Phishing_Websites'_Detection_with_Neural_Networks_using_fastai.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Phishing Websites' Detection with Neural Networks using fastai",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "0EDLUkUR-a7e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Filter the uneccesary warnings\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# Import pandas and numpy\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Fix the random seed\n",
        "np.random.seed(7)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IgotJCFa-56x",
        "colab_type": "code",
        "outputId": "403d610a-e6fa-494f-fa87-664ac408567f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1010
        }
      },
      "source": [
        "data = pd.read_csv('https://raw.githubusercontent.com/sayakpaul/Phishing-Websites-Detection/master/Phishing.csv')\n",
        "data.head(10).T"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>having_IP_Address</th>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>URL_Length</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>Shortining_Service</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <th>having_At_Symbol</th>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>double_slash_redirecting</th>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Prefix_Suffix</th>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>having_Sub_Domain</th>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SSLfinal_State</th>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Domain_registeration_length</th>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Favicon</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>port</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HTTPS_token</th>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Request_URL</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>URL_of_Anchor</th>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Links_in_tags</th>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SFH</th>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Submitting_to_email</th>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Abnormal_URL</th>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Redirect</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>on_mouseover</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RightClick</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>popUpWidnow</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iframe</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age_of_domain</th>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DNSRecord</th>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>web_traffic</th>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Page_Rank</th>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Google_Index</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Links_pointing_to_page</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Statistical_report</th>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Result</th>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             0  1  2  3  4  5  6  7  8  9\n",
              "having_IP_Address           -1  1  1  1  1 -1  1  1  1  1\n",
              "URL_Length                   1  1  0  0  0  0  0  0  0  1\n",
              "Shortining_Service           1  1  1  1 -1 -1 -1  1 -1 -1\n",
              "having_At_Symbol             1  1  1  1  1  1  1  1  1  1\n",
              "double_slash_redirecting    -1  1  1  1  1 -1  1  1  1  1\n",
              "Prefix_Suffix               -1 -1 -1 -1 -1 -1 -1 -1 -1 -1\n",
              "having_Sub_Domain           -1  0 -1 -1  1  1 -1 -1  1 -1\n",
              "SSLfinal_State              -1  1 -1 -1  1  1 -1 -1  1  1\n",
              "Domain_registeration_length -1 -1 -1  1 -1 -1  1  1 -1 -1\n",
              "Favicon                      1  1  1  1  1  1  1  1  1  1\n",
              "port                         1  1  1  1  1  1  1  1  1  1\n",
              "HTTPS_token                 -1 -1 -1 -1  1 -1  1 -1 -1  1\n",
              "Request_URL                  1  1  1 -1  1  1 -1 -1  1  1\n",
              "URL_of_Anchor               -1  0  0  0  0  0 -1  0  0  0\n",
              "Links_in_tags                1 -1 -1  0  0  0  0 -1  1  1\n",
              "SFH                         -1 -1 -1 -1 -1 -1 -1 -1 -1 -1\n",
              "Submitting_to_email         -1  1 -1  1  1 -1 -1  1  1  1\n",
              "Abnormal_URL                -1  1 -1  1  1 -1 -1  1  1  1\n",
              "Redirect                     0  0  0  0  0  0  0  0  0  0\n",
              "on_mouseover                 1  1  1  1 -1  1  1  1  1  1\n",
              "RightClick                   1  1  1  1  1  1  1  1  1  1\n",
              "popUpWidnow                  1  1  1  1 -1  1  1  1  1  1\n",
              "Iframe                       1  1  1  1  1  1  1  1  1  1\n",
              "age_of_domain               -1 -1  1 -1 -1  1  1 -1  1  1\n",
              "DNSRecord                   -1 -1 -1 -1 -1  1 -1 -1 -1 -1\n",
              "web_traffic                 -1  0  1  1  0  1 -1  0  1  0\n",
              "Page_Rank                   -1 -1 -1 -1 -1 -1 -1 -1  1 -1\n",
              "Google_Index                 1  1  1  1  1  1  1  1  1  1\n",
              "Links_pointing_to_page       1  1  0 -1  1 -1  0  0  0  0\n",
              "Statistical_report          -1  1 -1  1  1 -1 -1  1  1  1\n",
              "Result                      -1 -1 -1 -1  1  1 -1 -1  1 -1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TofXcsDj_AU2",
        "colab_type": "code",
        "outputId": "4443cb99-c7a8-4d69-c518-d5db180ef40c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "# Data columns\n",
        "data.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['having_IP_Address', 'URL_Length', 'Shortining_Service',\n",
              "       'having_At_Symbol', 'double_slash_redirecting', 'Prefix_Suffix',\n",
              "       'having_Sub_Domain', 'SSLfinal_State', 'Domain_registeration_length',\n",
              "       'Favicon', 'port', 'HTTPS_token', 'Request_URL', 'URL_of_Anchor',\n",
              "       'Links_in_tags', 'SFH', 'Submitting_to_email', 'Abnormal_URL',\n",
              "       'Redirect', 'on_mouseover', 'RightClick', 'popUpWidnow', 'Iframe',\n",
              "       'age_of_domain', 'DNSRecord', 'web_traffic', 'Page_Rank',\n",
              "       'Google_Index', 'Links_pointing_to_page', 'Statistical_report',\n",
              "       'Result'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nrABpH3O_N18",
        "colab_type": "code",
        "outputId": "ebed8710-4d00-46bf-dda7-d5bdd89ea85b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from collections import Counter\n",
        "\n",
        "classes = Counter(data['Result'].values)\n",
        "classes.most_common()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(1, 6157), (-1, 4898)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wP2UgHnR_Vt3",
        "colab_type": "code",
        "outputId": "cb0c9a5a-0952-49f5-f586-f2c5a6cf770b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        }
      },
      "source": [
        "class_dist = pd.DataFrame(classes.most_common(), columns=['Class', 'Num_Observations'])\n",
        "class_dist"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Class</th>\n",
              "      <th>Num_Observations</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>6157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-1</td>\n",
              "      <td>4898</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Class  Num_Observations\n",
              "0      1              6157\n",
              "1     -1              4898"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xF4TiCjN_Sbz",
        "colab_type": "code",
        "outputId": "36c37fac-8b5c-46a7-97c5-dac1f462bf6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1010
        }
      },
      "source": [
        "data.describe().T"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>having_IP_Address</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>0.313795</td>\n",
