Natural_Image_Classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Project_ML.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GVkS_uzlo36c"
      },
      "source": [
        "###Mounting Drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hqe9ukN6osTV",
        "outputId": "bff406eb-7c82-4a14-df07-ab5d4cb5fb23"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T_3LiY8jpfY0"
      },
      "source": [
        "###Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UBQMEwwuph-2"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "import pickle\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.feature_selection import SelectKBest\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.svm import SVC"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZgilH08_qnfQ"
      },
      "source": [
        "###Uploading data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R8Sxlo-gTJFs"
      },
      "source": [
        "Function to unpicle the files"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UVeLa0dyqo7z"
      },
      "source": [
        "def unpickle(file):\n",
        "  import pickle\n",
        "  with open(file, 'rb') as fo:\n",
        "    d = pickle.load(fo, encoding='bytes')\n",
        "    data = np.array(d[b'data'])\n",
        "    labels = np.array(d[b'labels'])\n",
        "    dic = {}\n",
        "    for i in range(data.shape[1]):\n",
        "      dic[i] = data.T[i]\n",
        "    dic['label'] = labels\n",
        "    df1 = pd.DataFrame(dic)\n",
        "  return df1"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YCZCYLVlTL6u"
      },
      "source": [
        "Unpickling all batches"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TO6dSTmUCIlZ"
      },
      "source": [
        "path = '/content/gdrive/MyDrive/ML Project/Project Data/'"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wLcxuDg4rFwp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "64a9b629-b3b3-4e3a-c279-a77480c09136"
      },
      "source": [
        "path1 = path + 'data_batch_1'\n",
        "data_batch_1 = unpickle(path1)\n",
        "print(data_batch_1)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "        0    1    2    3    4    5  ...  3067  3068  3069  3070  3071  label\n",
            "0      59   43   50   68   98  119  ...    57   104   140    84    72      6\n",
            "1     154  126  105  102  125  155  ...   133   136   139   142   144      9\n",
            "2     255  253  253  253  253  253  ...    72    79    83    83    84      9\n",
            "3      28   37   38   42   44   40  ...    48    38    28    37    46      4\n",
            "4     170  168  177  183  181  177  ...    79    78    82    78    80      1\n",
            "...   ...  ...  ...  ...  ...  ...  ...   ...   ...   ...   ...   ...    ...\n",
            "9995   18   18   21   23   24   23  ...    39    39    39    34    37      3\n",
            "9996  235  240  249  253  254  253  ...   174   179   182   181   181      9\n",
            "9997   71   60   74   73   87  125  ...    81    78    68    69    68      1\n",
            "9998  250  254  211   64   61   60  ...   132   133   215   255   254      1\n",
            "9999   62   61   60   59   94  123  ...   130   130   130   130   131      5\n",
            "\n",
            "[10000 rows x 3073 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q4r0lQms4e_6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "02d485dd-6310-405e-fa8f-01ead522fad4"
      },
      "source": [
        "path2 = path + 'data_batch_2'\n",
        "data_batch_2 = unpickle(path2)\n",
        "print(data_batch_2)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "        0    1    2    3    4    5  ...  3067  3068  3069  3070  3071  label\n",
            "0      35   27   25   26   24   22  ...   171   169   169   168   168      1\n",
            "1      20   20   18   19   21   23  ...    70    66   111    97    51      6\n",
            "2     116  115  155  142   99  116  ...     5     5    18    84   124      6\n",
            "3      98   91   82   69   59   60  ...   112   120   125   132   138      8\n",
            "4      85  102  109  118   49   14  ...    85    71    64    65    66      8\n",
            "...   ...  ...  ...  ...  ...  ...  ...   ...   ...   ...   ...   ...    ...\n",
            "9995  236  233  233  233  233  233  ...   196   196   197   198   195      8\n",
            "9996  231  231  232  230  230  231  ...   161   172   170   170   176      8\n",
            "9997  127  139  155  149  132  158  ...   199   199   197   192   191      7\n",
            "9998  190  200  208  208  208  208  ...   162   162   163   182   192      2\n",
            "9999  177  174  182  188  186  189  ...   106   105   119   127   136      5\n",
            "\n",
            "[10000 rows x 3073 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y5u8KWls4h7t",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96d416d4-bb90-4399-868f-b533c30e9e9f"
      },
      "source": [
        "path3 = path + 'data_batch_3'\n",
        "data_batch_3 = unpickle(path3)\n",
        "print(data_batch_3)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "        0    1    2    3    4    5  ...  3067  3068  3069  3070  3071  label\n",
            "0      26   17   13   13   13   14  ...    46    28    27    26    27      8\n",
            "1      94  101   95   94   94   97  ...   163   174   182   184   155      5\n",
            "2     183  158  166  167  169  171  ...   250   250   250   250   250      0\n",
            "3     255  254  255  255  255  255  ...   255   255   255   255   255      6\n",
            "4     182  156  156  144  118  132  ...    72    68    73    67    75      9\n",
            "...   ...  ...  ...  ...  ...  ...  ...   ...   ...   ...   ...   ...    ...\n",
            "9995   25   25   26   27   27   27  ...   175   175   175   178   155      2\n",
            "9996   65   66   69   73   75   72  ...    44    40    39    38    36      5\n",
            "9997  175  200  207  206  191  174  ...    98   118   124    49    32      3\n",
            "9998   28   59   67   71   30   59  ...    54    48    36    44    41      7\n",
            "9999   62   40   61   75   85  128  ...   129   127   127   124   116      1\n",
            "\n",
            "[10000 rows x 3073 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_V8QDPUX4jog",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a611aa26-6127-4002-92fe-3350f234971f"
      },
      "source": [
        "path4 = path + 'data_batch_4'\n",
        "data_batch_4 = unpickle(path4)\n",
        "print(data_batch_4)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "        0    1    2    3    4    5  ...  3067  3068  3069  3070  3071  label\n",
            "0     178  178  178  178  179  179  ...    82    80    80    80    77      0\n",
            "1      29   22   25   24   28   43  ...    26    29    29    31    30      6\n",
            "2      36   33   21   20   52   54  ...   177   175   173   170   170      0\n",
            "3      30   45   44   50   71   64  ...   104   107   106    99   147      2\n",
            "4      35   80   89   52   47   39  ...    99   104   107   110    92      7\n",
            "...   ...  ...  ...  ...  ...  ...  ...   ...   ...   ...   ...   ...    ...\n",
