{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Activity 3.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# import required packages from Keras\n",
    "from keras.models import Sequential \n",
    "from keras.layers import Dense, Activation \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow import random\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import required packages for plotting\n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib\n",
    "%matplotlib inline \n",
    "import matplotlib.patches as mpatches\n",
    "# import the function for plotting decision boundary\n",
    "from utils import plot_decision_boundary\n",
    "\n",
    "# define a seed for random number generator so the result will be reproducible\n",
    "seed = 1\n",
    "np.random.seed(seed)\n",
    "random.set_seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X size =  (3359, 2)\n",
      "Y size =  (3359, 1)\n",
      "Number of examples =  3359\n"
     ]
    }
   ],
   "source": [
    "# load the dataset, print the shapes of input and output and the number of examples\n",
    "feats = pd.read_csv('../data/outlier_feats.csv')\n",
    "target = pd.read_csv('../data/outlier_target.csv')\n",
    "print(\"X size = \", feats.shape)\n",
    "print(\"Y size = \", target.shape)\n",
    "print(\"Number of examples = \", feats.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the features and target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Feature 2')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# changing the size of the plots\n",
    "matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)\n",
    "\n",
    "class_1=plt.scatter(feats.loc[target['Class']==0,'feature1'], feats.loc[target['Class']==0,'feature2'], c=\"red\", s=40, edgecolor='k')\n",
    "class_2=plt.scatter(feats.loc[target['Class']==1,'feature1'], feats.loc[target['Class']==1,'feature2'], c=\"blue\", s=40, edgecolor='k')\n",
    "plt.legend((class_1, class_2),('Fail','Pass'))\n",
    "plt.xlabel('Feature 1')\n",
    "plt.ylabel('Feature 2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 2687 samples, validate on 672 samples\n",
      "Epoch 1/100\n",
      "2687/2687 [==============================] - 1s 214us/step - loss: 0.5405 - val_loss: 0.4194\n",
      "Epoch 2/100\n",
      "2687/2687 [==============================] - 0s 173us/step - loss: 0.3729 - val_loss: 0.3672\n",
      "Epoch 3/100\n",
      "2687/2687 [==============================] - 0s 176us/step - loss: 0.3459 - val_loss: 0.3574\n",
      "Epoch 4/100\n",
      "2687/2687 [==============================] - 1s 220us/step - loss: 0.3396 - val_loss: 0.3551\n",
      "Epoch 5/100\n",
      "2687/2687 [==============================] - 1s 249us/step - loss: 0.3378 - val_loss: 0.3545\n",
      "Epoch 6/100\n",
      "2687/2687 [==============================] - 1s 222us/step - loss: 0.3371 - val_loss: 0.3543\n",
      "Epoch 7/100\n",
      "2687/2687 [==============================] - 1s 274us/step - loss: 0.3369 - val_loss: 0.3543\n",
      "Epoch 8/100\n",
      "2687/2687 [==============================] - 1s 200us/step - loss: 0.3368 - val_loss: 0.3542\n",
      "Epoch 9/100\n",
      "2687/2687 [==============================] - 0s 182us/step - loss: 0.3367 - val_loss: 0.3542\n",
      "Epoch 10/100\n",
      "2687/2687 [==============================] - 1s 186us/step - loss: 0.3367 - val_loss: 0.3542\n",
      "Epoch 11/100\n",
      "2687/2687 [==============================] - 0s 173us/step - loss: 0.3367 - val_loss: 0.3542\n",
      "Epoch 12/100\n",
      "2687/2687 [==============================] - 0s 173us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 13/100\n",
      "2687/2687 [==============================] - 0s 173us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 14/100\n",
      "2687/2687 [==============================] - 0s 166us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 15/100\n",
      "2687/2687 [==============================] - 0s 171us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 16/100\n",
      "2687/2687 [==============================] - 0s 174us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 17/100\n",
      "2687/2687 [==============================] - 0s 174us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 18/100\n",
      "2687/2687 [==============================] - 0s 167us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 19/100\n",
      "2687/2687 [==============================] - 0s 169us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 20/100\n",
      "2687/2687 [==============================] - 0s 172us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 21/100\n",
      "2687/2687 [==============================] - 1s 212us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 22/100\n",
      "2687/2687 [==============================] - 1s 221us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 23/100\n",
      "2687/2687 [==============================] - 0s 167us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 24/100\n",
      "2687/2687 [==============================] - 0s 176us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 25/100\n",
      "2687/2687 [==============================] - 0s 168us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 26/100\n",
      "2687/2687 [==============================] - 0s 175us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 27/100\n",
      "2687/2687 [==============================] - 1s 199us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 28/100\n",
      "2687/2687 [==============================] - 0s 173us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 29/100\n",
      "2687/2687 [==============================] - 0s 185us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 30/100\n",
      "2687/2687 [==============================] - 0s 186us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 31/100\n",
      "2687/2687 [==============================] - 0s 170us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 32/100\n",
      "2687/2687 [==============================] - 0s 165us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 33/100\n",
      "2687/2687 [==============================] - 0s 173us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 34/100\n",