              "      <td>0.949534</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>URL_Length</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>-0.633198</td>\n",
              "      <td>0.766095</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Shortining_Service</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>0.738761</td>\n",
              "      <td>0.673998</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>having_At_Symbol</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>0.700588</td>\n",
              "      <td>0.713598</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>double_slash_redirecting</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>0.741474</td>\n",
              "      <td>0.671011</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Prefix_Suffix</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>-0.734962</td>\n",
              "      <td>0.678139</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>having_Sub_Domain</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>0.063953</td>\n",
              "      <td>0.817518</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SSLfinal_State</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>0.250927</td>\n",
              "      <td>0.911892</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Domain_registeration_length</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>-0.336771</td>\n",
              "      <td>0.941629</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Favicon</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>0.628584</td>\n",
              "      <td>0.777777</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>port</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>0.728268</td>\n",
              "      <td>0.685324</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HTTPS_token</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>0.675079</td>\n",
              "      <td>0.737779</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Request_URL</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>0.186793</td>\n",
              "      <td>0.982444</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>URL_of_Anchor</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>-0.076526</td>\n",
              "      <td>0.715138</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Links_in_tags</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>-0.118137</td>\n",
              "      <td>0.763973</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SFH</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>-0.595749</td>\n",
              "      <td>0.759143</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Submitting_to_email</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>0.635640</td>\n",
              "      <td>0.772021</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Abnormal_URL</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>0.705292</td>\n",
              "      <td>0.708949</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Redirect</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>0.115694</td>\n",
              "      <td>0.319872</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>on_mouseover</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>0.762099</td>\n",
              "      <td>0.647490</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RightClick</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>0.913885</td>\n",
              "      <td>0.405991</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>popUpWidnow</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>0.613388</td>\n",
              "      <td>0.789818</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iframe</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>0.816915</td>\n",
              "      <td>0.576784</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age_of_domain</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>0.061239</td>\n",
              "      <td>0.998168</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DNSRecord</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>0.377114</td>\n",
              "      <td>0.926209</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>web_traffic</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>0.287291</td>\n",
              "      <td>0.827733</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Page_Rank</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>-0.483673</td>\n",
              "      <td>0.875289</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Google_Index</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>0.721574</td>\n",
              "      <td>0.692369</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Links_pointing_to_page</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>0.344007</td>\n",
              "      <td>0.569944</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Statistical_report</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>0.719584</td>\n",
              "      <td>0.694437</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Result</th>\n",
              "      <td>11055.0</td>\n",
              "      <td>0.113885</td>\n",
              "      <td>0.993539</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               count      mean       std  min  25%  50%  75%  \\\n",
              "having_IP_Address            11055.0  0.313795  0.949534 -1.0 -1.0  1.0  1.0   \n",
              "URL_Length                   11055.0 -0.633198  0.766095 -1.0 -1.0 -1.0 -1.0   \n",
              "Shortining_Service           11055.0  0.738761  0.673998 -1.0  1.0  1.0  1.0   \n",
              "having_At_Symbol             11055.0  0.700588  0.713598 -1.0  1.0  1.0  1.0   \n",
              "double_slash_redirecting     11055.0  0.741474  0.671011 -1.0  1.0  1.0  1.0   \n",
              "Prefix_Suffix                11055.0 -0.734962  0.678139 -1.0 -1.0 -1.0 -1.0   \n",
              "having_Sub_Domain            11055.0  0.063953  0.817518 -1.0 -1.0  0.0  1.0   \n",
              "SSLfinal_State               11055.0  0.250927  0.911892 -1.0 -1.0  1.0  1.0   \n",
              "Domain_registeration_length  11055.0 -0.336771  0.941629 -1.0 -1.0 -1.0  1.0   \n",
              "Favicon                      11055.0  0.628584  0.777777 -1.0  1.0  1.0  1.0   \n",
              "port                         11055.0  0.728268  0.685324 -1.0  1.0  1.0  1.0   \n",
              "HTTPS_token                  11055.0  0.675079  0.737779 -1.0  1.0  1.0  1.0   \n",
              "Request_URL                  11055.0  0.186793  0.982444 -1.0 -1.0  1.0  1.0   \n",
              "URL_of_Anchor                11055.0 -0.076526  0.715138 -1.0 -1.0  0.0  0.0   \n",
              "Links_in_tags                11055.0 -0.118137  0.763973 -1.0 -1.0  0.0  0.0   \n",
              "SFH                          11055.0 -0.595749  0.759143 -1.0 -1.0 -1.0 -1.0   \n",
              "Submitting_to_email          11055.0  0.635640  0.772021 -1.0  1.0  1.0  1.0   \n",
              "Abnormal_URL                 11055.0  0.705292  0.708949 -1.0  1.0  1.0  1.0   \n",
              "Redirect                     11055.0  0.115694  0.319872  0.0  0.0  0.0  0.0   \n",
              "on_mouseover                 11055.0  0.762099  0.647490 -1.0  1.0  1.0  1.0   \n",
              "RightClick                   11055.0  0.913885  0.405991 -1.0  1.0  1.0  1.0   \n",
              "popUpWidnow                  11055.0  0.613388  0.789818 -1.0  1.0  1.0  1.0   \n",
              "Iframe                       11055.0  0.816915  0.576784 -1.0  1.0  1.0  1.0   \n",
              "age_of_domain                11055.0  0.061239  0.998168 -1.0 -1.0  1.0  1.0   \n",
              "DNSRecord                    11055.0  0.377114  0.926209 -1.0 -1.0  1.0  1.0   \n",
              "web_traffic                  11055.0  0.287291  0.827733 -1.0  0.0  1.0  1.0   \n",
              "Page_Rank                    11055.0 -0.483673  0.875289 -1.0 -1.0 -1.0  1.0   \n",
              "Google_Index                 11055.0  0.721574  0.692369 -1.0  1.0  1.0  1.0   \n",