            "9995  229  231  232  232  233  233  ...   106   151   174   181   190      0\n",
            "9996   68   59   53   46   52   60  ...    62    64    66    66    67      1\n",
            "9997  167  164  151  161  190  156  ...   115   130   132   142   130      6\n",
            "9998   45   46   46   47   48   48  ...    14    34    30    34    24      2\n",
            "9999  235  239  239  237  230  225  ...    25   114   182   175   174      8\n",
            "\n",
            "[10000 rows x 3073 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tQo_dy1H4lp8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "48a0d1f6-65ed-4cf8-e465-1ffd2b6834b0"
      },
      "source": [
        "path5 = path + 'data_batch_5'\n",
        "data_batch_5 = unpickle(path5)\n",
        "print(data_batch_5)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "        0    1    2    3    4    5  ...  3067  3068  3069  3070  3071  label\n",
            "0     255  252  253  250  238  233  ...   231   172   173   231   248      1\n",
            "1     127  126  127  127  128  128  ...    81    89   102   108   112      8\n",
            "2     116   64   19   29   36   40  ...    10     8     7     6     5      5\n",
            "3     205  213  235  232  112   98  ...    23    23    24    25    23      1\n",
            "4     189  184  181  186  191  177  ...    76    72    76    77    72      5\n",
            "...   ...  ...  ...  ...  ...  ...  ...   ...   ...   ...   ...   ...    ...\n",
            "9995  145  151  156  155  153  153  ...    59    57    56    53    54      2\n",
            "9996  255  254  255  254  254  254  ...    76    82    79    81    76      6\n",
            "9997   35   40   42   39   43   45  ...    93    87    77    66    50      9\n",
            "9998  189  186  185  184  182  181  ...   169   165   169   171   171      1\n",
            "9999  229  236  234  231  236  242  ...   171   174   173   162   161      1\n",
            "\n",
            "[10000 rows x 3073 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qmfzgRo44nhg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa210dbf-11b6-47ff-a7bc-4aea46c7c709"
      },
      "source": [
        "path_test = path + 'test_batch'\n",
        "test_batch = unpickle(path_test)\n",
        "print(test_batch)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "        0    1    2    3    4    5  ...  3067  3068  3069  3070  3071  label\n",
            "0     158  159  165  166  160  156  ...   175   145   124   129   110      3\n",
            "1     235  231  232  232  232  232  ...   153   163   178   191   199      8\n",
            "2     158  158  139  132  166  182  ...    52    37     8     3     7      8\n",
            "3     155  167  176  190  177  166  ...    72    53    50    52    50      0\n",
            "4      65   70   48   30   23   40  ...   130   147   136   146   117      6\n",
            "...   ...  ...  ...  ...  ...  ...  ...   ...   ...   ...   ...   ...    ...\n",
            "9995   76   76   77   76   75   76  ...   239   235   236   234   236      8\n",
            "9996   81   91   98  106  108  110  ...   160   173   129   147   160      3\n",
            "9997   20   19   15   15   14   13  ...    52    51    50    53    47      5\n",
            "9998   25   15   23   17   23   51  ...    78    78    80    81    80      1\n",
            "9999   73   98   99   77   59  146  ...    93    90    94    58    26      7\n",
            "\n",
            "[10000 rows x 3073 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8hvNDDSfTROT"
      },
      "source": [
        "Merging all batches"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iHhRAbevAP77",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f294cfb7-128c-4514-fd36-63d672b44d37"
      },
      "source": [
        "data_batch = pd.concat([data_batch_1, data_batch_2, data_batch_3, data_batch_4, data_batch_5], ignore_index=True)\n",
        "print(data_batch)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "         0    1    2    3    4    5  ...  3067  3068  3069  3070  3071  label\n",
            "0       59   43   50   68   98  119  ...    57   104   140    84    72      6\n",
            "1      154  126  105  102  125  155  ...   133   136   139   142   144      9\n",
            "2      255  253  253  253  253  253  ...    72    79    83    83    84      9\n",
            "3       28   37   38   42   44   40  ...    48    38    28    37    46      4\n",
            "4      170  168  177  183  181  177  ...    79    78    82    78    80      1\n",
            "...    ...  ...  ...  ...  ...  ...  ...   ...   ...   ...   ...   ...    ...\n",
            "49995  145  151  156  155  153  153  ...    59    57    56    53    54      2\n",
            "49996  255  254  255  254  254  254  ...    76    82    79    81    76      6\n",
            "49997   35   40   42   39   43   45  ...    93    87    77    66    50      9\n",
            "49998  189  186  185  184  182  181  ...   169   165   169   171   171      1\n",
            "49999  229  236  234  231  236  242  ...   171   174   173   162   161      1\n",
            "\n",
            "[50000 rows x 3073 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8-IknstHTUKh"
      },
      "source": [
        "###Converting to gray scale"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mr3NFICATmuV"
      },
      "source": [
        "Taking average of pixel values to get image data in gray scale"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3EJdJ1lQ2pXN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "83e84369-f55d-4313-ac2b-43999f3bed46"
      },
      "source": [
        "data_np = data_batch.iloc[:,:-1].to_numpy()\n",
        "B = data_np[:,0:1024]\n",
        "G = data_np[:,1024:2048]\n",
        "R = data_np[:,2048:3072]\n",
        "data_batch_g = (B+G+R)/3\n",
        "data_batch_g = pd.DataFrame(data_batch_g)\n",
        "data_batch_g['label'] = data_batch['label']\n",
        "print(data_batch_g)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "               0          1          2  ...       1022       1023  label\n",
            "0      61.333333  44.666667  47.000000  ...  32.333333  10.333333      6\n",
            "1       2.000000  47.666667  16.000000  ...  54.333333  54.666667      9\n",
            "2      84.333333  82.333333  82.333333  ...  82.333333  83.333333      9\n",
            "3      21.000000  30.000000  31.000000  ...  52.000000  61.000000      4\n",
            "4      12.000000  10.000000  17.666667  ...  74.666667  76.666667      1\n",
            "...          ...        ...        ...  ...        ...        ...    ...\n",
            "49995  81.333333   1.333333   6.666667  ...  42.333333  43.333333      2\n",
            "49996  40.000000  41.000000  43.000000  ...   0.666667  84.666667      6\n",
            "49997  64.000000  66.333333  67.666667  ...  43.333333  31.000000      9\n",
            "49998  42.666667  39.333333  38.333333  ...  14.666667  14.666667      1\n",
            "49999  61.666667  69.333333  68.333333  ...  78.000000  77.000000      1\n",
            "\n",
            "[50000 rows x 1025 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lfED1ORo3Euj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7969b954-339c-4829-e778-cdbf5eed0bf8"
