main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('churn.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "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>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
       "0  No phone service             DSL             No  ...               No   \n",
       "1                No             DSL            Yes  ...              Yes   \n",
       "2                No             DSL            Yes  ...               No   \n",
       "3  No phone service             DSL            Yes  ...              Yes   \n",
       "4                No     Fiber optic             No  ...               No   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No          No              No  Month-to-month              Yes   \n",
       "1          No          No              No        One year               No   \n",
       "2          No          No              No  Month-to-month              Yes   \n",
       "3         Yes          No              No        One year               No   \n",
       "4          No          No              No  Month-to-month              Yes   \n",
       "\n",
       "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "0           Electronic check          29.85         29.85    No  \n",
       "1               Mailed check          56.95        1889.5    No  \n",
       "2               Mailed check          53.85        108.15   Yes  \n",
       "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
       "4           Electronic check          70.70        151.65   Yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        7043 non-null   object \n",
      " 1   gender            7043 non-null   object \n",
      " 2   SeniorCitizen     7043 non-null   int64  \n",
      " 3   Partner           7043 non-null   object \n",
      " 4   Dependents        7043 non-null   object \n",
      " 5   tenure            7043 non-null   int64  \n",
      " 6   PhoneService      7043 non-null   object \n",
      " 7   MultipleLines     7043 non-null   object \n",
      " 8   InternetService   7043 non-null   object \n",
      " 9   OnlineSecurity    7043 non-null   object \n",
      " 10  OnlineBackup      7043 non-null   object \n",
      " 11  DeviceProtection  7043 non-null   object \n",
      " 12  TechSupport       7043 non-null   object \n",
      " 13  StreamingTV       7043 non-null   object \n",
      " 14  StreamingMovies   7043 non-null   object \n",
      " 15  Contract          7043 non-null   object \n",
      " 16  PaperlessBilling  7043 non-null   object \n",
      " 17  PaymentMethod     7043 non-null   object \n",
      " 18  MonthlyCharges    7043 non-null   float64\n",
      " 19  TotalCharges      7043 non-null   object \n",
      " 20  Churn             7043 non-null   object \n",
      "dtypes: float64(1), int64(2), object(18)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID          0\n",
       "gender              0\n",
       "SeniorCitizen       0\n",
       "Partner             0\n",
       "Dependents          0\n",
       "tenure              0\n",
       "PhoneService        0\n",
       "MultipleLines       0\n",
       "InternetService     0\n",
       "OnlineSecurity      0\n",
       "OnlineBackup        0\n",
       "DeviceProtection    0\n",
       "TechSupport         0\n",
       "StreamingTV         0\n",
       "StreamingMovies     0\n",
       "Contract            0\n",
       "PaperlessBilling    0\n",
       "PaymentMethod       0\n",
       "MonthlyCharges      0\n",
       "TotalCharges        0\n",
       "Churn               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.TotalCharges = pd.to_numeric(df['TotalCharges'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('customerID', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Churn'].replace(to_replace='Yes', value=1, inplace=True)\n",
    "df['Churn'].replace(to_replace='No', value=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SeniorCitizen                               0\n",
       "tenure                                      0\n",
       "MonthlyCharges                              0\n",
       "TotalCharges                               11\n",
       "Churn                                       0\n",
       "gender_Female                               0\n",
       "gender_Male                                 0\n",
       "Partner_No                                  0\n",
       "Partner_Yes                                 0\n",
       "Dependents_No                               0\n",
       "Dependents_Yes                              0\n",
       "PhoneService_No                             0\n",
       "PhoneService_Yes                            0\n",
       "MultipleLines_No                            0\n",
       "MultipleLines_No phone service              0\n",
       "MultipleLines_Yes                           0\n",
       "InternetService_DSL                         0\n",
       "InternetService_Fiber optic                 0\n",
       "InternetService_No                          0\n",
       "OnlineSecurity_No                           0\n",
       "OnlineSecurity_No internet service          0\n",
       "OnlineSecurity_Yes                          0\n",
       "OnlineBackup_No                             0\n",
       "OnlineBackup_No internet service            0\n",
       "OnlineBackup_Yes                            0\n",
       "DeviceProtection_No                         0\n",
       "DeviceProtection_No internet service        0\n",
       "DeviceProtection_Yes                        0\n",
       "TechSupport_No                              0\n",
       "TechSupport_No internet service             0\n",
       "TechSupport_Yes                             0\n",
       "StreamingTV_No                              0\n",
       "StreamingTV_No internet service             0\n",
       "StreamingTV_Yes                             0\n",
       "StreamingMovies_No                          0\n",
       "StreamingMovies_No internet service         0\n",
       "StreamingMovies_Yes                         0\n",
       "Contract_Month-to-month                     0\n",
       "Contract_One year                           0\n",
       "Contract_Two year                           0\n",
       "PaperlessBilling_No                         0\n",
       "PaperlessBilling_Yes                        0\n",
       "PaymentMethod_Bank transfer (automatic)     0\n",
       "PaymentMethod_Credit card (automatic)       0\n",