              "Links_pointing_to_page       11055.0  0.344007  0.569944 -1.0  0.0  0.0  1.0   \n",
              "Statistical_report           11055.0  0.719584  0.694437 -1.0  1.0  1.0  1.0   \n",
              "Result                       11055.0  0.113885  0.993539 -1.0 -1.0  1.0  1.0   \n",
              "\n",
              "                             max  \n",
              "having_IP_Address            1.0  \n",
              "URL_Length                   1.0  \n",
              "Shortining_Service           1.0  \n",
              "having_At_Symbol             1.0  \n",
              "double_slash_redirecting     1.0  \n",
              "Prefix_Suffix                1.0  \n",
              "having_Sub_Domain            1.0  \n",
              "SSLfinal_State               1.0  \n",
              "Domain_registeration_length  1.0  \n",
              "Favicon                      1.0  \n",
              "port                         1.0  \n",
              "HTTPS_token                  1.0  \n",
              "Request_URL                  1.0  \n",
              "URL_of_Anchor                1.0  \n",
              "Links_in_tags                1.0  \n",
              "SFH                          1.0  \n",
              "Submitting_to_email          1.0  \n",
              "Abnormal_URL                 1.0  \n",
              "Redirect                     1.0  \n",
              "on_mouseover                 1.0  \n",
              "RightClick                   1.0  \n",
              "popUpWidnow                  1.0  \n",
              "Iframe                       1.0  \n",
              "age_of_domain                1.0  \n",
              "DNSRecord                    1.0  \n",
              "web_traffic                  1.0  \n",
              "Page_Rank                    1.0  \n",
              "Google_Index                 1.0  \n",
              "Links_pointing_to_page       1.0  \n",
              "Statistical_report           1.0  \n",
              "Result                       1.0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AETL8Po9B2Eo",
        "colab_type": "text"
      },
      "source": [
        "Mapping the -1 labels to 0. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MzFxUnSK_i6w",
        "colab_type": "code",
        "outputId": "cd65fdca-4c97-4be0-fd01-159add95e48f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "data.rename(columns={'Result': 'Class'}, inplace=True)\n",
        "\n",
        "data['Class'] = data['Class'].map({-1:0, 1:1})\n",
        "data['Class'].unique()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U5vKgPxXvs5o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for col in data.columns:\n",
        "    data[col] = data[col].astype('category')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RLSwiZLEwIiz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "outputId": "042f22d4-6dcf-4d4f-fc36-35f8af2d0b97"
      },
      "source": [
        "data.info()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 11055 entries, 0 to 11054\n",
            "Data columns (total 31 columns):\n",
            "having_IP_Address              11055 non-null category\n",
            "URL_Length                     11055 non-null category\n",
            "Shortining_Service             11055 non-null category\n",
            "having_At_Symbol               11055 non-null category\n",
            "double_slash_redirecting       11055 non-null category\n",
            "Prefix_Suffix                  11055 non-null category\n",
            "having_Sub_Domain              11055 non-null category\n",
            "SSLfinal_State                 11055 non-null category\n",
            "Domain_registeration_length    11055 non-null category\n",
            "Favicon                        11055 non-null category\n",
            "port                           11055 non-null category\n",
            "HTTPS_token                    11055 non-null category\n",
            "Request_URL                    11055 non-null category\n",
            "URL_of_Anchor                  11055 non-null category\n",
            "Links_in_tags                  11055 non-null category\n",
            "SFH                            11055 non-null category\n",
            "Submitting_to_email            11055 non-null category\n",
            "Abnormal_URL                   11055 non-null category\n",
            "Redirect                       11055 non-null category\n",
            "on_mouseover                   11055 non-null category\n",
            "RightClick                     11055 non-null category\n",
            "popUpWidnow                    11055 non-null category\n",
            "Iframe                         11055 non-null category\n",
            "age_of_domain                  11055 non-null category\n",
            "DNSRecord                      11055 non-null category\n",
            "web_traffic                    11055 non-null category\n",
            "Page_Rank                      11055 non-null category\n",
            "Google_Index                   11055 non-null category\n",
            "Links_pointing_to_page         11055 non-null category\n",
            "Statistical_report             11055 non-null category\n",
            "Class                          11055 non-null category\n",
            "dtypes: category(31)\n",
            "memory usage: 337.7 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LlbilpDhwa2V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oU0kFdgUChXx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data.to_csv('data/Phishing.csv', index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w_fIA6zx_rQN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from fastai.tabular import * "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mUz1I6CvAo0R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "procs = [Categorify]\n",
        "valid_idx = range(len(data)-2211, len(data))\n",
        "dep_var = 'Class'\n",
        "cat_names = data.columns[:-1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GS7elBCCB0L1",
        "colab_type": "code",
        "outputId": "fe84550e-29d3-4a3f-81b5-1da1c4ee349c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "path = Path('content/data')\n",
        "databunch = TabularDataBunch.from_df(path, data, dep_var, valid_idx=valid_idx, procs=procs, cat_names=cat_names, bs=10)\n",
        "print(databunch.train_ds.cat_names) "
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['having_IP_Address', 'URL_Length', 'Shortining_Service',\n",
            "       'having_At_Symbol', 'double_slash_redirecting', 'Prefix_Suffix',\n",
            "       'having_Sub_Domain', 'SSLfinal_State', 'Domain_registeration_length',\n",
            "       'Favicon', 'port', 'HTTPS_token', 'Request_URL', 'URL_of_Anchor',\n",
            "       'Links_in_tags', 'SFH', 'Submitting_to_email', 'Abnormal_URL',\n",
            "       'Redirect', 'on_mouseover', 'RightClick', 'popUpWidnow', 'Iframe',\n",
            "       'age_of_domain', 'DNSRecord', 'web_traffic', 'Page_Rank',\n",
            "       'Google_Index', 'Links_pointing_to_page', 'Statistical_report'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "33L5jPxHFhIz",
        "colab_type": "code",
        "outputId": "98409e20-0833-43ae-f61a-f9b4fc6d9e11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        }
      },
      "source": [
        "databunch.label_list"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LabelLists;\n",
              "\n",
              "Train: LabelList (8844 items)\n",
              "x: TabularList\n",
