FinalProject_Log+SVM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Importing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import precision_recall_fscore_support, confusion_matrix, f1_score, precision_score, recall_score\n",
    "from sklearn import datasets, linear_model, preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import keras\n",
    "import keras.backend as K\n",
    "from keras.models import Model, Sequential\n",
    "from keras.layers import Dense, Activation\n",
    "from keras.utils.vis_utils import plot_model\n",
    "from IPython.display import clear_output\n",
    "from keras import optimizers\n",
    "from keras.models import load_model\n",
    "from keras.callbacks import Callback\n",
    "\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Investigating the Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          MIP        SIP      EKIP      SKIP    MDM-SR     SDM-SR    EKDM-SR  \\\n",
      "0  140.562500  55.683782 -0.234571 -0.699648  3.199833  19.110426   7.975532   \n",
      "1  102.507812  58.882430  0.465318 -0.515088  1.677258  14.860146  10.576487   \n",
      "2  103.015625  39.341649  0.323328  1.051164  3.121237  21.744669   7.735822   \n",
      "3  136.750000  57.178449 -0.068415 -0.636238  3.642977  20.959280   6.896499   \n",
      "4   88.726562  40.672225  0.600866  1.123492  1.178930  11.468720  14.269573   \n",
      "5   93.570312  46.698114  0.531905  0.416721  1.636288  14.545074  10.621748   \n",
      "\n",
      "      SKDM-SR  Target  \n",
      "0   74.242225       0  \n",
      "1  127.393580       0  \n",
      "2   63.171909       0  \n",
      "3   53.593661       0  \n",
      "4  252.567306       0  \n",
      "5  131.394004       0  \n"
     ]
    }
   ],
   "source": [
    "features = ['MIP', 'SIP','EKIP','SKIP','MDM-SR','SDM-SR','EKDM-SR','SKDM-SR','Target'] # Abbreviating features\n",
    "df =  pd.read_csv('pulsar_stars.csv',\n",
    "                names=features,na_values='?',header=None, skiprows = 1)\n",
    "print(df.head(6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17898 entries, 0 to 17897\n",
      "Data columns (total 9 columns):\n",
      "MIP        17898 non-null float64\n",
      "SIP        17898 non-null float64\n",
      "EKIP       17898 non-null float64\n",
      "SKIP       17898 non-null float64\n",
      "MDM-SR     17898 non-null float64\n",
      "SDM-SR     17898 non-null float64\n",
      "EKDM-SR    17898 non-null float64\n",
      "SKDM-SR    17898 non-null float64\n",
      "Target     17898 non-null int64\n",
      "dtypes: float64(8), int64(1)\n",
      "memory usage: 1.2 MB\n",
      "\n",
      " Since dtypes are all floating point numbers or integers, and there are no null values in the dataset \n",
      " No need to perform cleaning\n"
     ]
    }
   ],
   "source": [
    "# Checking information about the dtypes and non-null rows in the dataset\n",
    "df.info()\n",
    "print('\\n Since dtypes are all floating point numbers or integers, and there are no null values in the dataset \\n No need to perform cleaning')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>MIP</th>\n",
       "      <th>SIP</th>\n",
       "      <th>EKIP</th>\n",
       "      <th>SKIP</th>\n",
       "      <th>MDM-SR</th>\n",
       "      <th>SDM-SR</th>\n",
       "      <th>EKDM-SR</th>\n",
       "      <th>SKDM-SR</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17898.000000</td>\n",
       "      <td>17898.000000</td>\n",
       "      <td>17898.000000</td>\n",
       "      <td>17898.000000</td>\n",
       "      <td>17898.000000</td>\n",
       "      <td>17898.000000</td>\n",
       "      <td>17898.000000</td>\n",
       "      <td>17898.000000</td>\n",
       "      <td>17898.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>111.079968</td>\n",
       "      <td>46.549532</td>\n",
       "      <td>0.477857</td>\n",
       "      <td>1.770279</td>\n",
       "      <td>12.614400</td>\n",
       "      <td>26.326515</td>\n",
       "      <td>8.303556</td>\n",
       "      <td>104.857709</td>\n",
       "      <td>0.091574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>25.652935</td>\n",
       "      <td>6.843189</td>\n",
       "      <td>1.064040</td>\n",
       "      <td>6.167913</td>\n",
       "      <td>29.472897</td>\n",
       "      <td>19.470572</td>\n",
       "      <td>4.506092</td>\n",
       "      <td>106.514540</td>\n",
       "      <td>0.288432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.812500</td>\n",
       "      <td>24.772042</td>\n",
       "      <td>-1.876011</td>\n",
       "      <td>-1.791886</td>\n",
       "      <td>0.213211</td>\n",
       "      <td>7.370432</td>\n",
       "      <td>-3.139270</td>\n",
       "      <td>-1.976976</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>100.929688</td>\n",
       "      <td>42.376018</td>\n",
       "      <td>0.027098</td>\n",
       "      <td>-0.188572</td>\n",
       "      <td>1.923077</td>\n",
       "      <td>14.437332</td>\n",
       "      <td>5.781506</td>\n",
       "      <td>34.960504</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>115.078125</td>\n",
       "      <td>46.947479</td>\n",
       "      <td>0.223240</td>\n",
       "      <td>0.198710</td>\n",
       "      <td>2.801839</td>\n",
       "      <td>18.461316</td>\n",
       "      <td>8.433515</td>\n",
       "      <td>83.064556</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>127.085938</td>\n",
       "      <td>51.023202</td>\n",
       "      <td>0.473325</td>\n",
       "      <td>0.927783</td>\n",
       "      <td>5.464256</td>\n",
       "      <td>28.428104</td>\n",
       "      <td>10.702959</td>\n",
       "      <td>139.309331</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>192.617188</td>\n",
       "      <td>98.778911</td>\n",
       "      <td>8.069522</td>\n",
       "      <td>68.101622</td>\n",
       "      <td>223.392140</td>\n",
       "      <td>110.642211</td>\n",
       "      <td>34.539844</td>\n",
       "      <td>1191.000837</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                MIP           SIP          EKIP          SKIP        MDM-SR  \\\n",
       "count  17898.000000  17898.000000  17898.000000  17898.000000  17898.000000   \n",
       "mean     111.079968     46.549532      0.477857      1.770279     12.614400   \n",
       "std       25.652935      6.843189      1.064040      6.167913     29.472897   \n",
       "min        5.812500     24.772042     -1.876011     -1.791886      0.213211   \n",
       "25%      100.929688     42.376018      0.027098     -0.188572      1.923077   \n",
       "50%      115.078125     46.947479      0.223240      0.198710      2.801839   \n",
       "75%      127.085938     51.023202      0.473325      0.927783      5.464256   \n",
       "max      192.617188     98.778911      8.069522     68.101622    223.392140   \n",
       "\n",
       "             SDM-SR       EKDM-SR       SKDM-SR        Target  \n",
       "count  17898.000000  17898.000000  17898.000000  17898.000000  \n",
       "mean      26.326515      8.303556    104.857709      0.091574  \n",
       "std       19.470572      4.506092    106.514540      0.288432  \n",
       "min        7.370432     -3.139270     -1.976976      0.000000  \n",
       "25%       14.437332      5.781506     34.960504      0.000000  \n",
       "50%       18.461316      8.433515     83.064556      0.000000  \n",
       "75%       28.428104     10.702959    139.309331      0.000000  \n",
       "max      110.642211     34.539844   1191.000837      1.000000  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the statistical details of the dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation = df.corr()\n",
    "plt.figure(figsize=(10,8))\n",
    "sns.heatmap(correlation,annot=True,\n",
    "            cmap=sns.color_palette(\"inferno_r\"),\n",
    "            linewidth=1,edgecolor=\"k\")\n",
    "plt.title(\"Interdependency of Variables\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Pre-Processing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17898, 8)\n",
      "(17898, 1)\n"
     ]
    }
   ],
   "source": [
    "# Changing dataframe to array and separating target values from feature values\n",
    "pulsar_x= np.array((df.loc[:,'MIP':'SKDM-SR']))\n",
    "pulsar_y= np.array((df.loc[:,'Target']))\n",
    "pulsar_x=pulsar_x.reshape(pulsar_x.shape[0],8)\n",
    "pulsar_y=pulsar_y.reshape(pulsar_y.shape[0],1)\n",
    "print(pulsar_x.shape)\n",
    "print(pulsar_y.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13423, 1)\n",
      "(13423, 8)\n",
      "(4475, 1)\n"
     ]
    }
   ],
   "source": [
    "# Scaling Data\n",
    "x_scale = preprocessing.scale(pulsar_x)\n",
    "# splitting data into a 75:25 train test split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_scale, pulsar_y, test_size = .25)\n",
    "y_train = y_train.reshape((y_train.shape[0]), 1)\n",
    "print(y_train.shape)\n",
    "print(x_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Logistic Regression Without Feature Transformation\n",
    "\n",
    "##  4.1 With L1 Regularization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initializing lists for different accuracy values for train and test sets corresponding to different values of parameter C\n",
    "acc_train_logreg = [] \n",
    "acc_test_logreg = []\n",
    "Yhat_test_logreg = []\n",
    "c_logreg = []\n",
    "prec_test_logreg = []\n",
    "fscore_test_logreg =[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def logreg_model_L1(c , X_train, Y_train, X_test, Y_test):\n",
    "    # Create an object of logistic regression model using linear_model.\n",
    "    \n",
    "  \n",
    "    logreg = linear_model.LogisticRegression(C=c,penalty='l1', warm_start=True, solver='saga', max_iter = 5000)\n",
    "    \n",
    "    logreg.fit(X_train, Y_train)\n",
    "    \n",
    "    Yhat_train = logreg.predict(X_train)\n",
    "    \n",
    "  \n",
    "    # Adding training accuracy to acc_train_logreg\n",
    "    acc_train = np.mean(Yhat_train == Y_train)\n",
    "    acc_train_logreg.append(acc_train)\n",
    "    \n",
    "    # Printing values of C and no. of iterations\n",
    "    print(\"For C = %f\" % c)\n",
    "    print(\"Accuracy on training data = %f\" % acc_train)\n",
    "#     prec_train , recal_train , fscore_train,_ = precision_recall_fscore_support(Y_train, Yhat_train, average = 'weighted')\n",
    "#     print(\"Precision on train data = %f\" % prec)\n",
    "#     print(\"Recal on train data = %f\" % recal)\n",
    "#     print(\"fscore on train data = %f\" % fscore)\n",
    "  # Find the prediction on test sets\n",
    "    Yhat_test = logreg.predict(X_test)\n",
    "    # Adding testing accuracy to acc_test_logreg\n",
    "    acc_test = np.mean(Yhat_test == Y_test)\n",
    "    acc_test_logreg.append(acc_test)\n",
    "    print(\"Accuracy on test data = %f\" % acc_test)\n",
    "    prec , recal , fscore,_ = precision_recall_fscore_support(Y_test, Yhat_test, average = 'weighted')\n",
    "    prec_test_logreg.append(prec)\n",
    "    fscore_test_logreg.append(fscore)\n",
    "    print(\"Precision on test data = %f\" % prec)\n",
    "    print(\"Recal on test data = %f\" % recal)\n",
    "    print(\"fscore on test data = %f\" % fscore)\n",
    "    \n",
    "   # Find precision, recall, and fscore \n",
    "    \n",
    "    # Appending value of c for graphing purposes\n",
    "    c_logreg.append(c)\n",
    "    Yhat_test_logreg.append(Yhat_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 0.000100\n",
      "Accuracy on training data = 0.907845\n",
      "Accuracy on test data = 0.910168\n",
      "Precision on test data = 0.828405\n",
      "Recal on test data = 0.910168\n",
      "fscore on test data = 0.867364\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 0.001000\n",
      "Accuracy on training data = 0.873328\n",
      "Accuracy on test data = 0.875338\n",
      "Precision on test data = 0.953599\n",
      "Recal on test data = 0.951732\n",
      "fscore on test data = 0.943701\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 0.010000\n",
      "Accuracy on training data = 0.847745\n",
      "Accuracy on test data = 0.849857\n",
      "Precision on test data = 0.973630\n",
      "Recal on test data = 0.974302\n",
      "fscore on test data = 0.973133\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 0.100000\n",
      "Accuracy on training data = 0.843734\n",
      "Accuracy on test data = 0.845641\n",
      "Precision on test data = 0.978491\n",
      "Recal on test data = 0.978994\n",
      "fscore on test data = 0.978362\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 1.000000\n",
      "Accuracy on training data = 0.842276\n",
      "Accuracy on test data = 0.843441\n",
      "Precision on test data = 0.979882\n",
      "Recal on test data = 0.980335\n",
      "fscore on test data = 0.979893\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 10.000000\n",
      "Accuracy on training data = 0.842154\n",
      "Accuracy on test data = 0.843258\n",
      "Precision on test data = 0.980117\n",
      "Recal on test data = 0.980559\n",
      "fscore on test data = 0.980134\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 100.000000\n",
      "Accuracy on training data = 0.842154\n",
      "Accuracy on test data = 0.843258\n",
      "Precision on test data = 0.980117\n",
      "Recal on test data = 0.980559\n",
      "fscore on test data = 0.980134\n"
     ]
    }
   ],
   "source": [
    "# Call the above function i.e. logreg_model with different values of parameter 'c'.\n",
    "c_values = [0.0001, 0.001, 0.01, 0.1, 1, 10,100]\n",
    "\n",
    "for c in c_values:\n",
    "    logreg_model_L1(c, x_train, y_train, x_test, y_test)    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19cd58ad320>"
