IDS_Deep_Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Mining Course Project\n",
    "# Intrusion Detection System"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Name : Fraz Ahmad\n",
    "## ID: SP17-BSE-003"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oeq7pF1Svpi1"
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import operator\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from keras.regularizers import l1\n",
    "from keras.models import Sequential, load_model\n",
    "from keras.layers import Dense, Activation, Dropout,BatchNormalization\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.utils.np_utils import normalize\n",
    "from sklearn.utils import shuffle\n",
    "from tensorflow.keras.callbacks import TensorBoard\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from timeit import default_timer as timer\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mkQJWbvSvpjS"
   },
   "outputs": [],
   "source": [
    "dataPath = \"\"\n",
    "resultPath = 'results'\n",
    "if not os.path.exists(resultPath):\n",
    "    print('result path {} created.'.format(resultPath))\n",
    "    os.mkdir(resultPath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Tj_A3NYmvpju"
   },
   "outputs": [],
   "source": [
    "def loadData(fileName):\n",
    "    dataFile = os.path.join(dataPath, fileName)\n",
    "    df = pd.read_csv(dataFile)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6ZhhTrFrvpkY"
   },
   "outputs": [],
   "source": [
    "def clean_dataset(df):\n",
    "    assert isinstance(df, pd.DataFrame), \"df needs to be a pd.DataFrame\"\n",
    "    df.dropna(inplace=True)\n",
    "    indices_to_keep = ~df.isin([np.nan, np.inf, -np.inf]).any(1)\n",
    "    return df[indices_to_keep].astype(np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "DTVLM7LlwjgH",
    "outputId": "d2c7ea6d-a61d-48c1-c085-e4bca153e043"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount(\"/content/drive\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fdDn9ZVTxOVv"
   },
   "outputs": [],
   "source": [
    "dataPath = \"/content/drive/My Drive/Colab Notebooks/data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fWc504hMvplH"
   },
   "outputs": [],
   "source": [
    "data = loadData('IDS-2017-binaryclass.csv')\n",
    "data = data.astype('float64')\n",
    "data = clean_dataset(data)\n",
    "\n",
    "validation_data = data[0:1000]\n",
    "validation_data.to_csv('validation_data_IDS.csv')\n",
    "\n",
    "data = data[1000:]\n",
    "\n",
    "x_data , y_data = data.drop('Label',axis = 1), data.Label\n",
    "x_data = preprocessing.normalize(x_data.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 274
    },
    "colab_type": "code",
    "id": "yigUsw5dvplS",
    "outputId": "b2caa2b4-c4fd-418f-98ed-2b6b28db48cd"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Destination Port</th>\n",
       "      <th>Flow Duration</th>\n",
       "      <th>Total Fwd Packets</th>\n",
       "      <th>Total Backward Packets</th>\n",
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       "      <th>Bwd IAT Min</th>\n",
       "      <th>Fwd PSH Flags</th>\n",
       "      <th>Bwd PSH Flags</th>\n",
       "      <th>Fwd URG Flags</th>\n",
       "      <th>Bwd URG Flags</th>\n",
       "      <th>Fwd Header Length</th>\n",
       "      <th>Bwd Header Length</th>\n",
       "      <th>Fwd Packets/s</th>\n",
       "      <th>Bwd Packets/s</th>\n",
       "      <th>Min Packet Length</th>\n",
       "      <th>Max Packet Length</th>\n",
       "      <th>Packet Length Mean</th>\n",
       "      <th>Packet Length Std</th>\n",
       "      <th>Packet Length Variance</th>\n",
       "      <th>FIN Flag Count</th>\n",
       "      <th>SYN Flag Count</th>\n",
       "      <th>RST Flag Count</th>\n",
       "      <th>PSH Flag Count</th>\n",
       "      <th>ACK Flag Count</th>\n",
       "      <th>URG Flag Count</th>\n",
       "      <th>CWE Flag Count</th>\n",
       "      <th>ECE Flag Count</th>\n",
       "      <th>Down/Up Ratio</th>\n",
       "      <th>Average Packet Size</th>\n",
       "      <th>Avg Fwd Segment Size</th>\n",
       "      <th>Avg Bwd Segment Size</th>\n",
       "      <th>Fwd Header Length.1</th>\n",
       "      <th>Fwd Avg Bytes/Bulk</th>\n",
       "      <th>Fwd Avg Packets/Bulk</th>\n",
       "      <th>Fwd Avg Bulk Rate</th>\n",
       "      <th>Bwd Avg Bytes/Bulk</th>\n",
       "      <th>Bwd Avg Packets/Bulk</th>\n",
       "      <th>Bwd Avg Bulk Rate</th>\n",
       "      <th>Subflow Fwd Packets</th>\n",
       "      <th>Subflow Fwd Bytes</th>\n",
       "      <th>Subflow Bwd Packets</th>\n",
       "      <th>Subflow Bwd Bytes</th>\n",
       "      <th>Init_Win_bytes_forward</th>\n",
       "      <th>Init_Win_bytes_backward</th>\n",
       "      <th>act_data_pkt_fwd</th>\n",
       "      <th>min_seg_size_forward</th>\n",
       "      <th>Active Mean</th>\n",
       "      <th>Active Std</th>\n",
       "      <th>Active Max</th>\n",
       "      <th>Active Min</th>\n",
       "      <th>Idle Mean</th>\n",
       "      <th>Idle Std</th>\n",
       "      <th>Idle Max</th>\n",
       "      <th>Idle Min</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
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       "      <td>19.0</td>\n",
       "      <td>2149.0</td>\n",
       "      <td>5569.0</td>\n",
       "      <td>1059.0</td>\n",
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       "      <td>254.624870</td>\n",
       "      <td>1418.0</td>\n",
