DL1 cancer classification pytorch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
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     "data": {
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       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
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       "      <td>0</td>\n",
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       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
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      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('diabetes.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# Balancing Data For Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "df0 = df.query('Outcome ==0')[:269]\n",
    "df1 = df.query('Outcome ==1')\n",
    "df0 = df0.append(df1 , ignore_index=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "df = df0.copy()\n",
    "del df0 , df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "537"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "df = df.sample(frac=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
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       "      <td>0.378</td>\n",
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      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "168            3      122             78              0        0  23.0   \n",
       "301            0      131              0              0        0  43.2   \n",
       "244            4      116             72             12       87  22.1   \n",
       "523            1      102             74              0        0  39.5   \n",
       "221           12       88             74             40       54  35.3   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "168                     0.254   40        0  \n",
       "301                     0.270   26        1  \n",
       "244                     0.463   37        0  \n",
       "523                     0.293   42        1  \n",
       "221                     0.378   48        0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "Collapsed": "false"
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   "outputs": [
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       "      <td>162</td>\n",
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       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>37.2</td>\n",
       "      <td>0.652</td>\n",
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       "      <td>1</td>\n",
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      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "156            2       90             80             14       55  24.4   \n",
       "310            7      160             54             32      175  30.5   \n",
       "239            1      125             70             24      110  24.3   \n",
       "46             2      112             66             22        0  25.0   \n",
       "355            3      162             52             38        0  37.2   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "156                     0.249   24        0  \n",
       "310                     0.588   39        1  \n",
       "239                     0.221   25        0  \n",
       "46                      0.307   24        0  \n",
       "355                     0.652   24        1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "train_data  = df[:500]\n",
    "test_data = df[500:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "Collapsed": "false"
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   "outputs": [
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       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "151            0      165             90             33      680  52.3   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
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     "metadata": {},
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    "train_data.tail(1)"
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  {
   "cell_type": "code",
   "execution_count": 13,
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       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "294            8      176             90             34      300  33.7   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "294                     0.467   58        1  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[0:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "train_x = train_data.drop(columns=['Outcome']).values.tolist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "Collapsed": "false"
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   "outputs": [
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     "data": {
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    "train_x[0]"
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  {
   "cell_type": "code",
   "execution_count": 16,
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>156</td>\n",
       "      <td>2</td>\n",
       "      <td>90</td>\n",
       "      <td>80</td>\n",
       "      <td>14</td>\n",
       "      <td>55</td>\n",
       "      <td>24.4</td>\n",
       "      <td>0.249</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>310</td>\n",
       "      <td>7</td>\n",
       "      <td>160</td>\n",
       "      <td>54</td>\n",
       "      <td>32</td>\n",
       "      <td>175</td>\n",
       "      <td>30.5</td>\n",
       "      <td>0.588</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>239</td>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>70</td>\n",
       "      <td>24</td>\n",
       "      <td>110</td>\n",
       "      <td>24.3</td>\n",
       "      <td>0.221</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>112</td>\n",
       "      <td>66</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.307</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>355</td>\n",
       "      <td>3</td>\n",
       "      <td>162</td>\n",
       "      <td>52</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>37.2</td>\n",
       "      <td>0.652</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "156            2       90             80             14       55  24.4   \n",
       "310            7      160             54             32      175  30.5   \n",
       "239            1      125             70             24      110  24.3   \n",
       "46             2      112             66             22        0  25.0   \n",
       "355            3      162             52             38        0  37.2   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "156                     0.249   24        0  \n",
       "310                     0.588   39        1  \n",
       "239                     0.221   25        0  \n",
       "46                      0.307   24        0  \n",
       "355                     0.652   24        1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "test_x = test_data.drop(columns=['Outcome']).values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "train_y = train_data['Outcome'].tolist()\n",
    "test_y = test_data['Outcome'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[8.0, 176.0, 90.0, 34.0, 300.0, 33.7, 0.467, 58.0]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_x[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"fivethirtyeight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(train_y , label = 'train' , color='green' , alpha=0.5)\n",
    "plt.hist(test_y , label=\"test\" , color='blue',)\n",
    "plt.legend()\n",
    "plt.xticks(ticks=[0,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# Deep Learning Starts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch\n",
    "from torch.nn import functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>168</td>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.254</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>301</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>43.2</td>\n",
       "      <td>0.270</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>244</td>\n",
       "      <td>4</td>\n",
       "      <td>116</td>\n",
       "      <td>72</td>\n",