      ]
     },
     "execution_count": 74,
     "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": [
    "plt.plot(c_logreg, prec_test_logreg, 'ro-') \n",
    "plt.plot(c_logreg, acc_test_logreg,'bo-') \n",
    "plt.plot(c_logreg, fscore_test_logreg,'yo-') \n",
    "plt.grid()\n",
    "# Use the following function to have a legend\n",
    "plt.legend(['Test Precision', 'Test Accuracy', 'Fscore'], loc='lower right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 With L2 Regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "acc_train_logreg_L2 = [] \n",
    "acc_test_logreg_L2 = []\n",
    "Yhat_test_logreg_L2 = []\n",
    "c_logreg_L2 = []\n",
    "prec_test_logreg.clear()\n",
    "fscore_test_logreg.clear()\n",
    "print(prec_test_logreg)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "def logreg_model_L2(c , X_train, Y_train, X_test, Y_test):\n",
    "    # Create an object of logistic regression model using linear_model.\n",
    "    \n",
    "  \n",
    "    logreg = linear_model.LogisticRegression(C=c, warm_start=True, solver='saga', max_iter = 5000)\n",
    "    \n",
    "    logreg.fit(X_train, Y_train)\n",
    "    \n",
    "    Yhat_train = logreg.predict(X_train)\n",
    "    \n",
    "  \n",
    "    # Adding training accuracy to acc_train_logreg\n",
    "    acc_train = np.mean(Yhat_train == Y_train)\n",
    "    acc_train_logreg_L2.append(acc_train)\n",
    "    \n",
    "    # Printing values of C and no. of iterations\n",
    "    print(\"For C = %f\" % c)\n",
    "    print(\"Accuracy on training data = %f\" % acc_train)\n",
    "#     prec_train , recal_train , fscore_train,_ = precision_recall_fscore_support(Y_train, Yhat_train, average = 'weighted')\n",
    "#     print(\"Precision on train data = %f\" % prec)\n",
    "#     print(\"Recal on train data = %f\" % recal)\n",
    "#     print(\"fscore on train data = %f\" % fscore)\n",
    "  # Find the prediction on test sets\n",
    "    Yhat_test = logreg.predict(X_test)\n",
    "    # Adding testing accuracy to acc_test_logreg\n",
    "    acc_test = np.mean(Yhat_test == Y_test)\n",
    "    acc_test_logreg_L2.append(acc_test)\n",
    "    print(\"Accuracy on test data = %f\" % acc_test)\n",
    "    prec , recal , fscore,_ = precision_recall_fscore_support(Y_test, Yhat_test, average = 'weighted')\n",
    "    prec_test_logreg.append(prec)\n",
    "    fscore_test_logreg.append(fscore)\n",
    "    print(\"Precision on test data = %f\" % prec)\n",
    "    print(\"Recal on test data = %f\" % recal)\n",
    "    print(\"fscore on test data = %f\" % fscore)\n",
    "    \n",
    "   # Find precision, recall, and fscore \n",
    "    \n",
    "    # Appending value of c for graphing purposes\n",
    "    c_logreg_L2.append(c)\n",
    "    Yhat_test_logreg_L2.append(Yhat_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 0.000100\n",
      "Accuracy on training data = 0.890647\n",
      "Accuracy on test data = 0.892936\n",
      "Precision on test data = 0.936012\n",
      "Recal on test data = 0.931173\n",
      "fscore on test data = 0.911057\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 0.001000\n",
      "Accuracy on training data = 0.860506\n",
      "Accuracy on test data = 0.864522\n",
      "Precision on test data = 0.962439\n",
      "Recal on test data = 0.962235\n",
      "fscore on test data = 0.958145\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 0.010000\n",
      "Accuracy on training data = 0.848353\n",
      "Accuracy on test data = 0.850590\n",
      "Precision on test data = 0.972207\n",
      "Recal on test data = 0.972961\n",
      "fscore on test data = 0.971656\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 0.100000\n",
      "Accuracy on training data = 0.844524\n",
      "Accuracy on test data = 0.846557\n",
      "Precision on test data = 0.977327\n",
      "Recal on test data = 0.977877\n",
      "fscore on test data = 0.977139\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 1.000000\n",
      "Accuracy on training data = 0.842762\n",
      "Accuracy on test data = 0.843808\n",
      "Precision on test data = 0.979412\n",
      "Recal on test data = 0.979888\n",
      "fscore on test data = 0.979411\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 10.000000\n",
      "Accuracy on training data = 0.842154\n",
      "Accuracy on test data = 0.843258\n",
      "Precision on test data = 0.980117\n",
      "Recal on test data = 0.980559\n",
      "fscore on test data = 0.980134\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 100.000000\n",
      "Accuracy on training data = 0.842154\n",
      "Accuracy on test data = 0.843258\n",
      "Precision on test data = 0.980117\n",
      "Recal on test data = 0.980559\n",
      "fscore on test data = 0.980134\n"
     ]
    }
   ],
   "source": [
    "c_values = [0.0001, 0.001, 0.01, 0.1, 1, 10,100]\n",
    "\n",
    "for c in c_values:\n",
    "    logreg_model_L2(c, x_train, y_train, x_test, y_test)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19cd5d81da0>"
      ]
     },
     "execution_count": 78,
     "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": [
    "plt.plot(c_logreg_L2, prec_test_logreg, 'ro-') \n",
    "plt.plot(c_logreg_L2, acc_test_logreg,'bo-') \n",
    "plt.plot(c_logreg_L2, fscore_test_logreg,'yo-') \n",
    "plt.grid()\n",
    "# Use the following function to have a legend\n",
    "plt.legend(['Test Precision', 'Test Accuracy','Fscore'], loc='lower right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.3 Logistic Regression using Polynomial Feature Transformation\n",
    "## 4.3.1 Using L1 Regularization without PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "ply = PolynomialFeatures(degree = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13423, 45)\n",
      "(4475, 45)\n"
     ]
    }
   ],
   "source": [
    "X_transformed_train = ply.fit_transform(x_train)\n",
    "X_transformed_test = ply.fit_transform(x_test)\n",
    "print(X_transformed_train.shape)\n",
    "print(X_transformed_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 0.000100\n",
      "Accuracy on training data = 0.907784\n",
      "Accuracy on test data = 0.910168\n",
      "Precision on test data = 0.828405\n",
      "Recal on test data = 0.910168\n",
      "fscore on test data = 0.867364\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 0.001000\n",
      "Accuracy on training data = 0.870837\n",
      "Accuracy on test data = 0.873138\n",
      "Precision on test data = 0.956084\n",
      "Recal on test data = 0.954413\n",
      "fscore on test data = 0.947412\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 0.010000\n",
      "Accuracy on training data = 0.847745\n",
      "Accuracy on test data = 0.850407\n",
      "Precision on test data = 0.973924\n",
      "Recal on test data = 0.974525\n",
      "fscore on test data = 0.973313\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 0.100000\n",
      "Accuracy on training data = 0.842154\n",
      "Accuracy on test data = 0.843258\n",
      "Precision on test data = 0.981052\n",
      "Recal on test data = 0.981453\n",
      "fscore on test data = 0.981047\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 1.000000\n",
      "Accuracy on training data = 0.841304\n",
      "Accuracy on test data = 0.841974\n",
      "Precision on test data = 0.981299\n",
      "Recal on test data = 0.981676\n",
      "fscore on test data = 0.981354\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 10.000000\n",
      "Accuracy on training data = 0.841365\n",
      "Accuracy on test data = 0.841608\n",
      "Precision on test data = 0.981770\n",
      "Recal on test data = 0.982123\n",
      "fscore on test data = 0.981831\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 100.000000\n",
      "Accuracy on training data = 0.841365\n",
      "Accuracy on test data = 0.841608\n",
      "Precision on test data = 0.981770\n",
      "Recal on test data = 0.982123\n",
      "fscore on test data = 0.981831\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19cd5e0a908>"
      ]
     },
     "execution_count": 82,
     "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": [
    "acc_train_logreg = []\n",
    "acc_test_logreg = []\n",
    "c_logreg = []\n",
    "fscore_test_logreg.clear()\n",
    "prec_test_logreg.clear()\n",
    "\n",
    "\n",
    "# Call the function logreg_model.\n",
    "for c in c_values:\n",
    "    logreg_model_L1(c, X_transformed_train, y_train, X_transformed_test, y_test)\n",
    "    \n",
    "plt.plot(c_logreg, prec_test_logreg, 'ro-') \n",
    "plt.plot(c_logreg, acc_test_logreg,'bo-') \n",
    "plt.plot(c_logreg, fscore_test_logreg,'yo-')\n",
    "plt.grid()\n",
    "plt.legend(['Test Precision', 'Test Accuracy','Fscore'], loc='upper right')\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3.2 Using L1 Regularization with PCA\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(.95)\n",
    "pca_x_transformed_train = pca.fit_transform(X_transformed_train)\n",
    "pca_x_transformed_test = pca.transform(X_transformed_test)\n",
    "pca.n_components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 0.000100\n",
      "Accuracy on training data = 0.907845\n",
      "Accuracy on test data = 0.910168\n",
      "Precision on test data = 0.828405\n",
      "Recal on test data = 0.910168\n",
      "fscore on test data = 0.867364\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 0.001000\n",
      "Accuracy on training data = 0.870837\n",
      "Accuracy on test data = 0.873138\n",
      "Precision on test data = 0.956084\n",
      "Recal on test data = 0.954413\n",
      "fscore on test data = 0.947412\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 0.010000\n",
      "Accuracy on training data = 0.847745\n",
      "Accuracy on test data = 0.850407\n",
      "Precision on test data = 0.973924\n",
      "Recal on test data = 0.974525\n",
      "fscore on test data = 0.973313\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 0.100000\n",
      "Accuracy on training data = 0.842154\n",
      "Accuracy on test data = 0.843258\n",
      "Precision on test data = 0.981052\n",
      "Recal on test data = 0.981453\n",
      "fscore on test data = 0.981047\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 1.000000\n",
      "Accuracy on training data = 0.841304\n",
      "Accuracy on test data = 0.841974\n",
      "Precision on test data = 0.981299\n",
      "Recal on test data = 0.981676\n",
      "fscore on test data = 0.981354\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 10.000000\n",
      "Accuracy on training data = 0.841365\n",
      "Accuracy on test data = 0.841608\n",
      "Precision on test data = 0.981770\n",
      "Recal on test data = 0.982123\n",
      "fscore on test data = 0.981831\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 100.000000\n",
      "Accuracy on training data = 0.841365\n",
      "Accuracy on test data = 0.841608\n",
      "Precision on test data = 0.981770\n",
      "Recal on test data = 0.982123\n",
      "fscore on test data = 0.981831\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19cd647bda0>"
      ]
     },
     "execution_count": 84,
     "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": [
    "acc_train_logreg = []\n",
    "acc_test_logreg = []\n",
    "c_logreg = []\n",
    "fscore_test_logreg.clear()\n",
    "prec_test_logreg.clear()\n",
    "\n",
    "# Call the function logreg_model.\n",
    "for c in c_values:\n",
    "    logreg_model_L1(c, X_transformed_train, y_train, X_transformed_test, y_test)\n",
    "    \n",
    "plt.plot(c_logreg, prec_test_logreg, 'ro-') \n",
    "plt.plot(c_logreg, acc_test_logreg,'bo-') \n",
    "plt.plot(c_logreg, fscore_test_logreg,'yo-')\n",
    "plt.grid()\n",
    "plt.legend(['Test Precision', 'Test Accuracy', 'Fscore'], loc='upper right')\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3.3 Using L2 Regularization without PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 0.000100\n",
      "Accuracy on training data = 0.866097\n",
      "Accuracy on test data = 0.868738\n",
      "Precision on test data = 0.958629\n",
      "Recal on test data = 0.957989\n",
      "fscore on test data = 0.952546\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 0.001000\n",
      "Accuracy on training data = 0.853032\n",
      "Accuracy on test data = 0.856456\n",
      "Precision on test data = 0.968938\n",
      "Recal on test data = 0.969385\n",
      "fscore on test data = 0.967187\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 0.010000\n",
      "Accuracy on training data = 0.847137\n",
      "Accuracy on test data = 0.848757\n",
      "Precision on test data = 0.975510\n",
      "Recal on test data = 0.976089\n",
      "fscore on test data = 0.975100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 0.100000\n",
      "Accuracy on training data = 0.842884\n",
      "Accuracy on test data = 0.843991\n",
      "Precision on test data = 0.980588\n",
      "Recal on test data = 0.981006\n",
      "fscore on test data = 0.980543\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 1.000000\n",
      "Accuracy on training data = 0.841851\n",
      "Accuracy on test data = 0.842708\n",
      "Precision on test data = 0.981288\n",
      "Recal on test data = 0.981676\n",
      "fscore on test data = 0.981310\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 10.000000\n",
      "Accuracy on training data = 0.841486\n",
      "Accuracy on test data = 0.842158\n",
      "Precision on test data = 0.981063\n",
      "Recal on test data = 0.981453\n",
      "fscore on test data = 0.981116\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 100.000000\n",
      "Accuracy on training data = 0.841486\n",
      "Accuracy on test data = 0.842158\n",
      "Precision on test data = 0.981063\n",
