Basic_ANN.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Basic_ANN.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "8mw6J9xhF8ex",
        "tI_ZydqIF_rA",
        "2AwIv629fPE0",
        "lm8jotcqfbWP",
        "SqjP4hYHfgYe",
        "n3DLJ2Vkfo-C"
      ],
      "toc_visible": true,
      "mount_file_id": "1e7VBMRHQ-OaVpphDZFo4kolQM65GkdCU",
      "authorship_tag": "ABX9TyOImfHbpH0MxGY+Ru3i5K35",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/KamaljeetSahoo/Basic-ANN-Pytorch/blob/master/Basic_ANN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8mw6J9xhF8ex"
      },
      "source": [
        "# Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_mBLvoOzE9Wz",
        "outputId": "1a12d65c-4717-4119-a1ed-dc4380ab8b4b"
      },
      "source": [
        "!wget https://gist.githubusercontent.com/chaityacshah/899a95deaf8b1930003ae93944fd17d7/raw/3d35de839da708595a444187e9f13237b51a2cbe/pima-indians-diabetes.csv"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-02-27 06:40:26--  https://gist.githubusercontent.com/chaityacshah/899a95deaf8b1930003ae93944fd17d7/raw/3d35de839da708595a444187e9f13237b51a2cbe/pima-indians-diabetes.csv\n",
            "Resolving gist.githubusercontent.com (gist.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to gist.githubusercontent.com (gist.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 23610 (23K) [text/plain]\n",
            "Saving to: ‘pima-indians-diabetes.csv’\n",
            "\n",
            "pima-indians-diabet 100%[===================>]  23.06K  --.-KB/s    in 0.002s  \n",
            "\n",
            "2021-02-27 06:40:26 (10.8 MB/s) - ‘pima-indians-diabetes.csv’ saved [23610/23610]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tI_ZydqIF_rA"
      },
      "source": [
        "# Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hbMFCPV9FWEB"
      },
      "source": [
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "import torch\r\n",
        "import torch.nn as nn\r\n",
        "import torch.nn.functional as F\r\n",
        "import seaborn as sns\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "from sklearn.model_selection import train_test_split\r\n",
        "%matplotlib inline\r\n",
        "sns.set_style(\"whitegrid\")"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mlGhh16MGI9Y"
      },
      "source": [
        "# Data Visualisation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "9EfcI9qIGG6X",
        "outputId": "56f6a7a0-3849-41b2-a66f-fb5ab30683ea"
      },
      "source": [
        "df = pd.read_csv('pima-indians-diabetes.csv')\r\n",
        "df.head(10)"
      ],
      "execution_count": 3,
      "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>1. Number of times pregnant</th>\n",
              "      <th>2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test</th>\n",
              "      <th>3. Diastolic blood pressure (mm Hg)</th>\n",
              "      <th>4. Triceps skin fold thickness (mm)</th>\n",
              "      <th>5. 2-Hour serum insulin (mu U/ml)</th>\n",
              "      <th>6. Body mass index (weight in kg/(height in m)^2)</th>\n",
              "      <th>7. Diabetes pedigree function</th>\n",
              "      <th>8. Age (years)</th>\n",
              "      <th>9. Class variable (0 or 1)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>148</td>\n",
              "      <td>72</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>33.6</td>\n",
              "      <td>0.627</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>183</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>116</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25.6</td>\n",
              "      <td>0.201</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3</td>\n",
              "      <td>78</td>\n",
              "      <td>50</td>\n",
              "      <td>32</td>\n",
              "      <td>88</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0.248</td>\n",
              "      <td>26</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>10</td>\n",
              "      <td>115</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35.3</td>\n",
              "      <td>0.134</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2</td>\n",
              "      <td>197</td>\n",
              "      <td>70</td>\n",
              "      <td>45</td>\n",
              "      <td>543</td>\n",
              "      <td>30.5</td>\n",
              "      <td>0.158</td>\n",
              "      <td>53</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>8</td>\n",
              "      <td>125</td>\n",
              "      <td>96</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.232</td>\n",