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       "      <td>293.105263</td>\n",
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       "      <td>293.105263</td>\n",
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       "      <td>24.0</td>\n",
       "      <td>2149.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5569.0</td>\n",
       "      <td>29200.0</td>\n",
       "      <td>381.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>93565.72727</td>\n",
       "      <td>189559.186600</td>\n",
       "      <td>665108.0</td>\n",
       "      <td>36279.0</td>\n",
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       "      <td>4919.82671</td>\n",
       "      <td>10015471.0</td>\n",
       "      <td>9996137.0</td>\n",
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       "    <tr>\n",
       "      <th>1002</th>\n",
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       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>588.0</td>\n",
       "      <td>4097.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.230769</td>\n",
       "      <td>67.763257</td>\n",
       "      <td>1430.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>315.153846</td>\n",
       "      <td>531.570918</td>\n",
       "      <td>40.062443</td>\n",
       "      <td>0.222332</td>\n",
       "      <td>4677697.720</td>\n",
       "      <td>1.610000e+07</td>\n",
       "      <td>58400000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>117000000.0</td>\n",
       "      <td>9745203.583</td>\n",
       "      <td>2.270000e+07</td>\n",
       "      <td>58400000.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>117000000.0</td>\n",
       "      <td>9737989.417</td>\n",
       "      <td>2.270000e+07</td>\n",
       "      <td>58500000.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>0.111166</td>\n",
       "      <td>0.111166</td>\n",
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       "      <td>1430.0</td>\n",
       "      <td>173.518518</td>\n",
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       "      <td>45.230769</td>\n",
       "      <td>315.153846</td>\n",
       "      <td>272.0</td>\n",
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       "      <td>13.0</td>\n",
       "      <td>588.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4097.0</td>\n",
       "      <td>8192.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>145541.50000</td>\n",
       "      <td>73600.623530</td>\n",
       "      <td>197585.0</td>\n",
       "      <td>93498.0</td>\n",
       "      <td>58300000.00</td>\n",
       "      <td>172057.46460</td>\n",
       "      <td>58400000.0</td>\n",
       "      <td>58200000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>50372.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>9302.325581</td>\n",
       "      <td>215.000</td>\n",
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       "      <td>215.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>215.000</td>\n",
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       "      <td>215.0</td>\n",
       "      <td>215.0</td>\n",
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       "      <td>0.000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9302.325581</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>53722.0</td>\n",
       "      <td>10890.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1101.928375</td>\n",
       "      <td>275.482094</td>\n",
       "      <td>5445.000</td>\n",
       "      <td>6.447400e+03</td>\n",
       "      <td>10004.0</td>\n",
       "      <td>886.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10004.0</td>\n",
       "      <td>10004.000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>10004.0</td>\n",
       "      <td>10004.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>91.827365</td>\n",
       "      <td>183.654729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.464102</td>\n",
       "      <td>12.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Destination Port   Flow Duration  ...    Idle Min  Label\n",
       "1000              443.0      74336080.0  ...   9976027.0    0.0\n",
       "1001              443.0     115708519.0  ...   9996137.0    0.0\n",
       "1002              443.0     116942443.0  ...  58200000.0    0.0\n",
       "1003            50372.0           215.0  ...         0.0    0.0\n",
       "1004            53722.0         10890.0  ...         0.0    0.0\n",
       "\n",
       "[5 rows x 79 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 349
    },
    "colab_type": "code",
    "id": "Yd5GlFUSvpla",
    "outputId": "2465a7e7-1166-443a-d4dd-f7d036876e7d"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Destination Port</th>\n",
       "      <th>Flow Duration</th>\n",
       "      <th>Total Fwd Packets</th>\n",
       "      <th>Total Backward Packets</th>\n",
       "      <th>Total Length of Fwd Packets</th>\n",
       "      <th>Total Length of Bwd Packets</th>\n",
       "      <th>Fwd Packet Length Max</th>\n",
       "      <th>Fwd Packet Length Min</th>\n",
       "      <th>Fwd Packet Length Mean</th>\n",
       "      <th>Fwd Packet Length Std</th>\n",
       "      <th>Bwd Packet Length Max</th>\n",
       "      <th>Bwd Packet Length Min</th>\n",
       "      <th>Bwd Packet Length Mean</th>\n",
       "      <th>Bwd Packet Length Std</th>\n",
       "      <th>Flow Bytes/s</th>\n",
       "      <th>Flow Packets/s</th>\n",
       "      <th>Flow IAT Mean</th>\n",
       "      <th>Flow IAT Std</th>\n",