      "Recal on test data = 0.981453\n",
      "fscore on test data = 0.981116\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19c8005b6d8>"
      ]
     },
     "execution_count": 85,
     "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": [
    "acc_train_logreg_L2 = [] \n",
    "acc_test_logreg_L2 = []\n",
    "Yhat_test_logreg_L2 = []\n",
    "c_logreg_L2 = []\n",
    "fscore_test_logreg.clear()\n",
    "prec_test_logreg.clear()\n",
    "# Call the function logreg_model.\n",
    "for c in c_values:\n",
    "    logreg_model_L2(c, X_transformed_train, y_train, X_transformed_test, y_test)\n",
    "    \n",
    "plt.plot(c_logreg, prec_test_logreg, 'ro-') \n",
    "plt.plot(c_logreg, acc_test_logreg,'bo-') \n",
    "plt.plot(c_logreg, fscore_test_logreg,'yo-') \n",
    "plt.grid()\n",
    "plt.legend(['Test Precision', 'Test Accuracy', 'Fscore'], loc='upper right')\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3.4 Using L2 regularization with PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(.95)\n",
    "pca_x_transformed_train = pca.fit_transform(X_transformed_train)\n",
    "pca_x_transformed_test = pca.transform(X_transformed_test)\n",
    "pca.n_components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 0.000100\n",
      "Accuracy on training data = 0.866097\n",
      "Accuracy on test data = 0.868738\n",
      "Precision on test data = 0.958629\n",
      "Recal on test data = 0.957989\n",
      "fscore on test data = 0.952546\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 0.001000\n",
      "Accuracy on training data = 0.853032\n",
      "Accuracy on test data = 0.856456\n",
      "Precision on test data = 0.968938\n",
      "Recal on test data = 0.969385\n",
      "fscore on test data = 0.967187\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 0.010000\n",
      "Accuracy on training data = 0.847137\n",
      "Accuracy on test data = 0.848757\n",
      "Precision on test data = 0.975510\n",
      "Recal on test data = 0.976089\n",
      "fscore on test data = 0.975100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 0.100000\n",
      "Accuracy on training data = 0.842884\n",
      "Accuracy on test data = 0.843991\n",
      "Precision on test data = 0.980588\n",
      "Recal on test data = 0.981006\n",
      "fscore on test data = 0.980543\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 1.000000\n",
      "Accuracy on training data = 0.841851\n",
      "Accuracy on test data = 0.842708\n",
      "Precision on test data = 0.981288\n",
      "Recal on test data = 0.981676\n",
      "fscore on test data = 0.981310\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 10.000000\n",
      "Accuracy on training data = 0.841486\n",
      "Accuracy on test data = 0.842158\n",
      "Precision on test data = 0.981063\n",
      "Recal on test data = 0.981453\n",
      "fscore on test data = 0.981116\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 100.000000\n",
      "Accuracy on training data = 0.841486\n",
      "Accuracy on test data = 0.842158\n",
      "Precision on test data = 0.981063\n",
      "Recal on test data = 0.981453\n",
      "fscore on test data = 0.981116\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19c800d2da0>"
      ]
     },
     "execution_count": 87,
     "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": [
    "acc_train_logreg_L2 = [] \n",
    "acc_test_logreg_L2 = []\n",
    "Yhat_test_logreg_L2 = []\n",
    "c_logreg_L2 = []\n",
    "fscore_test_logreg.clear()\n",
    "prec_test_logreg.clear()\n",
    "# Call the function logreg_model.\n",
    "for c in c_values:\n",
    "    logreg_model_L2(c, X_transformed_train, y_train, X_transformed_test, y_test)\n",
    "    \n",
    "plt.plot(c_logreg, prec_test_logreg, 'ro-') \n",
    "plt.plot(c_logreg, acc_test_logreg,'bo-') \n",
    "plt.plot(c_logreg, fscore_test_logreg,'yo-') \n",
    "plt.grid()\n",
    "plt.legend(['Test Precision', 'Test Accuracy', 'Fscore'], loc='upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Support Vector Machine (SVM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1 Using Linear Kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_train_svm_linear = []\n",
    "acc_test_svm_linear = []\n",
    "c_svm_linear = []\n",
    "prec_test_svm_linear = []\n",
    "fscore_test_svm_linear = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "# Complete the function below:\n",
    "# In this function and next 2 functions, we are not passing the data matrices as parameters \n",
    "# because we can use global variables inside the functions.\n",
    "def svm_linear(c, X_train, Y_train, X_test, Y_test):\n",
    "    # TODO - Create an object of svm.SVC(probability = False, kernel = 'linear', C = c) - 5 points\n",
    "    svc_linear = svm.SVC(probability = False, kernel = 'linear', C = c)\n",
    "    \n",
    "    # TODO - Fit the classifier on the training set - 5 points\n",
    "    svc_linear.fit(X_train, Y_train)\n",
    "    # TODO - Find the prediction and accuracy on the training set - 5 points\n",
    "    Yhat_svc_linear_train = svc_linear.predict(X_train)\n",
    "    acc_train = np.mean(Yhat_svc_linear_train == Y_train)\n",
    "    \n",
    "    # Adding testing accuracy to acc_train_svm\n",
    "    acc_train_svm_linear.append(acc_train)\n",
    "    print('Train Accuracy = {0:f}'.format(acc_train))\n",
    "    \n",
    "    # TODO - Find the prediction and accuracy on the test set - 5 points\n",
    "    Yhat_svc_linear_test = svc_linear.predict(X_test)\n",
    "    acc_test = np.mean(Yhat_svc_linear_test == Y_test)\n",
    "    \n",
    "    # Adding testing accuracy to acc_test_svm\n",
    "    acc_test_svm_linear.append(acc_test)\n",
    "    print('Test Accuracy = {0:f}'.format(acc_test))\n",
    "    prec , recal , fscore,_ = precision_recall_fscore_support(Y_test, Yhat_svc_linear_test, average = 'weighted')\n",
    "    prec_test_svm_linear.append(prec)\n",
    "    fscore_test_svm_linear.append(fscore)\n",
    "    print(\"Precision on test data = %f\" % prec)\n",
    "    print(\"Recal on test data = %f\" % recal)\n",
    "    print(\"fscore on test data = %f\" % fscore)\n",
    "    \n",
    "    \n",
    "    # Appending value of c for graphing purposes\n",
    "    c_svm_linear.append(c)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0001, 0.001, 0.01, 0.1, 1, 10, 100]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy = 0.869925\n",
      "Test Accuracy = 0.872221\n",
      "Precision on test data = 0.955827\n",
      "Recal on test data = 0.954637\n",
      "fscore on test data = 0.947905\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy = 0.853579\n",
      "Test Accuracy = 0.856823\n",
      "Precision on test data = 0.970591\n",
      "Recal on test data = 0.970726\n",
      "fscore on test data = 0.968579\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy = 0.847684\n",
      "Test Accuracy = 0.848940\n",
      "Precision on test data = 0.975768\n",
      "Recal on test data = 0.976313\n",
      "fscore on test data = 0.975317\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy = 0.843734\n",
      "Test Accuracy = 0.845091\n",
      "Precision on test data = 0.980138\n",
      "Recal on test data = 0.980559\n",
      "fscore on test data = 0.980011\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy = 0.842884\n",
      "Test Accuracy = 0.843624\n",
      "Precision on test data = 0.980585\n",
      "Recal on test data = 0.981006\n",
      "fscore on test data = 0.980567\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy = 0.842701\n",
      "Test Accuracy = 0.843074\n",
      "Precision on test data = 0.980819\n",
      "Recal on test data = 0.981229\n",
      "fscore on test data = 0.980830\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy = 0.842641\n",
      "Test Accuracy = 0.843074\n",
      "Precision on test data = 0.980819\n",
      "Recal on test data = 0.981229\n",
      "fscore on test data = 0.980830\n"
     ]
    }
   ],
   "source": [
    "print(c_values)\n",
    "for c in c_values:\n",
    "    svm_linear(c,x_train,y_train, x_test, y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.8722213663743329, 0.8568228956649293, 0.8489403451827346, 0.8450907275053837, 0.8436242064854406, 0.8430742611029618, 0.8430742611029618]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19c8014de80>"
      ]
     },
     "execution_count": 91,
     "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": [
    "print(acc_test_svm_linear)\n",
    "# Plot training accuracy(Y-axis) v/s 'c' on X - Axis.\n",
    "# Plot test accuracy(Y-Axis) v/s 'c' on X - Axis.\n",
    "\n",
    "plt.plot(c_svm_linear, prec_test_svm_linear, 'ro-') \n",
    "plt.plot(c_svm_linear, acc_test_svm_linear,'bo-') \n",
    "plt.plot(c_svm_linear, fscore_test_svm_linear,'yo-') \n",
    "plt.grid()\n",
    "\n",
    "# Use the following function to have a legend\n",
    "plt.legend(['Test Precision', 'Test Accuracy','Fscore'], loc='upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2 Using Radial Basis Function (RBF) Kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_train_svm_rbf = []\n",
    "acc_test_svm_rbf = []\n",
    "c_svm_rbf = []\n",
    "prec_test_svm_rbf = []\n",
    "fscore_test_svm_rbf = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "# Complete the function below:\n",
    "# In this function and next 2 functions, we are not passing the data matrices as parameters \n",
    "# because we can use global variables inside the functions.\n",
    "def svm_rbf(c,X_train,Y_train, X_test, Y_test):\n",
    "    # TODO - Create an object of svm.SVC(probability = False, kernel = 'rbf', C = c) - 5 points\n",
    "    svc_rbf = svm.SVC(probability = False, kernel = 'rbf', C = c)\n",
    "    \n",
    "    # TODO - Fit the classifier on the trai  ning set - 5 points\n",
    "    svc_rbf.fit(X_train, Y_train)\n",
    "    # TODO - Find the prediction and accuracy on the training set - 5 points\n",
    "    Yhat_svc_rbf_train = svc_rbf.predict(X_train)\n",
    "    acc_train = np.mean(Yhat_svc_rbf_train == Y_train)\n",
    "    \n",
    "    # Adding testing accuracy to acc_train_svm\n",
    "    acc_train_svm_rbf.append(acc_train)\n",
    "    print('Train Accuracy = {0:f}'.format(acc_train))\n",
    "    \n",
    "    # TODO - Find the prediction and accuracy on the test set - 5 points\n",
    "    Yhat_svc_rbf_test = svc_rbf.predict(X_test)\n",
    "    acc_test = np.mean(Yhat_svc_rbf_test == Y_test)\n",
    "    \n",
    "    # Adding testing accuracy to acc_test_svm\n",
    "    acc_test_svm_rbf.append(acc_test)\n",
    "    print('Test Accuracy = {0:f}'.format(acc_test))\n",
    "    prec , recal , fscore,_ = precision_recall_fscore_support(Y_test, Yhat_svc_rbf_test, average = 'weighted')\n",
    "    prec_test_svm_rbf.append(prec)\n",
    "    fscore_test_svm_rbf.append(fscore)\n",
    "    print(\"Precision on test data = %f\" % prec)\n",
    "    print(\"Recal on test data = %f\" % recal)\n",
    "    print(\"fscore on test data = %f\" % fscore)\n",
    "    # Appending value of c for graphing purposes\n",
    "    c_svm_rbf.append(c)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy = 0.907845\n",
      "Test Accuracy = 0.910168\n",
      "Precision on test data = 0.828405\n",
      "Recal on test data = 0.910168\n",
      "fscore on test data = 0.867364\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy = 0.907845\n",
      "Test Accuracy = 0.910168\n",
      "Precision on test data = 0.828405\n",
      "Recal on test data = 0.910168\n",
      "fscore on test data = 0.867364\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy = 0.854430\n",
      "Test Accuracy = 0.855723\n",
      "Precision on test data = 0.971403\n",
      "Recal on test data = 0.971620\n",
      "fscore on test data = 0.969669\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy = 0.845861\n",
      "Test Accuracy = 0.847657\n",
      "Precision on test data = 0.978832\n",
      "Recal on test data = 0.979218\n",
      "fscore on test data = 0.978442\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy = 0.842276\n",
      "Test Accuracy = 0.843074\n",
      "Precision on test data = 0.979418\n",
      "Recal on test data = 0.979888\n",
      "fscore on test data = 0.979461\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy = 0.841790\n",
      "Test Accuracy = 0.842891\n",
      "Precision on test data = 0.980587\n",
      "Recal on test data = 0.981006\n",
      "fscore on test data = 0.980614\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy = 0.840878\n",
      "Test Accuracy = 0.841974\n",
      "Precision on test data = 0.980836\n",
      "Recal on test data = 0.981229\n",
      "fscore on test data = 0.980900\n"
     ]
    }
   ],
   "source": [
    "for c in c_values:\n",
    "    svm_rbf(c,x_train,y_train, x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19c801d0978>"
      ]
     },
     "execution_count": 95,
     "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": [
    "plt.plot(c_svm_rbf, prec_test_svm_rbf, 'ro-') \n",
    "plt.plot(c_svm_rbf, acc_test_svm_rbf,'bo-') \n",
    "plt.plot(c_svm_rbf, fscore_test_svm_rbf,'yo-') \n",
    "plt.grid()\n",
    "# Use the following function to have a legend\n",
    "plt.legend(['Test Precision', 'Test Accuracy', 'Fscore'], loc='lower right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3 Using Polynomial Kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_train_svm_poly = []\n",
    "acc_test_svm_poly = []\n",
    "c_svm_poly = []\n",
    "prec_test_svm_poly = []\n",
    "fscore_test_svm_poly = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "def svm_polynomial(c,X_train,Y_train, X_test, Y_test):\n",
    "    # TODO - Create an object of svm.SVC(probability = False, kernel = 'poly', C = c) - 5 points\n",
    "    svc_polynomial = svm.SVC(probability = False, kernel = 'poly', C = c)\n",