       "PaymentMethod_Electronic check              0\n",
       "PaymentMethod_Mailed check                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dum = pd.get_dummies(df)\n",
    "df_dum.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dum.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "      <th>gender_Female</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>Partner_No</th>\n",
       "      <th>Partner_Yes</th>\n",
       "      <th>Dependents_No</th>\n",
       "      <th>...</th>\n",
       "      <th>StreamingMovies_Yes</th>\n",
       "      <th>Contract_Month-to-month</th>\n",
       "      <th>Contract_One year</th>\n",
       "      <th>Contract_Two year</th>\n",
       "      <th>PaperlessBilling_No</th>\n",
       "      <th>PaperlessBilling_Yes</th>\n",
       "      <th>PaymentMethod_Bank transfer (automatic)</th>\n",
       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <th>PaymentMethod_Mailed check</th>\n",
       "    </tr>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SeniorCitizen  tenure  MonthlyCharges  TotalCharges  Churn  gender_Female  \\\n",
       "0              0       1           29.85         29.85      0              1   \n",
       "1              0      34           56.95       1889.50      0              0   \n",
       "2              0       2           53.85        108.15      1              0   \n",
       "3              0      45           42.30       1840.75      0              0   \n",
       "4              0       2           70.70        151.65      1              1   \n",
       "\n",
       "   gender_Male  Partner_No  Partner_Yes  Dependents_No  ...  \\\n",
       "0            0           0            1              1  ...   \n",
       "1            1           1            0              1  ...   \n",
       "2            1           1            0              1  ...   \n",
       "3            1           1            0              1  ...   \n",
       "4            0           1            0              1  ...   \n",
       "\n",
       "   StreamingMovies_Yes  Contract_Month-to-month  Contract_One year  \\\n",
       "0                    0                        1                  0   \n",
       "1                    0                        0                  1   \n",
       "2                    0                        1                  0   \n",
       "3                    0                        0                  1   \n",
       "4                    0                        1                  0   \n",
       "\n",
       "   Contract_Two year  PaperlessBilling_No  PaperlessBilling_Yes  \\\n",
       "0                  0                    0                     1   \n",
       "1                  0                    1                     0   \n",
       "2                  0                    0                     1   \n",
       "3                  0                    1                     0   \n",
       "4                  0                    0                     1   \n",
       "\n",
       "   PaymentMethod_Bank transfer (automatic)  \\\n",
       "0                                        0   \n",
       "1                                        0   \n",
       "2                                        0   \n",
       "3                                        1   \n",
       "4                                        0   \n",
       "\n",
       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "0                                      0                               1   \n",
       "1                                      0                               0   \n",
       "2                                      0                               0   \n",
       "3                                      0                               0   \n",
       "4                                      0                               1   \n",
       "\n",
       "   PaymentMethod_Mailed check  \n",
       "0                           0  \n",
       "1                           1  \n",
       "2                           1  \n",
       "3                           0  \n",
       "4                           0  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dum.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_0 = df_dum[df_dum['Churn'] == 0]\n",
    "class_1 = df_dum[df_dum['Churn'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5163, 1869)"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(class_0), len(class_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_1_over = class_1.sample(len(class_0), replace=True)\n",
    "test_over = pd.concat([class_1_over, class_0], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = test_over['Churn'].values\n",
    "X = test_over.drop(columns = ['Churn'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = X.columns.values\n",
    "scaler = MinMaxScaler(feature_range = (0,1))\n",
    "scaler.fit(X)\n",
    "X = pd.DataFrame(scaler.transform(X))\n",
    "X.columns = features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7228, 45), (7228,))"
      ]
     },
     "execution_count": 435,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert not np.any(np.isnan(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import Dense, Dropout, Flatten, Input\n",
    "from tensorflow.keras.models import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {},
   "outputs": [],
   "source": [
    "inp = Input((X_train.shape[-1]))\n",
    "\n",
    "x = Dense(1024, activation='relu')(inp)\n",
    "x = Dropout(0.5)(x)\n",
    "x = Dense(1024, activation='relu')(x)\n",
    "x = Dropout(0.5)(x)\n",
    "# x = Dense(64, activation='relu')(x)\n",
    "# x = Dropout(0.5)(x)\n",
    "# x = Dense(64, activation='relu')(x)\n",
    "# x = Dropout(0.5)(x)\n",
    "x = Dense(8, activation='relu')(x)\n",
    "pred = Dense(1, activation='sigmoid')(x)\n",
    "\n",
    "model = Model(inputs=[inp], outputs=[pred])\n",
    "opt = tf.keras.optimizers.SGD(learning_rate=5e-3)\n",
    "model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_58\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_58 (InputLayer)        [(None, 45)]              0         \n",
      "_________________________________________________________________\n",
      "dense_327 (Dense)            (None, 1024)              47104     \n",
      "_________________________________________________________________\n",
      "dropout_66 (Dropout)         (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_328 (Dense)            (None, 1024)              1049600   \n",
