 0.3137255 ]\n",
            "   [0.28235295 0.3137255  0.30980393]\n",
            "   [0.28235295 0.3137255  0.30980393]]\n",
            "\n",
            "  [[0.43529412 0.4627451  0.43137255]\n",
            "   [0.40784314 0.43529412 0.40784314]\n",
            "   [0.3882353  0.41568628 0.38431373]\n",
            "   ...\n",
            "   [0.26666668 0.29411766 0.28627452]\n",
            "   [0.27450982 0.29803923 0.29411766]\n",
            "   [0.30588236 0.32941177 0.32156864]]\n",
            "\n",
            "  [[0.41568628 0.44313726 0.4117647 ]\n",
            "   [0.3882353  0.41568628 0.38431373]\n",
            "   [0.37254903 0.4        0.36862746]\n",
            "   ...\n",
            "   [0.30588236 0.33333334 0.3254902 ]\n",
            "   [0.30980393 0.33333334 0.3254902 ]\n",
            "   [0.3137255  0.3372549  0.32941177]]]\n",
            "\n",
            "\n",
            " ...\n",
            "\n",
            "\n",
            " [[[0.13725491 0.69803923 0.92156863]\n",
            "   [0.15686275 0.6901961  0.9372549 ]\n",
            "   [0.16470589 0.6901961  0.94509804]\n",
            "   ...\n",
            "   [0.3882353  0.69411767 0.85882354]\n",
            "   [0.30980393 0.5764706  0.77254903]\n",
            "   [0.34901962 0.5803922  0.7411765 ]]\n",
            "\n",
            "  [[0.22352941 0.7137255  0.91764706]\n",
            "   [0.17254902 0.72156864 0.98039216]\n",
            "   [0.19607843 0.7176471  0.9411765 ]\n",
            "   ...\n",
            "   [0.6117647  0.7137255  0.78431374]\n",
            "   [0.5529412  0.69411767 0.80784315]\n",
            "   [0.45490196 0.58431375 0.6862745 ]]\n",
            "\n",
            "  [[0.38431373 0.77254903 0.92941177]\n",
            "   [0.2509804  0.7411765  0.9882353 ]\n",
            "   [0.27058825 0.7529412  0.9607843 ]\n",
            "   ...\n",
            "   [0.7372549  0.7647059  0.80784315]\n",
            "   [0.46666667 0.5294118  0.5764706 ]\n",
            "   [0.23921569 0.30980393 0.3529412 ]]\n",
            "\n",
            "  ...\n",
            "\n",
            "  [[0.28627452 0.30980393 0.3019608 ]\n",
            "   [0.20784314 0.24705882 0.26666668]\n",
            "   [0.21176471 0.26666668 0.3137255 ]\n",
            "   ...\n",
            "   [0.06666667 0.15686275 0.2509804 ]\n",
            "   [0.08235294 0.14117648 0.2       ]\n",
            "   [0.12941177 0.1882353  0.19215687]]\n",
            "\n",
            "  [[0.23921569 0.26666668 0.29411766]\n",
            "   [0.21568628 0.27450982 0.3372549 ]\n",
            "   [0.22352941 0.30980393 0.40392157]\n",
            "   ...\n",
            "   [0.09411765 0.1882353  0.28235295]\n",
            "   [0.06666667 0.13725491 0.20784314]\n",
            "   [0.02745098 0.09019608 0.1254902 ]]\n",
            "\n",
            "  [[0.17254902 0.21960784 0.28627452]\n",
            "   [0.18039216 0.25882354 0.34509805]\n",
            "   [0.19215687 0.3019608  0.4117647 ]\n",
            "   ...\n",
            "   [0.10588235 0.20392157 0.3019608 ]\n",
            "   [0.08235294 0.16862746 0.25882354]\n",
            "   [0.04705882 0.12156863 0.19607843]]]\n",
            "\n",
            "\n",
            " [[[0.7411765  0.827451   0.9411765 ]\n",
            "   [0.7294118  0.8156863  0.9254902 ]\n",
            "   [0.7254902  0.8117647  0.92156863]\n",
            "   ...\n",
            "   [0.6862745  0.7647059  0.8784314 ]\n",
            "   [0.6745098  0.7607843  0.87058824]\n",
            "   [0.6627451  0.7607843  0.8627451 ]]\n",
            "\n",
            "  [[0.7607843  0.8235294  0.9372549 ]\n",
            "   [0.7490196  0.8117647  0.9254902 ]\n",
            "   [0.74509805 0.80784315 0.92156863]\n",
            "   ...\n",
            "   [0.6784314  0.7529412  0.8627451 ]\n",
            "   [0.67058825 0.7490196  0.85490197]\n",
            "   [0.654902   0.74509805 0.84705883]]\n",
            "\n",