              "      <td>54</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   1. Number of times pregnant  ...  9. Class variable (0 or 1)\n",
              "0                            6  ...                           1\n",
              "1                            1  ...                           0\n",
              "2                            8  ...                           1\n",
              "3                            1  ...                           0\n",
              "4                            0  ...                           1\n",
              "5                            5  ...                           0\n",
              "6                            3  ...                           1\n",
              "7                           10  ...                           0\n",
              "8                            2  ...                           1\n",
              "9                            8  ...                           1\n",
              "\n",
              "[10 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rgT1QYIdGR_C",
        "outputId": "68b233ec-23d2-4151-85b4-ba22535c0283"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1. Number of times pregnant                                                    0\n",
              "2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test    0\n",
              "3. Diastolic blood pressure (mm Hg)                                            0\n",
              "4. Triceps skin fold thickness (mm)                                            0\n",
              "5. 2-Hour serum insulin (mu U/ml)                                              0\n",
              "6. Body mass index (weight in kg/(height in m)^2)                              0\n",
              "7. Diabetes pedigree function                                                  0\n",
              "8. Age (years)                                                                 0\n",
              "9. Class variable (0 or 1)                                                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "lGI4B8UBHcS2",
        "outputId": "2acf9fcf-6129-4a85-c82d-c635a6495232"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "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>1. Number of times pregnant</th>\n",
              "      <th>2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test</th>\n",
              "      <th>3. Diastolic blood pressure (mm Hg)</th>\n",
              "      <th>4. Triceps skin fold thickness (mm)</th>\n",
              "      <th>5. 2-Hour serum insulin (mu U/ml)</th>\n",
              "      <th>6. Body mass index (weight in kg/(height in m)^2)</th>\n",
              "      <th>7. Diabetes pedigree function</th>\n",
              "      <th>8. Age (years)</th>\n",
              "      <th>9. Class variable (0 or 1)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.845052</td>\n",
              "      <td>120.894531</td>\n",
              "      <td>69.105469</td>\n",
              "      <td>20.536458</td>\n",
              "      <td>79.799479</td>\n",
              "      <td>31.992578</td>\n",
              "      <td>0.471876</td>\n",
              "      <td>33.240885</td>\n",
              "      <td>0.348958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.369578</td>\n",
              "      <td>31.972618</td>\n",
              "      <td>19.355807</td>\n",
              "      <td>15.952218</td>\n",
              "      <td>115.244002</td>\n",
              "      <td>7.884160</td>\n",
              "      <td>0.331329</td>\n",
              "      <td>11.760232</td>\n",
              "      <td>0.476951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.078000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>27.300000</td>\n",
              "      <td>0.243750</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>30.500000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.372500</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>140.250000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>127.250000</td>\n",
              "      <td>36.600000</td>\n",
              "      <td>0.626250</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>846.000000</td>\n",
              "      <td>67.100000</td>\n",
              "      <td>2.420000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       1. Number of times pregnant  ...  9. Class variable (0 or 1)\n",
              "count                   768.000000  ...                  768.000000\n",
              "mean                      3.845052  ...                    0.348958\n",
              "std                       3.369578  ...                    0.476951\n",