              "having_IP_Address -1; URL_Length 1; Shortining_Service 1; having_At_Symbol 1; double_slash_redirecting -1; Prefix_Suffix -1; having_Sub_Domain -1; SSLfinal_State -1; Domain_registeration_length -1; Favicon 1; port 1; HTTPS_token -1; Request_URL 1; URL_of_Anchor -1; Links_in_tags 1; SFH -1; Submitting_to_email -1; Abnormal_URL -1; Redirect 0; on_mouseover 1; RightClick 1; popUpWidnow 1; Iframe 1; age_of_domain -1; DNSRecord -1; web_traffic -1; Page_Rank -1; Google_Index 1; Links_pointing_to_page 1; Statistical_report -1; ,having_IP_Address 1; URL_Length 1; Shortining_Service 1; having_At_Symbol 1; double_slash_redirecting 1; Prefix_Suffix -1; having_Sub_Domain 0; SSLfinal_State 1; Domain_registeration_length -1; Favicon 1; port 1; HTTPS_token -1; Request_URL 1; URL_of_Anchor 0; Links_in_tags -1; SFH -1; Submitting_to_email 1; Abnormal_URL 1; Redirect 0; on_mouseover 1; RightClick 1; popUpWidnow 1; Iframe 1; age_of_domain -1; DNSRecord -1; web_traffic 0; Page_Rank -1; Google_Index 1; Links_pointing_to_page 1; Statistical_report 1; ,having_IP_Address 1; URL_Length 0; Shortining_Service 1; having_At_Symbol 1; double_slash_redirecting 1; Prefix_Suffix -1; having_Sub_Domain -1; SSLfinal_State -1; Domain_registeration_length -1; Favicon 1; port 1; HTTPS_token -1; Request_URL 1; URL_of_Anchor 0; Links_in_tags -1; SFH -1; Submitting_to_email -1; Abnormal_URL -1; Redirect 0; on_mouseover 1; RightClick 1; popUpWidnow 1; Iframe 1; age_of_domain 1; DNSRecord -1; web_traffic 1; Page_Rank -1; Google_Index 1; Links_pointing_to_page 0; Statistical_report -1; ,having_IP_Address 1; URL_Length 0; Shortining_Service 1; having_At_Symbol 1; double_slash_redirecting 1; Prefix_Suffix -1; having_Sub_Domain -1; SSLfinal_State -1; Domain_registeration_length 1; Favicon 1; port 1; HTTPS_token -1; Request_URL -1; URL_of_Anchor 0; Links_in_tags 0; SFH -1; Submitting_to_email 1; Abnormal_URL 1; Redirect 0; on_mouseover 1; RightClick 1; popUpWidnow 1; Iframe 1; age_of_domain -1; DNSRecord -1; web_traffic 1; Page_Rank -1; Google_Index 1; Links_pointing_to_page -1; Statistical_report 1; ,having_IP_Address 1; URL_Length 0; Shortining_Service -1; having_At_Symbol 1; double_slash_redirecting 1; Prefix_Suffix -1; having_Sub_Domain 1; SSLfinal_State 1; Domain_registeration_length -1; Favicon 1; port 1; HTTPS_token 1; Request_URL 1; URL_of_Anchor 0; Links_in_tags 0; SFH -1; Submitting_to_email 1; Abnormal_URL 1; Redirect 0; on_mouseover -1; RightClick 1; popUpWidnow -1; Iframe 1; age_of_domain -1; DNSRecord -1; web_traffic 0; Page_Rank -1; Google_Index 1; Links_pointing_to_page 1; Statistical_report 1; \n",
              "y: CategoryList\n",
              "0,0,0,0,1\n",
              "Path: content/data;\n",
              "\n",
              "Valid: LabelList (2211 items)\n",
              "x: TabularList\n",
              "having_IP_Address -1; URL_Length -1; Shortining_Service 1; having_At_Symbol 1; double_slash_redirecting 1; Prefix_Suffix -1; having_Sub_Domain -1; SSLfinal_State -1; Domain_registeration_length 1; Favicon 1; port 1; HTTPS_token 1; Request_URL -1; URL_of_Anchor -1; Links_in_tags -1; SFH -1; Submitting_to_email 1; Abnormal_URL 1; Redirect 0; on_mouseover 1; RightClick 1; popUpWidnow 1; Iframe 1; age_of_domain 1; DNSRecord 1; web_traffic 0; Page_Rank -1; Google_Index 1; Links_pointing_to_page 1; Statistical_report -1; ,having_IP_Address 1; URL_Length -1; Shortining_Service 1; having_At_Symbol 1; double_slash_redirecting 1; Prefix_Suffix -1; having_Sub_Domain 1; SSLfinal_State 1; Domain_registeration_length -1; Favicon 1; port 1; HTTPS_token 1; Request_URL -1; URL_of_Anchor 0; Links_in_tags 1; SFH 1; Submitting_to_email 1; Abnormal_URL 1; Redirect 0; on_mouseover 1; RightClick 1; popUpWidnow 1; Iframe 1; age_of_domain 1; DNSRecord 1; web_traffic -1; Page_Rank -1; Google_Index 1; Links_pointing_to_page 0; Statistical_report -1; ,having_IP_Address -1; URL_Length -1; Shortining_Service -1; having_At_Symbol 1; double_slash_redirecting -1; Prefix_Suffix -1; having_Sub_Domain 1; SSLfinal_State 1; Domain_registeration_length -1; Favicon 1; port 1; HTTPS_token -1; Request_URL 1; URL_of_Anchor 0; Links_in_tags 1; SFH -1; Submitting_to_email 1; Abnormal_URL -1; Redirect 1; on_mouseover 1; RightClick 1; popUpWidnow 1; Iframe 1; age_of_domain 1; DNSRecord -1; web_traffic -1; Page_Rank -1; Google_Index 1; Links_pointing_to_page 1; Statistical_report 1; ,having_IP_Address 1; URL_Length -1; Shortining_Service 1; having_At_Symbol 1; double_slash_redirecting 1; Prefix_Suffix -1; having_Sub_Domain -1; SSLfinal_State -1; Domain_registeration_length 1; Favicon 1; port 1; HTTPS_token 1; Request_URL -1; URL_of_Anchor -1; Links_in_tags -1; SFH -1; Submitting_to_email 1; Abnormal_URL 1; Redirect 0; on_mouseover 1; RightClick 1; popUpWidnow 1; Iframe 1; age_of_domain 1; DNSRecord 1; web_traffic 0; Page_Rank -1; Google_Index 1; Links_pointing_to_page 0; Statistical_report 1; ,having_IP_Address -1; URL_Length -1; Shortining_Service 1; having_At_Symbol 1; double_slash_redirecting 1; Prefix_Suffix 1; having_Sub_Domain -1; SSLfinal_State -1; Domain_registeration_length -1; Favicon 1; port 1; HTTPS_token 1; Request_URL 1; URL_of_Anchor 1; Links_in_tags 0; SFH -1; Submitting_to_email 1; Abnormal_URL 1; Redirect 0; on_mouseover 1; RightClick 1; popUpWidnow 1; Iframe 1; age_of_domain 1; DNSRecord 1; web_traffic 1; Page_Rank -1; Google_Index 1; Links_pointing_to_page 1; Statistical_report 1; \n",
              "y: CategoryList\n",
              "0,1,1,0,1\n",
              "Path: content/data;\n",
              "\n",
              "Test: None"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "npjpsgmyFdPd",
        "colab_type": "code",
        "outputId": "721113fb-3439-4ee8-a6f5-f279d4c4b17a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "def emb_sz_rule(n_cat:int)->int: return min(600, round(1.6 * n_cat**0.56))\n",
        "emb_sz_rule(30)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RAByXLBjFmQo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "embedding_dict = {}\n",
        "\n",
        "for column in data.columns[:-1]:\n",
        "  embedding_dict[column] = 11"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "amgnLO-xGa_h",
        "colab_type": "code",
        "outputId": "41493197-e068-42ce-ed33-4d05f3b40126",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "learn = tabular_learner(databunch, layers=[200,100], emb_szs=embedding_dict, metrics=accuracy)\n",
        "learn.fit_one_cycle(5, 1e-2)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.235874</td>\n",
              "      <td>0.253502</td>\n",
              "      <td>0.889643</td>\n",
              "      <td>00:10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.220044</td>\n",
              "      <td>0.195389</td>\n",
              "      <td>0.929896</td>\n",
              "      <td>00:11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.180829</td>\n",
              "      <td>0.203281</td>\n",
              "      <td>0.925825</td>\n",
              "      <td>00:10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.144848</td>\n",
              "      <td>0.230486</td>\n",
              "      <td>0.933514</td>\n",
              "      <td>00:12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.139055</td>\n",
              "      <td>0.167894</td>\n",
              "      <td>0.933062</td>\n",
              "      <td>00:10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uyMGwzt3xvOB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn.save('stage-1')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IEnfmK0LH2t4",
        "colab_type": "code",
        "outputId": "b4f41c77-8b63-46b7-ec7b-c29413f8e7d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('ggplot')\n",
        "\n",
        "learn.recorder.plot_losses()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wwVqLY7wwv/Ybq88MG91O3mR977YaHOTzD44AG/eyvC8uqn0RJfGnDcDRg4Cn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\nLodTfGN4awAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lfhaa4pMIdxV",
        "colab_type": "code",
        "outputId": "60d72c06-211e-4b5d-86eb-15bb403b4772",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 782
        }
      },
      "source": [
        "learn.model"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TabularModel(\n",
              "  (embeds): ModuleList(\n",
              "    (0): Embedding(3, 11)\n",
              "    (1): Embedding(4, 11)\n",
              "    (2): Embedding(3, 11)\n",
              "    (3): Embedding(3, 11)\n",
              "    (4): Embedding(3, 11)\n",
              "    (5): Embedding(3, 11)\n",
              "    (6): Embedding(4, 11)\n",
              "    (7): Embedding(4, 11)\n",
              "    (8): Embedding(3, 11)\n",