            "  [[0.8156863  0.85882354 0.95686275]\n",
            "   [0.8039216  0.84705883 0.9411765 ]\n",
            "   [0.8        0.84313726 0.9372549 ]\n",
            "   ...\n",
            "   [0.6862745  0.7490196  0.8509804 ]\n",
            "   [0.6745098  0.74509805 0.84705883]\n",
            "   [0.6627451  0.7490196  0.84313726]]\n",
            "\n",
            "  ...\n",
            "\n",
            "  [[0.8117647  0.78039217 0.70980394]\n",
            "   [0.79607844 0.7647059  0.6862745 ]\n",
            "   [0.79607844 0.76862746 0.6784314 ]\n",
            "   ...\n",
            "   [0.5294118  0.5176471  0.49803922]\n",
            "   [0.63529414 0.61960787 0.5882353 ]\n",
            "   [0.65882355 0.6392157  0.5921569 ]]\n",
            "\n",
            "  [[0.7764706  0.74509805 0.6666667 ]\n",
            "   [0.7411765  0.70980394 0.62352943]\n",
            "   [0.7058824  0.6745098  0.5764706 ]\n",
            "   ...\n",
            "   [0.69803923 0.67058825 0.627451  ]\n",
            "   [0.6862745  0.6627451  0.6117647 ]\n",
            "   [0.6862745  0.6627451  0.6039216 ]]\n",
            "\n",
            "  [[0.7764706  0.7411765  0.6784314 ]\n",
            "   [0.7411765  0.70980394 0.63529414]\n",
            "   [0.69803923 0.6666667  0.58431375]\n",
            "   ...\n",
            "   [0.7647059  0.72156864 0.6627451 ]\n",
            "   [0.76862746 0.7411765  0.67058825]\n",
            "   [0.7647059  0.74509805 0.67058825]]]\n",
            "\n",
            "\n",
            " [[[0.8980392  0.8980392  0.9372549 ]\n",
            "   [0.9254902  0.92941177 0.96862745]\n",
            "   [0.91764706 0.9254902  0.96862745]\n",
            "   ...\n",
            "   [0.8509804  0.85882354 0.9137255 ]\n",
            "   [0.8666667  0.8745098  0.91764706]\n",
            "   [0.87058824 0.8745098  0.9137255 ]]\n",
            "\n",
            "  [[0.87058824 0.8666667  0.8980392 ]\n",
            "   [0.9372549  0.9372549  0.9764706 ]\n",
            "   [0.9137255  0.91764706 0.9647059 ]\n",
            "   ...\n",
            "   [0.8745098  0.8745098  0.9254902 ]\n",
            "   [0.8901961  0.89411765 0.93333334]\n",
            "   [0.8235294  0.827451   0.8627451 ]]\n",
            "\n",
            "  [[0.8352941  0.80784315 0.827451  ]\n",
            "   [0.91764706 0.9098039  0.9372549 ]\n",
            "   [0.90588236 0.9137255  0.95686275]\n",
            "   ...\n",
            "   [0.8627451  0.8627451  0.9098039 ]\n",
            "   [0.8627451  0.85882354 0.9098039 ]\n",
            "   [0.7921569  0.79607844 0.84313726]]\n",
            "\n",
            "  ...\n",
            "\n",
            "  [[0.5882353  0.56078434 0.5294118 ]\n",
            "   [0.54901963 0.5294118  0.49803922]\n",
            "   [0.5176471  0.49803922 0.47058824]\n",
            "   ...\n",
            "   [0.8784314  0.87058824 0.85490197]\n",
            "   [0.9019608  0.89411765 0.88235295]\n",
            "   [0.94509804 0.94509804 0.93333334]]\n",
            "\n",
            "  [[0.5372549  0.5176471  0.49411765]\n",
            "   [0.50980395 0.49803922 0.47058824]\n",
            "   [0.49019608 0.4745098  0.4509804 ]\n",
            "   ...\n",
            "   [0.70980394 0.7058824  0.69803923]\n",
            "   [0.7921569  0.7882353  0.7764706 ]\n",
            "   [0.83137256 0.827451   0.8117647 ]]\n",
            "\n",
            "  [[0.47843137 0.46666667 0.44705883]\n",
            "   [0.4627451  0.45490196 0.43137255]\n",
            "   [0.47058824 0.45490196 0.43529412]\n",
            "   ...\n",
            "   [0.7019608  0.69411767 0.6784314 ]\n",
            "   [0.6431373  0.6431373  0.63529414]\n",
            "   [0.6392157  0.6392157  0.6313726 ]]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_classes = 10\n",
        "print('shape before one hot encoding - ',y_train.shape)\n",
        "y_train = np_utils.to_categorical(y_train, n_classes)\n",