              "min                       0.000000  ...                    0.000000\n",
              "25%                       1.000000  ...                    0.000000\n",
              "50%                       3.000000  ...                    0.000000\n",
              "75%                       6.000000  ...                    1.000000\n",
              "max                      17.000000  ...                    1.000000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 653
        },
        "id": "-3sbMvelKvfR",
        "outputId": "4de7a505-ac50-4971-94fd-5020018f76bf"
      },
      "source": [
        "corr = df.corr()\r\n",
        "sns.heatmap(corr, annot=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2a904d2358>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2AwIv629fPE0"
      },
      "source": [
        "# Data Preparation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3a0NBspNNnil"
      },
      "source": [
        "X = df.drop('9. Class variable (0 or 1)', axis=1).values\r\n",
        "Y = df['9. Class variable (0 or 1)'].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bS0MqlJGMaE8"
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7vcTjpfROF9_"
      },
      "source": [
        "X_train = torch.FloatTensor(X_train)\r\n",
        "X_test = torch.FloatTensor(X_test)\r\n",
        "Y_train = torch.LongTensor(Y_train)\r\n",
        "Y_test = torch.LongTensor(Y_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lm8jotcqfbWP"
      },
      "source": [
        "# Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bOuEw1JDPfQc"
      },
      "source": [
        "HAPPY_COLORS_PALETTE = [\"#01BEFE\", \"#FFDD00\", \"#FF7D00\", \"#FF006D\", \"#93D30C\", \"#8F00FF\"]\r\n",
        "def ann_viz(model, view=True, filename=\"network.gv\"):\r\n",
        "    \"\"\"Vizualizez a Sequential model.\r\n",
        "\r\n",
        "    # Arguments\r\n",
        "        model: A Keras model instance.\r\n",
        "\r\n",
        "        view: whether to display the model after generation.\r\n",
        "\r\n",
        "        filename: where to save the vizualization. (a .gv file)\r\n",
        "\r\n",
        "        title: A title for the graph\r\n",
        "    \"\"\"\r\n",
        "    from graphviz import Digraph\r\n",
        "\r\n",
        "    input_layer = 0\r\n",
        "    hidden_layers_nr = 0\r\n",
        "    layer_types = []\r\n",
        "    hidden_layers = []\r\n",
        "    output_layer = 0\r\n",
        "    layers = [layer for layer in model.modules() if type(layer) == torch.nn.Linear]\r\n",
        "\r\n",
        "    for layer in layers:\r\n",
        "        if layer == layers[0]:\r\n",
        "            input_layer = layer.in_features\r\n",
        "            hidden_layers_nr += 1\r\n",
        "            if type(layer) == torch.nn.Linear:                \r\n",
        "                hidden_layers.append(layer.out_features)\r\n",
        "                layer_types.append(\"Dense\")\r\n",
        "            else:\r\n",
        "                raise Exception(\"Input error\")\r\n",
        "\r\n",
        "        else:\r\n",
        "            if layer == layers[-1]:\r\n",
        "                output_layer = layer.out_features\r\n",
        "            else:\r\n",
        "                hidden_layers_nr += 1\r\n",
        "                if type(layer) == torch.nn.Linear:\r\n",
        "\r\n",
        "                    hidden_layers.append(layer.out_features)\r\n",
        "                    layer_types.append(\"Dense\")\r\n",
        "                else:\r\n",
        "                    raise Exception(\"Hidden error\")\r\n",
        "        last_layer_nodes = input_layer\r\n",
        "        nodes_up = input_layer\r\n",
        "\r\n",
        "    g = Digraph(\"g\", filename=filename)\r\n",
        "    n = 0\r\n",
        "    g.graph_attr.update(splines=\"false\", nodesep=\"0.5\", ranksep=\"0\", rankdir='LR')\r\n",
        "    # Input Layer\r\n",
        "    with g.subgraph(name=\"cluster_input\") as c:\r\n",
        "        if type(layers[0]) == torch.nn.Linear:\r\n",
        "            the_label = \"Input Layer\"\r\n",
        "            if layers[0].in_features > 10:\r\n",
        "                the_label += \" (+\" + str(layers[0].in_features - 10) + \")\"\r\n",
        "                input_layer = 10\r\n",
        "            c.attr(color=\"white\")\r\n",
        "            for i in range(0, input_layer):\r\n",
        "                n += 1\r\n",
        "                c.node(str(n))\r\n",
        "                c.attr(labeljust=\"1\")\r\n",
        "                c.attr(label=the_label, labelloc=\"bottom\")\r\n",