      },
      "source": [
        "test_np = test_batch.iloc[:,:-1].to_numpy()\n",
        "B = test_np[:,0:1024]\n",
        "G = test_np[:,1024:2048]\n",
        "R = test_np[:,2048:3072]\n",
        "test_batch_g = (B+G+R)/3\n",
        "test_batch_g = pd.DataFrame(test_batch_g)\n",
        "test_batch_g['label'] = test_batch['label']\n",
        "print(test_batch_g)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              0          1          2  ...       1022       1023  label\n",
            "0     21.000000  20.333333  25.333333  ...  82.333333  66.000000      3\n",
            "1     64.333333  60.333333  61.333333  ...  18.000000  24.333333      8\n",
            "2     19.333333  17.000000  81.000000  ...   4.000000   7.333333      8\n",
            "3     68.000000   6.000000  12.000000  ...  60.333333  63.666667      0\n",
            "4     61.000000  71.666667  52.666667  ...  76.333333  48.333333      6\n",
            "...         ...        ...        ...  ...        ...        ...    ...\n",
            "9995  10.333333  10.666667  11.000000  ...  45.666667  46.333333      8\n",
            "9996  60.333333  71.000000  78.666667  ...  76.333333  78.333333      3\n",
            "9997  15.666667  14.666667  13.333333  ...  34.000000  30.666667      5\n",
            "9998  25.666667  18.000000  27.333333  ...  14.333333  12.000000      1\n",
            "9999  75.333333  19.333333  21.000000  ...  63.666667  26.333333      7\n",
            "\n",
            "[10000 rows x 1025 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iU2K3ZaJWQGT"
      },
      "source": [
        "###Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z8eY1F-1UPWw"
      },
      "source": [
        "Dividing into feature and target variable, and futher into training, validation and testing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yXP3C0QBA4oo"
      },
      "source": [
        "x_train = data_batch.iloc[:, :-1]\n",
        "y_train = data_batch.iloc[:, -1]\n",
        "x_test = test_batch.iloc[:, :-1]\n",
        "y_test = test_batch.iloc[:, -1]"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ii_zldyl8qfr"
      },
      "source": [
        "x_val = x_train.iloc[30000:, :]\n",
        "y_val = y_train.iloc[30000:]\n",
        "x_train = x_train.iloc[:30000, :]\n",
        "y_train = y_train.iloc[:30000]"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i0hGMkYg3hT3"
      },
      "source": [
        "x_train_g = data_batch_g.iloc[:, :-1]\n",
        "y_train_g = data_batch_g.iloc[:, -1]\n",
        "x_test_g = test_batch_g.iloc[:, :-1]\n",
        "y_test_g = test_batch_g.iloc[:, -1]"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AZ4UmHhj9Q3I"
      },
      "source": [
        "x_val_g = x_train_g.iloc[30000:, :]\n",
        "y_val_g = y_train_g.iloc[30000:]\n",
        "x_train_g = x_train_g.iloc[:30000, :]\n",
        "y_train_g = y_train_g.iloc[:30000]"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OnyDrT7YWS3K"
      },
      "source": [
        "Scaling the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qe9_vzXzCdKy"
      },
      "source": [
        "stdsc = StandardScaler()\n",
        "stdsc.fit(x_train)\n",
        "x_train_1 = stdsc.transform(x_train)\n",
        "x_val_1 = stdsc.transform(x_val)\n",
        "x_test_1 = stdsc.transform(x_test)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RFFEGmKUWabK"
      },
      "source": [
        "Dimensionality reduction using LDA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aNm2eziMKTPM"
      },
      "source": [
        "lda = LinearDiscriminantAnalysis()\n",
        "lda.fit(x_train_1, y_train)\n",
        "x_train_2 = lda.transform(x_train_1)\n",
        "x_val_2 = lda.transform(x_val_1)\n",
        "x_test_2 = lda.transform(x_test_1)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AaPJwlr0Wd2J"
      },
      "source": [
        "Feature reduction using Select K Best"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "STmUUW07L0wX"
      },
      "source": [
        "selkb = SelectKBest(k = 512)\n",
        "selkb.fit(x_train_1, y_train)\n",
        "x_train_3 = selkb.transform(x_train_1)\n",
        "x_val_3 = selkb.transform(x_val_1)\n",
        "x_test_3 = selkb.transform(x_test_1)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XopsxZpCmkhh"
      },
      "source": [
        "###Decision Tree classifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vvlFiCQjm5KB"
      },
      "source": [
        "Scaled Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wa5dXwdvm6wG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1d884aec-a8ca-40a8-ae4e-db6fe218c21f"
      },
      "source": [
        "model_dt_1 = DecisionTreeClassifier(max_depth = 15)\n",
        "model_dt_1.fit(x_train_1, y_train)\n",
        "print('Accuracy on training data : ', model_dt_1.score(x_train_1, y_train))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on training data :  0.7700333333333333\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oQk9s6AInHxH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "679aabdd-4aac-495f-e2fa-c377a0ba0a80"
      },
      "source": [
        "print('Accuracy on validation data : ', model_dt_1.score(x_val_1, y_val))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on validation data :  0.27105\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RqBD2ejrnODt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ea9276f-492b-4403-c425-963655905f7b"
      },
      "source": [
        "print('Accuracy on testing data : ', model_dt_1.score(x_test_1, y_test))"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on testing data :  0.2752\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s-rs9_wNoc8L",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "34651686-31c0-4946-eb20-42d8d91445cf"
      },
      "source": [
        "print('Cross Validation Score : ', cross_val_score(model_dt_1, x_train_1, y_train, cv=5))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cross Validation Score :  [0.26966667 0.26966667 0.26516667 0.26683333 0.27683333]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U8-KF7dwnbvc"
      },
      "source": [
        "Data with reduced dimensions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C2SreKM0nceT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d917326c-0708-4bb2-b416-c237809d87ac"
      },
      "source": [
        "model_dt_2 = DecisionTreeClassifier(max_depth = 15)\n",
        "model_dt_2.fit(x_train_2, y_train)\n",
        "print('Accuracy on training data : ', model_dt_2.score(x_train_2, y_train))"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on training data :  0.7939\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U2k10MOknjyE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d450c43-f54c-4f30-81d7-df483537ce2f"
      },
      "source": [
        "print('Accuracy on validation data : ', model_dt_2.score(x_val_2, y_val))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on validation data :  0.2861\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4cP-6EJ7nmDj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "861a31b5-c684-4e6c-fbbf-35c777b441e5"
      },
      "source": [