              "    (9): Embedding(3, 11)\n",
              "    (10): Embedding(3, 11)\n",
              "    (11): Embedding(3, 11)\n",
              "    (12): Embedding(3, 11)\n",
              "    (13): Embedding(4, 11)\n",
              "    (14): Embedding(4, 11)\n",
              "    (15): Embedding(4, 11)\n",
              "    (16): Embedding(3, 11)\n",
              "    (17): Embedding(3, 11)\n",
              "    (18): Embedding(3, 11)\n",
              "    (19): Embedding(3, 11)\n",
              "    (20): Embedding(3, 11)\n",
              "    (21): Embedding(3, 11)\n",
              "    (22): Embedding(3, 11)\n",
              "    (23): Embedding(3, 11)\n",
              "    (24): Embedding(3, 11)\n",
              "    (25): Embedding(4, 11)\n",
              "    (26): Embedding(3, 11)\n",
              "    (27): Embedding(3, 11)\n",
              "    (28): Embedding(4, 11)\n",
              "    (29): Embedding(3, 11)\n",
              "  )\n",
              "  (emb_drop): Dropout(p=0.0)\n",
              "  (bn_cont): BatchNorm1d(0, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (layers): Sequential(\n",
              "    (0): Linear(in_features=330, out_features=200, bias=True)\n",
              "    (1): ReLU(inplace)\n",
              "    (2): BatchNorm1d(200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (3): Linear(in_features=200, out_features=100, bias=True)\n",
              "    (4): ReLU(inplace)\n",
              "    (5): BatchNorm1d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (6): Linear(in_features=100, out_features=2, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3jTLzhMrH6F7",
        "colab_type": "code",
        "outputId": "ba71900a-d5d0-42b0-bcb3-82e60d02deec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "source": [
        "learn.lr_find();\n",
        "learn.recorder.plot()\n"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ICwvjyiuvZPr06QBcddVVhIVV/WXuc6W/PSnWMrTKRyd7JXrxfMj8Hn1Of9ix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\nJBIhhBAekUQihBDCI/8flb1SkNAuIc0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xbMLlIv9JQyD",
        "colab_type": "code",
        "outputId": "de297a82-0731-4788-d009-a2ed6d71b4e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "learn.load('stage-1')\n",
        "learn.fit_one_cycle(5, 1.5*1e-06)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.148217</td>\n",
              "      <td>0.176690</td>\n",
              "      <td>0.928087</td>\n",
              "      <td>00:11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.127078</td>\n",
              "      <td>0.173688</td>\n",
              "      <td>0.928991</td>\n",
              "      <td>00:11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.160250</td>\n",
              "      <td>0.172248</td>\n",
              "      <td>0.928087</td>\n",
              "      <td>00:11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.162649</td>\n",
              "      <td>0.195290</td>\n",
              "      <td>0.925373</td>\n",
              "      <td>00:11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.113820</td>\n",
              "      <td>0.176578</td>\n",
              "      <td>0.927635</td>\n",
              "      <td>00:10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YZv6Vq2mz3pr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "05371def-6193-4397-bf2d-c711d9cb1484"
      },
      "source": [
        "learn.load('stage-1')\n",
        "learn.fit(5, 1.5*1e-06)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.159023</td>\n",
              "      <td>0.176422</td>\n",
              "      <td>0.927635</td>\n",
              "      <td>00:10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.150491</td>\n",
              "      <td>0.165265</td>\n",
              "      <td>0.933967</td>\n",
              "      <td>00:10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.138136</td>\n",
              "      <td>0.170437</td>\n",
              "      <td>0.933062</td>\n",
              "      <td>00:10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.146531</td>\n",
              "      <td>0.185920</td>\n",
              "      <td>0.926278</td>\n",
              "      <td>00:10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.146488</td>\n",
              "      <td>0.197861</td>\n",
              "      <td>0.921303</td>\n",
              "      <td>00:11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0NmlUH-x0ND3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "225fdaf0-ea92-47b1-8ddc-198ee1995309"
      },
      "source": [
        "databunch = TabularDataBunch.from_df(path, data, dep_var, valid_idx=valid_idx, procs=procs, cat_names=cat_names, bs=32)\n",
        "learn = tabular_learner(databunch, layers=[200,100], emb_szs=embedding_dict, metrics=accuracy).to_fp16()\n",
        "learn.fit(5)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.206088</td>\n",
              "      <td>0.186887</td>\n",
              "      <td>0.921303</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.154100</td>\n",
              "      <td>0.184212</td>\n",
              "      <td>0.926730</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.140966</td>\n",
              "      <td>0.216007</td>\n",
              "      <td>0.916780</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.123015</td>\n",
              "      <td>0.162843</td>\n",
              "      <td>0.938942</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.123534</td>\n",
              "      <td>0.173642</td>\n",
              "      <td>0.917684</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yq9gOtFA2mpC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn.save('stage-2')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kx_wHaN808_0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "1b723e3c-8a8d-40bd-bb5a-66597fd2fad0"
      },
      "source": [
        "learn.recorder.plot_losses()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wDfCmuy06OCEFV3SUAFgTkjHGBNDUpHAdMB64R1U3i0gv4LnghRU8MV7nI1tSMMaYuqKa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\npM0DAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Z1Sf2rd1d0c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "070621e4-4c52-4954-9abc-7df63ceaaf77"
      },
      "source": [
        "learn.lr_find()\n",
        "learn.recorder.plot()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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s/kKDA5JjmTowideW7qS8qtr7wRnTRhWXV7HrYGnQjIQCN8lCRBwiUtzAw0HtfAsTwGpq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\nxhjj1v8HMnguLhOvrtAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SkCFMPz31hDx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "ef128c74-8ac0-4243-ba89-0a05d8988cc8"
      },
      "source": [
        "learn.load('stage-2')\n",
        "learn.fit(5)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.112382</td>\n",
              "      <td>0.185257</td>\n",
              "      <td>0.923112</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.100710</td>\n",
              "      <td>0.180946</td>\n",
              "      <td>0.921755</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.103667</td>\n",
              "      <td>0.166621</td>\n",
              "      <td>0.927182</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.089619</td>\n",
              "      <td>0.152128</td>\n",
              "      <td>0.936680</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.088446</td>\n",
              "      <td>0.172709</td>\n",
              "      <td>0.933514</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s-awtBd829PQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "0e83f726-bb7d-4a14-a41d-96bac2bb7599"
      },
      "source": [