    "\n",
    "    \n",
    "    # TODO - Fit the classifier on the training set - 5 points\n",
    "    # Use A and B to train and C and D to test.\n",
    "    svc_polynomial.fit(X_train, Y_train)\n",
    "    # TODO - Find the prediction and accuracy on the training set - 5 points\n",
    "    Yhat_svc_polynomial_train = svc_polynomial.predict(X_test)\n",
    "    acc_train = np.mean(Yhat_svc_polynomial_train == Y_train)\n",
    "    \n",
    "    # Adding testing accuracy to acc_train_svm\n",
    "    acc_train_svm_poly.append(acc_train)\n",
    "    print('Train Accuracy = {0:f}'.format(acc_train))\n",
    "    \n",
    "    # TODO - Find the prediction and accuracy on the test set - 5 points\n",
    "    Yhat_svc_polynomial_test = svc_polynomial.predict(X_test)\n",
    "    acc_test = np.mean(Yhat_svc_polynomial_test == Y_test)\n",
    "    \n",
    "    # Adding testing accuracy to acc_test_svm\n",
    "    acc_test_svm_poly.append(acc_test)\n",
    "    print('Test Accuracy = {0:f}'.format(acc_test))\n",
    "    prec , recal , fscore,_ = precision_recall_fscore_support(Y_test, Yhat_svc_polynomial_test, average = 'weighted')\n",
    "    prec_test_svm_poly.append(prec)\n",
    "    fscore_test_svm_poly.append(fscore)\n",
    "    print(\"Precision on test data = %f\" % prec)\n",
    "    print(\"Recal on test data = %f\" % recal)\n",
    "    print(\"fscore on test data = %f\" % fscore)\n",
    "    \n",
    "    # Appending value of c for graphing purposes\n",
    "    c_svm_poly.append(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy = 0.872483\n",
      "Test Accuracy = 0.874604\n",
      "Precision on test data = 0.952400\n",
      "Recal on test data = 0.951285\n",
      "fscore on test data = 0.943391\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy = 0.862822\n",
      "Test Accuracy = 0.864889\n",
      "Precision on test data = 0.961422\n",
      "Recal on test data = 0.961341\n",
      "fscore on test data = 0.957085\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy = 0.853708\n",
      "Test Accuracy = 0.855723\n",
      "Precision on test data = 0.970362\n",
      "Recal on test data = 0.970726\n",
      "fscore on test data = 0.968714\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy = 0.848787\n",
      "Test Accuracy = 0.850773\n",
      "Precision on test data = 0.972967\n",
      "Recal on test data = 0.973631\n",
      "fscore on test data = 0.972340\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy = 0.845141\n",
      "Test Accuracy = 0.847107\n",
      "Precision on test data = 0.977110\n",
      "Recal on test data = 0.977654\n",
      "fscore on test data = 0.976864\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy = 0.842772\n",
      "Test Accuracy = 0.844724\n",
      "Precision on test data = 0.979183\n",
      "Recal on test data = 0.979665\n",
      "fscore on test data = 0.979118\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\zafir\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy = 0.841860\n",
      "Test Accuracy = 0.843808\n",
      "Precision on test data = 0.978943\n",
      "Recal on test data = 0.979441\n",
      "fscore on test data = 0.978953\n"
     ]
    }
   ],
   "source": [
    "for c in c_values:\n",
    "    svm_polynomial(c,x_train,y_train, x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19c80246fd0>"
      ]
     },
     "execution_count": 99,
     "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": [
    "plt.plot(c_svm_poly,prec_test_svm_poly, 'ro-') \n",
    "plt.plot(c_svm_poly,acc_test_svm_poly,'bo-')\n",
    "plt.plot(c_svm_poly,fscore_test_svm_poly,'yo-') \n",
    "plt.grid()\n",
    "# Use the following function to have a legend\n",
    "plt.legend(['Test Precision', 'Test Accuracy','Fscore'], loc='lower right')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Neural Networks\n",
    "In the following section, we will run our data set through various neural networks with different hyper parameters. We will vary the activation function (tanh, reLU, sigmoid), the number of inputs in the hidden layer (10, 100, 200), and the number of hidden layers (1, 2, 5, with 100 nodes in each hidden layer). Weights and biases will be initialized randomly.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Metrics(Callback):\n",
    "    def on_train_begin(self, logs={}):\n",
    "     self.val_f1s = []\n",
    "     self.val_recalls = []\n",
    "     self.val_precisions = []\n",
    " \n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "     val_predict = (np.asarray(self.model.predict(self.validation_data[0])).round())\n",
    "     val_targ = self.validation_data[1]\n",
    "     _val_f1 = f1_score(val_targ, val_predict)\n",
    "     _val_recall = recall_score(val_targ, val_predict)\n",
    "     _val_precision = precision_score(val_targ, val_predict)\n",
    "    \n",
    "     self.val_f1s.append(_val_f1)\n",
    "     self.val_recalls.append(_val_recall)\n",
    "     self.val_precisions.append(_val_precision)\n",
    "        \n",
    "     print (\" — val_f1: %f — val_precision: %f — val_recall %f\" % \n",
    "            (_val_f1, _val_precision, _val_recall))\n",
    "     return\n",
    "\n",
    "metrics = Metrics()\n",
    "\n",
    "\n",
    "# Build and compile our network with user given parameters\n",
    "def neural_network(nin, nh, nout, nhl, act):\n",
    "    model = Sequential();\n",
    "    for x in range(0,nhl):\n",
    "        model.add(Dense(nh, input_shape=(nin,), activation=act, name=\"hidden\"+str(x)))\n",
    "    model.add(Dense(nout, activation=\"sigmoid\", name=\"output\"))  \n",
    "    # Compile model\n",
    "    opt = optimizers.Adam(lr=0.001) \n",
    "    model.compile(optimizer=opt, \n",
    "             loss=\"binary_crossentropy\",\n",
    "             metrics=[\"accuracy\"])  \n",
    "    return model\n",
    "\n",
    "# Train and validate our network on training and test data\n",
    "def train_network(model, x_train, y_train, x_test, y_test):\n",
    "    history = model.fit(x_train, y_train, epochs=10, batch_size=100, \n",
    "                validation_data=(x_test, y_test), callbacks=[metrics], verbose=1)\n",
    "    return history\n",
    "\n",
    "# Plot network accuracy\n",
    "def plot_acc(history, title):\n",
    "    plt.plot(history.history['acc'])\n",
    "    plt.plot(history.history['val_acc'])\n",
    "    plt.title(title)\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.legend(['acc', 'val_acc'], loc='lower right')\n",
    "    plt.show()\n",
    "\n",
    "# Plot network loss\n",
    "def plot_loss(history, title):\n",
    "    plt.plot(history.history['loss'])\n",
    "    plt.plot(history.history['val_loss'])\n",
    "    plt.title(title)\n",
    "    plt.ylabel('Loss')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.legend(['acc', 'val_acc'], loc='upper right')\n",
    "    plt.show()\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6.1 Network 8-100-1, Sigmoid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 13423 samples, validate on 4475 samples\n",
      "Epoch 1/10\n",
      "13423/13423 [==============================] - 1s 56us/step - loss: 0.1806 - acc: 0.9392 - val_loss: 0.1194 - val_acc: 0.9622\n",
      " — val_f1: 0.742770 — val_precision: 0.956863 — val_recall 0.606965\n",
      "Epoch 2/10\n",
      "13423/13423 [==============================] - 0s 18us/step - loss: 0.1073 - acc: 0.9680 - val_loss: 0.0981 - val_acc: 0.9707\n",
      " — val_f1: 0.814710 — val_precision: 0.944262 — val_recall 0.716418\n",
      "Epoch 3/10\n",
      "13423/13423 [==============================] - 0s 16us/step - loss: 0.0949 - acc: 0.9724 - val_loss: 0.0900 - val_acc: 0.9727\n",
      " — val_f1: 0.831492 — val_precision: 0.934783 — val_recall 0.748756\n",
      "Epoch 4/10\n",
      "13423/13423 [==============================] - 0s 18us/step - loss: 0.0892 - acc: 0.9730 - val_loss: 0.0860 - val_acc: 0.9743\n",
      " — val_f1: 0.846051 — val_precision: 0.915942 — val_recall 0.786070\n",
      "Epoch 5/10\n",
      "13423/13423 [==============================] - 0s 18us/step - loss: 0.0859 - acc: 0.9748 - val_loss: 0.0829 - val_acc: 0.9756\n",
      " — val_f1: 0.854860 — val_precision: 0.919771 — val_recall 0.798507\n",
      "Epoch 6/10\n",
      "13423/13423 [==============================] - 0s 16us/step - loss: 0.0839 - acc: 0.9750 - val_loss: 0.0808 - val_acc: 0.9756\n",
      " — val_f1: 0.855246 — val_precision: 0.917379 — val_recall 0.800995\n",
      "Epoch 7/10\n",
      "13423/13423 [==============================] - 0s 19us/step - loss: 0.0824 - acc: 0.9755 - val_loss: 0.0791 - val_acc: 0.9768\n",
      " — val_f1: 0.862434 — val_precision: 0.920904 — val_recall 0.810945\n",
      "Epoch 8/10\n",
      "13423/13423 [==============================] - 0s 17us/step - loss: 0.0816 - acc: 0.9761 - val_loss: 0.0781 - val_acc: 0.9768\n",
      " — val_f1: 0.862069 — val_precision: 0.923295 — val_recall 0.808458\n",
      "Epoch 9/10\n",
      "13423/13423 [==============================] - 0s 19us/step - loss: 0.0809 - acc: 0.9759 - val_loss: 0.0778 - val_acc: 0.9768\n",
      " — val_f1: 0.862434 — val_precision: 0.920904 — val_recall 0.810945\n",
      "Epoch 10/10\n",
      "13423/13423 [==============================] - 0s 17us/step - loss: 0.0803 - acc: 0.9760 - val_loss: 0.0766 - val_acc: 0.9770\n",
      " — val_f1: 0.863576 — val_precision: 0.923513 — val_recall 0.810945\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "hidden0 (Dense)              (None, 100)               900       \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 1,001\n",
      "Trainable params: 1,001\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy = 0.976983\n"
     ]
    }
   ],
   "source": [
    "K.clear_session()\n",
    "\n",
    "modelArr = []\n",
    "historyArr = []\n",
    "modelNameArr = []\n",
    "# Number of nodes in input layer = number of features = 8\n",
    "nin = x_train.shape[1];\n",
    "# Number of hidden layer nodes\n",
    "nh = 100;\n",
    "# Number of output nodes\n",
    "nout = 1;\n",
    "\n",
    "\n",
    "model_sig = neural_network(nin, nh, nout, 1, \"sigmoid\")\n",
    "sig_history = train_network(model_sig, x_train, y_train, x_test, y_test);\n",
    "model_sig.summary()\n",
    "plot_acc(sig_history, \"Sigmoid Accuracy\")\n",
    "plot_loss(sig_history, \"Sigmoid Loss\")\n",
    "\n",
    "modelArr.append(model_sig)\n",
    "historyArr.append(sig_history)\n",
    "modelNameArr.append(\"model_sig\")\n",
    "\n",
    "score, acc = model_sig.evaluate(x_test, y_test, verbose = 0)\n",
    "print(\"Validation Accuracy = %f\" % acc)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.2 Network 8-100-1, ReLU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "hidden0 (Dense)              (None, 100)               900       \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 1,001\n",
      "Trainable params: 1,001\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 13423 samples, validate on 4475 samples\n",
      "Epoch 1/10\n",
      "13423/13423 [==============================] - 1s 63us/step - loss: 0.3147 - acc: 0.9177 - val_loss: 0.1194 - val_acc: 0.9770\n",
      " — val_f1: 0.860999 — val_precision: 0.941003 — val_recall 0.793532\n",
      "Epoch 2/10\n",
      "13423/13423 [==============================] - 0s 16us/step - loss: 0.0949 - acc: 0.9756 - val_loss: 0.0822 - val_acc: 0.9770\n",
      " — val_f1: 0.862850 — val_precision: 0.928367 — val_recall 0.805970\n",
      "Epoch 3/10\n",
      "13423/13423 [==============================] - 0s 16us/step - loss: 0.0803 - acc: 0.9769 - val_loss: 0.0761 - val_acc: 0.9792\n",
      " — val_f1: 0.877147 — val_precision: 0.935211 — val_recall 0.825871\n",
      "Epoch 4/10\n",
      "13423/13423 [==============================] - 0s 17us/step - loss: 0.0776 - acc: 0.9770 - val_loss: 0.0746 - val_acc: 0.9781\n",
      " — val_f1: 0.871391 — val_precision: 0.922222 — val_recall 0.825871\n",
      "Epoch 5/10\n",
      "13423/13423 [==============================] - 0s 17us/step - loss: 0.0762 - acc: 0.9773 - val_loss: 0.0732 - val_acc: 0.9788\n",
      " — val_f1: 0.876141 — val_precision: 0.920548 — val_recall 0.835821\n",
      "Epoch 6/10\n",
      "13423/13423 [==============================] - 0s 19us/step - loss: 0.0754 - acc: 0.9776 - val_loss: 0.0710 - val_acc: 0.9797\n",
      " — val_f1: 0.880105 — val_precision: 0.935574 — val_recall 0.830846\n",
      "Epoch 7/10\n",
      "13423/13423 [==============================] - 0s 19us/step - loss: 0.0744 - acc: 0.9781 - val_loss: 0.0709 - val_acc: 0.9792\n",
      " — val_f1: 0.879377 — val_precision: 0.918699 — val_recall 0.843284\n",
      "Epoch 8/10\n",
      "13423/13423 [==============================] - 0s 17us/step - loss: 0.0735 - acc: 0.9783 - val_loss: 0.0691 - val_acc: 0.9808\n",
      " — val_f1: 0.887139 — val_precision: 0.938889 — val_recall 0.840796\n",
      "Epoch 9/10\n",
      "13423/13423 [==============================] - 0s 19us/step - loss: 0.0729 - acc: 0.9785 - val_loss: 0.0680 - val_acc: 0.9808\n",
      " — val_f1: 0.887139 — val_precision: 0.938889 — val_recall 0.840796\n",
      "Epoch 10/10\n",
      "13423/13423 [==============================] - 0s 17us/step - loss: 0.0723 - acc: 0.9785 - val_loss: 0.0674 - val_acc: 0.9806\n",