        "                c.attr(rank=\"same\")                \r\n",
        "                c.node_attr.update(\r\n",
        "                    width=\"0.65\",\r\n",
        "                    style=\"filled\",                    \r\n",
        "                    shape=\"circle\",\r\n",
        "                    color=HAPPY_COLORS_PALETTE[3],\r\n",
        "                    fontcolor=HAPPY_COLORS_PALETTE[3],\r\n",
        "                )\r\n",
        "    for i in range(0, hidden_layers_nr):\r\n",
        "        with g.subgraph(name=\"cluster_\" + str(i + 1)) as c:\r\n",
        "            if layer_types[i] == \"Dense\":\r\n",
        "                c.attr(color=\"white\")\r\n",
        "                c.attr(rank=\"same\")\r\n",
        "                the_label = f'Hidden Layer {i + 1}'\r\n",
        "                if layers[i].out_features > 10:\r\n",
        "                    the_label += \" (+\" + str(layers[i].out_features - 10) + \")\"\r\n",
        "                    hidden_layers[i] = 10\r\n",
        "                c.attr(labeljust=\"right\", labelloc=\"b\", label=the_label)\r\n",
        "                for j in range(0, hidden_layers[i]):\r\n",
        "                    n += 1\r\n",
        "                    c.node(\r\n",
        "                        str(n),\r\n",
        "                        width=\"0.65\",\r\n",
        "                        shape=\"circle\",\r\n",
        "                        style=\"filled\",\r\n",
        "                        color=HAPPY_COLORS_PALETTE[0],\r\n",
        "                        fontcolor=HAPPY_COLORS_PALETTE[0],\r\n",
        "                    )\r\n",
        "                    for h in range(nodes_up - last_layer_nodes + 1, nodes_up + 1):\r\n",
        "                        g.edge(str(h), str(n))\r\n",
        "                last_layer_nodes = hidden_layers[i]\r\n",
        "                nodes_up += hidden_layers[i]\r\n",
        "            else:\r\n",
        "                raise Exception(\"Hidden layer type not supported\")\r\n",
        "\r\n",
        "    with g.subgraph(name=\"cluster_output\") as c:\r\n",
        "        if type(layers[-1]) == torch.nn.Linear:\r\n",
        "            c.attr(color=\"white\")\r\n",
        "            c.attr(rank=\"same\")\r\n",
        "            c.attr(labeljust=\"1\")\r\n",
        "            for i in range(1, output_layer + 1):\r\n",
        "                n += 1\r\n",
        "                c.node(\r\n",
        "                    str(n),\r\n",
        "                    width=\"0.65\",\r\n",
        "                    shape=\"circle\",\r\n",
        "                    style=\"filled\",\r\n",
        "                    color=HAPPY_COLORS_PALETTE[4],\r\n",
        "                    fontcolor=HAPPY_COLORS_PALETTE[4],\r\n",
        "                    \r\n",
        "                )\r\n",
        "                for h in range(nodes_up - last_layer_nodes + 1, nodes_up + 1):\r\n",
        "                    g.edge(str(h), str(n))\r\n",
        "            c.attr(label=\"Output Layer\", labelloc=\"bottom\")\r\n",
        "            c.node_attr.update(\r\n",
        "                color=\"#2ecc71\", style=\"filled\", fontcolor=\"#2ecc71\", shape=\"circle\"\r\n",
        "            )\r\n",
        "\r\n",
        "    g.attr(arrowShape=\"none\")\r\n",
        "    g.edge_attr.update(arrowhead=\"none\", color=\"#707070\", penwidth=\"2\")\r\n",
        "    if view is True:\r\n",
        "        g.view()\r\n",
        "\r\n",
        "    return g"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nPUlxYViPYUK"
      },
      "source": [
        "class ANN(nn.Module):\r\n",
        "    def __init__(self, in_features = 8, hidden1 = 32, hidden2 = 32, out_features = 2):\r\n",
        "        super().__init__()\r\n",
        "        self.fc1 = nn.Linear(in_features, hidden1)\r\n",
        "        self.fc2 = nn.Linear(hidden1, hidden2)\r\n",
        "        self.out = nn.Linear(hidden2, out_features)\r\n",
        "\r\n",
        "    def forward(self, x):\r\n",
        "        x = F.relu(self.fc1(x))\r\n",
        "        x = F.relu(self.fc2(x))\r\n",
        "        x = self.out(x)\r\n",
        "        return x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S8t0JpjiRidE",
        "outputId": "d908f64f-86e8-4c9d-cb32-a57e585d2f7d"
      },
      "source": [
        "torch.manual_seed(20)\r\n",
        "model = ANN()\r\n",
        "model.train()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ANN(\n",
              "  (fc1): Linear(in_features=8, out_features=32, bias=True)\n",
              "  (fc2): Linear(in_features=32, out_features=32, bias=True)\n",