        "learn.recorder.plot_losses()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7s0xzinOEqizGY1WP3WiMWSgirYANzolVtVRHUl4wJo/ty39v7+N5v+PQCQb9\n7VvGvLnQM+b7JJ6aFEPXtDoMsp3cZdns3fbt6ABl4YoQ7hrUml/0bcmNfVqWKV9Z3euWbj/k2e4c\nxKwG1iojvUEC/7iuG3G2eapJnTi//YG0qB/P7y4+q9SV34ND2jOgXSpp9eLIPnhmFh9smBzDtZlW\nPc4/fbaKTfuOeYIFzjQ/juIcoTq4PzTGdDXG3Gm/32SMudpZ0aqO6i734UZEOKd1Az656xwAvl6z\nt8ScGfd6o4EWPDyYJnXiaGWXTN8YpDyGm2BmKDfJsVH8+YrOQZ/sz2vTICTZf/aJxnJFCGen1Qnq\nhA7Gx3ed45fRXhGa1Y3jSF4hN702nxkrdp3WuWoKg9s3pEFiDOe1acAzPgUuV+44QudmdYiQiuXK\nKEowQnVwp4nIJyKy1359JCIlSovXVMJlZeHGncEczEfQtlES8x8ezFu39PY8bdeJi6JBYgybylAW\npZmhyuPewW359oHzeWR4B4BSS4C4e4u/8oueAHx2z3l8+dsBIX1Gjxb16N/2NDodAml2iZAfsvZx\nZykmu9pGflExzVPiEBFEhF9f0Mazr35CNOkNEvhm7V7a/2kGb/64uRolVWoDod45JmF1uWtqvz63\nx2oF1VHuoyxSE2OoGx/lZ9rxpVFyLAMDWr22Tk3gg0XZLM/2HvP+wm089PEKCouKWW6fq7RQ1tKI\ndEWQ3iCBto2s1Uuvp2YHbUx0wk6IK80E5TRtGnobUjVIjCk3qTGcmbV6D9+uK7mqDORkYbFfdNvv\nLvZGj+07dpKzGiWxYsdh8gqKefzz1do/RTktQlUWqcaYScaYQvv1JlDuo6CIDBWRdSKSJSLjguwf\nICJLRKRQREYG7LtZRDbYr5tDlLNCVLeDOxARoWcLb86D2ydRFu7Y+sv/9SMAL3+3kT98tIL3Fmzj\nrXlb+fssK3ImWCZ0KPRtVd+zfcNrJVuiH7fzOBJOoz/56dCmYSI39W3B8C6N2XfsZJn+lXDn9rcX\nMXrSwnLnFRQVBzUrAhzLK/RToABX//t/nqKNinKqhKos9ovITSLisl83AWUG3ouIC3gJGAZ0BK4X\nkY4B07YBowkoSmjnczwG9AF6A4/ZyYCOEEqnvKrmiu5W+GmPFnWZNKY3658cxurxQ0qd/8tz0j3b\nT36xmgk+4bXLfFYokRVs5eoO0QXYfuBEiafUnGMnEYH4aEebL5aKK0J48oouXNerBQDzNx1g71H/\nGls1bbURKO8/v97AoL9963mfH7CyAFj22MW88ouevP+rfrQKKO+ycscRWj88nTd+UJOUcuqEeue4\nBStsdjewCxiJdZMvi95Alu3pZXbaAAAgAElEQVQMzwcmAyN8JxhjthhjlgOBCQJDgFnGmAPGmIPA\nLGBoiLKeMtVd7iMYl53dlPfH9uXlmywfQHRkRJk34l/0bcnbt1hO4td+2EyUy/uFplawvlMgL4zy\ntkG/453Ffo2Ivl2XQ6+WKaU+6VYV7hDbu/+7hEF//dYz/sHC7WQ8NJ30cdOqSbLyKSo2ftnXuw57\nlZ0xhr/PWs/mfcc9c/ILS64s6sRFMaRTY1KTYkrtFT/+i9V8sGh7CXPilyt3c9X//UjW3mOegIWj\neQXsO8Vujmt2HSF93DTW7Vbnem0ipMdAY8xW4HLfMRH5LfB8GYc1A7b7vM/GWimEQrBjm4V47CkT\nQlfVaqGPj+knFHzLdRQUGdLrx7PFp1RHYMbzqTKiWzOGdm7MzW8sYM66HCb9uIXbB7Ti+MlC1uw6\nwlif8hzVRdO63kzv4/lFVkXW/CJ+75MjkptfWG0roEA25hzj1e83sWDLgRJhrhtzjvHVqt3kHDvJ\n2AGtPePLsg8x6KyG5JdhhgLI8DE5/nVkV979aSvL7Gz530+xrsfiP15I/cQYjp8s5I53raoAFz73\nHQCTRvfivwu2MWv1HtY/OSzkB4GJdr2qz5ft5KzGZ5UzW6kpnM5j4P2VJkUFEZGxIrJIRBbl5ATv\n+hYK4WiGqggJMf43wKt7+Aes/eCTAFdRYiJdTBptrWB+3LiPf8xaT6fHZgKERVJcTKS/zyTn6ElP\nKXM3G/aETx+M+z9YxuSF24PmQ6zbfZTHP1/NS3M2cuSEt+7W2//bwoeLtpNfWExUGWZF32CGazKb\n88ld5/J/N/bwm+NO2nMrCl/GvLnQE5H36txNJfaXxnd2B8ZAM6BSszkdZVHe3XUHVpMkN2n2WCiE\ndKwxZqIxJtMYk5maWvHQy+JwXVpUgNdvzvRspzdIYO2fh9K/bQPevqW3n9/hdIiLdnF1jzS+XZfD\nC197czMN4eET+OjOfvz5is4AvPvTVurG+2emf7lqd9Dj8gqKOJFftRFDy0qJeAP4fLk3X8S3xeyc\ndTk8OGU5uw7nlfu0/6dLO/KP684GrEoBw7s04ckrOtO0jrUCG/nyPNLHTWPuhrI7Lv515royQ7Pd\nXPvKPI+ZbOn2Q2zMOcZ363OYE0J0lxLenM5avLw7w0KgrV3OfAcwCrghxHPPBP7i49S+GHioQlKG\nQu3RFZ4EPYBLujQhIkJ459ZQrX+h0zujHh8t8a9f9eQVXSr9cypCz5YppNdP4E+fruTFb7I8fozk\n2EjaNkri399aT+pPXekvb7+nv+ZIXiEb/zK8SuT85OfsUvf1aFGXJdu8imS8T3dDXwId3IHcel5G\nibGb+rbkmsw0zvrjlyHJmRgTybGThXy/Psfv9xWIMYYFPh0f1+85xuC/f+d5v/np4WEVoq6cGmX+\n0kTkqIgcCfI6ipVvUSp2Z717sG78a4APjDGrRGS8iFxun7+XiGQD1wCviMgq+9gDwJ+xFM5CYLw9\n5ghWp7za8SNuYj8x/n7oWZ6e1k5wbpDM7pQgtaWqi/qJMfRva8norpk1eWw/z8rhP/O3legkdzC3\noEpCS+es3cvJwiLue7/05kS3BNzk3eaih4a19xuvaEBBoLnOGotgyh39GNgulU/vPtczfv9F7YiN\niii3UOPPPqukm/q2KLG/39PfkD5uGgPK6J9SE8kvLKbdIzN4f2Hw3va1hTJXFsaY0Oo1lH78dGB6\nwNijPtsLsUxMwY59A3jjdD4/VGqRFYrYKFeVPMG5azrdMbA1l3RpwprdR8o5oup54vJOXODzZBsd\nGeFXBr3dH2ewZcIlFBcb/uDjALfybpy5fptyjjHmzYUM79KYwe0b8vVar3kmKTaSlvXj2X34JG0b\nlvzTO6d1fX41sDXfrsthnl0yvryVRVmkJERz4Hg+s+8fwMHcAprWjaNZ3TjesqPqfjWwFa98t4mE\nGBdp9eLZXk7trQ8WWjEp74/tS/smybz7k//Nc7fdDXLbgVwWbTnAyJfnefZ9eve5dGtet8LfpTrZ\nezSP/KJiHp+62hO6XRsJj5CQaqa6mx9VNlWxSnJFCFsmXOJ53yWtejK3y6JVaiID2qXyve1wjYmM\n4I+XdOTaV+aRa68wjuYVcOxkIR/6lIS/890lXNsrjeyDJ/hlKRV0K8paO5x07oZ9dGiSTI8WdRnc\noRGzVu/hozvP8RQA9PWdjDk3nUk/bvGUbHnn1t78deY6Xvl+U4kGSKfCnN+dj8GU8Om4uXNga1Li\noxnauQlfrdrDln1lKwt3IqQ7im/tn4dy/wdLmb6ipI/IV1EA/OenrUGVhVtxr9p5mA6Nkx1dLVeU\nPUes0OLaniFfvUHxYUI45lkolYNvJnxMZASdm9Vh9fihnl4Ym3KOs/+Yf7b3l6t2c8ubi3j0s1WV\nWsn2x6x9nlLzR/MKWbD5AJ2b1eHuQW349O5z/drKxvlkwrtLu7jrckW6IjjbvrFm7a14ZFed+KhS\nFQVA3fhofjWwNXXiomjfJImNOcc4WVjE3qN5vPHDZk/SYG5+Ife9v5RZq/fQ2KdJVWyUy1PJuHuL\nslcN7ta6vmw/kEvGQ9O57a1FXPLiDzw2dVW1J1Ze+Nx3DH3+e7+x1Xb/kNqeHK/KgvI99UrN5R6f\n4nq+dnr3TWxjzjHe/Wlrqcf/vK30aKVTJVhNrUY+N9dAMlvW47Kzm3oaUA3p5G1U1cWuwRWsz4cT\ndGiSTGGxYcaK3Tz88UrGf7GaeZv2k1dQxJy1OXzysxWseE2mv1W5X6v6/HlEJ969tQ+TRvfim98N\n9Ns/9/dWOPfirYdKKIINe61VmLvx1zs/bSXjoelc/e//OfIdQyFr7zHW7j7qaTX82dId/OnTlZ79\nxbVYY6gZCttnoUuLWokrQljw8GC+XruXOvHevAP3Tfb+D7xO5tXjhxAfHckv31jgMV1VZqvWhslW\nUmS35nU9RSLLyo6ecuc5nu3Ff7zQbxXQPCWeL3/bn/T6Fav1daq4FdZv319KP9vMdMOr8wF48Xpv\nC96uaf4riIgI4Re2KW+Q3fDKl+Yp8TxxeScem7qKT37ewVU90th+IJdv1u7lsamrgsqyeGvJ/vRV\nzddr93BtZnPunbzUb3z6yl1c2rXM2J8ai64sADBhVaJcqVwaJsdyfW9/x2OwKCJ3Vvcjwztw96DW\nNKsbx4yVwXMyKkJhkfXU+bhPG9R+IWbp10+M8TNTAbRvnFxpuTPlkeGjlOYF9GP37WWSEFO+PHN/\nP4hreqYx2q5n1q6R5cx3K+7+z84pVVG4ca9CTuQXeZ7mX5qTRfq4aaSPm8aSbaUrlKXbD7Fk20E6\nPfqlxylfHnuP5vnVW3vi89Xs9HmQeHi4FaV2z39/Dul8lcGuwyfItas9VwWqLHCXKK9uKZSqxrc5\nk2+HwrMaJ/HgkPZc1aMZy7YfIn3cNF75buNpf547LNe3Ve3FnRqf9nmrgogI8dzcA/HNrUiMKd9Y\n0Twlnr9ec7ZHafZo6V2NPF6GkhjgU5b/SF4hh3LzOWfC19z3wVL2HsnjrzPXefb/35zS/7+ueOlH\nrvq//5UoAxPIs1+uJeOhaZzIL6L3U1/zcsBvwN1zvlPTZMYOaO2puFxVfc/7Pf0NV/97XvkTKwlV\nFtgRF7UmeFYJlem/6c8PfxjEx3edwzmtS+aNpCZ5a2k9PWMtufmFGGNCzsVYvPUgv3h9vqfsRaF9\nXGSE8Nnd5/oVZqwJpNXztsDtne7tF79qpzdsunEZPpjSiIl08ct+lg/pzf9tCTrnnNb1eeWmnp4V\n4v3vL2V59mEO5hbw2dKd/LTZPw2r+BQc4YH5Nm7+79uNGAMdHvVPXrzX7nnv9kFNucMyFz4z0upW\neDpBB6Fy7KS1oliz6wgFpxERdyqossBycOvK4swjIkJIqxdPjxbBq9/3Sk8hMSaSRravYdXOI9z/\nwTK6PfFVmX+g2/bn8sLsDUxbvou5G/Zxy5sLefKL1RzNs/7AXRFWNNOIbo7VxnSEZnW9yuI/t/dh\nVK/mfvtfvzmThhVQFgB/GNo+6PhfruzCxr8M5z+39SEu2sWNfSxl8fXavX6lUqYu9a8G9I2d+BgK\n7y3YhjHGT2mU9v972dlN6WZHdm3ad5yeLet5ItfaN7b8OlVRbXeXjwms7SMzWLPL+TwnVRbUrqQ8\npfLo0CSZlU8MYfpv+iNihb5+8vMOjp4sZGAZWciPfLqCf8xez3/mW1FWK3cc4bUfNvPYVCtqJrKG\nOsha+vgtolwRTLi6K63tnhkTrurC4A6NSju0XAKLYILV/fGGPi1wRYgnAMW3oZO7oRfA7DV7iYty\n+RVKnB5iL/bHpq7i+dkbaPfHGXywcDs5R0/6JWm6uaB9Q14c1Y3m9bwRaL7O9vT68URHRrC2ChJU\nAwMvfswqu7ZXZaDKAvfKomb+ASvOUz8xBmPg+dneook7D5deUdX9WzoZYN4osB3cka6a+Vvr0CSJ\np67szKTRvTxjDZOslYQ70ut0OM+nhMw9g9oErTUWG+XijdGZJcYBioxheJcmfHWf1fv9vveXlYg2\nC8ybGdbZ8hm5C2L+/qPl9HpqNh8vsVYqD1zcjku6NgFgefYhRMTPHOfrx4l0RdA6NdGTeOkkOw/5\n//427StZtbiyUWVB+LVVVcKPlvVDy2coKjaesNvSCIxqqimICDf2aekXAvvEiE70Sq9Hpo8Po6K8\nOaYXYwe04u1bevPAkLPo1zp4pNj57fxDcL994HzAW/rEHV0FsGiL98n/6zV7uOKlH/2OLasHS/vG\nSdxzQVteuM7yLV2TaZndfCPQLju7id8xkRHC3A37/CKlyuObtXtYtOXUSt8FFvF8wifCzilUWaBm\nKKV8/n7N2SXGVu88wvGThRQXGw7apS7eW+BfDykpJpJVTwzxixKKiqg9f3btGiXx4R3nkOzTO6Oi\nRLoieHh4B7+op2AElvxwJzb2ziipsNwFGL9bn8Otby1in52t379tA247L4PuLeoxaUyvEseBtzRL\npCuCrKeG8fsh3kZO7t9D84CkyF/0tRz1//wmq8zv4Mstby5i5MvzyM0v5ODxfJ74fBWHcwuYsjib\nu/+zxK8jpRt3GfhLujbhscs6ltnXpLKoPb/a06A2VZ1VnCEzPYXfXdQO8Dp6h784l8F//45fv/cz\n3f88i1+9s8gTpeKOu7/3wrYkxET6Pfm5aqgZKpz4+K5z6Nsqhdn3DyAu2sUXvz7PLznw1V9apip3\nKY6b31jgd/yfLu3IHy/tCPjnujwyvINn+6ru3gCESFeE3z3i6p5pbPrLcI8Zzs21vZqTHBvJewu2\nnXIIbcdHZ/LBou1M+nELD05ZxgMfLmPail1s3nfck0PidryfLCjimp5pvHRDD8acW7IMvROossBd\nSLC6pVDCndv6t+KSLk14+9benrHdR/KYZjtSZ67a40nc+mW/dDY8NYzb+ltmjqt7estg1FQHdzjR\no0U9Jo/tRxu7Om/nZnX8Vm8XdWzERR0bsWrnYU9NLV/ifWpvxUa56NAkmT8Mbc/tA1ox7TfnMbBd\nKn+5quz+LKUVNbxrkFViprSOjIdzC4KuFsAK0Qb4arW3u+Ouw94ckraPzOBQbj47D+d5QrGrClUW\n2IUE1RCllENctIuXbuxB69TEoP0afImNcpUwDVzSpQmuCDmtsuJK6HRqmszmfcfZdqCk8zcpxt9s\nNuPe/tx5fmv7uDq8dRqdJYfYiZbuaDhfCouKOXv8VzzyyUq+W5/DZ3bIb6sGpZdtWbLtoF8NsEc/\nsxIX3fW4qgr91WKtLFRXKKfCRR39M6//6WMCWfroRUGP+dcN3Vn+2MVEqrKoEjo2ScYYeOjjFZ6x\nP1/RmTfH9PKrE1bZpNvBEJMXbmfxVn/H9RE71+b9Rdu5+Y0FntpSZWXyPz97A9sO5NIg0Yo4c+dU\nPHVl50qXvSwcLSQoIkOBFwAX8JoxZkLA/hjgbaAnsB+4zhizRUSigNeAHraMbxtjnnZKTtUVyqly\nXpsGjBvWnhYp8TRMiiEzPYUBbVM5ll9YatlvEQmaT6A4Q2e7Mu962xz0ze8GltkWtrIQEV4Y1Y17\nJy/lvQXb6dnS63g/cqJkKXaAkT3T+GbtHo+s1nnsB1mbD+/ox/UTf2KDnSE+tIpLxTj2yxURF/AS\ncBGQDSwUkanGGN9mwrcCB40xbURkFPAMcB1Wm9UYY0wXEYkHVovIe8aYLY4Ia0D0YU85BVwRwh0D\nW/uN1YmPcvSJVTk1mtaNo3/bBszdsI+mdWKrRFG4GdGtGdNX7GLmyt08e3VXj3/jcCnKIjEmkpm/\nHcD0Fbu5sGND/vPTNnpnpLBg8wHGf2HdMtPrx9O4Tqyn42CduKr9rTl5i+wNZBljNhlj8oHJwIiA\nOSOAt+ztKcBgsUIODJAgIpFAHJAPOJYWaTC1qlOeoigWvez8j7KSKJ3inNYNOHqykH3HvQ52X2Vx\ntk93yYQYFyLCJV2bEBPp4pbzMujcrI5fL3YR4R/XeeuJVbU508lPawb41v/NtseCzjHGFAKHgfpY\niuM4sAvYBvzNGHNqWSungFadVZTaybltQisB7wSt7FIoP2064ClAud9Hcbx2cy/+fWMP+rdtQEJ0\n6UaeeQ9dwCw7Kz09xORQJwhXA2pvoAhoCtQD5orIbGPMJt9JIjIWGAvQokXFG6Vr1VlFqZ30bJnC\nrPsGVEvv7nNaNyCtXhyf/byDT5ZkM2edN7N//sODSU2KYViXJgzr0qSMs0CTOnFgL0JEhIZJMVUe\nNgvOKosdgG9ZyjR7LNicbNvkVAfL0X0D8KUxpgDYKyI/ApmAn7IwxkwEJgJkZmZW+Opp1VlFqb20\n9Sn/UZW4IoRe6SlBQ1zrldH7vDy+//2gUyrBXlk4aYZaCLQVkQwRiQZGAVMD5kwFbra3RwLfGKsF\n1