        "y_test = np_utils.to_categorical(y_test, n_classes)\n",
        "print('shape after one hot encoding - ',y_train.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AkdiGaoX15qN",
        "outputId": "43b82b59-3d57-471f-de3c-a84c685d2991"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape before one hot encoding -  (50000, 1)\n",
            "shape after one hot encoding -  (50000, 10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Building The CNN Model"
      ],
      "metadata": {
        "id": "MwJEkbXQ3u0O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "#convolutional layers\n",
        "model.add(Conv2D(50, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu', input_shape=(32, 32, 3)))\n",
        "model.add(Conv2D(75, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu'))\n",
        "model.add(MaxPool2D(pool_size=(2,2)))\n",
        "model.add(Dropout(0.25))\n",
        "model.add(Flatten())\n",
        "\n",
        "#hidden layer \n",
        "model.add(Dense(500, activation='relu'))\n",
        "model.add(Dropout(0.4))\n",
        "model.add(Dense(250, activation='relu'))\n",
        "model.add(Dropout(0.3))\n",
        "\n",
        "#output layer\n",
        "model.add(Dense(10,activation='softmax'))\n",
        "\n",
        "#compiling\n",
        "model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam')"
      ],
      "metadata": {
        "id": "QpgZ8itR3zH3"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training The Model"
      ],
      "metadata": {
        "id": "qRrpIj_d6P0o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train, y_train, batch_size=128, epochs=10, validation_data=(x_test, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PWN68Cpp6SYU",
        "outputId": "39c731c3-2877-4c59-8a56-db3194d8ca9f"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "391/391 [==============================] - 417s 1s/step - loss: 1.4831 - accuracy: 0.4620 - val_loss: 1.1036 - val_accuracy: 0.6099\n",
            "Epoch 2/10\n",
            "391/391 [==============================] - 418s 1s/step - loss: 1.0621 - accuracy: 0.6256 - val_loss: 0.9380 - val_accuracy: 0.6712\n",
            "Epoch 3/10\n",
            "391/391 [==============================] - 418s 1s/step - loss: 0.8964 - accuracy: 0.6864 - val_loss: 0.8786 - val_accuracy: 0.6971\n",
            "Epoch 4/10\n",
            "391/391 [==============================] - 418s 1s/step - loss: 0.7743 - accuracy: 0.7273 - val_loss: 0.8608 - val_accuracy: 0.6996\n",
            "Epoch 5/10\n",
            "391/391 [==============================] - 419s 1s/step - loss: 0.6686 - accuracy: 0.7659 - val_loss: 0.8356 - val_accuracy: 0.7100\n",
            "Epoch 6/10\n",
            "391/391 [==============================] - 419s 1s/step - loss: 0.5734 - accuracy: 0.7986 - val_loss: 0.8631 - val_accuracy: 0.7110\n",
            "Epoch 7/10\n",
            "391/391 [==============================] - 419s 1s/step - loss: 0.4874 - accuracy: 0.8305 - val_loss: 0.8550 - val_accuracy: 0.7148\n",
            "Epoch 8/10\n",
            "391/391 [==============================] - 461s 1s/step - loss: 0.4269 - accuracy: 0.8504 - val_loss: 0.8863 - val_accuracy: 0.7262\n",
            "Epoch 9/10\n",
            "391/391 [==============================] - 421s 1s/step - loss: 0.3674 - accuracy: 0.8706 - val_loss: 0.8791 - val_accuracy: 0.7265\n",
            "Epoch 10/10\n",
            "391/391 [==============================] - 419s 1s/step - loss: 0.3195 - accuracy: 0.8879 - val_loss: 0.9183 - val_accuracy: 0.7277\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f10bed41190>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Making Prediction "
      ],
      "metadata": {
        "id": "l3qTiLBGGF-j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "classes = range(0,10)\n",
        "names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
        "class_labels = dict(zip(classes, names))\n",
        "batch = x_test[100:109]\n",