              "  (out): Linear(in_features=32, out_features=2, bias=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "NconaAe7PsxJ",
        "outputId": "b7e33405-5756-4a8c-dc7f-751311b1bab1"
      },
      "source": [
        "ann_viz(model, view=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<graphviz.dot.Digraph at 0x7f2a876ef588>"
            ],
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n -->\n<!-- Title: g Pages: 1 -->\n<svg width=\"496pt\" height=\"858pt\"\n viewBox=\"0.00 0.00 496.00 858.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 854)\">\n<title>g</title>\n<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-854 492,-854 492,4 -4,4\"/>\n<g id=\"clust1\" class=\"cluster\">\n<title>cluster_input</title>\n<polygon fill=\"none\" stroke=\"#ffffff\" points=\"0,-91 0,-759 84,-759 84,-91 0,-91\"/>\n<text text-anchor=\"middle\" x=\"42\" y=\"-98.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Input Layer</text>\n</g>\n<g id=\"clust2\" 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d=\"M55.495,-292.9577C78.9657,-261.022 125.9939,-197.0329 149.4809,-165.0752\"/>\n</g>\n<!-- 2&#45;&gt;10 -->\n<g id=\"edge10\" class=\"edge\">\n<title>2&#45;&gt;10</title>\n<path fill=\"none\" stroke=\"#707070\" stroke-width=\"2\" d=\"M51.9017,-290.7704C74.9509,-243.7273 130.0999,-131.169 153.1247,-84.1759\"/>\n</g>\n<!-- 2&#45;&gt;11 -->\n<g id=\"edge18\" class=\"edge\">\n<title>2&#45;&gt;11</title>\n<path fill=\"none\" stroke=\"#707070\" stroke-width=\"2\" d=\"M47.1396,-335.0208C67.5483,-418.3284 137.4246,-703.5612 157.85,-786.937\"/>\n</g>\n<!-- 2&#45;&gt;12 -->\n<g id=\"edge26\" class=\"edge\">\n<title>2&#45;&gt;12</title>\n<path fill=\"none\" stroke=\"#707070\" stroke-width=\"2\" d=\"M48.1459,-334.6069C69.4029,-406.9155 135.5826,-632.0351 156.8481,-704.3726\"/>\n</g>\n<!-- 2&#45;&gt;13 -->\n<g id=\"edge34\" class=\"edge\">\n<title>2&#45;&gt;13</title>\n<path fill=\"none\" stroke=\"#707070\" stroke-width=\"2\" d=\"M49.6942,-334.299C71.9193,-394.7803 133.1582,-561.4305 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149.4809,-790.9248\"/>\n</g>\n<!-- 6&#45;&gt;12 -->\n<g id=\"edge30\" class=\"edge\">\n<title>6&#45;&gt;12</title>\n<path fill=\"none\" stroke=\"#707070\" stroke-width=\"2\" d=\"M61.1014,-657.3354C83.8679,-672.8241 121.2015,-698.2231 143.9472,-713.6977\"/>\n</g>\n<!-- 6&#45;&gt;13 -->\n<g id=\"edge38\" class=\"edge\">\n<title>6&#45;&gt;13</title>\n<path fill=\"none\" stroke=\"#707070\" stroke-width=\"2\" d=\"M65.0228,-644C86.4878,-644 118.061,-644 139.6331,-644\"/>\n</g>\n<!-- 6&#45;&gt;14 -->\n<g id=\"edge46\" class=\"edge\">\n<title>6&#45;&gt;14</title>\n<path fill=\"none\" stroke=\"#707070\" stroke-width=\"2\" d=\"M61.1014,-630.6646C83.8679,-615.1759 121.2015,-589.7769 143.9472,-574.3023\"/>\n</g>\n<!-- 6&#45;&gt;15 -->\n<g id=\"edge54\" class=\"edge\">\n<title>6&#45;&gt;15</title>\n<path fill=\"none\" stroke=\"#707070\" stroke-width=\"2\" d=\"M55.495,-624.9577C78.9657,-593.022 125.9939,-529.0329 149.4809,-497.0752\"/>\n</g>\n<!-- 6&#45;&gt;16 -->\n<g id=\"edge62\" 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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SqjP4hYHfgYe"
      },
      "source": [
        "# Model Parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gFiQSlBfRvbd"
      },
      "source": [
        "loss_function = nn.CrossEntropyLoss()\r\n",
        "optimiser = torch.optim.Adam(model.parameters(), lr=0.0001)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n3DLJ2Vkfo-C"
      },
      "source": [
        "# Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4dkcBPtpSNHV",
        "outputId": "544ff888-96f3-423d-a235-0d9abcb4ba93"
      },
      "source": [
        "n_epoch = 10000\r\n",
        "loss_list = []\r\n",
        "for i in range(n_epoch):\r\n",
        "    Y_pred = model(X_train)\r\n",
        "    train_loss = loss_function(Y_pred, Y_train)\r\n",
        "    loss_list.append(train_loss.item())\r\n",
        "    if i%100==99:\r\n",
        "        print(f'Epoch :{i+1} loss :{train_loss.item()}')\r\n",
        "    optimiser.zero_grad()\r\n",
        "    train_loss.backward()\r\n",
        "    optimiser.step()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch :100 loss :0.6572433114051819\n",
            "Epoch :200 loss :0.5868644118309021\n",
            "Epoch :300 loss :0.5630478262901306\n",
            "Epoch :400 loss :0.5505251884460449\n",
            "Epoch :500 loss :0.5428259968757629\n",
            "Epoch :600 loss :0.5360120534896851\n",
            "Epoch :700 loss :0.5302756428718567\n",
            "Epoch :800 loss :0.5252026915550232\n",
            "Epoch :900 loss :0.5202588438987732\n",