       "      <th>Flow IAT Max</th>\n",
       "      <th>Flow IAT Min</th>\n",
       "      <th>Fwd IAT Total</th>\n",
       "      <th>Fwd IAT Mean</th>\n",
       "      <th>Fwd IAT Std</th>\n",
       "      <th>Fwd IAT Max</th>\n",
       "      <th>Fwd IAT Min</th>\n",
       "      <th>Bwd IAT Total</th>\n",
       "      <th>Bwd IAT Mean</th>\n",
       "      <th>Bwd IAT Std</th>\n",
       "      <th>Bwd IAT Max</th>\n",
       "      <th>Bwd IAT Min</th>\n",
       "      <th>Fwd PSH Flags</th>\n",
       "      <th>Bwd PSH Flags</th>\n",
       "      <th>Fwd URG Flags</th>\n",
       "      <th>Bwd URG Flags</th>\n",
       "      <th>Fwd Header Length</th>\n",
       "      <th>Bwd Header Length</th>\n",
       "      <th>Fwd Packets/s</th>\n",
       "      <th>Bwd Packets/s</th>\n",
       "      <th>Min Packet Length</th>\n",
       "      <th>Max Packet Length</th>\n",
       "      <th>Packet Length Mean</th>\n",
       "      <th>Packet Length Std</th>\n",
       "      <th>Packet Length Variance</th>\n",
       "      <th>FIN Flag Count</th>\n",
       "      <th>SYN Flag Count</th>\n",
       "      <th>RST Flag Count</th>\n",
       "      <th>PSH Flag Count</th>\n",
       "      <th>ACK Flag Count</th>\n",
       "      <th>URG Flag Count</th>\n",
       "      <th>CWE Flag Count</th>\n",
       "      <th>ECE Flag Count</th>\n",
       "      <th>Down/Up Ratio</th>\n",
       "      <th>Average Packet Size</th>\n",
       "      <th>Avg Fwd Segment Size</th>\n",
       "      <th>Avg Bwd Segment Size</th>\n",
       "      <th>Fwd Header Length.1</th>\n",
       "      <th>Fwd Avg Bytes/Bulk</th>\n",
       "      <th>Fwd Avg Packets/Bulk</th>\n",
       "      <th>Fwd Avg Bulk Rate</th>\n",
       "      <th>Bwd Avg Bytes/Bulk</th>\n",
       "      <th>Bwd Avg Packets/Bulk</th>\n",
       "      <th>Bwd Avg Bulk Rate</th>\n",
       "      <th>Subflow Fwd Packets</th>\n",
       "      <th>Subflow Fwd Bytes</th>\n",
       "      <th>Subflow Bwd Packets</th>\n",
       "      <th>Subflow Bwd Bytes</th>\n",
       "      <th>Init_Win_bytes_forward</th>\n",
       "      <th>Init_Win_bytes_backward</th>\n",
       "      <th>act_data_pkt_fwd</th>\n",
       "      <th>min_seg_size_forward</th>\n",
       "      <th>Active Mean</th>\n",
       "      <th>Active Std</th>\n",
       "      <th>Active Max</th>\n",
       "      <th>Active Min</th>\n",
       "      <th>Idle Mean</th>\n",
       "      <th>Idle Std</th>\n",
       "      <th>Idle Max</th>\n",
       "      <th>Idle Min</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2826876.0</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2826876.0</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2826876.0</td>\n",
       "      <td>2826876.0</td>\n",
       "      <td>2826876.0</td>\n",
       "      <td>2826876.0</td>\n",
       "      <td>2826876.0</td>\n",
       "      <td>2826876.0</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "      <td>2.826876e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.061642e+03</td>\n",
       "      <td>1.480118e+07</td>\n",
       "      <td>9.370409e+00</td>\n",
       "      <td>1.040590e+01</td>\n",
       "      <td>5.498556e+02</td>\n",
       "      <td>1.618326e+04</td>\n",
       "      <td>2.078039e+02</td>\n",
       "      <td>1.872888e+01</td>\n",
       "      <td>5.825496e+01</td>\n",
       "      <td>6.897668e+01</td>\n",
       "      <td>8.717405e+02</td>\n",
       "      <td>4.108860e+01</td>\n",
       "      <td>3.062590e+02</td>\n",
       "      <td>3.356670e+02</td>\n",
       "      <td>1.491562e+06</td>\n",
       "      <td>7.085144e+04</td>\n",
       "      <td>1.299726e+06</td>\n",
       "      <td>2.922281e+06</td>\n",
       "      <td>9.191991e+06</td>\n",
       "      <td>1.624999e+05</td>\n",
       "      <td>1.449817e+07</td>\n",
       "      <td>2.612779e+06</td>\n",
       "      <td>3.270387e+06</td>\n",
       "      <td>9.052294e+06</td>\n",
       "      <td>1.022820e+06</td>\n",
       "      <td>9.904341e+06</td>\n",
       "      <td>1.807540e+06</td>\n",
       "      <td>1.487625e+06</td>\n",
       "      <td>4.689619e+06</td>\n",
       "      <td>9.680954e+05</td>\n",
       "      <td>4.639538e-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.110767e-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.603305e+04</td>\n",
       "      <td>-2.276440e+03</td>\n",
       "      <td>6.392737e+04</td>\n",
       "      <td>7.002241e+03</td>\n",
       "      <td>1.644865e+01</td>\n",
       "      <td>9.513637e+02</td>\n",
       "      <td>1.721130e+02</td>\n",
       "      <td>2.952698e+02</td>\n",
       "      <td>4.866262e+05</td>\n",
       "      <td>3.528559e-02</td>\n",
       "      <td>4.639538e-02</td>\n",
       "      <td>2.426707e-04</td>\n",
       "      <td>2.982780e-01</td>\n",
       "      <td>3.153145e-01</td>\n",
       "      <td>9.482906e-02</td>\n",
       "      <td>1.110767e-04</td>\n",
       "      <td>2.437320e-04</td>\n",
       "      <td>6.838492e-01</td>\n",
       "      <td>1.921696e+02</td>\n",
       "      <td>5.825496e+01</td>\n",
       "      <td>3.062590e+02</td>\n",
       "      <td>-2.603305e+04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.370409e+00</td>\n",
       "      <td>5.498451e+02</td>\n",
       "      <td>1.040590e+01</td>\n",
       "      <td>1.618291e+04</td>\n",
       "      <td>6.992428e+03</td>\n",
       "      <td>1.988362e+03</td>\n",
       "      <td>5.424478e+00</td>\n",
       "      <td>-2.745474e+03</td>\n",