        "print('Accuracy on testing data : ', model_dt_2.score(x_test_2, y_test))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on testing data :  0.2845\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LVWQVSAXofHQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8dd1f1b7-c22a-40c4-8eb4-27818a9e20b9"
      },
      "source": [
        "print('Cross Validation Score : ', cross_val_score(model_dt_2, x_train_2, y_train, cv=5))"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cross Validation Score :  [0.43783333 0.44333333 0.43083333 0.44033333 0.4545    ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "655tb9vInuIg"
      },
      "source": [
        "Data with reduced features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DiLSfwkTnvAQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b749d0ba-70bb-4376-f3a6-59b0fa395639"
      },
      "source": [
        "model_dt_3 = DecisionTreeClassifier(max_depth = 15)\n",
        "model_dt_3.fit(x_train_3, y_train)\n",
        "print('Accuracy on training data : ', model_dt_3.score(x_train_3, y_train))"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on training data :  0.6761666666666667\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-95K0qRUnz3Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4fe84997-f70b-4483-9bb9-8c1284779a59"
      },
      "source": [
        "print('Accuracy on validation data : ', model_dt_3.score(x_val_3, y_val))"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on validation data :  0.22915\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TC1Lg2Pwn1YC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce31935c-77bf-4286-c3ba-b690660a1d34"
      },
      "source": [
        "print('Accuracy on testing data : ', model_dt_3.score(x_test_3, y_test))"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on testing data :  0.2378\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G-DjU6HRomat",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "905bd788-5dd3-41bb-ec12-81e9aa6e7063"
      },
      "source": [
        "print('Cross Validation Score : ', cross_val_score(model_dt_3, x_train_3, y_train, cv=5))"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cross Validation Score :  [0.22616667 0.22183333 0.2265     0.24033333 0.22266667]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rlHCwrQhbLWN"
      },
      "source": [
        "###Adaboost Classifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lQSzkStJbPcU"
      },
      "source": [
        "Scaled Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YUJsukgBbOhO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2012446-2499-4b60-90b8-526049fe8f0c"
      },
      "source": [
        "model_b_1 = AdaBoostClassifier(base_estimator = DecisionTreeClassifier(max_depth = 3), n_estimators = 10)\n",
        "model_b_1.fit(x_train_1, y_train)\n",
        "print('Accuracy on training data : ', model_b_1.score(x_train_1, y_train))"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on training data :  0.3372\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fIkwwliDbZ45",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f07bb545-53f1-41be-a7b3-4e1a93852c7f"
      },
      "source": [
        "print('Accuracy on validation data : ', model_b_1.score(x_val_1, y_val))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on validation data :  0.32105\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FDZnbN_WbeNE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cf061342-c90f-4c9d-9d13-597d90a7a9bd"
      },
      "source": [
        "print('Accuracy on testing data : ', model_b_1.score(x_test_1, y_test))"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on testing data :  0.3233\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QqVbXzV9bhet",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e67f79b1-da34-4ae0-860b-0fd755215660"
      },
      "source": [
        "print('Cross Validation Score : ', cross_val_score(model_b_1, x_train_1, y_train, cv=5))"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cross Validation Score :  [0.3175     0.32133333 0.312      0.32316667 0.318     ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zJExgf-eblRL"
      },
      "source": [
        "Data with reduced dimensions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uqfxazhwblri",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce1c17d0-0ec2-4470-bb71-34d9e1bb6527"
      },
      "source": [
        "model_b_2 = AdaBoostClassifier(base_estimator = DecisionTreeClassifier(max_depth = 3), n_estimators = 10)\n",
        "model_b_2.fit(x_train_2, y_train)\n",
        "print('Accuracy on training data : ', model_b_2.score(x_train_2, y_train))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on training data :  0.5183333333333333\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cxc-ZqmGbv-f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3876219d-e445-4205-92c6-cf0678c10bef"
      },
      "source": [
        "print('Accuracy on validation data : ', model_b_2.score(x_val_2, y_val))"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on validation data :  0.3237\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y3gmdH4eb8at",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce50a125-1079-4ce0-cd31-ee62a7430f29"
      },
      "source": [
        "print('Accuracy on testing data : ', model_b_2.score(x_test_2, y_test))"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on testing data :  0.323\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nvdyi7gScsbD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5748cf8f-e4fb-41a5-b6f4-57749a1f3cc0"
      },
      "source": [
        "print('Cross Validation Score : ', cross_val_score(model_b_2, x_train_2, y_train, cv=5))"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cross Validation Score :  [0.50166667 0.51233333 0.5075     0.53       0.51616667]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zjPmhij7cvwD"
      },
      "source": [
        "Data with reduced features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aDACwyVDcwKA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8505c5e3-d215-4ece-fe1a-033abca08259"
      },
      "source": [
        "model_b_3 = AdaBoostClassifier(base_estimator = DecisionTreeClassifier(max_depth = 3), n_estimators = 10)\n",
        "model_b_3.fit(x_train_3, y_train)\n",
        "print('Accuracy on training data : ', model_b_3.score(x_train_3, y_train))"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on training data :  0.2599\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "93cWqfd3c9Si",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0be0b281-5622-400d-e254-ca0b1e01ed9f"
      },
      "source": [
        "print('Accuracy on validation data : ', model_b_3.score(x_val_3, y_val))"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on validation data :  0.24515\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5KJ1lZLedVV1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "11e5185c-8f04-436e-c80f-b4c13ffe58bd"
      },
      "source": [
        "print('Accuracy on testing data : ', model_b_3.score(x_test_3, y_test))"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on testing data :  0.2526\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1BxjPG38dZMw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "99891c6f-1adc-453d-a582-906a0d154209"
      },
      "source": [