       "      <td>12</td>\n",
       "      <td>87</td>\n",
       "      <td>22.1</td>\n",
       "      <td>0.463</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>523</td>\n",
       "      <td>1</td>\n",
       "      <td>102</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39.5</td>\n",
       "      <td>0.293</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>221</td>\n",
       "      <td>12</td>\n",
       "      <td>88</td>\n",
       "      <td>74</td>\n",
       "      <td>40</td>\n",
       "      <td>54</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0.378</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>156</td>\n",
       "      <td>2</td>\n",
       "      <td>90</td>\n",
       "      <td>80</td>\n",
       "      <td>14</td>\n",
       "      <td>55</td>\n",
       "      <td>24.4</td>\n",
       "      <td>0.249</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>310</td>\n",
       "      <td>7</td>\n",
       "      <td>160</td>\n",
       "      <td>54</td>\n",
       "      <td>32</td>\n",
       "      <td>175</td>\n",
       "      <td>30.5</td>\n",
       "      <td>0.588</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>239</td>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>70</td>\n",
       "      <td>24</td>\n",
       "      <td>110</td>\n",
       "      <td>24.3</td>\n",
       "      <td>0.221</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>112</td>\n",
       "      <td>66</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.307</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>355</td>\n",
       "      <td>3</td>\n",
       "      <td>162</td>\n",
       "      <td>52</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>37.2</td>\n",
       "      <td>0.652</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>537 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "168            3      122             78              0        0  23.0   \n",
       "301            0      131              0              0        0  43.2   \n",
       "244            4      116             72             12       87  22.1   \n",
       "523            1      102             74              0        0  39.5   \n",
       "221           12       88             74             40       54  35.3   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "156            2       90             80             14       55  24.4   \n",
       "310            7      160             54             32      175  30.5   \n",
       "239            1      125             70             24      110  24.3   \n",
       "46             2      112             66             22        0  25.0   \n",
       "355            3      162             52             38        0  37.2   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "168                     0.254   40        0  \n",
       "301                     0.270   26        1  \n",
       "244                     0.463   37        0  \n",
       "523                     0.293   42        1  \n",
       "221                     0.378   48        0  \n",
       "..                        ...  ...      ...  \n",
       "156                     0.249   24        0  \n",
       "310                     0.588   39        1  \n",
       "239                     0.221   25        0  \n",
       "46                      0.307   24        0  \n",
       "355                     0.652   24        1  \n",
       "\n",
       "[537 rows x 9 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "train_x = torch.from_numpy(np.array(train_x))\n",
    "train_x = train_x.float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "train_y = torch.from_numpy(np.array(train_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 500)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_x) , len(train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([  3.0000, 122.0000,  78.0000,   0.0000,   0.0000,  23.0000,   0.2540,\n",
       "         40.0000])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "test_x = torch.from_numpy(np.array(test_x))\n",
    "test_x = test_x.float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "test_x = torch.from_numpy(np.array(test_x))\n",
    "test_x = test_x.float()\n",
    "test_y = torch.from_numpy(np.array(test_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# Building Network\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self ):\n",
    "        super().__init__()\n",
    "        self.input = nn.Linear(8 , 16)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.h1 = nn.Linear(16 , 142)\n",
    "        self.h2 = nn.Linear(142 , 72)\n",
    "        self.h3 = nn.Linear(72 , 204)\n",
    "        self.h4 = nn.Linear(204 , 32)\n",
    "        self.out = nn.Linear(32,2)\n",
    "        \n",
    "    def forward(self , x):\n",
    "        x = self.input(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.h1(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.h2(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.h3(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.h4(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.out(x)\n",
    "        \n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "model = Net()\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optim = torch.optim.Adam(model.parameters() , lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Net(\n",
       "  (input): Linear(in_features=8, out_features=16, bias=True)\n",
       "  (relu): ReLU()\n",
       "  (h1): Linear(in_features=16, out_features=142, bias=True)\n",
       "  (h2): Linear(in_features=142, out_features=72, bias=True)\n",
       "  (h3): Linear(in_features=72, out_features=204, bias=True)\n",
       "  (h4): Linear(in_features=204, out_features=32, bias=True)\n",
       "  (out): Linear(in_features=32, out_features=2, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# Training Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 272.49it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Loss Value')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "epochs = 100\n",
    "evey = 10\n",
    "loss_vals = []\n",
    "plt_x = []\n",
    "\n",
    "for epoch in tqdm(range(epochs)):\n",
    "    \n",
    "    y = model(train_x)\n",
    "    loss = criterion(y , train_y)    \n",
    "    \n",
    "    if epoch % evey ==0 :\n",
    "#         print( f\"Epoch:{epoch}  , Loss:{loss.squeeze_()}\")\n",
    "        \n",
    "        loss_vals.append(loss.data.item())\n",
    "        plt_x.append(epoch)\n",
    "    \n",
    "    loss.backward()\n",
    "    optim.step()\n",
    "    model.zero_grad()\n",
    "    \n",
    "plt.plot(plt_x , loss_vals , label=\"Loss\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss Value\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7297297297297297\n"
     ]
    }
   ],
   "source": [
    "correct = 0\n",
    "total =0\n",
    "\n",
    "for i in range(len(test_x)):    \n",
    "    predict= model.forward(test_x[i])\n",
    "    if torch.max(predict , 0)[1] == test_y[i]:\n",
    "        correct +=1\n",
    "    total+=1\n",
    "print(correct/total)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# Custom testing with random sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-0.7162,  0.8031], grad_fn=<AddBackward0>) tensor(0)\n",
      "tensor([ 1.9698, -2.3200], grad_fn=<AddBackward0>) tensor(1)\n",
      "tensor([ 1.0667, -1.5235], grad_fn=<AddBackward0>) tensor(1)\n",
      "tensor([-0.7851,  0.4942], grad_fn=<AddBackward0>) tensor(0)\n",
      "tensor([-0.1679, -0.3493], grad_fn=<AddBackward0>) tensor(1)\n",
      "tensor([0.1125, 0.0788], grad_fn=<AddBackward0>) tensor(1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(14, 20)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = 20\n",
    "correct = 0\n",
    "for i in range(total):\n",
    "    val = np.random.randint(low=0 , high=len(test_x))\n",
    "    vals = model.forward(test_x[val])\n",
    "    if torch.max( vals, 0)[1] == test_y[val]:\n",
    "        correct +=1\n",
    "    else:\n",
    "        print(vals , test_y[val])\n",
    "        \n",
    "correct  , total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}