      " — val_f1: 0.886275 — val_precision: 0.933884 — val_recall 0.843284\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9eN2Js8pcjYjI+NEvuIdhxrQqmhtrNO2HiFQchcUwpZIJXT4rIhVHYTFMqWQjGzv2caAnU+5SRETGjcJimFLJBDmH9Vs6y12KiMi4UVgMU++0H+s0biEiFURhMUzJxhqm18U1biEiFUVhMUxmRirZqLAQkYqisBiBVDLB01s7SWdz5S5FRGRcKCxGoDWZoCeb49lt+8pdiojIuFBYjMBC3dtCRCqMwmIE5s+cRl1VVOMWIlIxFBYjEIkYC5oT6lmISMVQWIzQwmSCde17yeVKes8lEZEJQWExQqlkI/t7sry460DhxiIik5zCYoRak8G9LdZs1qkoEZn6FBYjdPKcBuJR0yC3iFQEhcUIVcUinDynQYPcIlIRFBajkAoHud01yC0iU5vCYhRSyUZ27u9h696ucpciIlJSCotRSIWD3Gs3a9xCRKY2hcUoLGhOYIYGuUVkylNYjMK06hjzZ01jjQa5RWSKU1iMUirZyDr1LERkilNYjNLCZILNLx9k9/6ecpciIlIyCotRSiXDe3JvUe9CRKYuhcUopTTth4hUAIXFKE2fVkWysUZXRInIlKawGAOplkZN+yEiU5rCYgykkgk27tjP/u5MuUsRESkJhcUYSCUbcYentupUlIhMTQqLMbCwJZz2Q+MWIjJFlTQszOxcM3vazDaY2RVDbL/czNaZ2RNm9lszOy5v23vM7Nnw8Z5S1jlaRydqmDGtSnNEiciUVbKwMLMocA1wHtAKXGRmrYOa/QVY6u6LgRuBfwv3nQF8HngNcDrweTObXqpaR8vMSCUTmvZDRKasosLCzE4ws+rw9Zlm9lEzO6rAbqcDG9x9o7v3AKuAZfkN3P1ed++9ifVDwNzw9VuAu9x9l7vvBu4Czi3un1QerckEz2zrpCeTK3cpIiJjrtiexU1A1sxOBH4AzAd+WmCfFmBT3nJbuO5w3g/cPpx9zexDZrbazFZ3dHQUKKe0FiYbSWedZ7d3lrUOEZFSKDYscu6eAf4auNrdLwOaC+xjQ6wb8pZyZnYxsBT4+nD2dfeV7r7U3Zc2NTUVKKe0+u5toUFuEZmCig2LtJldBLwH+FW4Ll5gnzbgmLzluUD74EZmdjZwJXCBu3cPZ9+JZN7MaUyrirJW036IyBRUbFi8F3gt8BV3f97M5gM/KbDPI8BJZjbfzKqAFcAt+Q3M7DTguwRBsT1v053Am81sejiw/eZw3YQViRgLmhPqWYjIlBQrppG7rwM+ChB+eTe4+1cL7JMxs0sJvuSjwLXuvtbMrgJWu/stBKed6oFfmBnAS+5+gbvvMrMvEQQOwFXuvmsE/75xtbClkRtWbyKXcyKRoc6kiYhMTkWFhZndB1wQtn8M6DCz+9398iPt5+63AbcNWve5vNdnH2Hfa4Fri6lvomhNJjjQk+X5nfs5oam+3OWIiIyZYk9DNbr7XuAdwA/d/VXAYb/oK5UGuUVkqio2LGJm1gz8Lf0D3DLISbMbqIpGNAOtiEw5xYbFVQRjD8+5+yNmdjzwbOnKmpyqYhFOPrpe9+QWkSmn2AHuXwC/yFveCLyzVEVNZqnmRn6zbivuTjhoLyIy6RU73cdcM7vZzLab2TYzu8nM5hbes/KkWhLsPpBmy56ucpciIjJmij0N9UOC30gkCabduDVcJ4Okko2ABrlFZGopNiya3P2H7p4JH/8NlHd+jQlqQXMDZrBGv+QWkSmk2LDYYWYXm1k0fFwM7CxlYZNVXVWM42dNU89CRKaUYsPifQSXzW4FtgDLCaYAkSEsbGlknS6fFZEppKiwcPfeaTia3H22u19I8AM9GUIqmaB9Txe79veUuxQRkTExmjvlHXGqj0rWP8it3oWITA2jCQv9iOAwNO2HiEw1owmLIW9kJHBUXRUtR9UqLERkyjjiL7jNrJOhQ8GA2pJUNEWkkgmdhhKRKeOIYeHuDeNVyFSTSjZy1/pt7O/OMK26qFlVREQmrNGchpIjSCUTuMP6LToVJSKTn8KiRBa2aNoPEZk6FBYlMidRzcxpVZr2Q0SmBIVFiZgZrcmEehYiMiUoLEpoYUsjz27vpCeTK3cpIiKjorAooVQyQTrrPLOts9yliIiMisKihDTth4hMFQqLEjpuRh311TGNW4jIpKewKKFIxGht1iC3iEx+CosSa00mWNe+l2xOU2mJyOSlsCixVDLBwXSW53fsL3cpIiIjprAosf5fcmuQW0QmL4VFiZ04u56qWIR1GrcQkUlMYVFi8WiEU+Y0sEY9CxGZxBQW4yAVTvvhrkFuEZmcFBbjINXSyMsH0rTv6Sp3KSIiI1LSsDCzc83saTPbYGZXDLH9DWb2ZzPLmNnyQduyZvZY+LillHWWWt89uTUDrYhMUiULCzOLAtcA5wGtwEVm1jqo2UvA3wM/HeIQB939FeHjglLVOR4WHJ0gYrBGg9wiMkmV8n6fpwMb3H0jgJmtApYB63obuPsL4bYpPS1rbVWU45vqWadBbhGZpEp5GqoF2JS33BauK1aNma02s4fM7MKhGpjZh8I2qzs6OkZTa8kt1L0tRGQSK2VY2BDrhnM50LHuvhT4O+BqMzvhkIO5r3T3pe6+tKmpaaR1jotUspEte7rYua+73KWIiAxbKcOiDTgmb3ku0F7szu7eHj5vBO4DThvL4sZb3yC3ehciMgmVMiweAU4ys/lmVgWsAIq6qsnMpptZdfh6FvB68sY6JqP+e1soLERk8ilZWLh7BrgUuBNYD9zg7mvN7CozuwDAzF5tZm3A3wDfNbO14e4LgNVm9jhwL/BVd5/UYdFYF2fu9FrNESUik1Ipr4bC3W8Dbhu07nN5rx8hOD01eL8/AotKWVs5pDTILSKTlH7BPY5SyUae37Gffd2ZcpciIjIsCotxtLAlGORev0W9CxGZXBQW46h3kHuNpv0QkUlGYTGOZjdUM6u+SuMWIjLpKCzGkZnRmmxUWIjIpKOwGGcLkwme3dZJdyZb7lJERIqmsBhnqWQjmZzzzNZ95S5FRKRoCotx1j/thwa5RWTyUFiMs2Nn1NFQHdO4hYhMKgqLcRaJGAuSCfUsRGRSUViUQSqZYP2WTrK54czYLiJSPgqLMkglGzmYzvL8Dg1yi8jkoLAog95pPzRuISKThcKiDE5oqqcqFtG0HyIyaSgsyiAejXDq0Q3qWYjIpKGwKJNUOO2Huwa5RWTiU1iUSSqZYM/BNJtfPljuUkREClJYuMPvr4Z9HeP6tr2/5F6zWaeiRGTiU1jseBbu+yr84GzY+dy4ve2pRyeIGKzTj/NEZBJQWDSdDH//K+juhO+fDZv+NC5vW1sV5cTZ9RrkFpFJQWEBMHcpvP8uqD0KfnQ+rL91XN42pXtbiMgkobDoNfOEIDCOXgQ/vwQe/m7J3zKVTLB1bxc79nWX/L1EREZDYZFv2iz437fAKW+F2/8F7rwScrmSvV1rUr/kFpHJQWExWFUdvOvH8OoPwoPfhpveB+mukrxVKtkI6N4WIjLxxcpdwIQUicJbvw5HHQN3fQ46t8GK66Fuxpi+TWNtnGNm1LJWl8+KyASnnsXhmMHrPwbv/AFsXg3XvgV2vzjmb5NqblTPQkQmPIVFIYuWwyU3w75t8INzoP2xMT38wpYEL+w8QGdXekyPKyIylhQWxZj3V/C+30C0Cn74Vnj27jE7dO+4xfotnWN2TBGRsaawKNbsU4NLa2ceDz/9W/jzdWNy2P5pP3QqSkQmLoXFcCSa4b23w/Fnwi3/BPf+azC31CjMTtQwq75al8+KyISmsBiu6gb4u5/DaRfD/V+D//kIZEc33rCwJaFBbhGZ0EoaFmZ2rpk9bWYbzOyKIba/wcz+bGYZM1s+aNt7zOzZ8PGeUtY5bNE4XPBtOPNT8Nj1cP3fQNfIewapZIJnt++jK50dwyJFRMZOycLCzKLANcB5QCtwkZm1Dmr2EvD3wE8H7TsD+DzwGuB04PNmNr1UtY6IGZx5BSy7Bl74XTDwvbd9RIdKJRvJ5pxntmmQW0QmplL2LE4HNrj7RnfvAVYBy/IbuPsL7v4EMHhOjbcAd7n7LnffDdwFnFvCWkfutIvh726A3c/D98+B7euHfYiFfb/k1riFiExMpQyLFmBT3nJbuG7M9jWzD5nZajNb3dExvjcvGuDEs4KB71wGfvAWeP6BYe1+zIxaGmpiGrcQkQmrlGFhQ6wr9tKhovZ195XuvtTdlzY1NQ2ruDHXvBg+cHdwxdSP3wFP/KLoXc2M1uaE7ponIhNWKcOiDTgmb3kuUOxJ/dHsWz5HHQPvuwOOeQ388gPw+/8o+tLaVLKRp7buJZsb3aW4IiKlUMqweAQ4yczmm1kVsAK4pch97wTebGbTw4HtN4frJr7a6XDJL2Hhcrj7C/DrT0Cu8FVOC1sSdKVzfO2Op9iwXQPdIjKxlGzWWXfPmNmlBF/yUeBad19rZlcBq939FjN7NXAzMB0438y+6O4pd99lZl8iCByAq9x9V6lqHXOxanjH96BxLvzhaujcEkxIWFV32F3Obp3Dmac08f3fbWTlAxs59egGzl+S5PzFSY6defj9RETGg/kof4E8USxdutRXr15d7jIO9afvBTdSSp4GF/0c6o88trK9s4vbn9zKrY+3s/rF3QC84pijOH9JkrcvbmZOomY8qhaRCmFmj7r70oLtFBbj4Klfw43vh4Y5cPEvg1u4FqFt9wF+/cQWbn2inTWb92IGr5k/g/OXJDlvYTMzplWVuHARmeoUFhNN2+pgAkL3YLqQY04f1u7PdezjV49v4ZbHN/Ncx36iEeOvTpzFBUuSnJOaQ6ImXqLCRWQqU1hMRDufg+uXB7/0fuf3YcH5wz6Eu7N+Sye3PtHOrY+307b7IFWxCP/rlCbOX5LkrFPnUFsVLUHxIjIVKSwmqv074Gcrgp7GeV+D1/yfER/K3fnLppe59fF2fv3EFrZ3dlNXFeWc1jmcvzjJG05uoiqmuSJF5PAUFhNZzwG46QPw9K/htZfCOV+CyOi+1LM55+Hnd3Lr41u4fc0WXj6QJlET47yFzZy/JMkZx88gFlVwiMhACouJLpeFO66AP62E1F/Dhf8F8bG50imdzfH7DTu49bF2frNuG/u6M8yqr+Jti4LgeOWx04lEhvqRvIhUGoXFZOAOf/y/cNdn4djXwYrroW7GmL5FVzrLfU9v59bHt3D3+m10Z3K0HFXL2xcHwZFKJjBTcIhUKoXFZLLmJrj5w9B4DCx4O8w8EWacEFxiWz8nmA59DOzrznD3um3c+ng79z/TQSbnHD9rGm9fkuSCJc2cOLthTN5HRCYPhcVk88If4LZ/hh3PQC7vzntV9TBjfn945D9PmzXiIHn5QA93rNnKrU+08+BzO8k5fb8aP6d1Dk311TTUxDTOITLFKSwmq2wG9myCXc/Bzo3h83PB8+4XwfPmmapOwIzjDw2RmScM63TW9s4ubntiC7c+sYVHw1+N95pWFaWxNk6i91ETD5djwXPf8qHr66qiOsUlMsEpLKaibBpefqk/PPKf92wCz7uHVM1RQ4TI8cFz7VGHfYu23Qd45IVd7DmQZs/BDHu70uw5mGbvwfC5K8PecLmzO3PEcmMRCwMmVnTg5LeNq1cjUnLFhkXJJhKUEojGgy/+oaYLyXQHPY/BIfLSg/DkLxhwO5C6mUOHyMwTmDu9gbnTi5u4MJPNsa87E4ZJ+HxIuIShEy5vfvlg3+t09sh/qFTHIlTHItTEo1THI1THov3L4bbqWJSa3m3xyKDth26rzts2oG28v31VNKIekcggCoupIlYNTScHj8HSXcFtXwf0SDbCxvvh8Z8NbDttdnBfjqppEKsNLuftfY7XQawG4rUQqyEWr+WoeC1H9a6L10KiFmb27lML8Rn9+0T7pyRxd7rSuaHD5UDQg9nXnaEnk6MrnaU7k6M7k6U7naMrfO7sytCdydKVDrdlcn3bR9thzg+qeDRCPGrEopH+15FguSoaIRY1YpFgfTxcjkfC5/x9IwWOMaBNeKy8NvFYsK4q7xhVsUjfOl0OLaWksKgE8RqYvSB4DNazH3Y9P7BHsmczpA9C157gOd0FmfA5fWDguMlwRGJ9wWOxWmrjNdTGa5kzIJRq+8KIeC1U14Svw+e8sOpfXx+28FfnAAAIwElEQVSEZbwWYtV4rIa0VdNNnO6c0d0bOHmh0h9AObrTWbrC5/x1ve16sjkyWSeTy9GTCZ4zWSedzXGgJ0Mm56SzTiabI5NzejK5AW3S4b6FelKjFY1YX8hUx3oDpT948oMlHotQ1Rdm4fqY9QdRX9tD9x0QgpGwbcSIx4Jw6z1eLGoDwnTwvuq9TS4Ki0pXNQ2OXhg8ipVNByGS6QrD5GB/mGQO5q3rGqLdYfbpOQD7dx56nEzXwLGYIhhQFT4aIvG+EOnvKYWv+wImP3jC9bV5+1TVQXUDVDVAdX34OnyubgjaFcHdyYbBkg7DJJPNDQijIHQ8XJcLgyjXF0Y94eueTC5cH67L+IDl/u0ebh+4fPBguq/94OP1tunJDO9zH66g1xT0wgYGzcAeWG8Pa+A2IxqJEDWIRIyoBceKmBGN9D/HIta3PRIu92+HiOWtC9tFI/2PSP6y9beLhdti0f73CZ4jA5fDoIxFjGh0YLuIMakCU2EhwxeNh6eUEqV/L/cgnDJd/Y/eMMl09wfKkOu788KnKy+88tZ3dw69f6ar+Boj8bwQGRwo9cFVa1X1WHU9seoGYlUN1A4OnZrwdbx2zH5XM1ru3h9WmTBAwhBLh72m/GDLD8H84Mnkv8717ztw/8G9sEPf40BPpn9dzsnlgvqyOScXBnE252TD17m810Gbcn+ih+oNj3h0UMj0hcvg8IkMbBM+nzi7nivf1lraWkt6dJHRMoNYVfAYj3DqlctBNgyd9AHo3gc9+6B7b97rzuDR9zqvzYFdwZVr+esp4tvKokHADBU6kbxp6PsCxYZeHnGbvg0YEAfi+W3M+ntb8bqgZxqvhfi0/nV108JtdcH6eG3QLlLe2ZDdg8DI5HLB/7z5ATM4cMKg6Q2cTLZ/e/AcHCe/fWbAcxB2fcthwA1ul8nmBq0Ll7MD26aHaJfO5jiYDpan7+8p+eensBAZSiQCkXD8hDGYgiWXg/T+4YdO7+vOLeG93PMCp28U3wctD7XOBzwduc0RjuO5oNfVs3/4Y1fR6v4AGRA2df2Bkh8yRwqkeF3wB0S0KjhuNB6+roLo0F9rZkbUIFrm0JqsFBYi4yES6R/jmArcIdsThEb6QDDmlD6Q93r/oOcDYduDea/Dtgd2HNomc3DktVkkLzh6H2GYxAYHS972WPXAtkfcXt0fTJG8h0UGLkdi4R8eR2oTDR+926P9+00gCgsRGT6z8AKAasak5zVYLpcXPvsHhlD6YLAumw4Ca8AjXJfpLm57ek9wurGvbTrcltc2f/qdcWV5YZL3bNFBQROD5sWw/NqSVqOwEJGJJxIJx2zqy11JEFy5w4VJd3B60LPBcy4TPrIDnz079Pre156/nAnfM1N8m+nzSv4xKCxERI4kEoFIddGXSE9VE+ukmIiITEgKCxERKUhhISIiBSksRESkIIWFiIgUpLAQEZGCFBYiIlKQwkJERAqaMvfgNrMO4MVRHGIWsGOMypns9FkMpM9jIH0e/abCZ3GcuzcVajRlwmK0zGx1MTctrwT6LAbS5zGQPo9+lfRZ6DSUiIgUpLAQEZGCFBb9Vpa7gAlEn8VA+jwG0ufRr2I+C41ZiIhIQepZiIhIQQoLEREpqOLDwszONbOnzWyDmV1R7nrKycyOMbN7zWy9ma01s4+Vu6ZyM7Oomf3FzH5V7lrKzcyOMrMbzeyp8L+R15a7pnIys8vC/5+sMbOfmVlNuWsqpYoOCzOLAtcA5wGtwEVm1lreqsoqA3zC3RcAZwAfqfDPA+BjwPpyFzFBfBO4w91PBZZQwZ+LmbUAHwWWuvtCIAqsKG9VpVXRYQGcDmxw943u3gOsApaVuaaycfct7v7n8HUnwZdBS3mrKh8zmwu8Dfh+uWspNzNLAG8AfgDg7j3u/nJ5qyq7GFBrZjGgDmgvcz0lVelh0QJsyltuo4K/HPOZ2TzgNODh8lZSVlcD/wLkyl3IBHA80AH8MDwt930zm1buosrF3TcD3wBeArYAe9z9N+WtqrQqPSxsiHUVfy2xmdUDNwEfd/e95a6nHMzs7cB2d3+03LVMEDHglcB33P00YD9QsWN8Zjad4CzEfCAJTDOzi8tbVWlVeli0AcfkLc9linclCzGzOEFQXO/uvyx3PWX0euACM3uB4PTkm8zsJ+UtqazagDZ37+1p3kgQHpXqbOB5d+9w9zTwS+B1Za6ppCo9LB4BTjKz+WZWRTBAdUuZayobMzOCc9Lr3f3fy11PObn7p9x9rrvPI/jv4h53n9J/OR6Ju28FNpnZKeGqs4B1ZSyp3F4CzjCzuvD/N2cxxQf8Y+UuoJzcPWNmlwJ3ElzNcK27ry1zWeX0euAS4Ekzeyxc92l3v62MNcnE8U/A9eEfVhuB95a5nrJx94fN7EbgzwRXEf6FKT71h6b7EBGRgir9NJSIiBRBYSEiIgUpLEREpCCFhYiIFKSwEBGRghQWIsNgZlkzeyzvMWa/YjazeWa2ZqyOJzKWKvp3FiIjcNDdX1HuIkTGm3oWImPAzF4ws6+Z2Z/Cx4nh+uPM7Ldm9kT4fGy4fo6Z3Wxmj4eP3qkiomb2vfA+Cb8xs9qy/aNE8igsRIandtBpqHflbdvr7qcD3yaYsZbw9XXuvhi4HvhWuP5bwP3uvoRgjqXemQNOAq5x9xTwMvDOEv97RIqiX3CLDIOZ7XP3+iHWvwC8yd03hpMxbnX3mWa2A2h293S4fou7zzKzDmCuu3fnHWMecJe7nxQufxKIu/uXS/8vEzky9SxExo4f5vXh2gylO+91Fo0rygShsBAZO+/Ke34wfP1H+m+3+W7g9+Hr3wL/AH33+U6MV5EiI6G/WkSGpzZvRl4I7knde/lstZk9TPBH2EXhuo8C15rZPxPcaa53ptaPASvN7P0EPYh/ILjjmsiEpDELkTEQjlksdfcd5a5FpBR0GkpERApSz0JERApSz0JERApSWIiISEEKCxERKUhhISIiBSksRESkoP8PzOoipM1cYngAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.980559\n"
     ]
    }
   ],
   "source": [
    "nh = 100;\n",
    "    \n",
    "model_relu = neural_network(nin, nh, nout, 1, \"relu\")\n",
    "model_relu.summary()\n",
    "relu_history = train_network(model_relu, x_train, y_train, x_test, y_test);\n",
    "plot_acc(relu_history, \"ReLu Accuracy\")\n",
    "plot_loss(relu_history, \"ReLU Loss\")\n",
    "\n",
    "modelArr.append(model_relu)\n",
    "historyArr.append(relu_history)\n",
    "modelNameArr.append(\"model_relu\")\n",
    "\n",
    "score, acc = model_relu.evaluate(x_test, y_test, verbose = 0)\n",
    "print(\"Accuracy = %f\" % acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.3 Network 8-100-1, tanh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "hidden0 (Dense)              (None, 100)               900       \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 1,001\n",
      "Trainable params: 1,001\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 13423 samples, validate on 4475 samples\n",
      "Epoch 1/10\n",
      "13423/13423 [==============================] - 1s 60us/step - loss: 0.3004 - acc: 0.9350 - val_loss: 0.1132 - val_acc: 0.9761\n",
      " — val_f1: 0.853625 — val_precision: 0.948328 — val_recall 0.776119\n",
      "Epoch 2/10\n",
      "13423/13423 [==============================] - 0s 18us/step - loss: 0.0927 - acc: 0.9753 - val_loss: 0.0803 - val_acc: 0.9783\n",
      " — val_f1: 0.869799 — val_precision: 0.944606 — val_recall 0.805970\n",
      "Epoch 3/10\n",
      "13423/13423 [==============================] - 0s 19us/step - loss: 0.0802 - acc: 0.9765 - val_loss: 0.0761 - val_acc: 0.9779\n",
      " — val_f1: 0.869908 — val_precision: 0.922006 — val_recall 0.823383\n",
      "Epoch 4/10\n",
      "13423/13423 [==============================] - 0s 17us/step - loss: 0.0776 - acc: 0.9771 - val_loss: 0.0732 - val_acc: 0.9781\n",
      " — val_f1: 0.871391 — val_precision: 0.922222 — val_recall 0.825871\n",
      "Epoch 5/10\n",
      "13423/13423 [==============================] - 0s 16us/step - loss: 0.0760 - acc: 0.9771 - val_loss: 0.0713 - val_acc: 0.9792\n",
      " — val_f1: 0.878113 — val_precision: 0.927978 — val_recall 0.833333\n",
      "Epoch 6/10\n",
      "13423/13423 [==============================] - 0s 17us/step - loss: 0.0749 - acc: 0.9774 - val_loss: 0.0709 - val_acc: 0.9790\n",
      " — val_f1: 0.877922 — val_precision: 0.918478 — val_recall 0.840796\n",
      "Epoch 7/10\n",
      "13423/13423 [==============================] - 0s 17us/step - loss: 0.0741 - acc: 0.9776 - val_loss: 0.0692 - val_acc: 0.9792\n",
      " — val_f1: 0.878748 — val_precision: 0.923288 — val_recall 0.838308\n",
      "Epoch 8/10\n",
      "13423/13423 [==============================] - 0s 16us/step - loss: 0.0734 - acc: 0.9786 - val_loss: 0.0692 - val_acc: 0.9792\n",
      " — val_f1: 0.879377 — val_precision: 0.918699 — val_recall 0.843284\n",
      "Epoch 9/10\n",
      "13423/13423 [==============================] - 0s 17us/step - loss: 0.0728 - acc: 0.9783 - val_loss: 0.0680 - val_acc: 0.9792\n",
      " — val_f1: 0.879377 — val_precision: 0.918699 — val_recall 0.843284\n",
      "Epoch 10/10\n",
      "13423/13423 [==============================] - 0s 17us/step - loss: 0.0723 - acc: 0.9786 - val_loss: 0.0672 - val_acc: 0.9794\n",
      " — val_f1: 0.880829 — val_precision: 0.918919 — val_recall 0.845771\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.979441\n"
     ]
    }
   ],
   "source": [
    "nh = 100;\n",
    "    \n",
    "model_tanh = neural_network(nin, nh, nout, 1, \"relu\")\n",
    "model_tanh.summary()\n",
    "tanh_history = train_network(model_tanh, x_train, y_train, x_test, y_test);\n",
    "plot_acc(tanh_history, \"Tanh Accuracy\")\n",
    "plot_loss(tanh_history, \"Tanh Loss\")\n",
    "\n",
    "modelArr.append(model_tanh)\n",
    "historyArr.append(tanh_history)\n",
    "modelNameArr.append(\"model_tanh\")\n",
    "\n",
    "score, acc = model_tanh.evaluate(x_test, y_test, verbose = 0)\n",
    "print(\"Accuracy = %f\" % acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.4 Network 8-10-1, sigmoid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "hidden0 (Dense)              (None, 10)                90        \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 1)                 11        \n",
      "=================================================================\n",
      "Total params: 101\n",
      "Trainable params: 101\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 13423 samples, validate on 4475 samples\n",
      "Epoch 1/10\n",
      "13423/13423 [==============================] - 1s 50us/step - loss: 0.4587 - acc: 0.9039 - val_loss: 0.3280 - val_acc: 0.9484\n",
      " — val_f1: 0.670471 — val_precision: 0.785953 — val_recall 0.584577\n",
      "Epoch 2/10\n",
      "13423/13423 [==============================] - 0s 11us/step - loss: 0.2627 - acc: 0.9583 - val_loss: 0.2149 - val_acc: 0.9613\n",
      " — val_f1: 0.745214 — val_precision: 0.913357 — val_recall 0.629353\n",
      "Epoch 3/10\n",
      "13423/13423 [==============================] - 0s 11us/step - loss: 0.1887 - acc: 0.9644 - val_loss: 0.1681 - val_acc: 0.9649\n",