      "_________________________________________________________________\n",
      "dropout_67 (Dropout)         (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_329 (Dense)            (None, 8)                 8200      \n",
      "_________________________________________________________________\n",
      "dense_330 (Dense)            (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 1,104,913\n",
      "Trainable params: 1,104,913\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scheduler(epoch, lr):\n",
    "    if epoch < 25:\n",
    "        return lr\n",
    "    # elif 50 < epoch < 75:\n",
    "    #     return lr * 0.5\n",
    "    else:\n",
    "        return lr * 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x7f864c2e7378> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x7f864c2e7378> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "903/904 [============================>.] - ETA: 0s - loss: 0.6701 - accuracy: 0.5791WARNING:tensorflow:AutoGraph could not transform <function Model.make_test_function.<locals>.test_function at 0x7f8628e7d2f0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_test_function.<locals>.test_function at 0x7f8628e7d2f0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "904/904 [==============================] - 9s 9ms/step - loss: 0.6700 - accuracy: 0.5792 - val_loss: 0.5632 - val_accuracy: 0.7411\n",
      "Epoch 2/50\n",
      "373/904 [===========>..................] - ETA: 4s - loss: 0.5715 - accuracy: 0.7327"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-492-902d727b1c7c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m                     \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m                     \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m                     validation_batch_size=batch_size)\n\u001b[0m\u001b[1;32m      9\u001b[0m                     \u001b[0;31m# ,callbacks=callbacks)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1103\u001b[0m               \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtmp_logs\u001b[0m  \u001b[0;31m# No error, now safe to assign to logs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m               \u001b[0mend_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep_increment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1105\u001b[0;31m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mend_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1106\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_training\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1107\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/keras/callbacks.py\u001b[0m in \u001b[0;36mon_train_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m    452\u001b[0m     \"\"\"\n\u001b[1;32m    453\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_call_train_batch_hooks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 454\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mModeKeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'end'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_test_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/keras/callbacks.py\u001b[0m in \u001b[0;36m_call_batch_hook\u001b[0;34m(self, mode, hook, batch, logs)\u001b[0m\n\u001b[1;32m    294\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_begin_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    295\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'end'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 296\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_end_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    297\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    298\u001b[0m       \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Unrecognized hook: {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/keras/callbacks.py\u001b[0m in \u001b[0;36m_call_batch_end_hook\u001b[0;34m(self, mode, batch, logs)\u001b[0m\n\u001b[1;32m    314\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_times\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_time\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 316\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_hook_helper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhook_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    317\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    318\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_times\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_batches_for_timing_check\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/keras/callbacks.py\u001b[0m in \u001b[0;36m_call_batch_hook_helper\u001b[0;34m(self, hook_name, batch, logs)\u001b[0m\n\u001b[1;32m    354\u001b[0m       \u001b[0mhook\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    355\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'_supports_tf_logs'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 356\u001b[0;31m         \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    357\u001b[0m       \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    358\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnumpy_logs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Only convert once.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/keras/callbacks.py\u001b[0m in \u001b[0;36mon_train_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m   1018\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1019\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_train_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1020\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_update_progbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1021\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1022\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_test_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/keras/callbacks.py\u001b[0m in \u001b[0;36m_batch_update_progbar\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m   1083\u001b[0m       \u001b[0;31m# Only block async when verbose = 1.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1084\u001b[0m       \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numpy_or_python_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1085\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinalize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1086\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1087\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_finalize_progbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcounter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/keras/utils/generic_utils.