       "      <td>8.164183e+04</td>\n",
       "      <td>4.118132e+04</td>\n",
       "      <td>1.533534e+05</td>\n",
       "      <td>5.835905e+04</td>\n",
       "      <td>8.324742e+06</td>\n",
       "      <td>5.043017e+05</td>\n",
       "      <td>8.704811e+06</td>\n",
       "      <td>7.928369e+06</td>\n",
       "      <td>1.968070e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.827448e+04</td>\n",
       "      <td>3.366841e+07</td>\n",
       "      <td>7.501853e+02</td>\n",
       "      <td>9.980701e+02</td>\n",
       "      <td>1.000028e+04</td>\n",
       "      <td>2.264635e+06</td>\n",
       "      <td>7.174767e+02</td>\n",
       "      <td>6.034887e+01</td>\n",
       "      <td>1.861650e+02</td>\n",
       "      <td>2.812961e+02</td>\n",
       "      <td>1.947154e+03</td>\n",
       "      <td>6.887448e+01</td>\n",
       "      <td>6.054828e+02</td>\n",
       "      <td>8.400406e+02</td>\n",
       "      <td>2.593947e+07</td>\n",
       "      <td>2.544114e+05</td>\n",
       "      <td>4.509727e+06</td>\n",
       "      <td>8.049443e+06</td>\n",
       "      <td>2.447054e+07</td>\n",
       "      <td>2.951308e+06</td>\n",
       "      <td>3.359058e+07</td>\n",
       "      <td>9.529846e+06</td>\n",
       "      <td>9.643555e+06</td>\n",
       "      <td>2.454024e+07</td>\n",
       "      <td>8.595296e+06</td>\n",
       "      <td>2.875047e+07</td>\n",
       "      <td>8.891074e+06</td>\n",
       "      <td>6.281838e+06</td>\n",
       "      <td>1.716944e+07</td>\n",
       "      <td>8.312555e+06</td>\n",
       "      <td>2.103399e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.053871e-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.106725e+07</td>\n",
       "      <td>1.453202e+06</td>\n",
       "      <td>2.476501e+05</td>\n",
       "      <td>3.817052e+04</td>\n",
       "      <td>2.523561e+01</td>\n",
       "      <td>2.029018e+03</td>\n",
       "      <td>3.055940e+02</td>\n",
       "      <td>6.320356e+02</td>\n",
       "      <td>1.648144e+06</td>\n",
       "      <td>1.845008e-01</td>\n",
       "      <td>2.103399e-01</td>\n",
       "      <td>1.557600e-02</td>\n",
       "      <td>4.575023e-01</td>\n",
       "      <td>4.646411e-01</td>\n",
       "      <td>2.929787e-01</td>\n",
       "      <td>1.053871e-02</td>\n",
       "      <td>1.561002e-02</td>\n",
       "      <td>6.806016e-01</td>\n",
       "      <td>3.319673e+02</td>\n",
       "      <td>1.861650e+02</td>\n",
       "      <td>6.054828e+02</td>\n",
       "      <td>2.106725e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.501853e+02</td>\n",
       "      <td>9.986748e+03</td>\n",
       "      <td>9.980701e+02</td>\n",
       "      <td>2.264604e+06</td>\n",
       "      <td>1.434038e+04</td>\n",
       "      <td>8.454857e+03</td>\n",
       "      <td>6.368608e+02</td>\n",
       "      <td>1.085731e+06</td>\n",
       "      <td>6.489922e+05</td>\n",
       "      <td>3.936338e+05</td>\n",
       "      <td>1.026450e+06</td>\n",
       "      <td>5.774373e+05</td>\n",
       "      <td>2.364102e+07</td>\n",
       "      <td>4.605026e+06</td>\n",
       "      <td>2.437804e+07</td>\n",
       "      <td>2.337438e+07</td>\n",
       "      <td>3.975853e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.300000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-2.610000e+08</td>\n",
       "      <td>-2.000000e+06</td>\n",
       "      <td>-1.300000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.300000e+01</td>\n",
       "      <td>-1.400000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.200000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.221223e+10</td>\n",
       "      <td>-1.073741e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-3.221223e+10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-5.368707e+08</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.300000e+01</td>\n",
       "      <td>1.550000e+02</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.192923e+02</td>\n",
       "      <td>3.415264e+00</td>\n",
       "      <td>6.400000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.240000e+02</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>1.768374e+00</td>\n",
       "      <td>1.261750e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7.750000e+00</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.000000e+01</td>\n",
       "      <td>3.133900e+04</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>6.200000e+01</td>\n",
       "      <td>1.230000e+02</td>\n",
       "      <td>3.700000e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.400000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7.900000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7.200000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.586453e+03</td>\n",
       "      <td>1.094691e+02</td>\n",
       "      <td>1.155200e+04</td>\n",
       "      <td>1.382835e+02</td>\n",
       "      <td>3.087500e+04</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>4.500000e+01</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.500000e+01</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.400000e+01</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>6.158252e+01</td>\n",
       "      <td>1.987558e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>8.700000e+01</td>\n",
       "      <td>5.733333e+01</td>\n",
       "      <td>2.629068e+01</td>\n",
       "      <td>6.912000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>7.225000e+01</td>\n",