        "print('Cross Validation Score : ', cross_val_score(model_b_3, x_train_3, y_train, cv=5))"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cross Validation Score :  [0.24683333 0.2495     0.25366667 0.249      0.24233333]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OCJuXO7dPJcd"
      },
      "source": [
        "###Naive bayes classifer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xK7zD5IQb-nV"
      },
      "source": [
        "Scaled Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7L6Li_s4Po_D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2c2c6f25-6428-4877-e1dd-170c83718285"
      },
      "source": [
        "model_nb_1 = GaussianNB()\n",
        "model_nb_1.fit(x_train_1, y_train)\n",
        "print('Accuracy on training data : ', model_nb_1.score(x_train_1, y_train))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on training data :  0.2881666666666667\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "01gJIIDlcgy4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1c7dd42-8c4f-414c-db01-a00022cdb360"
      },
      "source": [
        "print('Accuracy on validation data : ', model_nb_1.score(x_val_1, y_val))"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on validation data :  0.2843\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G2LukxYvbzDp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d695df2e-6d6b-44db-95a7-39ae3e8d3bbf"
      },
      "source": [
        "print('Accuracy on testing data : ', model_nb_1.score(x_test_1, y_test))"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on testing data :  0.2968\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dcZ9j11TotDP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7104f263-52c1-4435-9161-4227081ed0cf"
      },
      "source": [
        "print('Cross Validation Score : ', cross_val_score(model_nb_1, x_train_1, y_train, cv=5))"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cross Validation Score :  [0.29166667 0.28566667 0.2775     0.29166667 0.2855    ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MWisiTv6cA5Y"
      },
      "source": [
        "Data with reduced dimensions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PV84aN59Pphf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ffd20537-2a22-4bc9-8279-4989344b19b8"
      },
      "source": [
        "model_nb_2 = GaussianNB()\n",
        "model_nb_2.fit(x_train_2, y_train)\n",
        "print('Accuracy on training data : ', model_nb_2.score(x_train_2, y_train))"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on training data :  0.5642\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H-rwDb83ckTJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "39c22c22-687e-4421-cf23-4cd3f01723a9"
      },
      "source": [
        "print('Accuracy on validation data : ', model_nb_2.score(x_val_2, y_val))"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on validation data :  0.34795\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mRBphCr3cXaD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "11c6fb61-4336-4270-a5db-d14941c8577d"
      },
      "source": [
        "print('Accuracy on testing data : ', model_nb_2.score(x_test_2, y_test))"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on testing data :  0.3424\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q6RYlq4rou1I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8490e5c4-2788-4521-9d26-cd489e619d6e"
      },
      "source": [
        "print('Cross Validation Score : ', cross_val_score(model_nb_2, x_train_2, y_train, cv=5))"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cross Validation Score :  [0.55716667 0.56333333 0.55783333 0.57683333 0.56116667]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OYLRxHuqcafi"
      },
      "source": [
        "Data with reduced features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q4qLTqfEPqLP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "87866726-0fcf-4f81-eff7-c82f12615471"
      },
      "source": [
        "model_nb_3 = GaussianNB()\n",
        "model_nb_3.fit(x_train_3, y_train)\n",
        "print('Accuracy on training data : ', model_nb_3.score(x_train_3, y_train))"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on training data :  0.23123333333333335\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DiHZJ3abcnAS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e7d0ff9-e95e-4c06-956c-0bb0f6b0622c"
      },
      "source": [
        "print('Accuracy on validation data : ', model_nb_3.score(x_val_3, y_val))"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on validation data :  0.2266\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7YZ-viHscm99",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "530b2469-5173-4371-d565-63e6ab4814b4"
      },
      "source": [
        "print('Accuracy on testing data : ', model_nb_3.score(x_test_3, y_test))"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on testing data :  0.2344\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xpJZjlKAowd1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4acc00ca-ca3c-4883-b728-1ac9206f9cb6"
      },
      "source": [
        "print('Cross Validation Score : ', cross_val_score(model_nb_3, x_train_3, y_train, cv=5))"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cross Validation Score :  [0.2355     0.23566667 0.224      0.23016667 0.22416667]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9iA5GiLUBeDe"
      },
      "source": [
        "###Multi-layer-perceptron"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3TSO5wdhdh7P"
      },
      "source": [
        "Scaled Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "umphO8lODDah",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e6a0e3b9-b4d7-4f89-c039-00f4cdefaab7"
      },
      "source": [
        "model_mlp_1 = MLPClassifier(learning_rate_init = 0.01, max_iter = 500)\n",
        "model_mlp_1.fit(x_train_1, y_train)\n",
        "print('Accuracy on training data : ', model_mlp_1.score(x_train_1, y_train))"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on training data :  0.5545333333333333\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kKGlw-cYiEtI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed2196dd-bb1c-42b5-fae0-b03d0da9588b"
      },
      "source": [
        "print('Accuracy on validation data : ', model_mlp_1.score(x_val_1, y_val))"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on validation data :  0.4283\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nkwM-QSbRJOi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4bb411f9-4315-4174-883e-f486fe3f28c5"
      },
      "source": [
        "print('Accuracy on testing data : ', model_mlp_1.score(x_test_1, y_test))"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on testing data :  0.429\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hwfBLBO8o2Z4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1a56a4b8-5e10-40c8-9327-3ed068d7c6c3"
      },
      "source": [
        "print('Cross Validation Score : ', cross_val_score(model_mlp_1, x_train_1, y_train, cv=5))"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cross Validation Score :  [0.4235     0.42766667 0.4175     0.42383333 0.422     ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9o-LCg0QdoWe"
      },
      "source": [