jbgAgARSQD6AmtxCG2rqiiKE/hWyXVze/+MoJ0aQyU2yuXp6liVOKYsbB/EPcBMYA3wgTFmlYiM\nF5HL7WmvA/VFJAu4Hxhnj78EJIrIKiylM8kYU3pLq9OXVY1QiqJUOtdmNi8xVlWtcCsbR9WTMWY6\nMD1g7FGf7TysMNnA444FG3cKNUMpiuIEDRK95qaGSTHsPXqyxioLzS5Aq84qiuIMIsKcB87ngvYN\nGWnXB9tzpOrDeCsDVRZo1VlFUZwjo0ECb4zuRbfmVjvW7IOh97oIJ8I1dLZK0aqziqI4zYB2qQzp\n1IjfXXxW+ZPDEFUWWEl5aohSFMVJYqNcvPKL4C1hawJqhkLbqiqKopSHKgsb1RWKoiilo8oCt89C\n1YWiKEppqLLA7pSnukJRFKVUVFmgSXmKoijlocoCrTqrKIpSHqossFYWqisURVFKR5UF6uBWFEUp\nD1UWaNVZRVGU8lBlgTq4FUVRykOVBVp1VlEUpTxUWWBVnVWfhaIoSuk4qixEZKiIrBORLBEZF2R/\njIi8b++fLyLpPvu6isg8EVklIitEJNYpOYuL0aWFoihKGTimLETEhdUedRjQEbheRDoGTLsVOGiM\naQP8A3jGPjYSeBe4wxjTCTgfKHBKVkDzLBRFUcrAyZVFbyDLGLPJGJMPTAZGBMwZAbxlb08BBovV\nhehiYLkxZhmAMWa/MabIKUG16qyiKErZOKksmgHbfd5n22NB5xhjCoHDQH2gHWBEZKaILBGR3zso\nJwZtfqQoilIW4dr8KBI4D+gF5AJfi8hiY8zXvpNEZCwwFqBFixYV/rBiLfehKIpSJk6uLHYAzX3e\np9ljQefYfoo6wH6sVcj3xph9xphcYDrQI/ADjDETjTGZxpjM1NTUCgtqjOZZKIqilIWTymIh0FZE\nMkQkGhgFTA2YMxW42d4eCXxjjDHATKCLiMTbSmQgsNopQTUpT1EUpWwcM0MZYwpF5B6sG78LeMMY\ns0pExgOLjDFTgdeBd0QkCziApVAwxhwUkeewFI4BphtjpjknK2jsrKIoSuk46rMwxkzHMiH5jj3q\ns50HXFPKse9ihc9WAUYd3IqiKGWgGdxAsfosFEVRykSVBdr8SFEUpTxUWaAObkVRlPJQZYE2P1IU\nRSkPVRZYSXmKoihK6aiyAFAHt6IoSpmossD2WaiDW1EUpVRUWaBVZxVFUcojXAsJViladVZRwouC\nggKys7PJy8urblFqDbGxsaSlpREVFVWh41VZYFed1aWFooQN2dnZJCUlkZ6ern+blYAxhv3795Od\nnU1GRkaFzqFmKOyqs9UthKIoHvLy8qhfv74qikpCRKhfv/5prdRUWWCZoVRbKEp4oYqicjnd66nK\nAkCT8hRF8WH//v1069aNbt260bhxY5o1a+Z5n5+fH9I5xowZw7p16xyWtOpQnwXuTnmKoigW9evX\nZ+nSpQA8/vjjJCYm8sADD/jNMcZgjCEiIvgz96RJkxyXsyrRlQVaG0pRlNDIysqiY8eO3HjjjXTq\n1Ildu3YxduxYMjMz6dSpE+PHj/fMPe+881i6dCmFhYXUrVuXcePGcfbZZ9OvXz/27t1bjd+iYujK\nAq06qyjhzBOfr2L1ziOVes6OTZN57LJOFTp27dq1vP3222RmZgIwYcIEUlJSKCwsZNCgQYwcOZKO\nHTv6HXP48GEGDhzIhAkTuP/++3njjTcYN27caX+PqsTRlYWIDBWRdSKSJSIlroyIxIjI+/b++SKS\nHrC/hYgcE5EHAo+tTHRloShKqLRu3dqjKADee+89evToQY8ePVizZg2rV5fsAB0XF8ewYcMA6Nmz\nJ1u2bKkqcSsNx1YWIuICXgIuArKBhSIy1RjjeyVvBQ4aY9qIyCjgGeA6n/3PATOcktGNMRp5oSjh\nSkVXAE6RkJDg2d6wYQMvvPACCxYsoG7dutx0001Bw1Ojo6M92y6Xi8LCwiqRtTJxcmXRG8gyxmwy\nxuQDk4ERAXNGAG/Z21OAwWLftUXkCmAzsMpBGTF2xVlVFYqinCpHjhwhKSmJ5ORkdu3axcyZM6tb\nJMdw0mfRDNju8z4b6FPaHGNMoYgcBuqLSB7wB6xVibMmKLs6uS4sFEU5VXr06EHHjh1p3749LVu2\n5Nxzz61ukRwjXB3cjwP/MMYcK8s8JCJjgbEALVq0qNAHuTtZqINbUZRgPP74457tNm3aeEJqwTJf\nv/POO0GP++GHHzzbhw4d8myPGjWKUaNGVb6gDuOkstgBNPd5n2aPBZuTLSKRQB1gP9YKZKSIPAvU\nBYpFJM8Y8y/fg40xE4GJAJmZmRXqYOQ2Q2khQUVRlNJxUlksBNqKSAaWUhgF3BAwZypwMzAPGAl8\nY6y7d3/3BBF5HDgWqCgqi2I1QymKopSLY8rC9kHcA8wEXMAbxphVIjIeWGSMmQq8DrwjIlnAASyF\nUqUY2xCl0VCKoiil46jPwhgzHZgeMPaoz3YecE0553jcEeE853fy7IqiKLUDLfdhowsLRVGU0jnj\nlYV7ZaFVZxVFUUrnjFcWxZqUpyhKAIMGDSqRYPf8889z5513lnpMYmIiADt37mTkyJFB55x//vks\nWrSozM9+/vnnyc3N9bwfPny4X+htdXHGKwtPnoVqC0VRbK6//nomT57sNzZ58mSuv/76co9t2rQp\nU6ZMqfBnByqL6dOnU7du3Qqfr7JQZeFZWai2UBTFYuTIkUybNs3T6GjLli3s3LmT7t27M3jwYHr0\n6EGXLl347LPPShy7ZcsWOnfuDMCJEycYNWoUHTp04Morr+TEiROeeXfeeaentPljjz0GwIsvvsjO\nnTsZNGgQgwYNAiA9PZ19+/YB8Nxzz9G5c2c6d+7M888/7/m8Dh06cPvtt9OpUycuvvhiv8+pLMI1\ng7vK0JWFooQ5M8bB7hWVe87GXWDYhFJ3p6Sk0Lt3b2bMmMGIESOYPHky1157LXFxcXzyySckJyez\nb98++vbty+WXX15q6P2///1v4uPjWbNmDcuXL6dHjx6efU899RQpKSkUFRUxePBgli9fzm9+8xue\ne+455syZQ4MGDfzOtXjxYiZNmsT8+fMxxtCnTx8GDhxIvXr12LBhA++99x6vvvoq1157LR999BE3\n3XRT5VwrG11ZFFv/ap6Foii++Jqi3CYoYwwPP/wwXbt25cILL2THjh3s2bOn1HN8//33npt2165d\n6dq1q2ffBx98QI8ePejevTurVq0KWtrclx9++IErr7yShIQEEhMTueqqq5g7dy4AGRkZdOvWDXCu\nBLquLFAHt6KENWWsAJxkxIgR3HfffSxZsoTc3Fx69uzJm2++SU5ODosXLyYqKor09PSgJcnLY/Pm\nzfztb39j4cKF1KtXj9GjR1foPG5iYmI82y6XyxEzlK4stNyHoihBSExMZNCgQdxyyy0ex/bhw4dp\n2LAhUVFRzJkzh61bt5Z5jgEDBvDf//4XgJUrV7J8+XLAKm2ekJBAnTp12LNnDzNmeNv2JCUlcfTo\n0RLn6t+/P59++im5ubkcP36cTz75hP79+5eY5xS6srD/VV2hKEog119/PVdeeaXHHHXjjTdy2WWX\n0aVLFzIzM2nfvn2Zx995552MGTOGDh060KFDB3r27AnA2WefTffu3Wnfvj3Nmzf3K20+duxYhg4d\nStOmTZkzZ45nvEePHowePZrevXsDcNttt9G9e/cq67onppbUu8jMzDTlxS8HY/+xk/R8cjbjR3Ti\nl/3SK18wRVFOmTVr1tChQ4fqFqPWEey6ishiY0xmKYd4OOPNUFGREQzv0pgWKfHVLYqiKErYcsab\noZJjo/i/G3tWtxiKoihhzRm/slAURVHKR5WFoihhSW3xp4YLp3s9VVkoihJ2xMbGsn//flUYlYQx\nhv379xMbG1vhczjqsxCRocALWJ3yXjPGTAjYHwO8DfTE6r19nTFmi4hcBEwAooF84EFjzDdOyqoo\nSviQlpZGdnY2OTk51S1KrSE2Npa0tLQKH++YshARF/AScBGQDSwUkanGGN+c9luBg8aYNiIyCngG\nuA7YB1xmjNkpIp2xWrjljBUAAAqISURBVLM2c0pWRVHCi6ioKDIyMqpbDMUHJ81QvYEsY8wmY0w+\nMBkYETBnBPCWvT0FGCwiYoz52Riz0x5fBcTZqxBFURSlGnBSWTQDtvu8z6bk6sAzxxhTCBwG6gfM\nuRpYYow56ZCciqIoSjmEdZ6FiHTCMk1dXMr+scBYgBYtWlShZIqiKGcWTiqLHUBzn/dp9liwOdki\nEgnUwXJ0IyJpwCfAL40xG4N9gDFmIjDRnp8jImVX9SqbBli+kpqCyussKq+zqLzOcirytgxlkpPK\nYiHQVkQysJTCKOCGgDlTgZuBecBI4BtjjBGRusA0YJwx5sdQPswYk3o6worIolDqo4QLKq+zqLzO\novI6ixPyOuazsH0Q92BFMq0BPjDGrBKR8SJyuT3tdaC+iGQB9wPj7PF7gDbAoyKy1H41dEpWRVEU\npWwc9VkYY6YD0wPGHvXZzgOuCXLck8CTTsqmKIqihI5mcHuZWN0CnCIqr7OovM6i8jpLpctba/pZ\nKIqiKM6hKwtFURSlXM54ZSEiQ0VknYhkici48o9wHhFpLiJzRGS1iKwSkXvt8RQRmSUiG+x/69nj\nIiIv2t9huYj0qCa5XSLys4h8Yb/PEJH5tlzvi0i0PR5jv8+y96dXg6x1RWSKiKwVkTUi0i+cr6+I\n3Gf/FlaKyHsiEhtu11dE3hCRvSKy0mfslK+piNxsz98gIjdXsbx/tX8Ty0XkEzsy073vIVvedSIy\nxGe8Su4hweT12fc7ETEi0sB+X/nX1xhzxr6wChxuBFphFS1cBnQMA7maAD3s7SRgPdAReBYrnBis\nyLFn7O3hwAysVuJ9gfnVJPf9wH+BL+z3HwCj7O2XgTvt7buAl+3tUcD71SDrW8Bt9nY0UDdcry9W\npYPNQJzPdR0dbtcXGAD0AFb6jJ3SNQVSgE32v/Xs7XpVKO/FQKS9/YyPvB3t+0MMkGHfN1xVeQ8J\nJq893hwr6nQr0MCp61tlP/hwfAH9gJk+7x8CHqpuuYLI+RlWQcZ1QBN7rAmwzt5+BbjeZ75nXhXK\nmAZ8DVwAfGH/SPf5/OF5rrX9w+5nb0fa86QKZa1j33wlYDwsry/esjgp9vX6AhgSjtcXSA+4+Z7S\nNQWuB17xGfeb57S8AfuuBP5jb/vdG9zXuKrvIcHkxaqrdzawBa+yqPTre6aboUKpX1Wt2CaE7sB8\noJExZpe9azfQyN4Oh+/xPPB7oNh+Xx84ZKx8m0CZQqkJ5iQZQA4wyTabvSYiCYTp9TXG7AD+BmwD\ndmFdr8WE7/X15VSvaTj8lt3cgvV0DmEqr4iMAHYYY5YF7Kp0ec90ZRHWiEgi8BHwW2PMEd99xnos\nCItQNhG5FNhrjFlc3bKESCTWcv7fxpjuwHG8CaFA2F3felgVmjOApkACMLRahaoA4XRNy0NEHgEK\ngf9UtyylISLxwMPAo+XNrQzOdGURSv2qakFEorAUxX+MMR/bw3tEpIm9vwmw1x6v7u9xLnC5iGzB\nKkV/AVbTq7pi1fwKlMkjrwTUBKsisoFsY8x8+/0ULOURrtf3QmCzMSbHGFMAfIx1zcP1+vpyqte0\nuq81IjIauBS40VZwlCFXdcrbGusBYpn9t5cGLBGRxmXIVWF5z3Rl4alfZUeSjMKqV1WtiIhglUJZ\nY4x5zmeXu5YW9r+f+Yz/0o6A6Asc9ln6O44x5iFjTJoxJh3rGn5jjLkRmINV8yuYvO7v4akJVoXy\n7ga2i8hZ9tBgYDVhen2xzE99RSTe/m245Q3L6xvAqV7TmcDFIlLPXlFdbI9VCWJ19/w9cLkxJtdn\n11RglB1plgG0BRZQjfcQY8wKY0xDY0y6/beXjRUYsxsnrq9Tjpia8sKKGliPFdHwSHXLY8t0HtZy\nfTmw1H4Nx7I7fw1sAGYDKfZ8wepKuBFYAWRWo+zn442GaoX1B5UFfAjE2OOx9vsse3+rapCzG7DI\nvsafYkWGhO31BZ4A1gIrgXewonLC6voC72H5VArsG9etFbmmWL6CLPs1porlzcKy6bv/7l72mf+I\nLe86YJjPeJXcQ4LJG7B/C14Hd6VfX83gVhRFUcrlTDdDKYqiKCGgykJRFEUpF1UWiqIoSrmoslAU\nRVHKRZWFoiiKUi6qLJQahYgUidVmd5mILBGRc8qZX1dE7grhvN+KSI3psVwViMibIjKy/JnKmYAq\nC6WmccIY080YczZW0bany5lfF6sKa1jik4GtKGGNKgulJpMMHASrjpaIfG2vNlbYBdYAJgCt7dXI\nX+25f7DnLBORCT7nu0ZEFojIehHpb8912T0OFtp9AX5ljzcRke/t8650z/dFRLaIyLP2Zy0QkTb2\n+Jsi8rKIzAeeFavnw6f2+X8Ska4+32mSffxyEbnaHr9YRObZ3/VDu4YYIjJBrB4oy0Xkb/bYNbZ8\ny0Tk+3K+k4jIv8TqzTAbaFiZ/1lKzUafapSaRpyILMXKUm6CVYcKIA+40hhzRKwGMD+JyFSsAoGd\njTHdAERkGFZRvj7GmFwRSfE5d6QxpreIDAcew6rJdCtWqYReIvL/7d3Pi5VVHMfx94cWSUYyhEQY\nuIhKoqIwkn4gKUKLWkgFFoHRLqkWCdImpaDaBLUQV5nFQAwU9IPCqCEXxTBFWTGB9A8UVGqE2Q9l\n+LT4nts8NPfhSUJinM9r81yee58759xh7nnOOcznez4wI+lD4G4qmvpZSecBF/S09xfb10raTiXz\n3tXOXwbcYnte0l7gK9tbJW0GJqn/MN89ur61faL17Ulgi+2Tkp4AdkraR0Vqr7NtLRTt2QPcYfu7\nzrm+Pt0AXEXVbriEihQ58K9+K3HOy2ARS83vnS/+m4FJSddQ8QbPSdpIxaSvYSEOu2sL8Ipb7o/t\n453nRoGNh6m6AVDZOdd11u5XUblAnwMHVIGPb9v+uqe9U53ji53zb9ieb49vA+5p7Tkk6WJJF7W2\n3je6wPbPqoTfq6kveKiCO7NUDPkfwMuqSoXvtctmgFclvd7pX1+fNgJTrV3fSzrU06dYhjJYxJJl\ne7bdaa+m8nlWA+ttn1alcK44w7f8sx3nWfjbEPCY7UVha21gupP6Mn7B9uS4ZvY8PnmGbfv7xwLT\ntu8f056bqJDBe4FHgc22H5a0obXzsKT1fX1qM6qIsbJnEUuWpHVUWctj1N3xj22g2ASsbS87QZWm\nHZkGHlLVAuAfy1DjfADsaDMIJF0paaWktcAPtl8C9lMR5+Ns6xxne17zCfBAe//bgaOu+iXTwCOd\n/k4AnwK3dvY/VrY2XQissn0QeJyqnIaky21/ZnsPVfBpVIJzUZ+Aj4FtbU/jUmDTwGcTy0hmFrHU\njPYsoO6QH2zr/q8B70r6hkqT/RbA9jFJM6oi9+/b3iXpeuALSaeAg1QBmT77qSWpL1XrPj8BW6l0\n3V2STgO/Att7rp+QNEfNWhbNBpqnqCWtOeA3FiK9nwH2tbbPA0/bflNVb2Gq7TdA7WGcAN6RtKJ9\nLjvbc89LuqKd+4iqET3X06e3qD2gI1Qset/gFstQUmcjzpK2FHaj7aP/d1si/qssQ0VExKDMLCIi\nYlBmFhERMSiDRUREDMpgERERgzJYRETEoAwWERExKINFREQM+gvtxgJYxmJE8gAAAABJRU5ErkJg\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ZMcIXKL4N8H",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "c5ddf0d7-4b64-4a4b-8937-689b38ea74d3"
      },
      "source": [
        "databunch = TabularDataBunch.from_df(path, data, dep_var, valid_idx=valid_idx, procs=procs, cat_names=cat_names, bs=64)\n",