            "Epoch :1000 loss :0.515230119228363\n",
            "Epoch :1100 loss :0.510227620601654\n",
            "Epoch :1200 loss :0.50494784116745\n",
            "Epoch :1300 loss :0.4981836676597595\n",
            "Epoch :1400 loss :0.49190887808799744\n",
            "Epoch :1500 loss :0.48609670996665955\n",
            "Epoch :1600 loss :0.47954466938972473\n",
            "Epoch :1700 loss :0.4729483425617218\n",
            "Epoch :1800 loss :0.46664443612098694\n",
            "Epoch :1900 loss :0.4604358971118927\n",
            "Epoch :2000 loss :0.4543372094631195\n",
            "Epoch :2100 loss :0.4480225145816803\n",
            "Epoch :2200 loss :0.44177505373954773\n",
            "Epoch :2300 loss :0.4354169964790344\n",
            "Epoch :2400 loss :0.42859625816345215\n",
            "Epoch :2500 loss :0.4218743145465851\n",
            "Epoch :2600 loss :0.4151095747947693\n",
            "Epoch :2700 loss :0.4086109697818756\n",
            "Epoch :2800 loss :0.4018287658691406\n",
            "Epoch :2900 loss :0.39586254954338074\n",
            "Epoch :3000 loss :0.3893703818321228\n",
            "Epoch :3100 loss :0.38313546776771545\n",
            "Epoch :3200 loss :0.377221941947937\n",
            "Epoch :3300 loss :0.37099456787109375\n",
            "Epoch :3400 loss :0.36536887288093567\n",
            "Epoch :3500 loss :0.36022889614105225\n",
            "Epoch :3600 loss :0.35543039441108704\n",
            "Epoch :3700 loss :0.3510311543941498\n",
            "Epoch :3800 loss :0.3468409776687622\n",
            "Epoch :3900 loss :0.34289753437042236\n",
            "Epoch :4000 loss :0.33921077847480774\n",
            "Epoch :4100 loss :0.3355608284473419\n",
            "Epoch :4200 loss :0.33200615644454956\n",
            "Epoch :4300 loss :0.32862767577171326\n",
            "Epoch :4400 loss :0.3252767026424408\n",
            "Epoch :4500 loss :0.32208138704299927\n",
            "Epoch :4600 loss :0.3188129961490631\n",
            "Epoch :4700 loss :0.3158900737762451\n",
            "Epoch :4800 loss :0.3131835162639618\n",
            "Epoch :4900 loss :0.31049028038978577\n",
            "Epoch :5000 loss :0.30781835317611694\n",
            "Epoch :5100 loss :0.3045908510684967\n",
            "Epoch :5200 loss :0.3017725348472595\n",
            "Epoch :5300 loss :0.29926058650016785\n",
            "Epoch :5400 loss :0.29692599177360535\n",
            "Epoch :5500 loss :0.2945350408554077\n",
            "Epoch :5600 loss :0.292147696018219\n",
            "Epoch :5700 loss :0.28982600569725037\n",
            "Epoch :5800 loss :0.28767865896224976\n",
            "Epoch :5900 loss :0.2849612832069397\n",
            "Epoch :6000 loss :0.28281694650650024\n",
            "Epoch :6100 loss :0.2805413007736206\n",
            "Epoch :6200 loss :0.2784760892391205\n",
            "Epoch :6300 loss :0.2764264941215515\n",
            "Epoch :6400 loss :0.2742573618888855\n",
            "Epoch :6500 loss :0.27208957076072693\n",
            "Epoch :6600 loss :0.2700267732143402\n",
            "Epoch :6700 loss :0.26801156997680664\n",
            "Epoch :6800 loss :0.2650045156478882\n",
            "Epoch :6900 loss :0.26234713196754456\n",
            "Epoch :7000 loss :0.2600288987159729\n",
            "Epoch :7100 loss :0.2579538822174072\n",
            "Epoch :7200 loss :0.2559097409248352\n",
            "Epoch :7300 loss :0.254019558429718\n",
            "Epoch :7400 loss :0.2522282600402832\n",
            "Epoch :7500 loss :0.25026097893714905\n",
            "Epoch :7600 loss :0.2484164834022522\n",
            "Epoch :7700 loss :0.2465151995420456\n",
            "Epoch :7800 loss :0.24467158317565918\n",
            "Epoch :7900 loss :0.24289099872112274\n",
            "Epoch :8000 loss :0.24121041595935822\n",
            "Epoch :8100 loss :0.23955725133419037\n",
            "Epoch :8200 loss :0.23791630566120148\n",
            "Epoch :8300 loss :0.23646825551986694\n",
            "Epoch :8400 loss :0.23493917286396027\n",
            "Epoch :8500 loss :0.23344439268112183\n",
            "Epoch :8600 loss :0.23213675618171692\n",
            "Epoch :8700 loss :0.23069967329502106\n",
            "Epoch :8800 loss :0.2293076366186142\n",
            "Epoch :8900 loss :0.2280476689338684\n",
            "Epoch :9000 loss :0.22674719989299774\n",
            "Epoch :9100 loss :0.2255103439092636\n",
            "Epoch :9200 loss :0.22431358695030212\n",
            "Epoch :9300 loss :0.22311420738697052\n",
            "Epoch :9400 loss :0.22192922234535217\n",
            "Epoch :9500 loss :0.2207174450159073\n",
            "Epoch :9600 loss :0.21950840950012207\n",