        "Data with reduced dimensions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "arFBhYJ0KtJB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "74daae2c-e003-46b4-87ea-1ef3ef1608a6"
      },
      "source": [
        "model_mlp_2 = MLPClassifier(learning_rate_init = 0.01, max_iter = 500)\n",
        "model_mlp_2.fit(x_train_2, y_train)\n",
        "print('Accuracy on training data : ', model_mlp_2.score(x_train_2, y_train))"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on training data :  0.5841666666666666\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yhgo2wiSitMj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea0e893b-2536-410c-c9ce-adc427e87212"
      },
      "source": [
        "print('Accuracy on validation data : ', model_mlp_2.score(x_val_2, y_val))"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on validation data :  0.3476\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gew2Us9yQtx2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d87934c7-f639-4af3-e469-4110322d25b2"
      },
      "source": [
        "print('Accuracy on testing data : ', model_mlp_2.score(x_test_2, y_test))"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on testing data :  0.3414\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a_yvuJ8Ko49j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1283a2ab-4761-4ddf-8641-8263a62fe016"
      },
      "source": [
        "print('Cross Validation Score : ', cross_val_score(model_mlp_2, x_train_2, y_train, cv=5))"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cross Validation Score :  [0.55616667 0.562      0.555      0.56766667 0.56316667]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CbeVwLhOdq7R"
      },
      "source": [
        "Data with reduced features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "odfiu2F4Mb48",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "65c48f45-6dd6-4fa5-9b9b-640a8fc066a7"
      },
      "source": [
        "model_mlp_3 = MLPClassifier(learning_rate_init = 0.01, max_iter = 500)\n",
        "model_mlp_3.fit(x_train_3, y_train)\n",
        "print('Accuracy on training data : ', model_mlp_3.score(x_train_3, y_train))"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on training data :  0.6612333333333333\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v-rXpIc6jGY6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc468372-3bb4-4f8e-9d96-84eb1ff38843"
      },
      "source": [
        "print('Accuracy on validation data : ', model_mlp_3.score(x_val_3, y_val))"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on validation data :  0.2925\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TTjm5x95Q3CJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d8fb6ae-e88b-4cea-e689-49e2d18c10ae"
      },
      "source": [
        "print('Accuracy on testing data : ', model_mlp_3.score(x_test_3, y_test))"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on testing data :  0.3039\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oEvXYbLmo7Fk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "59d88099-e38d-4a35-8d74-0e484dbfe28c"
      },
      "source": [
        "print('Cross Validation Score : ', cross_val_score(model_mlp_3, x_train_3, y_train, cv=5))"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cross Validation Score :  [0.293      0.28466667 0.2875     0.3055     0.29      ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FbeL1_nmYZ0o"
      },
      "source": [
        "###Visualisation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4o3267cA2YW1"
      },
      "source": [
        "Plotting figures for visualisation of differnt classes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7uHr0omY2e0f"
      },
      "source": [
        "def category(a):\n",
        "  dic = dict()\n",
        "  dic[0] = 'Airplane'\n",
        "  dic[1] = 'Automobile'\n",
        "  dic[2] = 'Bird'\n",
        "  dic[3] = 'Cat'\n",
        "  dic[4] = 'Deer'\n",
        "  dic[5] = 'Dog'\n",
        "  dic[6] = 'Frog'\n",
        "  dic[7] = 'Horse'\n",
        "  dic[8] = 'Ship'\n",
        "  dic[9] = 'Truck'\n",
        "  return dic[a]\n",
        "\n",
        "def plot_img(i):\n",
        "  arr1 = x_train.iloc[i, 0:1024]\n",
        "  arr2 = x_train.iloc[i, 1024:2048]\n",
        "  arr3 = x_train.iloc[i, 2048:3072]\n",
        "  arr4 = x_train_g.iloc[i, :]\n",
        "  arr1 = np.array(arr1)\n",
        "  arr2 = np.array(arr2)\n",
        "  arr3 = np.array(arr3)\n",
        "  arr4 = np.array(arr4)\n",
        "  img1 = arr1.reshape(32, 32, 1)\n",
        "  img2 = arr2.reshape(32, 32, 1)\n",
        "  img3 = arr3.reshape(32, 32, 1)\n",
        "  img4 = arr3.reshape(32, 32)\n",
        "  img = np.concatenate((img1, img2, img3), axis = 2)\n",
        "  plt.imshow(img, interpolation = 'nearest')\n",
        "  plt.axis('off')\n",
        "  plt.show()\n",
        "  print('\\n')\n",
        "  print('Class : ', y_train[i], ' : ', category(y_train[i]))\n",
        "  print('\\n')\n",
        "  plt.imshow(img4, cmap = 'gray', interpolation = 'nearest')\n",
        "  plt.axis('off')\n",
        "  plt.show()"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n23b2wedqNGz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "e9f792fe-3df2-479e-f7f0-85995543f4ee"
      },
      "source": [
        "plot_img(30)"
      ],
      "execution_count": 70,
      "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"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Class :  0  :  Airplane\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "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": {
        "id": "F5VY80DaqO3Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "1532705d-f964-45bf-eb6f-990d20558923"
      },
      "source": [
        "plot_img(4)"
      ],
      "execution_count": 71,
      "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"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Class :  1  :  Automobile\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "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": {
        "id": "FyUEKDUmqi6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "08f7743b-a480-4edb-b9f4-34cad5c5b514"
      },
      "source": [
        "plot_img(6)"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOcAAADnCAYAAADl9EEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAVd0lEQVR4nO2dOY8k2XWFb0TulVtl1r50T9f0LOzZwSEhyhBECRBlyJan36JfI9nyZBKiIBEgSIHUrN09M90z3bUvWVW5Z6wy5L7vAXTIZ5zPjIsXGRkZJwO45917o7IsTQgRHvGf+wKEEG4kTiECReIUIlAkTiECReIUIlCqvuC//v4fMZX737+8wHXd5o+cx9trPVxTi/hSOu0axjb7+xgbrB06j6/3+7jm7PoVxl5c/S/GegdTjG0czDBWa8ydxxezO1zTbNYxVonWMVbkGcbyfOI8Pui576GZWaOxhrGquc9nZnY/XmHs5sL9HCyn/JvNVx2MlcZuxO3ojM8552scT+/hs/j+3o74+fiXf/515DquN6cQgSJxChEoEqcQgSJxChEoEqcQgSJxChEoXiul0uBYe5NTw5/9z6+dxx/s/hjXdNstjC2TCsYWE06VL9adGWrLIrd9YWY22Odb8vYDji2abC1NCrZFirHbFmnkbVxTNvg7pzl/t2qFLYdhb9N5fK3u+axZF2Pj2R7GJjdjjL16/oPzeKVR4BqrpRg6PjnHWLfDltR0kmMsy2gd36vCc/mE3pxCBIrEKUSgSJxCBIrEKUSgSJxCBIrEKUSgeK2Uk8sbjO0fDTBWqbhT7MPOm55P43T4ycsXGHt5wpUFB/tuW2FWsgUwqN5iLOs9xVjc4Xu1SrmqZnLnrmQYVrnio+6xN3p9tku6La4wWaXu+59kbHtYxv7A/cUWxm5f8GP3/Hd/cB5vP+CKj4O3tjHW9FQ0jSf83VZL/jyL3Oe8vrnCJUm65PMBenMKESgSpxCBInEKESgSpxCBInEKESjebO3z59wH5tGbnI07eveh8/iLb77FNbM5b6RvdzlzOVm4+7mYmX3x7HPn8c7+27hmo5tgLIs5O3n8grO1VvL1D+ruHki+fjTNOt/7YX8HY9N73uj99Gv35w3au7im2+P/9nSDixVmJ3zO8wt3D6SjQz7fWoevIyv43idLfuaqdT7n7citi/mMM7IRXz6iN6cQgSJxChEoEqcQgSJxChEoEqcQgSJxChEoXivl9Svuo1LaAmPjjdfO40nMtkde5Y3v64Mhxt5+9whjF5fuz5t5NiF/9iVbIlnM92N9k+0ZK9mSqjXc1zIY8nfurLn7/ZiZTcbuvklmZtcXPGKgSNyPQrPn6ROUcPHD50suclgNNzAWb7t7CK01+Xe5vRth7OyU7322YrsqXfEzMp25N8xnmc/+8jTkAvTmFCJQJE4hAkXiFCJQJE4hAkXiFCJQJE4hAsVrpWQr7r9yd8nVG+nc3Yen0ebeN4Ndtg7KBqeot9/injnjwl11MF3wtbeMr+PmhtPr3TpPXt4/5GnTqV06j98X/Fmz0TXGmhW+jim7X9btuVP9WZ17Kl3OuHfPv/8b3+OiPMXY47r7nJWSyzquT7kXULLkZ65SZdtpCT2VzMzKyL2u0+V7H5X8WYTenEIEisQpRKBInEIEisQpRKBInEIEisQpRKB4rZQGtJ03M0sXnOof7LobOJ1c8PTn8fIEY2X8HGMff/AOxv7y793X0a5zpUU659jz555KnFtuxd9qcUVCXndXuhyPX+GajS6n+fcH3MSrO+Tp4XX4n55lbEV8d+yuIDEze/FfXIGUTL7DWPTAvW5+yXbJ3hvcxKu1zvfDYn6G4wqvW1tz6yLxWHS1mK8Rr+GPXiGE+JMgcQoRKBKnEIEicQoRKBKnEIEicQoRKF4rZXLLsyR6m5xivxm7p003O7wzfzrzNFvKuLHW069eYuzsxG1HdLtNXLOz8wBj2484vT7/YYax11dsHbS67vkrG1s9XDPoeSyA+Bhj1Tp/73rsrqjIEm4mVqSeSouCq1mefMg2y4+O3LHuGjcnG2zxDJv5vI2xJOHfc3LDtl+euD+vVffYJTnrhdCbU4hAkTiFCBSJU4hAkTiFCBSJU4hA8WZro4KzcbGn/8p0cec8vrPDPWcqxv1XTk95o/e45Azk+Na9Ebna5E3qNzOO9bs8fqDZ4U3lvY1DjLUa7p9gZ7DnWeMbk8z3Kk05652m7nEHZY3/v8e3PGG7x8lm+/nf8TiGBvRU2tvlXlF1z/14/jlncke3c4wtx1zkUIJ70N/ka8w9jgOhN6cQgSJxChEoEqcQgSJxChEoEqcQgSJxChEoXitlOuGpwJUZ67pbc582nXPqOjaOtRq86TmO2ErpDtxjEPIKb7JfJGylzC+4R8zRwfsY67fYcrDUvSE6vee0/KDt2WBd42ucL3lzvlXd96So8CPy4lvuMTXY4b5JP/6UrZSWuSeEpzkXYSxnbOtlKW9gTxb8fDcqfP2ttjtW8ThcUcyWDqE3pxCBInEKESgSpxCBInEKESgSpxCBInEKESheK6XSYO0ullz9MP3BnaJeXfNO/+197rHS9owzuIcKGDOzbtVtwQx3OOd9dcWfVck9VQcrPudyyjZRI3L3uIkrPA17dM3nq7a5+uFmwpbUYgpWRZWv4/UJPz57h9wnqNnh0QrVpdsKWizYPipXfI2HB2wt9T2W1LmnJ1S7415XxvxZnskmiN6cQgSKxClEoEicQgSKxClEoEicQgSKxClEoPgbfJVcvVEuOWW/1XO38K8s+HzZhHPNBTTBMjNLllxZcH3tToeXNa5iaNe4ff/W9j7Gtjd4bMHWOjc2s9RtwdQ8k5XTCldojD0Nyo4veHTF+bG7emPERR2WrT7CWHedr+P8+iuM9SO3TbFWfw/XbO/zdPP9A55UHmVc0TR5wg3bksx9//OILa75im1EQm9OIQJF4hQiUCROIQJF4hQiUCROIQJF4hQiULxWiqU8QbleZeujU3dXdtRy/rgsYWsmavB1rDW5iuTm0l05k/Pp7MmbPNn6YOMIY9UqWx/LGd+rmrlT9lHFM4sm4QqeZy/d07zNzM7uOBbDHJXijq99WLI98M6A//ezOf8ASdVtb1TSa1wTxfxZ9RZ/1s6mu5mYmdlm7yHGxjP31O5VylU/7So3NSP05hQiUCROIQJF4hQiUCROIQJF4hQiUCROIQLFa6X0+twAqdnmXfsljKRvr3ODrCznNHSWcbOl6T1XAlSmbsuhUeVrt4WnE9OCK0+iKs9DyTP+3o2aO5bm3EDt3p3JNzOzcvwEY610yLHS/b0blQNcc373O4w9qnIlzmHzA4ylsft7L+ZciXOfnGGsGHGjsajgRmPrbY4Vsdu+m4zZDqy3Bxgj9OYUIlAkTiECReIUIlAkTiECReIUIlD84xhWvME6j7gfUFq6M25zPp3Np5yRrdV5YQ96zpiZNWJ3f5561sM17cobGKusHmOsWOxgrFXjcQGWu/8fo5wzf3tdvsbd9Z9hbJFzv6XZyL2J/eXlD7hmUP0SY/2Sf5eH23wfvz7/znk8jjjbWYs4s52s+D4uFxxbdH6DsbzuzvaPl56eRHecUbYP/8F5WG9OIQJF4hQiUCROIQJF4hQiUCROIQJF4hQiULxWSnHJFkbRKjCWxO6+LfUW99mp17jHSpzwZ5UZTxMuMvfX297/BNfU8ncxdnXKG+ZrVU9/pBbbTnni3vC/WPD3arY4ZR97ftH++h7G6j237TTa4ntf90yGHi95d/7F4guMdXbd74tmzlbKasmFBZWcR2iUxn2azke/x1ij5h7xMBzyeIo45WvENX/0CiHEnwSJU4hAkTiFCBSJU4hAkTiFCBSJU4hA8Vop7x1+irF8jccg5DV3P5q9de7B0+xzpUhUcMr76opHDIxmbguj0nwL1yyXXEGy8IynaLa4V02S8LrFzN0DaTbjKp3cU7GS52zb9Lo85bnVcdtEJ1cjXLOssJVy5pmw3blhi64ycF9HOv4e16zFbNENWo8wVq3zc5Wt+Jzthtv2O9zl8Q41415MhN6cQgSKxClEoEicQgSKxClEoEicQgSKxClEoHitlI8+/jnG4j6n5eNO23l8vcmp90qDrZmK8YiEL5/xSICbVxfO4y/PeYRDrcq2R6vjrtwwM6un3DyrTDktP7t3N9bKSh5PUa/z/ZhP+TpefO9unmVm1mm6rzEv+BGZplw5czW5wdjj9BHGRifuZl2vvv8a19QS/l3WO+5nwMxs/1EfY/cZW0jFuvs5HtY89lGD9ULozSlEoEicQgSKxClEoEicQgSKxClEoEicQgSK10p566OfYqyscZOpvOpOh1crXGlRyfl8UYtT5fMvuELj5LU7nT9acpq/2/FM3z7nmRxrDV63PeQpzxs9dzp/Oud75atySZdsb0zveFrzsnBXs8SF53zL1xyD85mZjQu2e6LYXbFSi3gWzVffskXU3+TPuq2yvVFr8289Bdvs5panbx/t/ARjn+78k/O43pxCBIrEKUSgSJxCBIrEKUSgSJxCBIo3W7vW543BWcG6zqk1S40zeEXJm9Gbng3nqadXzcU3XzmPl7Ax38xsa/d9jH377BRji4hHNUQz3sRePXBnJyPjPjtnr77H2GzOGdn5nLOJFehLFJWcNbbmHYZK6CNlZvb6nLO8g777t3nw8BDXrFZ87xcJf+dkxbHukK9/uXKPqEjG3EeqYZxRtg/ch/XmFCJQJE4hAkXiFCJQJE4hAkXiFCJQJE4hAsVrpcTsYFjpafufQm+ZLOcN20Wd7YZiwpuQoylvYs+m7v4xg60jXLO64p4zs0u2ADLPyIh0yvbGDXxepcE3f7HgzdyLBX/WZM73qkIjsSv8mx0e8eOzvcfjNTyTPKws3RbSLD3HNUePHmKsmvMYhHnyJcbi6jHGktxt3bQ7bPcU/AjzNfzxS4QQfwokTiECReIUIlAkTiECReIUIlAkTiECxWulLDy9apIF9+5ZJu4RA3npPm5mlnna32fGfWzm92wrxA23vVFt89e+u2Yr4vrMk14v+V5lOVfcdNb33GuWbKUUCZ9vvuAqnWV+ibEIRjxUa1wds3novnYzs7feYbvq/Ibtqjo4MFHMa5IZPzu7gw8xZvE+hsoOPwfPnt46j+9tcZ+jdoNHkRB6cwoRKBKnEIEicQoRKBKnEIEicQoRKBKnEIHitVJyT6VFwRl2a9bdbe7TlWfEwN0ZxkYpN5Ja21jH2F//4q+cx0/n7lS4mdnr0QnGth5zOUUReRqepWx9JOZuMtXucZr/8jXfq2XCVsrbnwwxZi33D3pzz5Us69vcWMsibpC1mPJzNdxyN/jKSv7NNne4Ed3WFv8ucbyJsbsFWx9b6+5zNiq85vKUbURCb04hAkXiFCJQJE4hAkXiFCJQJE4hAkXiFCJQvFZKkrhnQpiZRZ6lEc1RyXlNrck2RXOdJxB3ZhybvHA35PrJ+1u45vH7nq5mMVcdJAv+n/vtf3JjsOtrt+XQ6vL3mi94xkffM+Pjo5++gbGXl8/cgS7bHvsPdzE2GHDFSqfNNtEic1efTOaeBnAlf+fj6y8wNlxnK2U1Z3um3xo4j6eeSq3Vkq+f0JtTiECROIUIFIlTiECROIUIFIlTiEDxb3xPOPuUL7lnTrUK05qrvPm32+NN1PmCN76fvPoaY9988a37s5o/wjXLIbf9X8CYCTOzjRaPBIgLvldbg3ecxxstnr69SjmL3t/kQoA04+ufTK6dxw8OObMdecZr/OqXv8FYbY2vf/uh+5mrVzibf37Km/2TnDfuj6acNR42eYxDv+NudJRV+V2XFfydCb05hQgUiVOIQJE4hQgUiVOIQJE4hQgUiVOIQPFaKbUaj+NNp9wXp1p3bx5f5u50vZnZ6cVnGHv6u88x1q10MNZOm87jX//HH3BN4xFv9L7x2Edrj9nCeHTIvWWOL9wbovOEJ4dX63WM7YAVYWZWlLxhvpi7z7kWs4Xx8tk3GPv1b3h0xeF7/NgVXff7opZt4JpszPdjuMWf9f3L7zD29J5HPPzib9y9qXYP2Q6cZWzpEHpzChEoEqcQgSJxChEoEqcQgSJxChEoEqcQgeK1Um5T7n2TrLjCZAYuy8UdWyKnt7/C2PU5V6Xs1t7H2EbktnTGniqX2jmMVjaz+oLtjeP8Ocbe/Vvu3XNTuK/l9pR/mq09tks++in/3zbbbmvJzOz62l1Vc3XFlkK7w32Onjw5xFjvkG24Moep6Cnfj/MTHvMxG/G6ZMXW2N30HmMnT9y9h9rdbVxzds1WIaE3pxCBInEKESgSpxCBInEKESgSpxCBInEKESh+K2XKE5RnY26ElS/cqe27KVcBFEu2ZvprPEZ7fu9u4mVm1h66rZQYGjSZmdWaXOXSS7lFf7zDlSeDLbYwen13FcyrZ2z3RMYjI0YX/H+7yrgqaGfXbX28PmHb4+aaLYyyxs3Etvl2WKPhvh9RxNVCqxU3zzp7PsZYu8YX8s4nRxibgs1yfcvPaa3B9hehN6cQgSJxChEoEqcQgSJxChEoEqcQgSJxChEoXitlMWG7JKrwfIpa173bv7/mSYe/YCuiu+VpNLbJVRNRbeg8vj/8ANccn/B3vv+GKxXeO3gPY50Op9gfHLoth5tT/l4vvuLzLcZss1TW2Bapt9xW1s6++x6amZ0fszWzKthmsZKvPzK3LdJb50ZjR4/dk6bNzK6+5cqqDBrAmZmNRzyJ+vzMbc+scra/NjwzbAi9OYUIFIlTiECROIUIFIlTiECROIUIFIlTiEDxWymjpxirNDjVvIrc6fB6l1PXe+/zCPA05R39WYP/X4p7d/XJ+JIthekdxxZnXDnz+W+5wddGj29zXHNXwfzs52wtPTrawdhwi3+X3jbbEa0N928Tx7u45vqEKzcuR1wtVDReYczSGizieSj1NY5F/JWt2+FqlqKYYGw6dTd6y2JuANds8hwVQm9OIQJF4hQiUCROIQJF4hQiUCROIQLFm63dbXF4Dr1e/v+k7sxfWeX/gvqAM6HJLbf9n19iyG6/dk8Trk89fYJWngnKNU9/npJ75hQ5Z15vL9xFApOUz/fmkXscgJnZKuWM4eg1T1eOp+4b2ezwdz46+hhjOwecnbxdcgr16sqdJS0SzvRX6vwsfvwXj3hdfouxwjxZ+8z9rEbw3JuZRTFv9if05hQiUCROIQJF4hQiUCROIQJF4hQiUCROIQLFa6VsZtybZbXHIw0uj929VC6PL3BNtsYbtquJZwzCCW+Kb47AVog9u6Ez/l7tt9gS2XjMqfKK5/rt0n2vzl/wvcpvOc2/feS5VwX3F2qt9pzHR/fcC6iW8wb2jR3enL875H5L+fLEefz1Cd+PVsc3CoN/62zJ1ke1xvaMXbt/69U9P4vpki0uQm9OIQJF4hQiUCROIQJF4hQiUCROIQJF4hQiUKLS0xpfCPHnQ29OIQJF4hQiUCROIQJF4hQiUCROIQJF4hQiUP4PxFUdj8alcY8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Class :  2  :  Bird\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "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": {
        "id": "mRt62iTyqtXx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "3a30cf13-2586-4822-e83f-1c5cc152c612"
      },
      "source": [