        "learn = tabular_learner(databunch, layers=[300,150], emb_szs=embedding_dict, metrics=accuracy).to_fp16()\n",
        "learn.fit(5)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.158269</td>\n",
              "      <td>0.167228</td>\n",
              "      <td>0.925373</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.153388</td>\n",
              "      <td>0.167898</td>\n",
              "      <td>0.933967</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.137706</td>\n",
              "      <td>0.202665</td>\n",
              "      <td>0.926278</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.131929</td>\n",
              "      <td>0.153038</td>\n",
              "      <td>0.936228</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.114318</td>\n",
              "      <td>0.135939</td>\n",
              "      <td>0.937585</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1P4iHP2J5bK5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn.save('stage-3')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UJl8YbqJ4S5D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "3e7de875-b425-400d-d65e-89d767ae867f"
      },
      "source": [
        "learn.recorder.plot_losses()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EziTrajpLLBTOUweO1nH9kx9yvN6521tyXDQPXTeWWeOyyU51ZsM8cryBb/1p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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MEWYRfja4bKg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "87984057-38be-49d2-c8de-25ca012c753e"
      },
      "source": [
        "learn.lr_find()\n",
        "learn.recorder.plot()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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brTrGwoX8b+0/5RUOVdTRaDUECLy3Poc7Zh77i//f7/cTEhjA1VMGeChK5Ski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\n16UWAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "02e7AxC64s4H",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "34dd77c8-3e6d-444c-9317-5b542dffd942"
      },
      "source": [
        "learn.load('stage-3')\n",
        "learn.fit(5, 1e-04)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.095814</td>\n",
              "      <td>0.145458</td>\n",
              "      <td>0.935776</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.092159</td>\n",
              "      <td>0.139997</td>\n",
              "      <td>0.936680</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.085358</td>\n",
              "      <td>0.142936</td>\n",
              "      <td>0.937585</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.078285</td>\n",
              "      <td>0.138763</td>\n",
              "      <td>0.939846</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.079426</td>\n",
              "      <td>0.140977</td>\n",
              "      <td>0.939394</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BW3IZYLk4za2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "6b8e80d5-25d6-4c2b-9b54-fc8557c8d4d5"
      },
      "source": [
        "learn.recorder.plot_losses()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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We1qPm0d3pV/H6BrH4tqFkHP8NI9c0YfEqFBvl/tVYIAw3x7z4Y178sGdR4oY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\nGeh6AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0EBYtLWj47Av",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "7e50c44a-9f58-4a8f-dbf0-eb2f81dd9389"
      },
      "source": [
        "learn.load('stage-3')\n",
        "learn.fit(10, 1e-04, 0.6)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.099098</td>\n",
              "      <td>0.149151</td>\n",
              "      <td>0.932610</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.090199</td>\n",
              "      <td>0.145723</td>\n",
              "      <td>0.934419</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.089418</td>\n",
              "      <td>0.143439</td>\n",
              "      <td>0.936680</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.082492</td>\n",
              "      <td>0.139634</td>\n",
              "      <td>0.936680</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.077620</td>\n",
              "      <td>0.140658</td>\n",
              "      <td>0.939394</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.080575</td>\n",
              "      <td>0.135422</td>\n",
              "      <td>0.938037</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.070920</td>\n",
              "      <td>0.138414</td>\n",
              "      <td>0.937585</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.072288</td>\n",
              "      <td>0.141489</td>\n",
              "      <td>0.934419</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.076184</td>\n",
              "      <td>0.136850</td>\n",
              "      <td>0.942560</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.072719</td>\n",
              "      <td>0.139091</td>\n",
              "      <td>0.940298</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N6s2Wll7_eqF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn.save('stage-4')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JXpJl8FC_gMS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "22575289-5a63-4ad2-bbc2-84ada4a784dc"
      },
      "source": [
        "learn.unfreeze()\n",
        "learn.fit_one_cycle(10, 1e-04, wd=0.6)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.070832</td>\n",
              "      <td>0.142825</td>\n",
              "      <td>0.938942</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.067276</td>\n",
              "      <td>0.136672</td>\n",
              "      <td>0.940298</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.071374</td>\n",
              "      <td>0.139362</td>\n",
              "      <td>0.941203</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.068225</td>\n",
              "      <td>0.142303</td>\n",
              "      <td>0.939846</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.069698</td>\n",
              "      <td>0.137403</td>\n",
              "      <td>0.934871</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.061990</td>\n",
              "      <td>0.138733</td>\n",
              "      <td>0.938489</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.063320</td>\n",
              "      <td>0.143927</td>\n",
              "      <td>0.939394</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.064499</td>\n",
              "      <td>0.142344</td>\n",
              "      <td>0.942108</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.056307</td>\n",
              "      <td>0.144487</td>\n",
              "      <td>0.940298</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.062314</td>\n",
              "      <td>0.140876</td>\n",
              "      <td>0.943012</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-YXkO3Kx_1NJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "b0c82970-8da4-42dd-87cf-1dcb16087ae3"
      },
      "source": [
        "learn.recorder.plot_losses()"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lVBAaLJRSSgUVVdcFCBc6glup8FFYWEhWVhZ5eXl1XZRTRlxcHGlpaURHR1fp9Ros0FYo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\n/wM2DkBiYgHzigAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q9ipQ88SA4WY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "preds,y,losses = learn.get_preds(with_loss=True)\n",
        "interp = ClassificationInterpretation(learn, preds, y, losses)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9SXBh_UyA5GI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "outputId": "1bdf1441-a30a-463c-8a6a-b947367d1e84"
      },
      "source": [
        "interp.plot_confusion_matrix()"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dO3dm0ODBABx62Cf47S0313NENWP85Bm8/taiVZZNeWE202bO+Yttk6Tblp3p2LEDXbt0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\nSCrO0EgqztBIKu6/AUi7AURAILOpAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bMLNY_FkCQeF",
        "colab_type": "text"
      },
      "source": [
        "- 0 corresponds to phishing webistes\n",
        "- 1 corresponds to normal websites"
      ]
    }
  ]
}