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, current, values, finalize)\u001b[0m\n\u001b[1;32m    639\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    640\u001b[0m       \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 641\u001b[0;31m       \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    642\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    643\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py37/lib/python3.7/site-packages/ipykernel/iostream.py\u001b[0m in \u001b[0;36mflush\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    350\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpub_thread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mschedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    351\u001b[0m                 \u001b[0;31m# and give a timeout to avoid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 352\u001b[0;31m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mevt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush_timeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    353\u001b[0m                     \u001b[0;31m# write directly to __stderr__ instead of warning because\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    354\u001b[0m                     \u001b[0;31m# if this is happening sys.stderr may be the problem.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py37/lib/python3.7/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    550\u001b[0m             \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    551\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 552\u001b[0;31m                 \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    553\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    554\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py37/lib/python3.7/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    298\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    299\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 300\u001b[0;31m                     \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    301\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    302\u001b[0m                     \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "batch_size = 8\n",
    "callbacks = [#tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5,patience=10, min_lr=1e-6, verbose=1),\n",
    "            tf.keras.callbacks.LearningRateScheduler(scheduler)\n",
    "]\n",
    "history = model.fit(X_train, y_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=50, validation_data=(X_test, y_test),\n",
    "                    validation_batch_size=batch_size)\n",
    "                    # ,callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7666236162185669\n",
      "26\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.plot(history.history['accuracy'])\n",
    "print(max(history.history['val_accuracy']))\n",
    "print(history.history['val_accuracy'].index(max(history.history['val_accuracy'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.48730507493019104\n",
      "27\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['val_loss'])\n",
    "plt.plot(history.history['loss'])\n",
    "\n",
    "print(min(history.history['val_loss']))\n",
    "print(history.history['val_loss'].index(min(history.history['val_loss'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x7f8628f99730> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x7f8628f99730> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"
     ]
    }
   ],
   "source": [
    "pred = model.predict(X_test)\n",
    "\n",
    "pred_int = []\n",
    "for i in pred:\n",
    "    if i <= 0.5:\n",
    "        pred_int.append(0)\n",
    "    else:\n",
    "        pred_int.append(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.82      0.86      1002\n",
      "           1       0.85      0.92      0.88      1064\n",
      "\n",
      "    accuracy                           0.87      2066\n",
      "   macro avg       0.87      0.87      0.87      2066\n",
      "weighted avg       0.87      0.87      0.87      2066\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred_int))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()\n",
    "result = model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7831558567279767\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "prediction_test = model.predict(X_test)\n",
    "# Print the prediction accuracy\n",
    "print (metrics.accuracy_score(y_test, prediction_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 416,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = model.predict(X_test)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.91      0.88      1036\n",
      "           1       0.69      0.55      0.61       373\n",
      "\n",
      "    accuracy                           0.81      1409\n",
      "   macro avg       0.77      0.73      0.75      1409\n",
      "weighted avg       0.81      0.81      0.81      1409\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomForestClassifier(n_estimators=10).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.90      0.86      1033\n",
      "           1       0.62      0.44      0.51       374\n",
      "\n",
      "    accuracy                           0.78      1407\n",
      "   macro avg       0.72      0.67      0.69      1407\n",
      "weighted avg       0.76      0.78      0.77      1407\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "model = KNeighborsClassifier()\n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.83      0.83      1033\n",
      "           1       0.53      0.52      0.52       374\n",
      "\n",
      "    accuracy                           0.75      1407\n",
      "   macro avg       0.68      0.67      0.68      1407\n",
      "weighted avg       0.75      0.75      0.75      1407\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "model = SVC()\n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.89      0.86      1033\n",
      "           1       0.61      0.49      0.54       374\n",
      "\n",
      "    accuracy                           0.78      1407\n",
      "   macro avg       0.72      0.69      0.70      1407\n",
      "weighted avg       0.77      0.78      0.77      1407\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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