       "      <td>3.400000e+01</td>\n",
       "      <td>7.200000e+01</td>\n",
       "      <td>6.400000e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>6.200000e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.230000e+02</td>\n",
       "      <td>2.510000e+02</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.400000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.430000e+02</td>\n",
       "      <td>3.239711e+06</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>1.880000e+02</td>\n",
       "      <td>4.840000e+02</td>\n",
       "      <td>8.100000e+01</td>\n",
       "      <td>3.600000e+01</td>\n",
       "      <td>5.000000e+01</td>\n",
       "      <td>2.616295e+01</td>\n",
       "      <td>2.820000e+02</td>\n",
       "      <td>7.700000e+01</td>\n",
       "      <td>1.810000e+02</td>\n",
       "      <td>7.850000e+01</td>\n",
       "      <td>1.666667e+05</td>\n",
       "      <td>2.325581e+04</td>\n",
       "      <td>3.391426e+05</td>\n",
       "      <td>6.964541e+05</td>\n",
       "      <td>2.483072e+06</td>\n",
       "      <td>6.400000e+01</td>\n",
       "      <td>1.259412e+06</td>\n",
       "      <td>2.095842e+05</td>\n",
       "      <td>6.679364e+04</td>\n",
       "      <td>9.565768e+05</td>\n",
       "      <td>4.800000e+01</td>\n",
       "      <td>9.927400e+04</td>\n",
       "      <td>1.836619e+04</td>\n",
       "      <td>1.584578e+04</td>\n",
       "      <td>6.062425e+04</td>\n",
       "      <td>4.500000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.200000e+02</td>\n",
       "      <td>1.040000e+02</td>\n",
       "      <td>1.204819e+04</td>\n",
       "      <td>7.380074e+03</td>\n",
       "      <td>3.600000e+01</td>\n",
       "      <td>5.320000e+02</td>\n",
       "      <td>1.198000e+02</td>\n",
       "      <td>1.752712e+02</td>\n",
       "      <td>3.072000e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.495000e+02</td>\n",
       "      <td>5.000000e+01</td>\n",
       "      <td>1.810000e+02</td>\n",
       "      <td>1.200000e+02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>1.880000e+02</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>4.840000e+02</td>\n",
       "      <td>8.192000e+03</td>\n",
       "      <td>2.350000e+02</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.553500e+04</td>\n",
       "      <td>1.200000e+08</td>\n",
       "      <td>2.197590e+05</td>\n",
       "      <td>2.919220e+05</td>\n",
       "      <td>1.290000e+07</td>\n",
       "      <td>6.554530e+08</td>\n",
       "      <td>2.482000e+04</td>\n",
       "      <td>2.325000e+03</td>\n",
       "      <td>5.940857e+03</td>\n",
       "      <td>7.125597e+03</td>\n",
       "      <td>1.953000e+04</td>\n",
       "      <td>2.896000e+03</td>\n",
       "      <td>5.800500e+03</td>\n",
       "      <td>8.194660e+03</td>\n",
       "      <td>2.071000e+09</td>\n",
       "      <td>4.000000e+06</td>\n",
       "      <td>1.200000e+08</td>\n",
       "      <td>8.480026e+07</td>\n",
       "      <td>1.200000e+08</td>\n",
       "      <td>1.200000e+08</td>\n",
       "      <td>1.200000e+08</td>\n",
       "      <td>1.200000e+08</td>\n",
       "      <td>8.460293e+07</td>\n",
       "      <td>1.200000e+08</td>\n",
       "      <td>1.200000e+08</td>\n",
       "      <td>1.200000e+08</td>\n",
       "      <td>1.200000e+08</td>\n",
       "      <td>8.441801e+07</td>\n",
       "      <td>1.200000e+08</td>\n",
       "      <td>1.200000e+08</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.644908e+06</td>\n",
       "      <td>5.838440e+06</td>\n",
       "      <td>3.000000e+06</td>\n",
       "      <td>2.000000e+06</td>\n",
       "      <td>1.448000e+03</td>\n",
       "      <td>2.482000e+04</td>\n",
       "      <td>3.337143e+03</td>\n",
       "      <td>4.731522e+03</td>\n",
       "      <td>2.240000e+07</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.560000e+02</td>\n",
       "      <td>3.893333e+03</td>\n",
       "      <td>5.940857e+03</td>\n",
       "      <td>5.800500e+03</td>\n",
       "      <td>4.644908e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.197590e+05</td>\n",
       "      <td>1.287034e+07</td>\n",
       "      <td>2.919220e+05</td>\n",
       "      <td>6.554530e+08</td>\n",
       "      <td>6.553500e+04</td>\n",
       "      <td>6.553500e+04</td>\n",
       "      <td>2.135570e+05</td>\n",
       "      <td>1.380000e+02</td>\n",
       "      <td>1.100000e+08</td>\n",
       "      <td>7.420000e+07</td>\n",
       "      <td>1.100000e+08</td>\n",
       "      <td>1.100000e+08</td>\n",
       "      <td>1.200000e+08</td>\n",
       "      <td>7.690000e+07</td>\n",
       "      <td>1.200000e+08</td>\n",
       "      <td>1.200000e+08</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Destination Port   Flow Duration  ...      Idle Min         Label\n",
       "count       2.826876e+06    2.826876e+06  ...  2.826876e+06  2.826876e+06\n",
       "mean        8.061642e+03    1.480118e+07  ...  7.928369e+06  1.968070e-01\n",
       "std         1.827448e+04    3.366841e+07  ...  2.337438e+07  3.975853e-01\n",
       "min         0.000000e+00   -1.300000e+01  ...  0.000000e+00  0.000000e+00\n",
       "25%         5.300000e+01    1.550000e+02  ...  0.000000e+00  0.000000e+00\n",
       "50%         8.000000e+01    3.133900e+04  ...  0.000000e+00  0.000000e+00\n",
       "75%         4.430000e+02    3.239711e+06  ...  0.000000e+00  0.000000e+00\n",
       "max         6.553500e+04    1.200000e+08  ...  1.200000e+08  1.000000e+00\n",
       "\n",