        "labels = np.argmax(y_test[100:109], axis=-1)\n",
        "predictions = model.predict(batch, verbose = 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lKk4N5WjGJ9W",
        "outputId": "6b4ab1a6-f5de-409f-c8ca-1cc753eb44ef"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 119ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9lNFmVVxLOhX",
        "outputId": "dd405f57-be62-4a17-e31f-7dbed489c0b3"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1.34123356e-05 4.75169941e-07 3.88241699e-03 9.16136801e-03\n",
            "  9.47082400e-01 2.68850178e-02 8.67174094e-05 1.28801651e-02\n",
            "  4.43045838e-06 3.63720505e-06]\n",
            " [5.89963784e-05 2.13207750e-04 1.54295843e-02 1.07506558e-01\n",
            "  1.09290883e-01 5.28385937e-01 3.64564732e-02 2.01845869e-01\n",
            "  3.03623470e-04 5.08851546e-04]\n",
            " [1.64308986e-07 1.50475870e-07 1.02895510e-05 3.53532215e-03\n",
            "  9.85445922e-07 5.78474661e-04 9.95874345e-01 1.17187255e-07\n",
            "  1.00777321e-07 4.27574243e-08]\n",
            " [5.51024470e-07 7.91706185e-08 3.29216971e-04 9.58991647e-01\n",
            "  2.08043159e-04 3.04359966e-03 3.70379761e-02 3.79158475e-04\n",
            "  3.12742060e-07 9.45339525e-06]\n",
            " [8.05018985e-09 9.99970675e-01 1.24046976e-14 7.88658310e-13\n",
            "  1.09704643e-16 1.38120182e-14 3.06708671e-16 1.25064720e-14\n",
            "  1.49578182e-06 2.77816889e-05]\n",
            " [3.53218297e-06 9.90591824e-01 4.88240248e-09 1.20498376e-08\n",
            "  1.28938327e-11 5.59329139e-10 4.93687722e-08 3.51537457e-13\n",
            "  8.65654461e-03 7.48077407e-04]\n",
            " [9.45901312e-03 5.51528123e-04 2.26885557e-01 3.52629364e-01\n",
            "  3.27666145e-04 2.01947391e-02 3.69418740e-01 1.37004085e-04\n",
            "  1.93834677e-02 1.01288129e-03]\n",
            " [2.59015764e-09 5.25229948e-09 1.55259040e-05 4.86928820e-06\n",
            "  3.82784776e-07 1.08030378e-07 9.99979138e-01 1.28598729e-10\n",
            "  1.38578216e-09 1.23809840e-09]\n",
            " [3.94632742e-02 4.61438522e-02 7.05310435e-04 1.95417330e-01\n",
            "  5.17326271e-06 7.52776815e-03 2.92295869e-02 1.62983325e-03\n",
            "  6.66337788e-01 1.35399839e-02]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for image in predictions:\n",
        "  print(np.sum(image)) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K47g50u3LVPr",
        "outputId": "cc75a684-47c4-4b95-a440-53bea9546a12"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.99999994\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "0.9999999\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_result = np.argmax(predictions, axis=-1)\n",
        "print(class_result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TWtDre2ULe6H",
        "outputId": "42b850a8-40c3-4cb6-d281-7f5c6354feb8"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[4 5 6 3 1 1 6 6 8]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Object Detection"
      ],
      "metadata": {
        "id": "XsDeqG5nL1-Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig , axs = plt.subplots(3, 3, figsize = (19,6))\n",
        "fig.subplots_adjust(hspace = 1)\n",
        "axs = axs.flatten()\n",
        "for i, img in enumerate(batch):\n",
        "  for key, value in class_labels.items():\n",
        "    if class_result[i]==key:\n",
        "      title = 'Prediction: {}\\nActual: {}'.format(class_labels[key], class_labels[labels[i]])\n",
        "      axs[i].set_title(title)\n",
        "      axs[i].axes.get_xaxis().set_visible(False)\n",
        "      axs[i].axes.get_yaxis().set_visible(False)\n",
        "    axs[i].imshow(img)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "MoU2sElGL0P6",
        "outputId": "c4ff613e-39d1-4806-fcb8-014625e10fa9"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1368x432 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}