      "2687/2687 [==============================] - 0s 170us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 35/100\n",
      "2687/2687 [==============================] - 0s 165us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 36/100\n",
      "2687/2687 [==============================] - 0s 169us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 37/100\n",
      "2687/2687 [==============================] - 0s 170us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 38/100\n",
      "2687/2687 [==============================] - 0s 165us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 39/100\n",
      "2687/2687 [==============================] - 0s 172us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 40/100\n",
      "2687/2687 [==============================] - 0s 166us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 41/100\n",
      "2687/2687 [==============================] - 0s 169us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 42/100\n",
      "2687/2687 [==============================] - 0s 183us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 43/100\n",
      "2687/2687 [==============================] - 0s 179us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 44/100\n",
      "2687/2687 [==============================] - 0s 186us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 45/100\n",
      "2687/2687 [==============================] - 0s 181us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 46/100\n",
      "2687/2687 [==============================] - 0s 169us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 47/100\n",
      "2687/2687 [==============================] - 0s 176us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 48/100\n",
      "2687/2687 [==============================] - 0s 177us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 49/100\n",
      "2687/2687 [==============================] - 1s 188us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 50/100\n",
      "2687/2687 [==============================] - 0s 176us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 51/100\n",
      "2687/2687 [==============================] - 0s 179us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 52/100\n",
      "2687/2687 [==============================] - 0s 177us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 53/100\n",
      "2687/2687 [==============================] - 0s 168us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 54/100\n",
      "2687/2687 [==============================] - 0s 173us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 55/100\n",
      "2687/2687 [==============================] - 1s 194us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 56/100\n",
      "2687/2687 [==============================] - 0s 183us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 57/100\n",
      "2687/2687 [==============================] - 0s 168us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 58/100\n",
      "2687/2687 [==============================] - 0s 170us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 59/100\n",
      "2687/2687 [==============================] - 0s 164us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 60/100\n",
      "2687/2687 [==============================] - 0s 166us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 61/100\n",
      "2687/2687 [==============================] - 0s 172us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 62/100\n",
      "2687/2687 [==============================] - 0s 175us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 63/100\n",
      "2687/2687 [==============================] - 0s 181us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 64/100\n",
      "2687/2687 [==============================] - 0s 167us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 65/100\n",
      "2687/2687 [==============================] - 1s 192us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 66/100\n",
      "2687/2687 [==============================] - 1s 200us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 67/100\n",
      "2687/2687 [==============================] - 0s 185us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 68/100\n",
      "2687/2687 [==============================] - 0s 180us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 69/100\n",
      "2687/2687 [==============================] - 0s 180us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 70/100\n",
      "2687/2687 [==============================] - 0s 174us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 71/100\n",
      "2687/2687 [==============================] - 0s 177us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 72/100\n",
      "2687/2687 [==============================] - 0s 175us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 73/100\n",
      "2687/2687 [==============================] - 1s 241us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 74/100\n",
      "2687/2687 [==============================] - 1s 237us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 75/100\n",
      "2687/2687 [==============================] - 1s 212us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 76/100\n",
      "2687/2687 [==============================] - 1s 205us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 77/100\n",
      "2687/2687 [==============================] - 1s 199us/step - loss: 0.3367 - val_loss: 0.3541\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 78/100\n",
      "2687/2687 [==============================] - 1s 204us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 79/100\n",
      "2687/2687 [==============================] - 1s 227us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 80/100\n",
      "2687/2687 [==============================] - 1s 206us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 81/100\n",
      "2687/2687 [==============================] - 1s 221us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 82/100\n",
      "2687/2687 [==============================] - 1s 221us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 83/100\n",
      "2687/2687 [==============================] - 1s 216us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 84/100\n",