       "[8 rows x 79 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zYhotJXqvplr"
   },
   "outputs": [],
   "source": [
    "def Model(inputDim):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(1024, activation = 'relu' , input_shape = (inputDim,)))\n",
    "    model.add(Dense(512, activation = 'relu', kernel_regularizer='l1'))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Dense(256, activation = 'relu', kernel_regularizer='l2'))\n",
    "    model.add(Dense(256, activation = 'relu', kernel_regularizer='l1'))\n",
    "    model.add(Dense(128, activation = 'relu'))\n",
    "    model.add(Dropout(0.2))\n",
    "    model.add(Dense(64, activation = 'relu'))\n",
    "    model.add(Dense(1 , activation = 'sigmoid'))\n",
    "    \n",
    "    opt = keras.optimizers.Adam(learning_rate=0.01)\n",
    "    model.compile(optimizer= opt, loss = 'binary_crossentropy', metrics = ['accuracy'])\n",
    "    model.summary()\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "g79VVtQIvpl6"
   },
   "outputs": [],
   "source": [
    "def TrainAndTest(x_data, y_data,optimizer='adam',epochs=50, batch_size = 256):\n",
    "    \n",
    "    #Creating data for analysis\n",
    "    time_gen = int(time.time())\n",
    "    #global model_name\n",
    "    file_name=\"IDS_binaryclass\"\n",
    "    model_name = \"{}_{}\".format(file_name, time_gen)\n",
    "    #$ tensorboard --logdir=logs/\n",
    "    #tensorboard = TensorBoard(log_dir='logs/{}'.format(model_name))\n",
    "    \n",
    "    X_train , X_test , y_train, y_test = train_test_split(x_data, y_data, test_size=0.20, random_state=42)\n",
    "    y_train = y_train.values.reshape(y_train.shape[0],1)\n",
    "    y_test = y_test.values.reshape(y_test.shape[0],1)\n",
    "\n",
    "    print(\"X train shape : \", X_train.shape , \" y train shape : \" , y_train.shape)\n",
    "      \n",
    "    model = Model(x_data.shape[1])\n",
    "    \n",
    "    history = model.fit(x=X_train,y= y_train, epochs=epochs, batch_size=batch_size,verbose=1)\n",
    "    \n",
    "    pred = model.evaluate(X_test, y_test, verbose = 1)\n",
    "    \n",
    "    print(model.metrics_names)\n",
    "    val_acc, val_loss = pred[1]*100, pred[0]*100\n",
    "    print('Test Data: accuracy: {:.2f}%: loss: {:.2f}'.format(val_acc, val_loss))\n",
    "\n",
    "    model.save(\"results/models/{}acc{}.model\".format(model_name,val_acc))\n",
    "\n",
    "    resultFile = os.path.join(resultPath, file_name)\n",
    "    with open('{}.result'.format(resultFile), 'a') as fout:\n",
    "        fout.write('{} results...'.format(model_name))\n",
    "        fout.write('\\taccuracy: {:.2f} loss: {:.2f}'.format(val_acc, val_loss))\n",
    "\n",
    "    return model ,history, val_acc, val_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "Y24Nssyy03Hu",
    "outputId": "6d8f8958-d51b-47cc-ebe8-450a1978d7af"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2826876, 78)"
      ]
     },
     "execution_count": 31,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "Qn9NaSskvpmF",
    "outputId": "37b970c0-cf62-4106-e463-31d98d2e32fa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X train shape :  (2261500, 78)  y train shape :  (2261500, 1)\n",
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_22 (Dense)             (None, 1024)              80896     \n",
      "_________________________________________________________________\n",
      "dense_23 (Dense)             (None, 512)               524800    \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 512)               2048      \n",
      "_________________________________________________________________\n",
      "dense_24 (Dense)             (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "dense_25 (Dense)             (None, 256)               65792     \n",
      "_________________________________________________________________\n",
      "dense_26 (Dense)             (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_27 (Dense)             (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dense_28 (Dense)             (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 846,081\n",
      "Trainable params: 845,057\n",
      "Non-trainable params: 1,024\n",
      "_________________________________________________________________\n",
      "Epoch 1/30\n",
      "8834/8834 [==============================] - 32s 4ms/step - loss: 7.3014 - accuracy: 0.9585\n",
      "Epoch 2/30\n",
      "8834/8834 [==============================] - 32s 4ms/step - loss: 7.1169 - accuracy: 0.9684\n",
      "Epoch 3/30\n",
      "8834/8834 [==============================] - 32s 4ms/step - loss: 7.0995 - accuracy: 0.9735\n",
      "Epoch 4/30\n",
      "8834/8834 [==============================] - 31s 4ms/step - loss: 7.0889 - accuracy: 0.9765\n",
      "Epoch 5/30\n",
      "8834/8834 [==============================] - 31s 4ms/step - loss: 7.0818 - accuracy: 0.9781\n",
      "Epoch 6/30\n",
      "8834/8834 [==============================] - 32s 4ms/step - loss: 7.0746 - accuracy: 0.9790\n",
      "Epoch 7/30\n",
      "8834/8834 [==============================] - 32s 4ms/step - loss: 7.0707 - accuracy: 0.9795\n",
      "Epoch 8/30\n",
      "8834/8834 [==============================] - 32s 4ms/step - loss: 7.0670 - accuracy: 0.9803\n",
      "Epoch 9/30\n",
      "8834/8834 [==============================] - 32s 4ms/step - loss: 7.0638 - accuracy: 0.9808\n",
      "Epoch 10/30\n",
      "8834/8834 [==============================] - 32s 4ms/step - loss: 7.0621 - accuracy: 0.9811\n",