      " — val_f1: 0.768095 — val_precision: 0.945455 — val_recall 0.646766\n",
      "Epoch 4/10\n",
      "13423/13423 [==============================] - 0s 13us/step - loss: 0.1563 - acc: 0.9670 - val_loss: 0.1452 - val_acc: 0.9669\n",
      " — val_f1: 0.784257 — val_precision: 0.947183 — val_recall 0.669154\n",
      "Epoch 5/10\n",
      "13423/13423 [==============================] - 0s 11us/step - loss: 0.1392 - acc: 0.9683 - val_loss: 0.1316 - val_acc: 0.9683\n",
      " — val_f1: 0.795389 — val_precision: 0.945205 — val_recall 0.686567\n",
      "Epoch 6/10\n",
      "13423/13423 [==============================] - 0s 11us/step - loss: 0.1283 - acc: 0.9695 - val_loss: 0.1221 - val_acc: 0.9698\n",
      " — val_f1: 0.807418 — val_precision: 0.946488 — val_recall 0.703980\n",
      "Epoch 7/10\n",
      "13423/13423 [==============================] - 0s 13us/step - loss: 0.1204 - acc: 0.9706 - val_loss: 0.1149 - val_acc: 0.9701\n",
      " — val_f1: 0.810734 — val_precision: 0.937908 — val_recall 0.713930\n",
      "Epoch 8/10\n",
      "13423/13423 [==============================] - 0s 13us/step - loss: 0.1140 - acc: 0.9712 - val_loss: 0.1089 - val_acc: 0.9718\n",
      " — val_f1: 0.825967 — val_precision: 0.928571 — val_recall 0.743781\n",
      "Epoch 9/10\n",
      "13423/13423 [==============================] - 0s 13us/step - loss: 0.1087 - acc: 0.9724 - val_loss: 0.1037 - val_acc: 0.9730\n",
      " — val_f1: 0.834473 — val_precision: 0.927052 — val_recall 0.758706\n",
      "Epoch 10/10\n",
      "13423/13423 [==============================] - 0s 13us/step - loss: 0.1041 - acc: 0.9730 - val_loss: 0.0993 - val_acc: 0.9736\n",
      " — val_f1: 0.839674 — val_precision: 0.925150 — val_recall 0.768657\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.973631\n"
     ]
    }
   ],
   "source": [
    "# We are changing the number of hidden units here\n",
    "nh = 10;\n",
    "    \n",
    "model_ten = neural_network(nin, nh, nout, 1, \"sigmoid\")\n",
    "model_ten.summary()\n",
    "ten_history = train_network(model_ten, x_train, y_train, x_test, y_test);\n",
    "plot_acc(ten_history, \"10 Hidden Unit Accuracy\")\n",
    "plot_loss(ten_history, \"10 Hidden Unit Loss\")\n",
    "\n",
    "modelArr.append(model_ten)\n",
    "historyArr.append(ten_history)\n",
    "modelNameArr.append(\"model_ten\")\n",
    "\n",
    "score, acc = model_ten.evaluate(x_test, y_test, verbose = 0)\n",
    "print(\"Accuracy = %f\" % acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.5 Network 8-200-1, sigmoid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "hidden0 (Dense)              (None, 200)               1800      \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 1)                 201       \n",
      "=================================================================\n",
      "Total params: 2,001\n",
      "Trainable params: 2,001\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 13423 samples, validate on 4475 samples\n",
      "Epoch 1/10\n",
      "13423/13423 [==============================] - 1s 77us/step - loss: 0.1775 - acc: 0.9425 - val_loss: 0.1127 - val_acc: 0.9649\n",
      " — val_f1: 0.766716 — val_precision: 0.952030 — val_recall 0.641791\n",
      "Epoch 2/10\n",
      "13423/13423 [==============================] - 0s 23us/step - loss: 0.1028 - acc: 0.9689 - val_loss: 0.0962 - val_acc: 0.9716\n",
      " — val_f1: 0.825309 — val_precision: 0.923077 — val_recall 0.746269\n",
      "Epoch 3/10\n",
      "13423/13423 [==============================] - 0s 23us/step - loss: 0.0922 - acc: 0.9725 - val_loss: 0.0886 - val_acc: 0.9736\n",
      " — val_f1: 0.840970 — val_precision: 0.917647 — val_recall 0.776119\n",
      "Epoch 4/10\n",
      "13423/13423 [==============================] - 0s 22us/step - loss: 0.0876 - acc: 0.9741 - val_loss: 0.0840 - val_acc: 0.9739\n",
      " — val_f1: 0.840816 — val_precision: 0.927928 — val_recall 0.768657\n",
      "Epoch 5/10\n",
      "13423/13423 [==============================] - 0s 21us/step - loss: 0.0849 - acc: 0.9752 - val_loss: 0.0818 - val_acc: 0.9750\n",
      " — val_f1: 0.850667 — val_precision: 0.916667 — val_recall 0.793532\n",
      "Epoch 6/10\n",
      "13423/13423 [==============================] - 0s 22us/step - loss: 0.0833 - acc: 0.9754 - val_loss: 0.0807 - val_acc: 0.9765\n",
      " — val_f1: 0.861295 — val_precision: 0.918310 — val_recall 0.810945\n",
      "Epoch 7/10\n",
      "13423/13423 [==============================] - 0s 22us/step - loss: 0.0820 - acc: 0.9757 - val_loss: 0.0789 - val_acc: 0.9768\n",
      " — val_f1: 0.862434 — val_precision: 0.920904 — val_recall 0.810945\n",
      "Epoch 8/10\n",
      "13423/13423 [==============================] - 0s 21us/step - loss: 0.0813 - acc: 0.9759 - val_loss: 0.0779 - val_acc: 0.9768\n",
      " — val_f1: 0.862434 — val_precision: 0.920904 — val_recall 0.810945\n",
      "Epoch 9/10\n",
      "13423/13423 [==============================] - 0s 22us/step - loss: 0.0808 - acc: 0.9763 - val_loss: 0.0771 - val_acc: 0.9768\n",
      " — val_f1: 0.862069 — val_precision: 0.923295 — val_recall 0.808458\n",
      "Epoch 10/10\n",
      "13423/13423 [==============================] - 0s 20us/step - loss: 0.0807 - acc: 0.9760 - val_loss: 0.0775 - val_acc: 0.9770\n",
      " — val_f1: 0.864652 — val_precision: 0.916435 — val_recall 0.818408\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.976983\n"
     ]
    }
   ],
   "source": [
    "nh = 200;\n",
    "    \n",
    "model_twohund = neural_network(nin, nh, nout, 1, \"sigmoid\")\n",
    "model_twohund.summary()\n",
    "twohund_history = train_network(model_twohund, x_train, y_train, x_test, y_test);\n",
    "plot_acc(twohund_history, \"200 Hidden Unit Accuracy\")\n",
    "plot_loss(twohund_history, \"200 Hidden Unit Loss\")\n",
    "\n",
    "modelArr.append(model_twohund)\n",
    "historyArr.append(twohund_history)\n",
    "modelNameArr.append(\"model_twohund\")\n",
    "\n",
    "score, acc = model_twohund.evaluate(x_test, y_test, verbose = 0)\n",
    "print(\"Accuracy = %f\" % acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.6 Network 8-100-100-1 sigmoid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "hidden0 (Dense)              (None, 100)               900       \n",
      "_________________________________________________________________\n",
      "hidden1 (Dense)              (None, 100)               10100     \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 11,101\n",
      "Trainable params: 11,101\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 13423 samples, validate on 4475 samples\n",
      "Epoch 1/10\n",
      "13423/13423 [==============================] - 1s 86us/step - loss: 0.2479 - acc: 0.8979 - val_loss: 0.1436 - val_acc: 0.9560\n",
      " — val_f1: 0.680713 — val_precision: 0.976744 — val_recall 0.522388\n",
      "Epoch 2/10\n",
      "13423/13423 [==============================] - 0s 23us/step - loss: 0.1140 - acc: 0.9662 - val_loss: 0.0958 - val_acc: 0.9709\n",
      " — val_f1: 0.819444 — val_precision: 0.927673 — val_recall 0.733831\n",
      "Epoch 3/10\n",
      "13423/13423 [==============================] - 0s 24us/step - loss: 0.0925 - acc: 0.9732 - val_loss: 0.0859 - val_acc: 0.9743\n",
      " — val_f1: 0.846462 — val_precision: 0.913545 — val_recall 0.788557\n",
      "Epoch 4/10\n",
      "13423/13423 [==============================] - 0s 22us/step - loss: 0.0863 - acc: 0.9751 - val_loss: 0.0815 - val_acc: 0.9752\n",
      " — val_f1: 0.852980 — val_precision: 0.912181 — val_recall 0.800995\n",
      "Epoch 5/10\n",
      "13423/13423 [==============================] - 0s 24us/step - loss: 0.0836 - acc: 0.9756 - val_loss: 0.0795 - val_acc: 0.9761\n",
      " — val_f1: 0.859025 — val_precision: 0.913165 — val_recall 0.810945\n",
      "Epoch 6/10\n",
      "13423/13423 [==============================] - 0s 24us/step - loss: 0.0819 - acc: 0.9764 - val_loss: 0.0770 - val_acc: 0.9768\n",
      " — val_f1: 0.862434 — val_precision: 0.920904 — val_recall 0.810945\n",
      "Epoch 7/10\n",
      "13423/13423 [==============================] - 0s 20us/step - loss: 0.0806 - acc: 0.9768 - val_loss: 0.0769 - val_acc: 0.9774\n",
      " — val_f1: 0.867628 — val_precision: 0.916898 — val_recall 0.823383\n",
      "Epoch 8/10\n",
      "13423/13423 [==============================] - 0s 23us/step - loss: 0.0794 - acc: 0.9774 - val_loss: 0.0750 - val_acc: 0.9774\n",
      " — val_f1: 0.866225 — val_precision: 0.926346 — val_recall 0.813433\n",
      "Epoch 9/10\n",
      "13423/13423 [==============================] - 0s 21us/step - loss: 0.0796 - acc: 0.9774 - val_loss: 0.0741 - val_acc: 0.9781\n",
      " — val_f1: 0.871391 — val_precision: 0.922222 — val_recall 0.825871\n",
      "Epoch 10/10\n",
      "13423/13423 [==============================] - 0s 20us/step - loss: 0.0788 - acc: 0.9774 - val_loss: 0.0738 - val_acc: 0.9779\n",
      " — val_f1: 0.868176 — val_precision: 0.934097 — val_recall 0.810945\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0D2GINUgkzZD0uqTVkm5Msv56SSslLZP0R0njwuXTJL0oaUW47pLIPvdJekvS0vA1Lc7P0F55WTHLNvjAROdc90yaNImamhrGjBnD6NGjueyyy6isrKSiooJ58+Zx3HHHdek4He03adIkvva1r3H22WczdepUrr/+egBuu+02FixYwOTJkznppJNYsWJF2j+bzCztBwWQlA38HTgfqAIWAZea2crINu8FXjKzfZI+D5xjZpdIehdgZvaGpCOBl4HjzWynpPuAx8zswa6WpaKiwiorK9PyuR5ftokv/Gox//eFM5g2tigtx3TOxWvVqlUcf/zxmS5Gr5bsO5L0splVdLZvnDWSU4DVZrbGzOqB+UCba9vMbIGZ7QtnFwKl4fK/m9kb4fRGYAswKsaydlnLwERv3nLOOSDeIBkDrI/MV4XLOvIZ4IBHiEk6BcgD3ows/k7Y5PVDSfnJDibpKkmVkiqrq6u7X/oOtA5M9CBxzsVn+fLlTJs2rc3r1FNPzXSxkorzqq1klw8kbUeTdDlQAZzdbvlo4BfAFWaW6JT4CrCZIFzmAjcANx1wIrO54XoqKirS2n5XXlbMEh/h7lyfksmrmnpi8uTJLF269JCcK9UujjhrJFXA2Mh8KbCx/UaSzgO+Bsw0s7rI8uHA48DXzWxhYrmZbbJAHXAvQRPaITW9rIgNO/fzzm5/YqJzfUFBQQHbtm1L+Q9mf2RmbNu2jYKCng+yjrNGsgiYKGkCsAGYA3w8uoGk6cDdwAwz2xJZngc8DNxvZr9tt89oM9uk4KfFbODVGD9DUuXjEk9M3MGFk308iXO9XWlpKVVVVaSzmbs/KSgooLS0tMf7xxYkZtYo6WrgaSAbuMfMVki6Cag0s0eB7wFDgd+GVc51ZjYT+BhwFjBS0pXhIa80s6XAPEmjCJrOlgKfi+szdGTSkYmBiR4kzvUFubm5TJgwIdPF6LdiHdluZk8AT7Rb9s3I9Hkd7PdL4JcdrHtfOsvYE/k52Zw4ZrjfCdg55/CR7T1WXlbMch+Y6JxzHiQ9VT6umPrGZlZs3JXpojjnXEZ5kPRQecudgL15yzk3sHmQ9NARhQUc6QMTnXPOgyQV08cVs8RvleKcG+A8SFJQXlbMxl21bN7lAxOdcwOXB0kKysvCGzh685ZzbgDzIEnBpCMLycvJ8jsBO+cGNA+SFOTlZDF5TCFL1vuVW865gcuDJEXlZUU+MNE5N6B5kKSovMwHJjrnBjYPkhS13AnYByY65wYoD5IUHT68gDFFg/zKLefcgOVBkgbTy4p8YKJzbsDyIEkDH5jonBvIPEjSoLWfxGslzrmBx4MkDU4YPZx8H5jonBugPEjSIDEw0WskzrmByIMkTcrHFfPqht3UNTZluijOOXdIxRokkmZIel3Sakk3Jll/vaSVkpZJ+qOkcZF1V0h6I3xdEVl+kqTl4TFvl6Q4P0NXlZcVUd/UzIqNuzNdFOecO6RiCxJJ2cCdwIXACcClkk5ot9kSoMLMpgAPAv8Z7jsC+BZwKnAK8C1JxeE+dwFXARPD14y4PkN3tDwx0ftJnHMDTJw1klOA1Wa2xszqgfnArOgGZrbAzPaFswuB0nD6/cAfzGy7me0A/gDMkDQaGG5mL5qZAfcDs2P8DF12WDgwcYmPcHfODTBxBskYYH1kvipc1pHPAE92su+YcLqrxzykppcVeYe7c27AiTNIkvVdWNINpcuBCuB7nezbnWNeJalSUmV1dXUXipu68rJiNu2qZdOu/YfkfM451xvEGSRVwNjIfCmwsf1Gks4DvgbMNLO6TvatorX5q8NjApjZXDOrMLOKUaNG9fhDdEfLwMS13rzlnBs44gySRcBESRMk5QFzgEejG0iaDtxNECJbIqueBi6QVBx2sl8APG1mm4AaSaeFV2t9Engkxs/QLS0DE715yzk3gOTEdWAza5R0NUEoZAP3mNkKSTcBlWb2KEFT1lDgt+FVvOvMbKaZbZd0M0EYAdxkZtvD6c8D9wGDCPpUnqSX8IGJzrmBKLYgATCzJ4An2i37ZmT6vIPsew9wT5LllcCJaSxmWpWPK+a+v7xNXWMT+TnZmS6Oc87Fzke2p1liYOKrG3xgonNuYPAgSbPEwMQl3rzlnBsgPEjS7DB/YqJzboDxIIlB+bhivwTYOTdgeJDEoLysiM27a9m40wcmOuf6Pw+SGLTcwNGbt5xzA4AHSQyOb3liojdvOef6Pw+SGOTlZDGl1AcmOucGBg+SmJSXFbNi4y5qG/yJic65/s2DJCbTy4ppaDJWbNyV6aI451ysPEhiUj6uCPA7ATvn+j8PkpgcNqyA0mIfmOic6/88SGJUXlbM4nU7CJ4K7Jxz/ZMHSYzKy4p4Z3cdG3fVZroozjkXGw+SGLU+MdGbt5xz/ZcHSYyOHz2cgtwslqzzDnfnXP/lQRKj3Owspowp8g5351y/5kESs+njinxgonOuX/MgiVm5D0x0zvVzHiQxa7kTsA9MdM71U7EGiaQZkl6XtFrSjUnWnyVpsaRGSRdFlr9X0tLIq1bS7HDdfZLeiqybFudnSNWoYfmMHeEDE51z/VdOXAeWlA3cCZwPVAGLJD1qZisjm60DrgT+JbqvmS0ApoXHGQGsBn4f2eTLZvZgXGVPt/KyYhau2YaZISnTxXHOubSKs0ZyCrDazNaYWT0wH5gV3cDM3jazZUDzQY5zEfCkme2Lr6jxKi8r9oGJzrl+K84gGQOsj8xXhcu6aw7w63bLviNpmaQfSspPtpOkqyRVSqqsrq7uwWnTp7WfxJu3nHP9T5xBkqwNp1s3nZI0GpgMPB1Z/BXgOOBkYARwQ7J9zWyumVWYWcWoUaO6c9q0O270MApys7yfxDnXL8UZJFXA2Mh8KbCxm8f4GPCwmTUkFpjZJgvUAfcSNKH1arnZWUwpLWKxj3B3zvVDcQbJImCipAmS8giaqB7t5jEupV2zVlhLQUGv9Wzg1TSUNXblZcWs9IGJzrl+KLYgMbNG4GqCZqlVwANmtkLSTZJmAkg6WVIVcDFwt6QVif0ljSeo0Tzf7tDzJC0HlgMlwC1xfYZ0Ki8roqHJeHWDD0x0zvUvsV3+C2BmTwBPtFv2zcj0IoImr2T7vk2Sznkze196S3lotNwJeN0OKsaPyHBpnHMufXxk+yFSMjSfshGDfYS7c67f8SA5hMrLivyJic65fqdLQSLp6MR4DUnnSLpGUlG8Ret/ppcVs6Wmjg0792e6KM45lzZdrZE8BDRJOgb4GTAB+FVspeqnWgYm+mXAzrl+pKtB0hxehfUPwI/M7DpgdHzF6p9aBib6CHfnXD/S1SBpkHQpcAXwWLgsN54i9V+JgYlLfIS7c64f6WqQfAp4N/AdM3tL0gTgl/EVq/8qLytmxcbdPjDROddvdClIzGylmV1jZr+WVAwMM7NbYy5bv1ReVkRjs7HcByY65/qJrl619Zyk4eGzQV4B7pX0g3iL1j+1DEz0fhLnXD/R1aatQjPbDXwEuNfMTgLOi69Y/VfLwETvJ3HO9RNdDZKc8GaJH6O1s931UDAwcacPTHTO9QtdDZKbCG6++KaZLZJ0FPBGfMXq38rHFVNdU0fVDh+Y6Jzr+7p000Yz+y3w28j8GuCjcRWqv2sdmLiDsSMGZ7g0zjmXmq52tpdKeljSFknvSHpIUtK79rrOHXfEMAblZrPER7g75/qBrjZt3UvwUKojCW7t/rtwmeuBnOwsppQWeoe7c65f6GqQjDKze82sMXzdB2T2Qeh9XPm4Ylb6wETnXD/Q1SDZKulySdnh63JgW5wF6+/Ky4ppbDaWVfnAROdc39bVIPk0waW/m4FNwEUEt01xPTS9LLgLvzdvOef6uq7eImWdmc00s1FmdpiZzSYYnOh6qGRoPuNGDvYR7s65Pi+VJyRe39kGkmZIel3Sakk3Jll/lqTFkholXdRuXZOkpeHr0cjyCZJekvSGpN9IykvhM2RUeVmxD0x0zvV5qQSJDrpSygbuBC4ETgAulXRCu83WAVeS/CFZ+81sWviaGVn+XeCHZjYR2AF8poflz7jysiK27vGBic65vi2VIOnsZ/QpwGozW2Nm9cB8YFabA5i9bWbLgOaunFCSgPcBD4aLfg7M7lape5HpkYGJzjnXVx00SCTVSNqd5FVDMKbkYMYA6yPzVeGyriqQVClpoaREWIwEdoZPazzoMSVdFe5fWV1d3Y3THjrHHTGMwXk+MNE517cd9BYpZjYshWMna/rqTmdAmZltDO/r9ayk5cDurh7TzOYCcwEqKip6ZSeED0x0zvUHqTRtdaYKGBuZLwU2dnVnM9sYvq8BngOmA1uBIkmJAOzWMXuj8jIfmOic69viDJJFwMTwKqs8YA7BbVY6JalYUn44XQKcAay04PKmBQTjWCB4hvwjaS/5IeQDE51zfV1sQRL2Y1xNcPv5VcADZrZC0k2SZgJIOllSFXAxcLekFeHuxwOVkl4hCI5bzWxluO4G4HpJqwn6TH4W12c4FHxgonOur+vSbeR7ysyeAJ5ot+ybkelFBM1T7ff7KzC5g2OuIbgirF8YOTSf8T4w0TnXh8XZtNX3NdbBrqrYT+MDE51zfZkHycHMuwjmfxyaGjvfNgXTxxX7wETnXJ/lQXIwFZ+GTa/Aop/Eeppy7ydxzvVhHiQHc8JsmHgBPHtLrE1cxx4eDEz0fhLnXF/kQXIwEnzg+9DcBE/eENtpcrKzmFpaxGIf4e6c64M8SDpTPA7OuRFeewxeezy205SPK2LVpt3sr/eBic65vsWDpCve/QU4bBI88WWoq4nlFK0DE71W4pzrWzxIuiI7Fz78I9i9