            "Epoch :9700 loss :0.21830879151821136\n",
            "Epoch :9800 loss :0.21689511835575104\n",
            "Epoch :9900 loss :0.21559348702430725\n",
            "Epoch :10000 loss :0.21428601443767548\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "PbCjt3msZ5Hs",
        "outputId": "b6ac0c80-0ff9-41ca-8d6d-2bd5815a341b"
      },
      "source": [
        "plt.plot(range(n_epoch), loss_list)\r\n",
        "plt.ylabel('loss')\r\n",
        "plt.xlabel('Epoch')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Epoch')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uciJyn-cajw9"
      },
      "source": [
        "predictions = []\r\n",
        "with torch.no_grad():\r\n",
        "    for i, data in enumerate(X_test):\r\n",
        "        y_pred = model(data)\r\n",
        "        predictions.append(y_pred.argmax().item())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZI7QKusVa8Ey",
        "outputId": "e784aad5-5beb-4cb1-c2ab-7b348637f065"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\r\n",
        "cm = confusion_matrix(Y_test.numpy(), predictions)\r\n",
        "cm"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[83, 24],\n",
              "       [24, 23]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "XDXMxP2aclcr",
        "outputId": "5f8e1aa4-052e-4722-f622-2bfa26052413"
      },
      "source": [
        "plt.figure(figsize=(10,6))\r\n",
        "sns.heatmap(cm, annot=True)\r\n",
        "plt.xlabel('Actual values')\r\n",
        "plt.ylabel('Prediction values')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(69.0, 0.5, 'Prediction values')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iRcrJydH+/fsDUBoAAPAnq3RW6tXkh1q1aiWn06mQkBANHjxYH3/8sb/rAgAAfmYYDp9egeK1s9KgQQOdPXtW7dq1U0ZGhqKjo+VyWWTLOwAAYHleOysZGRkyDENTp05Vw4YNdfjwYb300kuBqA0AAPiR4fLtFSheOyt79+5V9+7dFRERodGjRweiJgAAEAAuizzI0GtnZc2aNerdu7cmTpyoNWvWqKqqKhB1AQAAP7PKzIrDMLzfuHTu3DmtW7dOy5Yt0/bt29W1a1fNmDHDr4WdO7LPr+cH8MsaxXcPdgmAbVVWFgf0eoVt+/p0/G+/XFZLlXhWo7uBLrnkEnXr1k39+vVTQkKC8vPz/V0XAACApBrMrKxdu1YffvihtmzZoptuuklpaWnKzMwMRG0AAMCP6symcLm5uerbt6+mTZumsLCwQNQEAAACwCqbwnkNK88//3wg6gAAAAFWZ+4GAgAACCavnRUAAFA3BfL2Y18QVgAAsKk6M2C7bds2vfzyyzp06JCqqqpkGIYcDocKCgoCUR8AAPATq8yseA0rTz75pCZNmqT27durXj1GXAAAqCvqzDJQo0aNdOuttwaiFgAAgJ/xGlY6d+6sOXPmqFevXhfss5KQkODXwgAAgH/VmZmVXbt2SZL27Nnj/szhcGj+/Pn+q0o8nwQIliqXM9glAAiQOjOz8tZbbwWiDgAAEGB1Zmbl+PHjevnll7V161ZJ0k033aRRo0apUaNGfi8OAAD4j1U6K15v75k8ebLCw8P1wgsv6IUXXlBERIQmTZoUiNoAAAC8d1aKi4v10ksvud+PHj1aAwcO9GtRAADA/ywyX+u9s1K/fn198skn7vfbtm1T/fr1/VoUAADwP5fh8OkVKF47K08//bQef/xxnThxQoZhqHHjxpo9e3YgagMAAH5UZwZs27Vrp/fff18nTpyQJEVERPi9KAAAgB9UG1Zyc3M1cOBAvfHGG7/4/b333uu3ogAAgP+5gl1ADVUbVk6fPi1JOnnyZMCKAQAAgWPI4stAw4YNkyQlJiaqY8eOF3y3bds2/1YFAAD8zmWR24G83g00ffr0Gn0GAACsxSWHT69AqbazsmPHDu3YsUNHjx69YG7lxIkTcjp5dggAAAiMasPKuXPndOrUKTmdzgvmViIiIvTiiy8GpDgAAOA/VplZcRiG5wdEHzx4UHFxcYGqx61+/SsCfk0APHUZCKaqswcDer1VMek+HZ9cmu31Z5KSkhQeHq569eopJCREixcv1rFjxzRu3Dh3xsjMzFTjxo2rPYfXmZWnnnpKFRUV7vfff/+9RowYUcNfAwAAmJUhh0+vmpo3b55yc3O1ePFiSVJWVpYSExO1cuVKJSYmKisry+PxXsPKd999p8jISPf7xo0bq7y8vMYFAgAA/FhBQYFSU1MlSampqcrPz/f48153sK1Xr54OHTqkFi1aSDq/LORwWGONCwAAVM/XTeGys7OVnf3/l4LS09OVnv7zpaURI0bI4XC4vy8vL1d0dLQkqVmzZl6bIF7DytixY3XHHXeoU6dOMgxD27Zt07Rp0y729wEAACbja1ipLpz82IIFCxQTE6Py8nLde++9uuqqqy743uFweG2CeA0r3bp10+LFi7Vr1y5J0uTJkxUVFeXtMAAAYHKBuBsoJiZGktSkSRMlJydr9+7datKkicrKyhQdHa2ysjKvuaLamZWioiJJ0t69e3X48GFFR0crOjpahw8f1t69e2vx1wAAAMHgcvj28ubUqVPuByGfOnVK69ev129+8xslJSUpJydHkpSTk6OePXt6PE+1nZU33nhD06dP1+zZs3/2ncPh0Pz5871XCQAAbKu8vFyjRo2SJDmdTqWkpKhbt27q0KGDxo4dq4ULF6pFixbKzMz0eB6v+6wEC/usAMHBPitA8AR6n5Xc2Dt8On5gydu1VIln1XZWVq5c6fHAXr161XoxAAAgcEzZrfgF1YaVNWvWSDrfwtmxY4e6dOkiSdq8ebOuv/56wgoAABbn691AgVJtWJk1a5Yk6b777lNeXp77fuiysjJNmjQpMNUBAAC/cVlk3zSvO9j+cCfQD5o2bapDhw75tSgAAIAfeN1nJTExUSNGjFC/fv0kScuWLVPXrl39XhgAAPAvy8+s/GDq1KlatWqVtm7dKun8bnXJycl+LwwAAPiX5WdWfuzaa69VeHi4unbtqtOnT+vEiROKiIjwd20AAMCParKxmxl4nVl599139cgjj2jq1KmSpNLSUvcGLwAAAP7mNaz861//0oIFC9ydlCuvvFJHjx71e2EAAMC/XHL49AoUr8tAYWFhCgsLc7+vqqrya0EAACAw6syAbadOnfTqq6+qsrJS69ev19tvv62kpKRA1AYAAPyozsysTJw4UVFRUWrbtq2ys7N16623auzYsYGoDQAA+JHLx1egeOysOJ1O9evXT8uXL9fQoUMDVRMAAICbx85KSEiIWrduzY61AADUQYaPr0DxOrNSUVGhfv366Xe/+50aNGjg/vzVV1/1a2EAAMC/rDKz4jWsjBkzJhB1AACAALP8DrZnzpzRggULVFxcrLZt22rIkCEKDa3RhrcAAMACrBJWqp1Zefzxx7Vnzx61bdtW69at0+zZswNZFwAAgCQPnZWioiJ98MEHkqQhQ4YoLS0tYEUBAAD/M6w+s/LjJR+WfwAAqHussgxUbQopLCzUDTfcIEkyDENnzpzRDTfcIMMw5HA4tH379oAVCQAAap/lw8rnn38eyDoAAAB+Ees7AADYVJ15kCEAAKib6symcAAAoG6y/MwKAACo26wSVjw+yBAAACDY6KwAAGBTDNgCAABTY8AWAACYmlVmVggrAADYlFWWgRiwBQAApkZnBQAAm3JZpLdCWAEAwKaYWQEAAKZmjb4KMysAAMDk6KwAAGBTLAMBAABTY1M4AABgatwNBAAATM0aUYUBWwAAYHJ0VgAAsCkGbAEAgKkxswIAAEzNGlGFsAIAgG1ZZRmIAVsAAOA3TqdTqampevDBByVJBw4cUFpampKTkzV27FidPXvW6zkIKwAA2JRLhk+vmpg/f