        "plot_img(59)"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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K4RSJUwinUCulolbKo1elVClukJWX2N5YkpR9iewSM8sm4fEJdYanRnfJ6IeywDbLaIDT8k1ccGODRjg4W+BrTjq4qqa7sQVjsxU+5grG8DOryDOLiHnQI83LNjY2guvLJbZLsgw/l7LEzdXqEp9jqxmuaDIzSzvh88/J6AeNYxDiCULiFMIpEqcQTpE4hXCKxCmEUyROIZzyOQ2+SLOomtgiy3BqO69xWptNO66ANWNmVpKZFjmoVuim+H/SoDWAseUcV5fMMzy9uiDnP1+EG3ytsENkcQsfbzbFNlFGrAM032ZOJkqz3wBr2JaS6qQ4DlsOtNkcmdi9XODnMitx47gywsdsboX3RbjY5rHQm1MIp0icQjhF4hTCKRKnEE6ROIVwCu8hRLJgRjJ1ZRFONRaGM6sJ+S64kZD/IQXO8naa4V4va+02Pt4KZ/fwx+FmJ2OcJT2e4CzvYhG+V2WFrzkbT2AsWuJznJKM+MlxuFdQTLKugwHObLORCyuSikbTt9MUnwf7yL4Cv0Uzs2yFP9wvyDGjJcjyEk1oHIMQTxASpxBOkTiFcIrEKYRTJE4hnCJxCuEU/uE76R9Tk/4xJ0cHwfXeAH9EzdySYR/3nEF2iZnZ/Tt3guvFCFsAZ9ZxDyFmpcwzbFNM5uTj/Dx8zIg8mkabTF0m1kdBLIwKjE/o9/G9Oru9DWPcLsH3CrFGejvNyMf5ObFLIsPPMyIfvtdo6jixUh69g5DenEK4ReIUwikSpxBOkTiFcIrEKYRTJE4hnEKtlJhN6iWTi1HKnqX5R+u4AUuVkV4vpIfQg5OwpRPl+G+VSzzq4OgIT3mekXNcALvEzGy5DN+rTgtbRN0ethW6ZGp0BKZGm5nNgBXErAhWebJNbJbJBFfVNMDU6EaCqzrW1rDdMyYVPEaujVaRgFEk/E2ncQxCPDFInEI4ReIUwikSpxBOkTiFcIrEKYRTqJWyMjKtmbTUv7QZTudXxQncczLDdsNoE6fl2y18HuM6nCo/WWD75RBUspiZTcZjGFuxXmgJGT/QCttLjR6erNwmVTqtNrZL1pv4PLbPng2u7x/iZ2YxPl6UYCuojkjlDLiPSYJ/i53+CMaqBN+r/GgPxmryQBtgZERBRkaUdOp1GL05hXCKxCmEUyROIZwicQrhFIlTCKdInEI4hVopBWlylOJMuW0Nw+nrf3rzu3DPp/d3yfF6MLa2EbYAzHBTqAsXzsM9Rw8fwFiywLerSYa9VGBqtJkZcjcaZKp4TObDrCbY+lgQC6mfhC2Y5sY63tMhVhtpDrcir4R8GW7WVeZ4Fk1JGnwlxMJYH+Dzn85wg7KyDN//KMJ24KNPStGbUwi3SJxCOEXiFMIpEqcQTpE4hXCKxCmEU6iV0ozxCPZ90rDoo9Owz3JUb8A9SRenrk8znEbf+/QujI2G4cZPww1cxbBx/iKMpV1s6bDZIHUPNw3rdMPVJ9MpvuZZjO0Bdh7jKW5CFjXC1keng695EWODoCRzSNi+WRk+/+UCN+Oa4J+pVU1sl1RdbBOVpBmaDcJVUgm55k6MbSyE3pxCOEXiFMIpEqcQTpE4hXCKxCmEU2i2ducszq7e+gnup3OjvBBcL69+De5p5vjj5bKawVhCPjgfl+Hs2XspzkAOG7i1fz7A2c7lAmdJ024bxhLQhydv4o/b2y18PDYiYX4G3+PFMnxtEemLk6Q4axyTjHLdwh/FV8NwrADP0sxsRb4qj8jI9CXJGscR3le2toLrnRhXg1w7j0dowHN45B1CiP8TJE4hnCJxCuEUiVMIp0icQjhF4hTCKdRK+ePXrsNYw3Da+KNPwm3uVzVpV09S13WET7Ou6SWAPTiVX4OpxWZm7Q4+xw7ptxQZ/mgbTQjHQwTMIjAOwMwsIvcRfwJuVoMuN+x+PC7MnkHU5Kdakz5BkWGLKyaxFIzyMDPrNsLn8vtfeg7u+cMvvwhjCL05hXCKxCmEUyROIZwicQrhFIlTCKdInEI4JfrfSJULIf7n6M0phFMkTiGcInEK4RSJUwinSJxCOEXiFMIp/wVKSchQVo0bRAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Class :  3  :  Cat\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "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": {
        "id": "WMq9c8kUqv0m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "c44d0207-53ad-445a-95e6-63d759481942"
      },
      "source": [
        "plot_img(82)"
      ],
      "execution_count": 74,
      "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"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Class :  4  :  Deer\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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G3O/cuTPGkqXTWk6/0/2la6b5JVRJlKpqyP6ia06zV1rjBmvJSqG/tby8HGP79u2LsfSetsbPLL0j1ODLqhSRtwjFKVIUxSlSFMUpUhTFKVIUzNbS+AHayLuwsNB5nKYTf/zxxzFG6+g8VlZWOo9TJo42ZVN2jyYXU4+b2dnZzuPUk4jOgzLKtDE7bc6nLCNl2Ckje+HChRhLmfk+WfnW+ByppxL1A0pQhpr6ZyX8cooURXGKFEVxihRFcYoURXGKFEVxihQFrRTaRE2Tl1NKmTawk71BVgT1MkqbqCmFTlCLfupxQz2QhoaGOo/TyALa6L24uBhjNMk5/T0qEqD3g2ybdM2tZeuDxirQhn6C7K+5ubkYS0UJpAkqIkn45RQpiuIUKYriFCmK4hQpiuIUKYriFCnKmr62goj8s/jlFCmK4hQpiuIUKYriFCmK4hQpiuIUKcp/AHGC76K80TRuAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RsqLrlQ2q1ro",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "9e6c0459-8ce6-4ab0-90fb-442d11a8e2fd"
      },
      "source": [
        "plot_img(156)"
      ],
      "execution_count": 75,
      "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"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Class :  5  :  Dog\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Blgg1NSN7gyo+0jraB92rjY2NGOudOZOaoc3OzsY1CwsLMfbyyy8PXvfNKVIUxSlSFMUpUhTFKVIUxSlSFMzW0qFhyq7Ozc0NXqfsGH0e9YghUj8gyhZSBpLGGVCRAPWPSdnh3rEQlG2mDGrKvlO/H+ovRIfiKbuaMtvr6+txDRUy/PTTTzFGvyc9c+k5pows/WYJ35wiRVGcIkVRnCJFUZwiRVGcIkVRnCJFQSuFeqLQgeK1tbXB65SWp1Qz9QmiXkbpM2nv1OuFDr6TXUKWVNo/WQf0XUeOHIkxsnuSHXHjxo24Znp6OsaoyGHbtm0xlsY40D7o2aED7DR6g565VHhARQc9+OYUKYriFCmK4hQpiuIUKYriFCmK4hQpyjbqRyMi/x6+OUWKojhFiqI4RYqiOEWKojhFiqI4RYryHyCV0CuvAtpHAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0qtWd5tbrEyd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "93cbed4d-41e3-42c9-c90f-f6d753db940c"
      },
      "source": [
        "plot_img(19)"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TEKdQnIQ4heIkxCmmlbIscPaAlUWCzI0gxLZHZNQ/WgY41WKS4Vir21OPJ23DIhpgu6Qd4nYMrx6dwNiLx3+EsaMH+hzv9PBS/tYankezjtteFwujQNlUtzBSwy5JU6NVg5H5UwMWl4jI1bNj9fjiCncjL6q4bcjcyi5pbcLYzsE9GOtW9XYeprvIAl+E/OVAcRLiFIqTEKdQnIQ4heIkxCkUJyFOMa2UxRIXkjKtFLBsbC0nhyH+n1hmOHtgtMSxpL6lHl8ZmSzNZB3GfvbRe3gemzgz4pe//iWM/fY3L9Tjj9rYbui0cBGyVh2Pi62l/lx/nqMcW1XZLdxzZrOG72M6x4XBXr/Se9WM+tjiigz7qLWN53j0/hGMHezexr8309+5wPBSosiUmgq/nIQ4heIkxCkUJyFOoTgJcQrFSYhTzCWkIMIbimPBK1NJqNdLKQqjS3KIpzI0Vo2HRkfp+bm+groy2kz0jFpAwxtcj6Z/hVdr4wqumZNk+u/lCR5zfD2BMenfwFBgtirXn82qhmsS3T3CG9/rKV41LhZ4jqOV3tYi2sI1iaysicoavo+tTTz/IsRJH2tg437NeM5RxBpChPzFQHES4hSKkxCnUJyEOIXiJMQpFCchTjGtlDEo0S8issrxhvOZ6MvyY6P786rAy+FvBnjpvb/Ena1LYMGkxtL7WYatmeMnj/Bv3Rhdnkt8r8pIv1fFEtseNWNZfl7iR5onVusK3TZb39mFY7IEbzg/ucDWUq+Fx3X276rH8+olHBMbSRObG7hO0JOnz2Fs670Pcayld7YOE2w9BsBetOCXkxCnUJyEOIXiJMQpFCchTqE4CXEKxUmIU0wr5cMPPoaxyxXOjPj6/LV6/HzQh2OWGc5yuS5wVkoUY3ugVdWzBPI5tkuqbVz7ZmcP15XZMiyM0rCdRoFuL5WG/XJxja2lgfF/OwcdmUVEurt6rZ0fHd2HY04usNX28rX+DoiIrAW4VcN+d189vrjAllkjwhkwzUBvnSAicjHE72NZweO2b91Sj6cBvvex0U0dwS8nIU6hOAlxCsVJiFMoTkKcQnES4hSKkxCnBKVRQv5/Pn8MgwtQiElEZLTQrY8bI4NktsIFlZZGNstwhMv0z0Ebh0rN6AzdxIWkmgFeDg8m2O6Zz3CsrOrnvFnhYmLPT09hbGj8356OcOZMu6Nf98Emvh+fff4HGIsTfK9+fFu3S0REPr6rt0hIjBYaNaPNR1rHRbcKIztps4Htnt2WHqum+JqrNTyPna2WOhF+OQlxCsVJiFMoTkKcQnES4hSKkxCnUJyEOMW0Uj7/w1cwGBsFiyqgl0QQ4f+CssTL2omx5F0U2IJZZaD/h/FboeBYEOIY7gIjEhjZCiH4f8yNvjJTy3Yy/m9XC2xJyVKPFREuNDY0Oo4HRj+au8C2ERHpNfSsoFoFF89KQvwOr4x7JRVsfcRWWxnwbPrXuAjZf3/6KYz907/+C60UQv6coDgJcQrFSYhTKE5CnEJxEuIUipMQp5gFvp48/hLG6sZu/wRkJARGT4skNlrcGxkOkXFOVFQpjvFlm+3BDUskMTISrOJOATBhQsPiagY4liZ4/lEdt1lHLlGeY0snMwqXDa+vYCxd4QJrcaDHIsHX/PWjr2Hsk08+gbHD9w5h7P77uLDZYqVf91kfFwy7Nu4Hgl9OQpxCcRLiFIqTEKdQnIQ4heIkxCnmam1pbF4ej3EpfkRg1HoJjY301jjznGDlNTZWZI0FWRtjddVapUbzXy6NTeXGf2qjidsINBq4dhKaB9rkLSKSgc3yIiJXl3jlsgESI0REZhO9zlFe4HfxtI+7aHc2t2AsMByCs8trGBOQwFEx7u9HP/5rfD4Av5yEOIXiJMQpFCchTqE4CXEKxUmIUyhOQpxiWik1o4R8YSxtI7DpYVsilqUjlpUCQ/h8ZW79ljUNI2jYLCiUGPWWLNspNzaVj4bY+kDP03pmUmKbpWrU5wmM+z++GarHC3wLpd3BrRM2NrowZr1XhfXMwCZ8a0wUvrte+OUkxCkUJyFOoTgJcQrFSYhTKE5CnEJxEuIUsx0DIeSHg19OQpxCcRLiFIqTEKdQnIQ4heIkxCkUJyFO+V8R2H2t3k/y8gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Class :  6  :  Frog\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "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": {
        "id": "V68PeW42rK2J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "42924a57-b124-4f9a-f3c9-37f11fd867f1"
      },
      "source": [
        "plot_img(7)"
      ],
      "execution_count": 77,
      "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"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Class :  7  :  Horse\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "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": {
        "id": "ecku7GIarR2k",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "dd534253-4e6a-4da8-98ed-38b312b28dd4"
      },
      "source": [
        "plot_img(106)"
      ],
      "execution_count": 78,
      "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"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Class :  8  :  Ship\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "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": {
        "id": "WZQhIYa60Z8D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "55c385c2-0ff6-4ca4-88a9-bb22c54eda1b"
      },
      "source": [
        "plot_img(1)"
      ],
      "execution_count": 79,
      "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"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Class :  9  :  Truck\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "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"
          }
        }
      ]
    }
  ]
}