ERb8RyynaL0TsAeJc65v8SDpqrGnQMWn4KW7YOPStB9+xJA8JpQM8Su3nHN9jgdJd5z7LRhcAo9dG3TAp9n0siKWrNvhAxOdc32KB0l3DCqCC2+FjUtg0U/TfvjpZcVs3VPP+u0+MNE513d4kHTXpI/AMefBH2+GXRvSemgfmOic64s8SLqrZWxJAzyV3rElLQMTPUicc32IB0lPjJgAZ98Aq34Hrz+ZtsO2Dkz0IHHO9R0eJD11+j/DYSeEY0v2pO2wwcDEGvbVx3ujSOecSxcPkp7KzoUP/Qh2rYfn0je2pLysmCZ/YqJzrg/xIElF2alw0pWw8K7gLsFp0Dow0Zu3nHN9gwdJqs77NgweAb9Lz9iSloGJa32Eu3Oub/AgSdWgYphxK2xcDJX3pOWQPjDROdeXeJCkw4kfhaPeC8/8G+zelPLhysuK2ba3nnXb96WhcM45F69Yg0TSDEmvS1ot6cYk68+StFhSo6SLIsunSXpR0gpJyyRdEll3n6S3JC0NX9Pi/AxdIsGHfpC2sSXl3k/inOtDYgsSSdnAncCFwAnApZJOaLfZOuBK4Fftlu8DPmlmk4AZwI8kFUXWf9nMpoWv9N9BsSdGHAVnfRlWPgJ/fzqlQx17xDCG5GV7P4lzrk+Is0ZyCrDazNaYWT0wH5gV3cDM3jazZUBzu+V/N7M3wumNwBZgVIxlTY/Tr4FRx8Hj/wL1e3t8mOwsMXWsD0x0zvUNcQbJGGB9ZL4qXNYtkk4B8oA3I4u/EzZ5/VBSfgf7XSWpUlJldXV1d0/bMzl54diSdfDcrSkdqrysmNc2+8BE51zvF2eQKMmybl2GJGk08AvgU2aWqLV8BTgOOBkYASTtlDCzuWZWYWYVo0YdwsrMuHdD+SfhxTth8/IeH6Z8XBFNzcYr631gonOud4szSKqAsZH5UmBjV3eWNBx4HPi6mS1MLDezTRaoA+4laELrXc77t+Cy4BTGlkwf6x3uzrm+Ic4gWQRMlDRBUh4wB3i0KzuG2z8M3G9mv223bnT4LmA28GpaS50Og0fAjP+ADZXw8r09OkTxkDyOKhnCEg8S51wvF1uQmFkjcDXwNLAKeMDMVki6SdJMAEknS6oCLgbulrQi3P1jwFnAlUku850naTmwHCgBbonrM6Rk8sVw1DnB2JKazT06xPSyYpas2+kDE51zvZoGwh+piooKq6ysPPQn3vYm/M+74bgPwMX3dXv3eS+t5WsPv8rzXz6HcSOHpL98zjl3EJJeNrOKzrbzke1xGnl0MLZkxcPwxh+6vbsPTHT77mNoAAAUnklEQVTO9QUeJHE74xooORYevx7qu3fLk3cdPoyh+Tk+MNE516t5kMQtJx8+9EPYuQ6e/263dg0GJhZ6jcQ516t5kBwK48+A6ZfDi3fAOys63z7CByY653o7D5JD5fyboaAQfvdFaG7ufPtQ4omJPjDROddbeZAcKoNHwPv/HaoWweL7urzb9LLgXpUvvrk1poI551xqPEgOpSmXwISz4A/fhpp3urRL0eA8Tj96JLc/u5p/f2IV9Y1dr80459yh4EFyKEnwwR9C4354+itd3u2eK0/m8tPKmPvCGj561195a2vP7yzsnHPp5kFyqJUcA2f+C7z6EKx+pku7FORmc8vsydz9iZNYt30fH7z9Tzz0cpWPeHfO9QoeJJnwnmth5ER4rHtjS94/6QieuvZMJo8p5Eu/fYVrf7OUmtqGGAvqnHOd8yDJhJaxJWvhhe91a9fRhYP41WdP40vnv4vHlm3iA7f/yW/s6JzLKA+STJlwJky7DP56O7yzslu7ZmeJfz53Ig/842k0N8PFP36ROxespqnZm7qcc4eeB0kmnX8z5A+Hx67r1tiShJPGjeCJL57JjBOP4HtPv84nfvYS7+yujaGgzjnXMQ+STBoyEt7/HVi/EJbc36NDFA7K5b8vnc5/fnQKS9btZMaPXuCZlV27tNg559LBgyTTpl4K48+EP3wT9mzp0SEk8bGTx/LYNe9hdOEg/t/9lXzrkVepbejZ0xmdc647PEgyTYIP/gAa9sPTX03pUEePGsrDXzidz7xnAj9/cS2z7/wLb7xTk6aCOudcch4kvcGod8F7roflv4U3n03pUPk52XzjQydw76dOprqmjg/f8WfmvbTWx5w452LjQdJbvOc6GHlMMLakYX/Kh3vvsYfx5LVncvL4EXzt4Vf5/C8Xs3NffRoK6pxzbXmQ9Ba5BcHYkh1vwQvfT8shDxtWwM8/dQpf/cBx/PG1d7jwtj/x0pptaTm2c84lxBokkmZIel3Sakk3Jll/lqTFkholXdRu3RWS3ghfV0SWnyRpeXjM2yUpzs9wSE04K+h8/8ttsOW1tBwyK0tcddbRPPT508nPyeLSnyzkB3/4O41NfvNH51x6xBYkkrKBO4ELgROASyWd0G6zdcCVwK/a7TsC+BZwKnAK8C1JxeHqu4CrgInha0ZMHyEzLrgF8ofCY9f2aGxJR6aUFvHYNWcye/oYbv/jG8yZu5CqHd179K9zziUTZ43kFGC1ma0xs3pgPjAruoGZvW1my4D2fzHfD/zBzLab2Q7gD8AMSaOB4Wb2ogW9x/cDs2P8DIfekJJgoOK6F2HpL9N66KH5OfzgY9O4bc40Xttcw4W3/YnHl21K6zmccwNPnEEyBlgfma8Kl6Wy75hwutNjSrpKUqWkyurq6i4XuleYfjmMOwN+/w3Yk/6yz5o2hieuOZOjRg3lC79azI0PLfNH+TrneizOIEnWd9HVa1A72rfLxzSzuWZWYWYVo0aN6uJpewkp6Hiv3wu//1ospygbOZgHP/duPn/O0fymcj0f/u8/s2KjP87XOdd9cQZJFTA2Ml8KbExx36pwuifH7FtGHRtcErzsN7DmuVhOkZudxQ0zjuOXnzmVmtpG/uHOv3LPn9/yMSfOuW6JM0gWARMlTZCUB8wBHu3ivk8DF0gqDjvZLwCeNrNNQI2k08KrtT4JPBJH4XuFM78EI44KburYEN/NGM84poSnrj2LMyeWcNNjK/n0fYvYuqcutvM55/qX2ILEzBqBqwlCYRXwgJmtkHSTpJkAkk6WVAVcDNwtaUW473bgZoIwWgTcFC4D+DzwU2A18CbwZFyfIeNyC4Lbp2xfA7/8KCz9NezfGcupRgzJ46dXVPBvMyfxlze3ceFtf+JPb/SxviXnXEZoIDRjVFRUWGVlZaaL0XN/vQNe+jHsWg/ZeXD0++CE2XDshTCoKO2nW7VpN//86yWs3rKHfzz7KL50/rHk5fjYVecGGkkvm1lFp9t5kPQRZrDhZVjxMKx8JPZQ2V/fxE2PreTXf1vHlNJCbp8znfElQ9J2fOdc7+dBEtEvgiSqs1A57gNQUJiWUz25fBM3PLSMpmbj5tkn8g/Tx9CfbibgnOuYB0lEvwuSqGiorPg/2F3VGiqT/iGoqaQYKht27ue6+Uv529vbKRmaz7SxhUwpLWJKaSFTS4soHpKXpg/jnOtNPEgi+nWQRJlBVSWs/L+0h0pjUzMPLa7ipTXbeaVqJ2u27iXxn07ZiMEtoTKltJATxxQyJD8njR/MOZcJHiQRAyZIojoMlXNh0uyUayo1tQ0s37CLV9bvYlnVTpZV7WLDzuD291mCiYcNY0ppIVPGFjGttIhjjxjmHfbO9TEeJBEDMkiiYg6VhOqaOpZV7eSVqtZw2b43eAZKXnYWxx85nKlhzWXq2EKOKhlKVpb3tzjXW3mQRAz4IIlqbg76VGIOFQAzo2rHfl4JQ+WV9Tt5dcMu9tYHz5Ifmp/DiWOGh8ESNIuNKRrknfnO9RIeJBEeJB04hKGS0NRsvFm9h1fWB+GyrGonqzbVUB8+H2XkkLygv2VsUUufy8ih+Wktg3OuazxIIjxIuiADoZJQ19jEa5tq2jSLvbFlT0tn/piiQUwLayxTSouYXFrIUO/Mdy52HiQRHiTdlAiVxDiVaKiUngQjjg6eLz/iqOAhXDHYU9fIqxt2tYTLK+t3UrWj9Vn2w/JzKBmWT8nQPEqG5jMyfG99hfPD8hmSl+3NZc71gAdJhAdJCqKh8tpjsHNt2/XDRofBEoZL4r14POSkt0lq2546lm3YxapNu9myu46texKverbtqWPHvoak+xXkZjFySBAqozoJnsJBuX4BgHMhD5IID5I0qt8X3ERy22rY/iZsezOY3vYm7Nvaup2yoHBsJGCOaQ2cojLIyk570Rqamtm+t57qmjq27a1na01r2GzbU091GDpb99SxfW89Tc0H/refkyVGDs1rCZ6SoXmM6iB4RgzJIyfbL2l2/VdXg8Qbml335A2GI04MXu3t33lguGxbDVXzoW5363ZZuTBiQvKazLDRwYO9eiA3O4vDhxdw+PCCTrdtbjZ27m8IgqamjuowbNrXct7csofqPXXUN7Z/GnRgcF42wwpyGFaQ2+Z9eGI6P6eD9Yn5HA8j1+d5kLj0GVQEY04KXlFmsLc6EjCR2syaBdAYedZK7uAwYI5qV5M5BgaP6HHItJeVJUYMCWoV7zp82EG3NTNq6hpbgyas6WzbW09NbSM1tQ3heyO79tVTtX0fu8PldR0EUNSg3OyWUEkWNO1DqO36XIbm55CbLe8HchnjQeLiJ8HQw4LXuHe3XdfcDLs3tGsqexM2vwqvPQ7NkWfJFxS2dvIPLoFBxUF4DSpufRWE8wWFkJ2e/7wlMbwgl+EFuUzo5h2Q6xub2wRNTW1DS8hEl9XUNlJTF7zvrm1kw879LetqGzoPo+wsMTg3m0F54SucHpyXzaDcnGA6sr7NdMv2OQzKDefDZYnpvOwsDyrXIQ8Sl1lZWVA0Nngd/d6265oaYOe6ts1k29+E9X+D/TvaNpclk18IgwqTB03LK0kQ5Q5KW80nLyeLkUPzUxoL09DU3CZwdrcLob11jeyrb2J/QxP7w/d99a3T2/fup7ahiX31wXa1DU00NHWvbzRLMDgvh4IwXAbnZbdMtw2tIJAS2yRbPqjlGDkt+/rtc/o2DxLXe2Xnhn0nRydf39QItbuCUEm8ane2nd8fmd+1oXV9tKZzwHnzDx400WVDSoLa0ZCStF+llpCbndXSDJcuDU3NrcFTHwZPQyP765vZV9/YYSglgigaSrv2NxywbWKAaVflZKkldNoHVmvNqG2NqTWs2i7PkjAzgmspgvfmZsOAZjMwaDawcJ2ZYYn5Zlq2SyzvyrbBMa1lWzPIzRZ5OVnkZQdBGUwH7/nt5tusz87qc1cOepC4vis7B4aMDF7dYQb1e5KHzQFhtBN2rodNy4L5hr0dHzdvWFCWRLAMLjlwfvDI1mV5Q9JW8+mu3OwscrOzGF6QG8vxG5ua2dfQRG0YUomgapluEzwdLG9oZE9dI9U1dS3rEyGW5IK7fiUnSweET2I6v33w5GSRl5PdJqSiIXb5aWWx3x3Cg8QNPBLkDwteRWXd27exvjVo9m0PLnneuzV839Y6v3tDED77tkJTffJj5RQkD5sD5sMAKijMWPB0V052FsNjCiozo66xOahJNQRBlKhJ7WtowsyQhIAsiSwJKfjqhMhS0O+VlVgW2VZqfRciKyu6T/JtIbh4IytyfICGZqO+sbn11dREXZv5A6frIvMN7bapi27f2ExtQzO79zcm2b+pZb7Z4MNTR/ftIJE0A7gNyAZ+ama3tlufD9wPnARsAy4xs7clXQZ8ObLpFKDczJZKeg4YDSSGOV9gZlvi/BzOtcjJa71woCvMoK7mwKBped/WOr/1jeC9YV/yY2XlhjWaksh7SRCIEqAD3yGyjIOsO9h+Ha1rt03OoKDZr6AoCL3EdBr7nIJTioLcoI+mOG1H7Z8am5pbwi5OsQWJpGzgTuB8oApYJOlRM1sZ2ewzwA4zO0bSHOC7BGEyD5gXHmcy8IiZLY3sd5mZ+QhD1/tJUDA8eI04qmv71O9LHjQt0+H7xiVBONXXBIFFL23vyc47MFzavBcmWRYubwlJ1xOHaoxSnDWSU4DVZrYGQNJ8YBYQDZJZwLfD6QeBOyTJ2g63vxT4dYzldK53yRsMeWXdb3ZLMKPljpdYa8i0ee/uOjrfr2FfcPFD7c6gb6nlvd2yvdXBFXi14To7SMe8stsGUFfCKHdwcOFDdn7wnpMfNCPGcDcFF4gzSMYA6yPzVcCpHW1jZo2SdgEjgci9NriEIHCi7pXUBDwE3GID4T4vznVVokOgL2huDmpUyQKno2W7qlqnm5PfXy0pZQeBkpMXvGfntQZNdn6Sdcm27WhdQbtj5UFWTnCrIGUHIaas4JWVHSxrM63W6ZZtE9O9/98yziBJ9unb/8E/6DaSTgX2mdmrkfWXmdkGScMIguQTBP0sbQ8sXQVcBVBW1sNfds65eGVlhbWJHjyiIFELah84jfuhsa711RSZbqwNLn5orA0unGg/X7sbmqo73t+a0v8ddEodBFBWktBJElYf/01wS6IYxRkkVcDYyHwpsLGDbaok5QCFwPbI+jm0a9Yysw3he42kXxE0oR0QJGY2F5gLwU0bU/okzrneRwouoc4bAoVjDs05mxrbBkubkErMh6FkTdDcFL43B014LcvaTze3btsy3dyNbQ9yjpzO7z2XqjiDZBEwUdIEYANBKHy83TaPAlcALwIXAc8mmqkkZQEXA2clNg7DpsjMtkrKBT4EPBPjZ3DOuVbZOcErr3u3yunvYguSsM/jauBpgst/7zGzFZJuAirN7FHgZ8AvJK0mqInMiRziLKAq0VkfygeeDkMkmyBEfhLXZ3DOOdc5fx6Jc865pLr6PBK/U5pzzrmUeJA455xLiQeJc865lHiQOOecS4kHiXPOuZR4kDjnnEvJgLj8V1I1sLaHu5fQ9t5fA51/H638u2jLv4+2+sP3Mc7MRnW20YAIklRIquzKddQDhX8frfy7aMu/j7YG0vfhTVvOOedS4kHinHMuJR4knZub6QL0Mv59tPLvoi3/PtoaMN+H95E455xLiddInHPOpcSDxDnnXEo8SA5C0gxJr0taLenGTJcnUySNlbRA0ipJKyR9MdNl6g0kZUtaIumxTJcl0yQVSXpQ0mvhfyfvznSZMkXSdeH/J69K+rWk+B9RmGEeJB2QlA3cCVwInABcKumEzJYqYxqBL5nZ8cBpwBcG8HcR9UVgVaYL0UvcBjxlZscBUxmg34ukMcA1QIWZnUjwAL45B9+r7/Mg6dgpwGozW2Nm9cB8YFaGy5QRZrbJzBaH0zUEfyQO0UOyeydJpcAHgZ9muiyZJmk4wRNNfwZgZvVmtjOzpcqoHGBQ+GjwwcDGDJcndh4kHRsDrI/MVzHA/3gCSBoPTAdeymxJMu5HwL8CzZkuSC9wFFAN3Bs29f1U0oB8qLmZbQC+D6wDNgG7zOz3mS1V/DxIOqYkywb0tdKShgIPAdea2e5MlydTJH0I2GJmL2e6LL1EDlAO3GVm04G9wIDsU5RUTNByMQE4Ehgi6fLMlip+HiQdqwLGRuZLGQBV1I5IyiUIkXlm9r+ZLk+GnQHMlPQ2QZPn+yT9MrNFyqgqoMrMErXUBwmCZSA6D3jLzKrNrAH4X+D0DJcpdh4kHVsETJQ0QVIeQYfZoxkuU0ZIEkH79yoz+0Gmy5NpZvYVMys1s/EE/108a2b9/ldnR8xsM7Be0rHhonOBlRksUiatA06TNDj8/+ZcBsCFBzmZLkBvZWaNkq4Gnia48uIeM1uR4WJlyhnAJ4DlkpaGy75qZk9ksEyud/lnYF74o2sN8KkMlycjzOwlSQ8CiwmudlzCALhVit8ixTnnXEq8acs551xKPEicc86lxIPEOedcSjxInHPOpcSDxDnnXEo8SJxLA0lNkpZGXmkb2S1pvKRX03U859LNx5E4lx77zWxapgvhXCZ4jcS5GEl6W9J3Jf0tfB0TLh8n6Y+SloXvZeHywyU9LOmV8JW4vUa2pJ+Ez7n4vaRBGftQzrXjQeJcegxq17R1SWTdbjM7BbiD4K7BhNP3m9kUYB5we7j8duB5M5tKcL+qxN0UJgJ3mtkkYCfw0Zg/j3Nd5iPbnUsDSXvMbGiS5W8D7zOzNeGNLzeb2UhJW4HRZtYQLt9kZiWSqoFSM6uLHGM88AczmxjO3wDkmtkt8X8y5zrnNRLn4mcdTHe0TTJ1kekmvH/T9SIeJM7F75LI+4vh9F9pfQTrZcCfw+k/Ap+HlmfCDz9UhXSup/xXjXPpMShyZ2QInl+euAQ4X9JLBD/cLg2XXQPcI+nLBE8XTNwt94vAXEmfIah5fJ7gSXvO9VreR+JcjMI+kgoz25rpsjgXF2/acs45lxKvkTjnnEuJ10icc86lxIPEOedcSjxInHPOpcSDxDnnXEo8SJxzzqXk/wPNZvUA6p4wpwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.977877\n"
     ]
    }
   ],
   "source": [
    "nh = 100;\n",
    "num_layers = 2;\n",
    "    \n",
    "model_twolayer = neural_network(nin, nh, nout, num_layers, \"sigmoid\")\n",
    "model_twolayer.summary()\n",
    "twolayer_history = train_network(model_twolayer, x_train, y_train, x_test, y_test);\n",
    "plot_acc(twolayer_history, \"2 Hidden Layer (100) Accuracy\")\n",
    "plot_loss(twolayer_history, \"2 Hidden Layer (100) Loss\")\n",
    "\n",
    "modelArr.append(model_twolayer)\n",
    "historyArr.append(twolayer_history)\n",
    "modelNameArr.append(\"model_twolayer\")\n",
    "\n",
    "score, acc = model_twolayer.evaluate(x_test, y_test, verbose = 0)\n",
    "print(\"Accuracy = %f\" % acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.7 Network 8-100-100-100-100-100-1, sigmoid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "hidden0 (Dense)              (None, 100)               900       \n",
      "_________________________________________________________________\n",
      "hidden1 (Dense)              (None, 100)               10100     \n",
      "_________________________________________________________________\n",
      "hidden2 (Dense)              (None, 100)               10100     \n",
      "_________________________________________________________________\n",
      "hidden3 (Dense)              (None, 100)               10100     \n",
      "_________________________________________________________________\n",
      "hidden4 (Dense)              (None, 100)               10100     \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 41,401\n",
      "Trainable params: 41,401\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 13423 samples, validate on 4475 samples\n",
      "Epoch 1/10\n",
      "13423/13423 [==============================] - 1s 105us/step - loss: 0.2414 - acc: 0.9239 - val_loss: 0.0937 - val_acc: 0.9747\n",
      " — val_f1: 0.852288 — val_precision: 0.898072 — val_recall 0.810945\n",
      "Epoch 2/10\n",
      "13423/13423 [==============================] - 0s 29us/step - loss: 0.0933 - acc: 0.9743 - val_loss: 0.0851 - val_acc: 0.9752\n",
      " — val_f1: 0.853369 — val_precision: 0.909859 — val_recall 0.803483\n",
      "Epoch 3/10\n",
      "13423/13423 [==============================] - 0s 29us/step - loss: 0.0863 - acc: 0.9767 - val_loss: 0.0781 - val_acc: 0.9768\n",
      " — val_f1: 0.863158 — val_precision: 0.916201 — val_recall 0.815920\n",
      "Epoch 4/10\n",
      "13423/13423 [==============================] - 0s 29us/step - loss: 0.0824 - acc: 0.9776 - val_loss: 0.0746 - val_acc: 0.9781\n",
      " — val_f1: 0.872727 — val_precision: 0.913043 — val_recall 0.835821\n",
      "Epoch 5/10\n",
      "13423/13423 [==============================] - 0s 29us/step - loss: 0.0805 - acc: 0.9775 - val_loss: 0.0727 - val_acc: 0.9785\n",
      " — val_f1: 0.875648 — val_precision: 0.913514 — val_recall 0.840796\n",
      "Epoch 6/10\n",
      "13423/13423 [==============================] - 0s 30us/step - loss: 0.0803 - acc: 0.9768 - val_loss: 0.0731 - val_acc: 0.9797\n",
      " — val_f1: 0.884956 — val_precision: 0.899743 — val_recall 0.870647\n",
      "Epoch 7/10\n",
      "13423/13423 [==============================] - 0s 30us/step - loss: 0.0828 - acc: 0.9774 - val_loss: 0.0714 - val_acc: 0.9790\n",
      " — val_f1: 0.877604 — val_precision: 0.920765 — val_recall 0.838308\n",
      "Epoch 8/10\n",
      "13423/13423 [==============================] - 0s 29us/step - loss: 0.0786 - acc: 0.9783 - val_loss: 0.0713 - val_acc: 0.9792\n",
      " — val_f1: 0.878431 — val_precision: 0.925620 — val_recall 0.835821\n",
      "Epoch 9/10\n",
      "13423/13423 [==============================] - 0s 30us/step - loss: 0.0773 - acc: 0.9786 - val_loss: 0.0731 - val_acc: 0.9799\n",
      " — val_f1: 0.887500 — val_precision: 0.891960 — val_recall 0.883085\n",
      "Epoch 10/10\n",
      "13423/13423 [==============================] - 0s 30us/step - loss: 0.0769 - acc: 0.9794 - val_loss: 0.0698 - val_acc: 0.9797\n",
      " — val_f1: 0.881046 — val_precision: 0.928375 — val_recall 0.838308\n"
     ]
    },
    {
     "data": {