77atGnjfv/cc89p+PDhWrVqlSIjI7Vw4UKv5yCsAABgU4aPL29KSkr00UcfaciQIeevZxjatGmTevfuLUkaNGiQCgoKvJ6HmRUAAGzK15mV7OxsZWdnu9+np6crPT3d/X7mzJmaOHGiTp48KUn67rvvFBkZqdDQ8/EjNjZWpaWlXq9DWAEAAL/KT8PJj61Zs0ZRUVFq3769Nm/e7NN1CCsAANiU4cebl7dv367Vq1dr3bp1OnPmjE6cOKEZM2aooqJCVVVVCg0NVUlJiWJiYryei5kVAABsyuXjy5MJEyZo3bp1Wr16tZ5//nl16dJFf/3rX9W5c2etWLFCkrRkyRIlJSV5rZOwAgCATQXibqCfmjhxot544w0lJyfr2LFjSktL83qMwzAMU25gV7/+FcEuAbClKpcz2CUAtlV19mBAr/fQlUN9Ov4f+9+tpUo8o7MCAABMjbACn8THN9eKFe9ox44Cbd+er1Gj7rvg+zFjHlBlZbGaNLk8SBUCdVN8fAvlr3xPu3et0a6dq/Xw6BGSpL88PVHbt63SJ1tX6sO8t9W8uffhRdhXMJaBfg2WgeCT2NhoxcZGa+fOPYqICNfGjXlKS3tAhYX/UXx8c/3jHxm65po2Skzsp/Ly74JdLmqAZSBriI2NVvPYaO34f3/3tmxersFD7tM33xzW8eMnJEmjR92ndu3aatToJ4JcLWoq0MtAD1zpfV7Ek3/uf6+WKvGMzgp8UlJSpp0790iSTpw4qcLCrxQXFytJysj4b02ePFMmzcOApZWUlGnHBX/3/qO4FrHuoCJJ4eEN+fsHjwwf/xcoAd9nZdGiRRo8eHCgL4sAaNUqXr//fYK2bNmhlJRkHTpUok8//TzYZQF1XqtW8fr9de21ecsOSdIz0x7XXXcO0fcVFbot2bf/ckbdxlOXq/HSSy8F+pIIgPDwhlqw4DU9+uhfVFVVpcceG61p0/4a7LKAOi88vKHezf6nxj/63+6uypSpc9S6TSctWLBEo/58b5ArBHznl85K//79q/3uyJEj/rgkgig0NFTvvPOa3nlniXJzlysh4RpdeWVLbd26XJIUF9dcmzYt0x/+MEClpd8GuVqg7ggNDdV72f/UggVLlJPz4c++f3vBYn3w/lv6C//hgGoEcinHF34JK+Xl5Xr99dcVGRl5weeGYWjYsGH+uCSC6LXXnlVh4Vd68cW5kqS9e7/QFVfc4P7+iy/Wq2vXFAZsgVr2z6y/6vPCr5T5Qpb7s6uvbq2vvvofSdKA/r31xRdFwSoPFmCVZSC/hJXu3bvr5MmTateu3c++69y5sz8uiSDp2rWT7rxzsD799HNt3nz+v+ymTs3QihVrglwZULfd3LWT7r5riHZ/+pk+2bpSkjRlymzde+8wtW3bRi6XS8XFB/XnUdwJhOq5LDKAza3LAC7ArctA8AT61uW7W93u0/Fvfb24lirxjKcuAwBgU6bsVvwCwgoAADYVyF1ofUFYAQDApmx9NxAAADA/q9wNxHb7AADA1OisAABgU8ysAAAAU2NmBQAAmJpVZlYIKwAA2JRJ94X9GQZsAQCAqdFZAQDAphiwBQAApsbMCgAAMDWr3A3EzAoAADA1OisAANgUMysAAMDUrHLrMmEFAACbYsAWAACYGgO2AAAAtYDOCgAANsWALQAAMDUGbAEAgKlZpbPCzAoAADA1OisAANiUVe4GIqwAAGBTLmZWAACAmVkjqhBWAACwLQZsAQAAagGdFQAAbMoqnRXCCgAANsWmcAAAwNTorAAAAFOzyj4rDNgCAABTo7MCAIBNMbMCAABMjZkVAABgav7urJw5c0Z33nmnzp49K6fTqd69e+uRRx7RgQMHNH78eB07dkwJCQnKyMhQWFhYtedhZgUAAPhFWFiY5s2bp/fff185OTn6+OOPtXPnTj333HMaPny4Vq1apcjISC1cuNDjeQgrAADYlEuGTy9vHA6HwsPDJUlVVVWqqqqSw+HQpk2b1Lt3b0nSoEGDVFBQ4PE8LAMBAGBTgbh12el06vbbb1dxcbHuuOMOtWzZUpGRkQoNPR9BYmNjVVpa6vEchBUAAGzK5ePMSnZ2trKzs93v09PTlZ6efsHPhISEKDc3VxUVFRo1apT27dt30dchrAAAYFO+dlZ+KZxUJzIyUp07d9bOnTtVUVGhqqoqhYaGqqSkRDExMR6PZWYFAAD4xdGjR1VRUSFJqqys1IYNG9SmTRt17txZK1askCQtWbJESUlJHs9DZwUAAJvydRnIm7KyMj3xxBNyOp0yDEN9+vRRjx49dPXVV2vcuHHKzMxUu3btlJaW5vE8DsOk29fVr39FsEsAbKnK5Qx2CYBtVZ09GNDr/Ta6k0/HF5ZtraVKPKOzAgCATfm7s1JbCCsAANgUT10GAACoBXRWAACwKZaBAACAqVllGYiwAgCATRmGK9gl1AgzKwAAwNTorAAAYFM1eXKyGRBWAACwKZPuC/szhBUAAGyKzgoAADA1q3RWGLAFAACmRmcFAACbYlM4AABgamwKBwAATM0qMyuEFQAAbMoqdwMxYAsAAEyNzgoAADbFMhAAADA17gYCAACmZpXOCjMrAADA1OisAABgU1a5G4iwAgCATVllGYiwAgCATTFgCwAATM0q2+0zYAsAAEyNzgoAADbFMhAAADA1BmwBAICpWWVmhbACAIBNWaWzwoAtAAAwNTorAADYlFU6K4QVAABsyhpRRXIYVolVAADAlphZAQAApkZYAQAApkZYAQAApkZYAQAApkZYAQAApkZYAQAApkZYQa1at26devfureTkZGVlZQW7HMA2Jk2apMTERKWkpAS7FKDWEVZQa5xOp6ZNm6a5c+cqLy9PS5cu1VdffRXssgBbuP322zV37txglwH4BWEFtWb37t1q1aqVWrZsqbCwMPXr108FBQXBLguwhU6dOqlx48bBLgPwC8IKak1paaliY2Pd72NiYlRaWhrEigAAdQFhBQAAmBphBbUmJiZGJSUl7velpaWKiYkJYkUAgLqAsIJa06FDB+3fv18HDhzQ2bNnlSwONCoAAASISURBVJeXp6SkpGCXBQCwOJ66jFq1du1azZw5U06nU4MHD9ZDDz0U7JIAWxg/fry2bNmi7777Tk2aNNHDDz+stLS0YJcF1ArCCgAAMDWWgQAAgKkRVgAAgKkRVgAAgKkRVgAAgKkRVgAAgKkRVgATys/P1zXXXKOioiKvP/vmm2/q9OnTv/paixcv1rRp03718bV9HgD4KcIKYEJLly5Vx44dlZeX5/Vn58+f71NYAQCzI6wAJnPy5Elt27ZNM2bMuCCsOJ1OzZkzRykpKerfv7/eeustzZ8/X2VlZbrnnnt09913S5Kuv/569zHLly/XE088IUlavXq10tLSlJqaquHDh+vIkSPV1uByuZSUlKSKigr3Z7169dKRI0dqdJ4nnnhCy5cvd7//cU1z587V4MGD1b9/f7344ouSpFOnTmnkyJEaMGCAUlJStGzZsov9YwNQh4UGuwAAFyooKNAtt9yi1q1b6/LLL9eePXvUvn17ZWdn6+DBg8rJyVFoaKiOHTumyy67TG+++abmzZunqKgoj+ft2LGj3n33XTkcDr333nuaO3euO8j8VL169ZSUlKRVq1Zp8ODB2rVrl1q0aKGmTZte1Hl+6t///re+/vprLVy4UIZh6KGHHtLWrVt19OhRRUdHKysrS5J0/Pjxi/tDA1CnEVYAk8nLy9Of/vQnSVLfvn2Vl5en9u3ba+PGjRo2bJhCQ8//tb3ssssu6rwlJSUaN26cvv32W509e1bx8fEef75v37565ZVXNHjwYOXl5alv376/6jw/tn79eq1fv16pqamSzndU9u/frxtvvFFz5szRs88+qx49eujGG2+8qN8NQN1GWAFM5NixY9q0aZO+/PJLORwOOZ1OORwOPfbYY7/qfGfOnHH/8/Tp0zV8+HD17NlTmzdv1ssvv+zx2Ouvv17FxcU6evSo8vPz3c95qsl5QkJC5HK5JJ1fUjp37pwkyTAMjRw5UsOGDfvZMYsXL9batWuVmZmpLl26aPTo0b/qdwZQ9zCzApjIihUrNHDgQK1Zs0arV6/W2rVrFR8fr08++URdu3ZVdna2qqqqJJ0PNpIUHh6ukydPus/RtGlTFRUVyeVyKT8/3/358ePHFRMTI0nKycnxWovD4dBtt92mWbNmqU2bNrr88strfJ64uDjt3btX0vlZmR/Cyh/+8ActWrTIXW9paanKy8tVWlqqBg0aaODAgRoxYoQ+++yzmv+hAajz6KwAJrJ06VI98MADF3zWq1cvLV26VFOmTNH+/fs1YMAAhYaGaujQobrrrrs0dOhQ3X///YqOjtZbb72lCRMm6MEHH1RUVJTat2+vU6dOSZJGjx6tMWPGqHHjxurcubO++eYbr/X07dtXQ4YM0ezZs92f1eQ8Q4cO1Z///GcNGDBAt9xyixo2bCjpfFgpKipyd1YaNmyoZ599Vl9//bUyMjJUr149hYaG6umnn/61f4QA6iCeugwAAEyNZSAAAGBqhBUAAGBqhBUAAGBqhBUAAGBqhBUAAGBqhBUAAGBqhBUAAGBqhBUAAGBq/wsLp+ejs5Nh6wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QeLQH2W4eBYw"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}