      "Epoch 11/30\n",
      "8834/8834 [==============================] - 32s 4ms/step - loss: 7.0593 - accuracy: 0.9816\n",
      "Epoch 12/30\n",
      "8834/8834 [==============================] - 32s 4ms/step - loss: 7.0572 - accuracy: 0.9821\n",
      "Epoch 13/30\n",
      "8834/8834 [==============================] - 32s 4ms/step - loss: 7.0556 - accuracy: 0.9821\n",
      "Epoch 14/30\n",
      "8834/8834 [==============================] - 31s 4ms/step - loss: 7.0525 - accuracy: 0.9824\n",
      "Epoch 15/30\n",
      "8834/8834 [==============================] - 32s 4ms/step - loss: 7.0488 - accuracy: 0.9826\n",
      "Epoch 16/30\n",
      "8834/8834 [==============================] - 32s 4ms/step - loss: 7.0457 - accuracy: 0.9828\n",
      "Epoch 17/30\n",
      "8834/8834 [==============================] - 32s 4ms/step - loss: 7.0447 - accuracy: 0.9831\n",
      "Epoch 18/30\n",
      "8834/8834 [==============================] - 31s 4ms/step - loss: 7.0437 - accuracy: 0.9834\n",
      "Epoch 19/30\n",
      "8834/8834 [==============================] - 31s 4ms/step - loss: 7.0423 - accuracy: 0.9839\n",
      "Epoch 20/30\n",
      "8834/8834 [==============================] - 32s 4ms/step - loss: 7.0442 - accuracy: 0.9838\n",
      "Epoch 21/30\n",
      "8834/8834 [==============================] - 32s 4ms/step - loss: 7.0407 - accuracy: 0.9843\n",
      "Epoch 22/30\n",
      "8834/8834 [==============================] - 32s 4ms/step - loss: 7.0404 - accuracy: 0.9843\n",
      "Epoch 23/30\n",
      "8834/8834 [==============================] - 31s 4ms/step - loss: 7.0370 - accuracy: 0.9847\n",
      "Epoch 24/30\n",
      "8834/8834 [==============================] - 31s 4ms/step - loss: 7.0362 - accuracy: 0.9848\n",
      "Epoch 25/30\n",
      "8834/8834 [==============================] - 32s 4ms/step - loss: 7.0336 - accuracy: 0.9849\n",
      "Epoch 26/30\n",
      "8834/8834 [==============================] - 32s 4ms/step - loss: 7.0372 - accuracy: 0.9846\n",
      "Epoch 27/30\n",
      "8834/8834 [==============================] - 31s 4ms/step - loss: 7.0348 - accuracy: 0.9851\n",
      "Epoch 28/30\n",
      "8834/8834 [==============================] - 31s 4ms/step - loss: 7.0319 - accuracy: 0.9853\n",
      "Epoch 29/30\n",
      "8834/8834 [==============================] - 31s 4ms/step - loss: 7.0291 - accuracy: 0.9855\n",
      "Epoch 30/30\n",
      "8834/8834 [==============================] - 31s 4ms/step - loss: 7.0291 - accuracy: 0.9855\n",
      "17668/17668 [==============================] - 30s 2ms/step - loss: 7.2260 - accuracy: 0.9638\n",
      "['loss', 'accuracy']\n",
      "Test Data: accuracy: 96.38%: loss: 722.60\n",
      "INFO:tensorflow:Assets written to: results/models/IDS_binaryclass_1597501785acc96.3769257068634.model/assets\n"
     ]
    }
   ],
   "source": [
    "model ,history , val_acc , val_loss = TrainAndTest(x_data, y_data, epochs = 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 545
    },
    "colab_type": "code",
    "id": "qDHMKuHGvpmU",
    "outputId": "aa6f9da5-e728-47b5-88e0-f0626afaabfe"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AD6AnsD2wRtJg4EMR8UhEBDAVOHKb9sQq98or8Pd/n7p4zp4N//mfKRROPNHP1DUroEqCYAiwouz9ymxauSeBo7Pxo4C+kgZExF9IwbA6G+6KiIXZ+iub2SYAkk6RVCupdu3atRUU15q0cSP84hfpds6XXgrf+Q4sWQJnnw077JB36cwsJ211c5fTgfGSniA1/awC6iTtCewNDCVV9BMlfaYlG46IyyOiJiJqBg0a1EbFLZgIuOEG2HvvdEvnT38a5s+Hyy6DXXbJu3RmlrNKgmAVUH5Z6NBs2nsi4qWIODoi9gP+JZu2jnR08EhErI+I9cBMYFy2/tCtbdPawJYtcM896YHrxx8PH/pQen/HHenB62ZmVBYEc4BRkkZI6gEcD9xWvoCkgZJK2zobuCobf5F0pNBd0vako4WFEbEaeEPS32S9hb4J3NoG+2MAzz0HP/lJuq//IYfAiy/CVVfB44/DF76Qd+nMrJNp9jqCiNgs6TTgLqAbcFVEPCPpHKA2Im4DJgDnSQrgQeDUbPUZwERgPunE8Z8i4vZs3veBq4EdSEcKM9tqpwrpzTdhxgy4+urUBVSCL34RLrgg9QzyOQAza4JSp53qUFNTE7W1tXkXo/PYsiVV+ldfnULgrbdgr73g5JNTD6ChQ5vbgpkVgKS5EVHT1HxfWVyN3ngDLr44BcDzz0PfvvD1r6cAGDfOT/cysxZxEFSb22+Hv/s7eOkl+Pzn4T/+A446Cnr3zrtkZlalHATVYs0a+MEPYPr0dCHYTTfBQQflXSoz6wL8kNjOLiI1Ae29N9xyC/zsZ+mhMA4BM2sjPiLozJYtg1NOgVmz4OCD4Yor4GMfy7tUZtbF+IigM9q8Od37f5990vMALrss3SHUIWBm7cBHBJ3NvHnpHkBz56bbQl96KQxp9DZMZmZtwkcEnckFF0BNTXok5I03pnMCDgEza2c+IugsLrwQzjoLjjsuNQX17593icysIBwEncFvfgOnnw5f/Sr8/vfpGcFmZh3ETUN5mz499Qw69FC49lqHgJl1OAdBnmbOhBNOSM8HuOkm6NEj7xKZWQE5CPLy0ENwzDGpi+gf/+hbRJhZbhwEeXj8cfjKV2DYMPjTn+DDH867RGZWYA6Cjvbss/ClL0G/fulpYTvvnHeJzKzgHAQd6YUX0sNiunWDe++F3XZrfh0zs3bm7qMdZc2a9JjI9evT7SJGjcq7RGZmgIOgY7z+emoOeumldCSw7755l8jM7D0Ogva2cSN8+cuwcGHqHTRuXN4lMjN7HwdBe7viCvjLX+CGG9L5ATOzTsYni9vTu+/ClCnpWQLHHZd3aczMGuUjgvZ0ww3w4ovpVtJmZp2Ujwjay5YtcP756crhww/PuzRmZk3yEUF7ufNOeOYZ+N3vQMq7NGZmTfIRQXs5/3zYfXf42tfyLomZ2Vb5iKA9PPww/PnP8Ktfwfbb510aM7Ot8hFBe7jgAhg4EL71rbxLYmbWrIqCQNKhkhZJWirprEbm7y5plqSnJN0vaWg2/XOS5pUNGyUdmc27WtLzZfPGtO2u5WT+/HTh2A9+4FtLm1lVaLZpSFI34BLgi8BKYI6k2yJiQdliU4CpEXGNpInAecCJETEbGJNtpz+wFLi7bL0zImJG2+xKJ/Hzn0OfPnDqqXmXxMysIpUcEYwFlkbEsojYBEwDJjVYZjRwXzY+u5H5AMcCMyNiQ2sL2+ktXw7XXw/f+54fPm9mVaOSIBgCrCh7vzKbVu5J4Ohs/Cigr6QBDZY5Hri+wbRzs+akiyT1bOzDJZ0iqVZS7dq1aysobo5++UvYbjv40Y/yLomZWcXa6mTx6cB4SU8A44FVQF1ppqTBwCeAu8rWORv4GHAg0B84s7ENR8TlEVETETWDBg1qo+K2g7Vr4Te/Sc8gHjo079KYmVWsku6jq4DyJ6gMzaa9JyJeIjsikLQjcExErCtb5DjgDxHxbtk6q7PRdyT9lhQm1etXv0p3Gj3jjLxLYmbWIpUcEcwBRkkaIakHqYnntvIFJA2UVNrW2cBVDbYxmQbNQtlRApIEHAk83fLidxLr18Ovfw1HHgl77513aczMWqTZIIiIzcBppGadhcD0iHhG0jmSjsgWmwAskrQY2AU4t7S+pOGkI4oHGmz6OknzgfnAQOBn27QnebriivTwmTMbbd0yM+vUFBF5l6FiNTU1UVtbm3cx3m/TJhg5Mj16cvbsvEtjZvYBkuZGRE1T832LiW113XWwahVceWXeJTEzaxXfYmJbbNmSbicxZgwcckjepTEzaxUfEWyLW2+FRYtg2jTfatrMqpaPCForIt1qeuRIOOaYvEtjZtZqPiJorQcegMceg8sug+7+M5pZ9fIRQWtdfDHsvDOcdFLeJTEz2yYOgtbYuBHuvhuOOw522CHv0piZbRMHQWs8+CC8/TYcdljeJTEz22YOgtaYORN69oQJE/IuiZnZNnMQtMbMmSkE/AQyM+sCHAQt9fzz6doBNwuZWRfhIGipmTPTq4PAzLoIB0FLzZxZf5M5M7MuwEHQEhs3wn33paMB31LCzLoIB0FLPPggbNjgZiEz61IcBC1R6jb6uc/lXRIzszbjIGiJmTNh/Hh3GzWzLsVBUCl3GzWzLspBUKlSt9HDD8+3HGZmbcxBUCl3GzWzLspBUAl3GzWzLsxBUImHHnK3UTPrshwElXC3UTPrwhwElXC3UTPrwhwEzVm+HJ591s1CZtZlOQia47uNmlkX5yBozsyZMGIE7LVX3iUxM2sXDoKt2bgRZs1yt1Ez69IqCgJJh0paJGmppLMamb+7pFmSnpJ0v6Sh2fTPSZpXNmyUdGQ2b4SkR7Nt3iCpR9vuWhtwt1EzK4Bmg0BSN+AS4DBgNDBZ0ugGi00BpkbEvsA5wHkAETE7IsZExBhgIrABuDtb5wLgoojYE3gd+HYb7E/bmjkTevRwt1Ez69IqOSIYCyyNiGURsQmYBkxqsMxo4L5sfHYj8wGOBWZGxAZJIgXDjGzeNcCRLS18uyt1G+3TJ++SmJm1m0qCYAiwouz9ymxauSeBo7Pxo4C+kgY0WOZ44PpsfACwLiI2b2WbAEg6RVKtpNq1a9dWUNw24m6jZlYQbXWy+HRgvKQngPHAKqCuNFPSYOATwF0t3XBEXB4RNRFRM2jQoDYqbgXcbdTMCqJ7BcusAnYrez80m/aeiHiJ7IhA0o7AMRGxrmyR44A/RMS72ftXgX6SumdHBR/YZu5K3UY/+tG8S2Jm1q4qOSKYA4zKevn0IDXx3Fa+gKSBkkrbOhu4qsE2JlPfLEREBOlcwrHZpJOAW1te/Hbyzju+26iZFUazQZD9Yj+N1KyzEJgeEc9IOkfSEdliE4BFkhYDuwDnltaXNJx0RPFAg02fCfxY0lLSOYMrt2lP2tJDD8Fbb7lZyMwKoZKmISLiTuDOBtN+UjY+g/oeQA3XXU4jJ4IjYhmpR1Ln426jZlYgvrK4Me42amYF4iBo6IUXYOFCNwuZWWE4CBpyt1EzKxgHQUN33gnDh7vbqJkVhoOgnLuNmlkBOQjKzZ2buo0eckjeJTEz6zAOgnKLF6fXffbJtxxmZh3IQVBuyRLo3j2dIzAzKwgHQbnFi9P9hbpXdJ2dmVmX4CAot2QJjBqVdynMzDqUg6AkApYudRCYWeE4CEpWr049hhwEZlYwDoKSJUvSq4PAzArGQVDiIDCzgnIQlCxZkm49PWxY3iUxM+tQDoKSJUtg5Ejo1i3vkpiZdSgHQYm7jppZQTkIALZscddRMyssBwHAqlWwcaODwMwKyUEA7jFkZoXmIID6INhrr3zLYWaWAwcBpCDo1QuGDMm7JGZmHc5BACkI9twTtvOfw8yKxzUfpNtP+/yAmRWUg6CuDpYtcxCYWWE5CF58ETZtchCYWWE5CNx11MwKrqIgkHSopEWSlko6q5H5u0uaJekpSfdLGlo2b5ikuyUtlLRA0vBs+tWSnpc0LxvGtNVOtYiDwMwKrtkgkNQNuAQ4DBgNTJY0usFiU4CpEbEvcA5wXtm8qcAvImJvYCzwctm8MyJiTDbM24b9aL0lS6BPHxg8OJePNzPLWyVHBGOBpRGxLCI2AdOASQ2WGQ3cl43PLs3PAqN7RNwDEBHrI2JDm5S8rZS6jkp5l8TMLBeVBMEQYEXZ+5XZtHJPAkdn40cBfSUNAPYC1km6WdITkn6RHWGUnJs1J10kqWdjHy7pFEm1kmrXrl1b0U61iO86amYF11Yni08Hxkt6AhgPrALqgO7AZ7L5BwIjgZOzdc4GPpZN7w+c2diGI+LyiKiJiJpBgwa1UXEzmzfD8887CMys0CoJglXAbmXvh2bT3hMRL0XE0RGxH/Av2bR1pKOHeVmz0mbgFmD/bP7qSN4BfktqgupYy5enMHAQmFmBVRIEc4BRkkZI6gEcD9xWvoCkgZJK2zobuKps3X6SSj/lJwILsnUGZ68CjgSe3pYdaRX3GDIzaz4Isl/ypwF3AQuB6RHxjKRzJB2RLTYBWCRpMbALcG62bh2pWWiWpPmAgCuyda7Lps0HBgI/a7O9qpSDwMyM7pUsFBF3Anc2mPaTsvEZwIwm1r0H2LeR6RNbVNL2sGQJ9O0LO++cd0nMzHJT7CuLlyxJzyBw11EzKzAHgZuFzKzgihsEmzalXkMOAjMruOIGwbJlsGWLg8DMCq+4QeAeQ2ZmgIPAQWBmhVfsINhpJxgwIO+SmJnlqthB4KMBMzMHgZlZ0RUzCDZuhBUrHARmZhQ1CJ57DiIcBGZmFDUI3GPIzOw9DgIzs4IrbhAMHAj9+uVdEjOz3BU3CHw0YGYGOAjMzAqveEGwYQOsWpWeQ2BmZgUMgqVL06uPCMzMgCIGweLF6dVBYGYGFDEISl1H99wz33KYmXUSxQyCXXdND603M7OCBoGbhczM3uMgMDMruGIFwRtvwJo1DgIzszLFCgJ3HTUz+4BiBYFvNmdm9gHFDAJ3HTUze0/xgmDIEOjdO++SmJl1GhUFgaRDJS2StFTSWY3M313SLElPSbpf0tCyecMk3S1poaQFkoZn00dIejTb5g2SerTVTjXJPYbMzD6g2SCQ1A24BDgMGA1MljS6wWJTgKkRsS9wDnBe2bypwC8iYm9gLPByNv0C4KKI2BN4Hfj2tuxIRRwEZmYfUMkRwVhgaUQsi4hNwDRgUoNlRgP3ZeOzS/OzwOgeEfcARMT6iNggScBEYEa2zjXAkdu0J81Ztw5eecVBYGbWQCVBMARYUfZ+ZTat3JPA0dn4UUBfSQOAvYB1km6W9ISkX2RHGAOAdRGxeSvbBEDSKZJqJdWuXbu2sr1qjHsMmZk1qq1OFp8OjJf0BDAeWAXUAd2Bz2TzDwRGAie3ZMMRcXlE1EREzaBBg1pfwlIQ+DkEZmbvU0kQrAJ2K3s/NJv2noh4KSKOjoj9gH/Jpq0j/dKflzUrbQZuAfYHXgX6Sere1Dbb3JIlIMHIke36MWZm1aaSIJgDjMp6+fQAjgduK19A0kBJpW2dDVxVtm4/SaWf8hOBBRERpHMJx2bTTwJubf1uVGDxYhg2DHr1atePMTOrNs0GQfZL/jTgLmAhMD0inpF0jqQjssUmAIskLQZ2Ac7N1q0jNQvNkjQfEHBFts6ZwI8lLSWdM7iyzfaqMe4xZGbWKKUf59WhpqYmamtrW75iBPTvD5Mnw6WXtn3BzMw6MUlzI6KmqfnFuLL41VdT91EfEZiZfUAxgsBdR83MmuQgMDMruOIEwXbbwYgReZfEzKzTKU4QDB8OPdr/vnZmZtWme/OLdAFjxqQgMDOzDyhGEJz1gTtnm5lZphhNQ2Zm1iQHgZlZwTkIzMwKzkFgZlZwDgIzs4JzEJiZFZyDwMys4BwEZmYFV1XPI5C0FnihlasPBF5pw+J0Bl1tn7w/nV9X26eutj/Q+D7tHhFNPvS9qoJgW0iq3dqDGapRV9sn70/n19X2qavtD7Run9w0ZGZWcA4CM7OCK1IQXJ53AdpBV9sn70/n19X2qavtD7RinwpzjsDMzBpXpCMCMzNrhIPAzKzgChEEkg6VtEjSUklV/5QaScslzZc0T1Jt3uVpDUlXSXpZ0tNl0/pLukfSkux1pzzL2BJN7M9PJa3Kvqd5kiXpatIAAAMISURBVA7Ps4wtIWk3SbMlLZD0jKQfZtOr+Ttqap+q8nuS1EvSY5KezPbn37PpIyQ9mtV3N0hq9hm9Xf4cgaRuwGLgi8BKYA4wOSIW5FqwbSBpOVATEVV7IYykzwLrgakRsU827efAaxFxfhbYO0XEmXmWs1JN7M9PgfURMSXPsrWGpMHA4Ih4XFJfYC5wJHAy1fsdNbVPx1GF35MkAX0iYr2k7YGHgR8CPwZujohpkv4HeDIiLtvatopwRDAWWBoRyyJiEzANmJRzmQovIh4EXmsweRJwTTZ+Dek/aVVoYn+qVkSsjojHs/E3gYXAEKr7O2pqn6pSJOuzt9tnQwATgRnZ9Iq+oyIEwRBgRdn7lVTxl58J4G5JcyWdkndh2tAuEbE6G/8rsEuehWkjp0l6Kms6qppmlHKShgP7AY/SRb6jBvsEVfo9SeomaR7wMnAP8BywLiI2Z4tUVN8VIQi6ooMjYn/gMODUrFmiS4nUZlnt7ZaXAXsAY4DVwIX5FqflJO0I3AT8Q0S8UT6vWr+jRvapar+niKiLiDHAUFLrx8das50iBMEqYLey90OzaVUrIlZlry8DfyD9A+gK1mTtuKX23JdzLs82iYg12X/ULcAVVNn3lLU73wRcFxE3Z5Or+jtqbJ+q/XsCiIh1wGxgHNBPUvdsVkX1XRGCYA4wKjuT3gM4Hrgt5zK1mqQ+2YkuJPUBDgGe3vpaVeM24KRs/CTg1hzLss1KFWbmKKroe8pORF4JLIyIX5bNqtrvqKl9qtbvSdIgSf2y8R1IHWIWkgLh2Gyxir6jLt9rCCDrDnYx0A24KiLOzblIrSZpJOkoAKA78Ptq3B9J1wMTSLfMXQP8G3ALMB0YRrrd+HERURUnYJvYnwmk5oYAlgPfK2tf79QkHQw8BMwHtmST/5nUpl6t31FT+zSZKvyeJO1LOhncjfSjfnpEnJPVEdOA/sATwAkR8c5Wt1WEIDAzs6YVoWnIzMy2wkFgZlZwDgIzs4JzEJiZFZyDwMys4BwEZmYF5yAwMyu4/w/3fi1rJUTh5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "acc = history.history['accuracy']\n",
    "loss = history.history['loss']\n",
    "\n",
    "epochs = range(len(acc))\n",
    "\n",
    "plt.plot(epochs, acc, 'r', label='Training accuracy')\n",
    "plt.title('Training accuracy')\n",
    "plt.savefig('graph_training_acc_96%.png')\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(epochs, loss, 'b', label='Training Loss')\n",
    "plt.title('Training loss')\n",
    "plt.savefig('graph_training_loss_7.2260.png')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "POuleVeEvpma"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Hua4wFTbvpmr"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Wbtv34CXvpmx"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "IDS Deep Learning.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}