      "image/png": 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IDAv/ptdZZFu/7UXhX8B6jaKkUXSylyJLDvoaVWXFVJWNXdtwIXF3El3JUxX9ociZQHAW0H0qMfQkz7waaUplKTPryplZV8GSxgZm1JUzs7b8VGKYWVdOY2055SXj776DyUbJQl6b7o4zE8HRPcGUOEC0U7S3rI6Oqvm0lp3PwVlXsJdZ7ErOINY1nZc6Kjm2d6grX06GU+sYfKDBlZUURToaT7c7Bx2Opac6INPrp1WXUVFaWBVcKuV09CRJnOwlcTJJoqs3mO9K0t4VrDt+soeW9m4Oh81EhxJddPWemQRqy0uCir+ugksWTQuTQEWYBIL5GXXlBXeMJjMlC8ns5LF+ScCPvkxf625o3U3JyZZ+RduL6zlYMptXfSkvF7+Bpu5G9vTN4BWfRVtXDSSCchWlRcyoraCxtpyGGWW8dUk5jTXBcnqqKS8BnJRDyj245N4dTy97sM09Uqbf9nT50/tmXf7UezqdPX2Rq116OdbZQ1M8wdHOHo53Dn2/Q2VpcdiWHSSX6JUu06pL+7Vxpzs5X8sv6FTKae8OK/uuMyv8gevbB5Rp706SqQuzpryEGbXlzKgrZ/W8qafOCmbUVUTOCMqpKlPVMtHoG5VA70k48hLdB5s4ujeGH91NWdsrVJ/YR2WyrV/RQz6NV3wmr6RW8qrP5BWfyV6fxQGbSUVNPY01YYVfU8682nLW1JbTUFPeLxFUT5BmnWRfiraTvacvlUwnlQHJ5eiJHl492snREz20dw1970BNecmpyyWnRpJLfVUpvSkfNBG0h4mgvTvzPQm15SXUVZZSWxH8nTO1kmWVtdRVlFJXWUpduD5YLqGuopQpYfma8hJKijVQ9WSlZDHZdLfDkRehZSe07CB5aAe9B7dT0bEPwykHZrgR9wa2+0z2+lpaSufSVjmfrtoFpKYuYuqUKacSwhtqy3lHmAymVpZSVFT4CWAkSoqLmF5TzvSa7G/q6u1LhUll+ORyrLOHl1s6OHaihxM9fZgFlX1tpGKfP62qX8U+VIVfVxlcmlk8yb4fGT1KFhPVyWPQ8iK07DiVGGjZCYn9p4r0UsLu1Gxe8rm87JfQO20p9YtWsXDpKmZNq+O82nIuqy6jVL8mR1VpcdAEN6O2Iut9epIpSops0iVjGT+ULAqZO5w4EiSCIzv7J4WO04P09hVX0FK+kB19S9mafAMvpuawm/nUzzuPS5c08vrF0/mzhfVUl+ufw3hVVqKELfml2qEQuAcDzJ06S4gkhpNHT5crq6Wv4TwON/4O2+tn8x9tDWw+Us+rqemUdBWzet5ULrtoOu89ZzoXLZyqTkgRyZpqi/EklQrGG4qeIaTPGLoTp8tV1kPjMlhxNV1Tl7K9bw7/cXw6j+wr4oU9CVIejDOzZn49V18xjcvOmc5FC+qpLNNliiJydpQsXou+3rMba2iooSq62qCv+/Tr18yExvNh9Q3QcB40LiNRu4RfHyriyVeO8qvdR4nF20g5lBV38roFU7n5Tedy2TnTWaPkICKjSMmipxOavj94Bd7bOXzlPpKHohSXDz7MRNW00/PldcHopI3LoPE8qKynrbOXX79ylF/tbuXJJ1uJxZ/BPWjDXjN/Kh9481Jef840LlpQrxucRCRnlCx6T8L3/jyywgZU6OHYQ5X1wQByg40plM3YQ8WlWYVzvLOHJ/cc5cltzfxq9wtsP5g4lRwuXlDPh9Yt5bJzpvO6+VOVHERkzChZVE6FDz57unLP0yMpD7Z18f6NW3kh3oZ7MBrmxQvr+fC687jsnGmsVnIQkTxSsigqhmmL8x0FT7zYwvMH2viLK5ZwxfkzWD1/igZPE5FxQ8linGhqTlBdVswtbzlfN16JyLijO33GiVi8jeWz65QoRGRcUrIYB1IpZ3tzOyvm1OU7FBGRQSlZjAOvHu2kozvJSiULERmnlCzGgVg8uDt75ZwpeY5ERGRwShbjQFNzGyVFxtKZNfkORURkUEoW40AsnuDcGTW6VFZExi0li3EgFk+oCUpExjUlizw73N5FS3u3roQSkXFNySLPmk51bitZiMj4pWSRZ+kroXRmISLjWU6ThZldaWY7zWyXmd06yPaFZrbZzJ4zs8fMbF64/k1m9mxk6jKzd+Qy1nxpak4wf1oldRXZjUorIpIPOUsWZlYM3ANcBawAbjSzFQOK3QlsdPdVwO3AHQDuvsXdX+furwPeDHQCP81VrPnUFE+wcrY6t0VkfMvlmcWlwC533+3uPcADwDUDyqwANofzWwbZDnA98CN378xZpHnS0Z1kz5ETaoISkXEvl8liLrAvsrw/XBe1DVgfzl8L1JrZ9AFlbgD+ZbA3MLObzGyrmW1taWkZhZDH1o5mdW6LSGHIZbIYbPhUH7B8C3C5mT0DXA4cAJKnXsBsNnAh8JPB3sDdN7j7Wndf29jYODpRjyEN8yEihSKXz7PYD8yPLM8D4tEC7h4HrgMwsxpgvbu3RYq8E/iuu/fmMM68icXbmFZdxsy68nyHIiIyrFyeWTwFLDWzxWZWRtCc9FC0gJk1mFk6hk8A9w54jRsZoglqImhqTrByTh2Wh8e4ioiMRM6ShbsngZsJmpC2Aw+6e8zMbjezq8NiVwA7zexFYCbwpfT+ZraI4Mzk8VzFmE+9fSlePNihzm0RKQg5fayquz8MPDxg3Wci85uATUPs+wpndohPGC8d6qCnL8WK2UoWIjL+6Q7uPGlqVue2iBQOJYs8icXbqCwtZnFDdb5DERHJSMkiT5riCZbNrqW4SJ3bIjL+KVnkgbufuhJKRKQQKFnkwb6jJ2nvSqq/QkQKhpJFHjQ1B/cd6kooESkUShZ5EIsnKC4yzp9Vm+9QRESyomSRB7F4gnMba6goLc53KCIiWVGyyIOmeEJ3botIQVGyGGOtHd0cTHTpSigRKShKFmPs1DO31bktIgVEyWKMpYf5UDOUiBQSJYsxFosnmDu1kqlVZfkORUQka0oWYywWb9NZhYgUHCWLMdTZk2TPkRPq3BaRgqNkMYa2N7fjrmHJRaTwKFmMoaZ4OMyHzixEpMAoWYyhpuYEU6tKmTOlIt+hiIiMiJLFGIrFg2HJzfQMCxEpLEoWY6S3L8WOg+26GU9ECpKSxRjZ3XKCnmRKndsiUpCULMZILOzc1mWzIlKIlCzGSFM8QXlJEYsbqvMdiojIiClZjJFYPMGy2XWUFOuQi0jhUc01BtydWLxNTVAiUrCULMbAgeMnSXQldSWUiBSsnCYLM7vSzHaa2S4zu3WQ7QvNbLOZPWdmj5nZvMi2BWb2UzPbbmZNZrYol7HmUvoZFjqzEJFClbNkYWbFwD3AVcAK4EYzWzGg2J3ARndfBdwO3BHZthH4W3dfDlwKHM5VrLkWiycoMlg2S8lCRApTLs8sLgV2uftud+8BHgCuGVBmBbA5nN+S3h4mlRJ3fwTA3TvcvTOHseZUUzzBOY01VJYV5zsUEZGzkstkMRfYF1neH66L2gasD+evBWrNbDpwHnDczL5jZs+Y2d+GZyr9mNlNZrbVzLa2tLTk4COMjiZ1botIgctlshhsACQfsHwLcLmZPQNcDhwAkkAJ8Lvh9kuAc4D3nfFi7hvcfa27r21sbBzF0EfPsRM9xNu61LktIgUtl8liPzA/sjwPiEcLuHvc3a9z9zXAJ8N1beG+z4RNWEnge8BFOYw1Z9LP3NYwHyJSyHKZLJ4ClprZYjMrA24AHooWMLMGM0vH8Ang3si+9WaWPl14M9CUw1hzJqZnWIjIBJCzZBGeEdwM/ATYDjzo7jEzu93Mrg6LXQHsNLMXgZnAl8J9+wiaoDab2fMETVpfz1WsuRSLJ5g9pYJp1WX5DkVE5KyV5PLF3f1h4OEB6z4Tmd8EbBpi30eAVbmMbyw0hc+wEBEpZLqDO4dO9vTxcksHK9RfISIFTskih3YcTJBydCWUiBQ8JYscOn0llJKFiBS2jMnCzG42s/qxCGaiicUT1FWUMK++Mt+hiIi8JtmcWcwCnjKzB8OBAQe72U4GEYsnWDGnDh0yESl0GZOFu38KWAp8k+Au6pfM7K/NbEmOYytoyb4UO5oTuhlPRCaErPos3N2Bg+GUBOqBTWb2NzmMraDtOXKC7mRK/RUiMiFkvM/CzD4I/DFwBPgG8DF37w3vvH4J+KvchliY0p3bunNbRCaCbG7KawCuc/e90ZXunjKzt+UmrMIXiycoKyliSWNNvkMREXnNsmmGehg4ml4ws1ozez2Au2/PVWCFLhZvY9msWkqLdXWyiBS+bGqyrwEdkeUT4ToZgrvTFE/oZjwRmTCySRYWdnADQfMTOR5TqtA1t3VxrLNXndsiMmFkkyx2m9kHzaw0nD4E7M51YIUsFk93buuyWRGZGLJJFn8O/DbBU+z2A68HbsplUIWuKZ7ADJbNqs13KCIioyJjc5K7HyZ4cJFkKRZvY3FDNdXlaq0TkYkhm/ssKoD/AqwEKtLr3f1PcxhXQYvFE6xZMDXfYYiIjJpsmqH+iWB8qN8HHid4lnZ7LoMqZG2dvRw4flLDfIjIhJJNsjjX3T8NnHD3+4C3AhfmNqzCFWsOnrmtK6FEZCLJJln0hn+Pm9kFwBRgUc4iKnBNcQ3zISITTzY9sBvC51l8CngIqAE+ndOoClhTPMHMunIaasrzHYqIyKgZNlmEgwUm3P0Y8ARwzphEVcBicQ1LLiITz7DNUOHd2jePUSwFr6u3j10tHRrmQ0QmnGz6LB4xs1vMbL6ZTUtPOY+sAL14qJ2+lKtzW0QmnGz6LNL3U/xlZJ2jJqkzpIf5UDOUiEw02dzBvXgsApkIYvE2astLmFdfme9QRERGVTZ3cL93sPXuvnH0wylsTfEEy+fUUVRk+Q5FRGRUZdMMdUlkvgJYB/wGULKI6Es525vbueHS+fkORURk1GXTDPWB6LKZTSEYAiQjM7sS+CpQDHzD3b88YPtC4F6gkeBpfH/k7vvDbX3A82HRV9396mzeM19eaT3Byd4+XQklIhPS2QyL2gkszVTIzIqBe4D/RDC0+VNm9pC7N0WK3QlsdPf7zOzNwB3Ae8JtJ939dWcRX16oc1tEJrJs+ix+QHD1EwSX2q4AHszitS8Fdrn77vB1HgCuAaLJYgXwkXB+C/C97MIef2LxNsqKizh3Rk2+QxERGXXZnFncGZlPAnvTTUUZzAX2RZbTD06K2gasJ2iquhaoNbPp7t4KVJjZ1vA9v+zuZyQSM7uJ8EFMCxYsyCKk3GmKJ1g6s4aykmxuXRERKSzZ1GyvAk+6++Pu/nOg1cwWZbHfYJcE+YDlW4DLzewZ4HKCp/Elw20L3H0t8IfAP5jZkjNezH2Du69197WNjY1ZhJQb7k5TPKGb8URkwsomWfwbkIos94XrMtkPRC8NmgfEowXcPe7u17n7GuCT4bq29Lbw727gMWBNFu+ZF4cS3bSe6FHntohMWNkkixJ370kvhPNlWez3FLDUzBabWRnBo1kfihYws4ZwsEKATxBcGYWZ1ZtZeboM8Ab693WMK03pZ1jMVee2iExM2SSLFjM7ddmqmV0DHMm0k7snCQYh/AmwHXjQ3WNmdnvk9a4AdprZi8BM4Evh+uXAVjPbRtDx/eUBV1GNK7EDwZVQy3VmISITVDYd3H8O3G9md4fL+4FB7+oeyN0fBh4esO4zkflNwKZB9vsFBfQ0vlg8waLpVdSUn82VyCIi4182N+W9DFxmZjWAubuevz1AU3OCC9UEJSITWMZmKDP7azOb6u4d7t4e9id8cSyCKwSJrl5ePdqpx6iKyISWTZ/FVe5+PL0QPjXvD3IXUmHRM7dFZDLIJlkUp69MAjCzSkAPmA41nRrmQ8lCRCaubHpkvw1sNrP/Ey7/CXBf7kIqLLF4gsbacmbUVuQ7FBGRnMmmg/tvzOw54PcI7sr+MbAw14EVili8TTfjiciEl+1ARgcJ7uJeT/A8i+05i6iAdCf72HW4Q01QIjLhDXlmYWbnEdx1fSPQCvwrwaWzbxqj2Ma9lw51kEy5hiUXkQlvuGaoHcD/A97u7rsAzOwjw5SfdGLxYJgPXQklIhPdcM1Q6wman7aY2dfNbB2DjyQ7aTXFE9SUl7BwWlW+QxERyakhk4W7f9fd3wUsIxj19SPATDNv7vNtAAAQcUlEQVT7mpm9ZYziG9di8QTLZ9dSVKQcKiITW8YObnc/4e73u/vbCIYZfxa4NeeRjXOplLO9OaEroURkUhjRY93c/ai7/293f3OuAioUe492cqKnT53bIjIp6BmgZ0md2yIymShZnKWmeIKSImPpzJp8hyIiknNKFmcpFk+wdGYt5SXF+Q5FRCTnlCzOUiyuzm0RmTyULM7C4fYujnR0a5gPEZk0lCzOQkzDkovIJKNkcRbSz7BYrmQhIpOEksVZaIonWDCtirqK0nyHIiIyJpQszkIs3qYmKBGZVJQsRqi9q5dXWjt1JZSITCpKFiO042A7ACvnKlmIyOShZDFCsQPBMB8aE0pEJhMlixGKxRNMry5jRm15vkMRERkzOU0WZnalme00s11mdsaw5ma20Mw2m9lzZvaYmc0bsL3OzA6Y2d25jHMkmpoTrJhTh5meYSEik0fOkoWZFQP3AFcBK4AbzWzFgGJ3AhvdfRVwO3DHgO1fAB7PVYwj1ZNM8eKhdjVBicikk8szi0uBXe6+2917gAeAawaUWQFsDue3RLeb2cXATOCnOYxxRF463E5vn2tYchGZdHKZLOYC+yLL+8N1UdsInvUNcC1Qa2bTzawI+DvgY8O9gZndZGZbzWxrS0vLKIU9tCYN8yEik1Quk8Vgjfo+YPkW4HIzewa4HDgAJIG/AB52930Mw903uPtad1/b2Ng4GjEPKxZPUFVWzKLp1Tl/LxGR8aQkh6+9H5gfWZ4HxKMF3D0OXAdgZjXAendvM7PfAn7XzP4CqAHKzKzD3fP67O+m5gTLZtVSXKTObRGZXHKZLJ4ClprZYoIzhhuAP4wWMLMG4Ki7p4BPAPcCuPu7I2XeB6zNd6JIpZzt8QTvWDOwJU1EZOLLWTOUuyeBm4GfANuBB909Zma3m9nVYbErgJ1m9iJBZ/aXchXPa7XvWCft3Un1V4jIpJTLMwvc/WHg4QHrPhOZ3wRsyvAa3wK+lYPwRiTdua0roURkMtId3FmKxRMUFxnnzazNdygiImNOySJLsXgb5zbWUFFanO9QRETGnJJFlpqaE+qvEJFJS8kiC0c6ujmU6FZ/hYhMWkoWWYipc1tEJjkliyycGuZjtgYQFJHJSckiC7F4G/PqK5lSVZrvUERE8kLJIgtN8YSeuS0ik5qSRQYnupPsaT2hZ1iIyKSmZJHBjoMJ3DUsuYhMbkoWGehKKBERJYuMmuIJ6qtKmT2lIt+hiIjkjZJFBrF4gpVzpmCmZ1iIyOSlZDGM3r4UOw+2qwlKRCY9JYthvNzSQU9fSp3bIjLpKVkMI3YgvHNbyUJEJjkli2E0NSeoKC1icUNNvkMREckrJYthxOJtLJtVR3GROrdFZHJTshiCu9MU1zMsRERAyWJI+4+dJNGV1JVQIiIoWQwpfee2xoQSEVGyGFJTvI0ig/Nn1uY7FBGRvCvJdwDjVVNzgiWNNVSWFec7FBHJQm9vL/v376erqyvfoYxLFRUVzJs3j9LSs3suj5LFEGLxBK9fPC3fYYhIlvbv309tbS2LFi3S8DwDuDutra3s37+fxYsXn9VrqBlqEEdP9NDc1qXObZEC0tXVxfTp05UoBmFmTJ8+/TWddSlZDKJJndsiBUmJYmiv9djkNFmY2ZVmttPMdpnZrYNsX2hmm83sOTN7zMzmRdY/bWbPmlnMzP48l3EOFIu3AehRqiIioZwlCzMrBu4BrgJWADea2YoBxe4ENrr7KuB24I5wfTPw2+7+OuD1wK1mNidXsQ4UiyeYM6WC+uqysXpLEZFxLZdnFpcCu9x9t7v3AA8A1wwoswLYHM5vSW939x537w7Xl+c4zjM0NSdYoSYoEZFTcnk11FxgX2R5P8FZQtQ2YD3wVeBaoNbMprt7q5nNB/4vcC7wMXePD3wDM7sJuAlgwYIFoxL0yZ4+drd08NYLZ4/K64nI2Pv8D2Kn+h5Hy4o5dXz27SszlnvHO97Bvn376Orq4kMf+hA33XQTP/7xj7ntttvo6+ujoaGBzZs309HRwQc+8AG2bt2KmfHZz36W9evXj2rMoymXyWKw3hQfsHwLcLeZvQ94AjgAJAHcfR+wKmx++p6ZbXL3Q/1ezH0DsAFg7dq1A1/7rGw/mCDleua2iJyde++9l2nTpnHy5EkuueQSrrnmGt7//vfzxBNPsHjxYo4ePQrAF77wBaZMmcLzzz8PwLFjx/IZdka5TBb7gfmR5XlAv7OD8GzhOgAzqwHWu3vbwDJmFgN+F9iUw3iB6JVQShYihSqbM4Bcueuuu/jud78LwL59+9iwYQNvfOMbT93fMG1acP/Wz372Mx544IFT+9XX1499sCOQy76Ap4ClZrbYzMqAG4CHogXMrMHM0jF8Arg3XD/PzCrD+XrgDcDOHMZ6SiyeYEplKXOnVo7F24nIBPLYY4/xs5/9jF/+8pds27aNNWvWsHr16kEvW3X3grrUN2fJwt2TwM3AT4DtwIPuHjOz283s6rDYFcBOM3sRmAl8KVy/HHjSzLYBjwN3uvvzuYo1qinexorZdQX1JYrI+NDW1kZ9fT1VVVXs2LGDX/3qV3R3d/P444+zZ88egFPNUG95y1u4++67T+073puhcnqVkbs/7O7nufsSd/9SuO4z7v5QOL/J3ZeGZf4sfQWUuz/i7qvcfXX4d0Mu40xL9qXYcbBdTVAiclauvPJKkskkq1at4tOf/jSXXXYZjY2NbNiwgeuuu47Vq1fzrne9C4BPfepTHDt2jAsuuIDVq1ezZcuWPEc/PI0NFbH7yAm6kylWzlWyEJGRKy8v50c/+tGg26666qp+yzU1Ndx3331jEdao0HAfEafv3NY9FiIiUUoWEU3xBGUlRSxprM53KCIi44qSRUQsnmDZrFpKinVYRESiVCuG3J2m5oQ6t0VEBqFkEYq3dXG8s1djQomIDELJIhQ7oGHJRUSGomQRampOYAbLZ9fmOxQRkXFHySIUiyc4p6GaqjLdeiIiY6OmpibfIWRNNWOoKZ7gooXjeyAvEcnSj26Fg6M8QtCsC+GqL4/uaxYQnVkAxzt7OHD8pK6EEpHX5OMf/zj/+I//eGr5c5/7HJ///OdZt24dF110ERdeeCHf//73s3qtjo6OIffbuHEjq1atYvXq1bznPe8B4NChQ1x77bWsXr2a1atX84tf/GJ0P5y7T4jp4osv9rP185dafOHHf+hPvHj4rF9DRPKrqakp3yH4b37zG3/jG994ann58uW+d+9eb2trc3f3lpYWX7JkiadSKXd3r66uHvK1ent7B93vhRde8PPOO89bWlrc3b21tdXd3d/5znf6V77yFXd3TyaTfvz48TNec7BjBGz1LOpYNUMR9FeAroQSkddmzZo1HD58mHg8TktLC/X19cyePZuPfOQjPPHEExQVFXHgwAEOHTrErFmzhn0td+e22247Y79HH32U66+/noaGBuD08zEeffRRNm7cCEBxcTFTpozubQBKFgRXQs2qq2B6TXm+QxGRAnf99dezadMmDh48yA033MD9999PS0sLTz/9NKWlpSxatIiurq6MrzPUfp6n52Coz4JgAEH1V4jIaLjhhht44IEH2LRpE9dffz1tbW3MmDGD0tJStmzZwt69e7N6naH2W7duHQ8++CCtra3A6edjrFu3jq997WsA9PX1kUiM7jPIJ32y6Ort4+WWE3rmtoiMipUrV9Le3s7cuXOZPXs27373u9m6dStr167l/vvvZ9myZVm9zlD7rVy5kk9+8pNcfvnlrF69mo9+9KMAfPWrX2XLli1ceOGFXHzxxcRisVH9XBb0bxS+tWvX+tatW0e8X0t7N1/4YRPvXDuf31nakIPIRGQsbN++neXLl+c7jHFtsGNkZk+7+9pM+076PovG2nLuunFNvsMQERnXJn2yEBHJp+eff/7UvRJp5eXlPPnkk3mKaHBKFiIyYeTrSqHX4sILL+TZZ5/N+fu81i6HSd/BLSITQ0VFBa2tra+5UpyI3J3W1lYqKirO+jV0ZiEiE8K8efPYv38/LS0t+Q5lXKqoqGDevHlnvb+ShYhMCKWlpSxevDjfYUxYaoYSEZGMlCxERCQjJQsREclowtzBbWYtQHaDrgyuATgySuEUOh2L/nQ8+tPxOG0iHIuF7t6YqdCESRavlZltzeaW98lAx6I/HY/+dDxOm0zHQs1QIiKSkZKFiIhkpGRx2oZ8BzCO6Fj0p+PRn47HaZPmWKjPQkREMtKZhYiIZKRkISIiGU36ZGFmV5rZTjPbZWa35juefDKz+Wa2xcy2m1nMzD6U75jyzcyKzewZM/thvmPJNzObamabzGxH+G/kt/IdUz6Z2UfC/ycvmNm/mNnZD+laACZ1sjCzYuAe4CpgBXCjma3Ib1R5lQT+u7svBy4D/nKSHw+ADwHb8x3EOPFV4MfuvgxYzSQ+LmY2F/ggsNbdLwCKgRvyG1VuTepkAVwK7HL33e7eAzwAXJPnmPLG3Zvd/TfhfDtBZTA3v1Hlj5nNA94KfCPfseSbmdUBbwS+CeDuPe5+PL9R5V0JUGlmJUAVEM9zPDk12ZPFXGBfZHk/k7hyjDKzRcAaYHw923Fs/QPwV0Aq34GMA+cALcD/CZvlvmFm1fkOKl/c/QBwJ/Aq0Ay0uftP8xtVbk32ZDHY8xcn/bXEZlYD/DvwYXdP5DuefDCztwGH3f3pfMcyTpQAFwFfc/c1wAlg0vbxmVk9QSvEYmAOUG1mf5TfqHJrsieL/cD8yPI8JvipZCZmVkqQKO539+/kO548egNwtZm9QtA8+WYz+3Z+Q8qr/cB+d0+faW4iSB6T1e8Be9y9xd17ge8Av53nmHJqsieLp4ClZrbYzMoIOqgeynNMeWPBk+6/CWx397/Pdzz55O6fcPd57r6I4N/Fo+4+oX85DsfdDwL7zOz8cNU6oCmPIeXbq8BlZlYV/r9ZxwTv8J/Uj1V196SZ3Qz8hOBqhnvdPZbnsPLpDcB7gOfN7Nlw3W3u/nAeY5Lx4wPA/eEPq93An+Q5nrxx9yfNbBPwG4KrCJ9hgg/9oeE+REQko8neDCUiIllQshARkYyULEREJCMlCxERyUjJQkREMlKyEBkBM+szs2cj06jdxWxmi8zshdF6PZHRNKnvsxA5Cyfd/XX5DkJkrOnMQmQUmNkrZvY/zOzX4XRuuH6hmW02s+fCvwvC9TPN7Ltmti2c0kNFFJvZ18PnJPzUzCrz9qFEIpQsREamckAz1Lsi2xLufilwN8GItYTzG919FXA/cFe4/i7gcXdfTTDGUnrkgKXAPe6+EjgOrM/x5xHJiu7gFhkBM+tw95pB1r8CvNndd4eDMR509+lmdgSY7e694fpmd28wsxZgnrt3R15jEfCIuy8Nlz8OlLr7F3P/yUSGpzMLkdHjQ8wPVWYw3ZH5PtSvKOOEkoXI6HlX5O8vw/lfcPpxm+8G/iOc3wz8Nzj1nO+6sQpS5GzoV4vIyFRGRuSF4JnU6ctny83sSYIfYTeG6z4I3GtmHyN40lx6pNYPARvM7L8QnEH8N4InromMS+qzEBkFYZ/FWnc/ku9YRHJBzVAiIpKRzixERCQjnVmIiEhGShYiIpKRkoWIiGSkZCEiIhkpWYiISEb/HzKBQya/X6BnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.979665\n"
     ]
    }
   ],
   "source": [
    "nh = 100;\n",
    "num_layers = 5;\n",
    "    \n",
    "model_fivelayer = neural_network(nin, nh, nout, num_layers, \"sigmoid\")\n",
    "model_fivelayer.summary()\n",
    "fivelayer_history = train_network(model_fivelayer, x_train, y_train, x_test, y_test);\n",
    "plot_acc(fivelayer_history, \"5 Hidden Layer (100) Accuracy\")\n",
    "plot_loss(fivelayer_history, \"5 Hidden Layer (100) Loss\")\n",
    "\n",
    "modelArr.append(model_fivelayer)\n",
    "historyArr.append(fivelayer_history)\n",
    "modelNameArr.append(\"model_fivelayer\")\n",
    "\n",
    "score, acc = model_fivelayer.evaluate(x_test, y_test, verbose = 0)\n",
    "print(\"Accuracy = %f\" % acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accuracy Graphs of All Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1280 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(18, 16), dpi= 80, facecolor='w', edgecolor='k')\n",
    "fig.tight_layout()\n",
    "\n",
    "for index, history in enumerate(historyArr):\n",
    "    plt.subplot(3,3,index+1)\n",
    "    plt.plot(history.history['acc'])\n",
    "    plt.plot(history.history['val_acc'])\n",
    "    plt.title(modelNameArr[index] + \" Accuracy\")\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.legend(['acc', 'val_acc'], loc='lower right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loss Graphs of All Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1280 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(18, 16), dpi= 80, facecolor='w', edgecolor='k')\n",
    "fig.tight_layout()\n",
    "\n",
    "for index, history in enumerate(historyArr):\n",
    "    plt.subplot(3,3,index+1)\n",
    "    plt.plot(history.history['loss'])\n",
    "    plt.plot(history.history['val_loss'])\n",
    "    plt.title(modelNameArr[index] + \" Loss\")\n",
    "    plt.ylabel('Loss')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.legend(['loss', 'val_loss'], loc='upper right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Accuracies of all models after 10 Epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model_sig Validation Accuracy = 97.698%\n",
      "model_relu Validation Accuracy = 98.056%\n",
      "model_tanh Validation Accuracy = 97.944%\n",
      "model_ten Validation Accuracy = 97.363%\n",
      "model_twohund Validation Accuracy = 97.698%\n",
      "model_twolayer Validation Accuracy = 97.788%\n",
      "model_fivelayer Validation Accuracy = 97.966%\n",
      "\n",
      "\n",
      "model_sig Train Accuracy = 97.601%\n",
      "model_relu Train Accuracy = 97.854%\n",
      "model_tanh Train Accuracy = 97.862%\n",
      "model_ten Train Accuracy = 97.303%\n",
      "model_twohund Train Accuracy = 97.601%\n",
      "model_twolayer Train Accuracy = 97.735%\n",
      "model_fivelayer Train Accuracy = 97.936%\n"
     ]
    }
   ],
   "source": [
    "for index, model in enumerate(modelArr):\n",
    "    score, acc = model.evaluate(x_test, y_test, verbose = 0)\n",
    "    print(modelNameArr[index] + \" Validation Accuracy = \" + \"{:.3%}\".format(acc))\n",
    "\n",
    "print('\\n')\n",
    "\n",
    "for index, history in enumerate(historyArr):\n",
    "    \n",
    "    print(modelNameArr[index] + \" Train Accuracy = \" + \n",
    "          \"{:.3%}\".format(history.history[\"acc\"][9]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}