      "2687/2687 [==============================] - 1s 207us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 85/100\n",
      "2687/2687 [==============================] - 1s 201us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 86/100\n",
      "2687/2687 [==============================] - 1s 206us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 87/100\n",
      "2687/2687 [==============================] - 1s 217us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 88/100\n",
      "2687/2687 [==============================] - 1s 212us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 89/100\n",
      "2687/2687 [==============================] - 1s 202us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 90/100\n",
      "2687/2687 [==============================] - 1s 196us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 91/100\n",
      "2687/2687 [==============================] - 1s 188us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 92/100\n",
      "2687/2687 [==============================] - 1s 202us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 93/100\n",
      "2687/2687 [==============================] - 1s 205us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 94/100\n",
      "2687/2687 [==============================] - 1s 240us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 95/100\n",
      "2687/2687 [==============================] - 1s 207us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 96/100\n",
      "2687/2687 [==============================] - 1s 199us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 97/100\n",
      "2687/2687 [==============================] - 1s 197us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 98/100\n",
      "2687/2687 [==============================] - 1s 215us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 99/100\n",
      "2687/2687 [==============================] - 1s 199us/step - loss: 0.3367 - val_loss: 0.3541\n",
      "Epoch 100/100\n",
      "2687/2687 [==============================] - 0s 180us/step - loss: 0.3367 - val_loss: 0.3541\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x142dc5240>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logistic Regression model\n",
    "np.random.seed(seed)\n",
    "random.set_seed(seed)\n",
    "model_1 = Sequential()\n",
    "model_1.add(Dense(1, activation='sigmoid', input_dim=2)) \n",
    "model_1.compile(optimizer='sgd', loss='binary_crossentropy') \n",
    "\n",
    "# train the model for 100 epoches and batch size 5\n",
    "model_1.fit(feats, target, batch_size=5, epochs=100, verbose=1, validation_split=0.2, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the decision boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Logistic Regression')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)\n",
    "plot_decision_boundary(lambda x: model_1.predict(x), feats, target) \n",
    "plt.title(\"Logistic Regression\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate the loss and accuracy on the test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 2687 samples, validate on 672 samples\n",
      "Epoch 1/200\n",
      "2687/2687 [==============================] - 1s 216us/step - loss: 0.4889 - val_loss: 0.3847\n",
      "Epoch 2/200\n",
      "2687/2687 [==============================] - 1s 190us/step - loss: 0.3135 - val_loss: 0.2946\n",
      "Epoch 3/200\n",
      "2687/2687 [==============================] - 1s 196us/step - loss: 0.2509 - val_loss: 0.2408\n",
      "Epoch 4/200\n",
      "2687/2687 [==============================] - 1s 190us/step - loss: 0.2078 - val_loss: 0.2044\n",
      "Epoch 5/200\n",
      "2687/2687 [==============================] - 1s 192us/step - loss: 0.1776 - val_loss: 0.1790\n",
      "Epoch 6/200\n",
      "2687/2687 [==============================] - 1s 192us/step - loss: 0.1562 - val_loss: 0.1609\n",
      "Epoch 7/200\n",
      "2687/2687 [==============================] - 1s 192us/step - loss: 0.1402 - val_loss: 0.1472\n",
      "Epoch 8/200\n",
      "2687/2687 [==============================] - 1s 190us/step - loss: 0.1279 - val_loss: 0.1362\n",
      "Epoch 9/200\n",
      "2687/2687 [==============================] - 1s 194us/step - loss: 0.1185 - val_loss: 0.1276\n",
      "Epoch 10/200\n",
      "2687/2687 [==============================] - 1s 193us/step - loss: 0.1108 - val_loss: 0.1209\n",
      "Epoch 11/200\n",
      "2687/2687 [==============================] - 1s 193us/step - loss: 0.1046 - val_loss: 0.1153\n",
      "Epoch 12/200\n",
      "2687/2687 [==============================] - 1s 235us/step - loss: 0.0994 - val_loss: 0.1106\n",
      "Epoch 13/200\n",
      "2687/2687 [==============================] - 0s 184us/step - loss: 0.0949 - val_loss: 0.1066\n",
      "Epoch 14/200\n",
      "2687/2687 [==============================] - 1s 186us/step - loss: 0.0909 - val_loss: 0.1030\n",
      "Epoch 15/200\n",
      "2687/2687 [==============================] - 0s 182us/step - loss: 0.0873 - val_loss: 0.1000\n",
      "Epoch 16/200\n",
      "2687/2687 [==============================] - 0s 182us/step - loss: 0.0840 - val_loss: 0.0973\n",
      "Epoch 17/200\n",
      "2687/2687 [==============================] - 1s 211us/step - loss: 0.0811 - val_loss: 0.0948\n",
      "Epoch 18/200\n",
      "2687/2687 [==============================] - 1s 204us/step - loss: 0.0784 - val_loss: 0.0925\n",
      "Epoch 19/200\n",
      "2687/2687 [==============================] - 1s 216us/step - loss: 0.0759 - val_loss: 0.0902\n",
      "Epoch 20/200\n",
      "2687/2687 [==============================] - 1s 211us/step - loss: 0.0736 - val_loss: 0.0881\n",
      "Epoch 21/200\n",
      "2687/2687 [==============================] - 1s 240us/step - loss: 0.0714 - val_loss: 0.0861\n",
      "Epoch 22/200\n",
      "2687/2687 [==============================] - 1s 188us/step - loss: 0.0694 - val_loss: 0.0840\n",
      "Epoch 23/200\n",
      "2687/2687 [==============================] - 0s 185us/step - loss: 0.0673 - val_loss: 0.0821\n",
      "Epoch 24/200\n",
      "2687/2687 [==============================] - 1s 192us/step - loss: 0.0654 - val_loss: 0.0804\n",
      "Epoch 25/200\n",
      "2687/2687 [==============================] - 1s 191us/step - loss: 0.0635 - val_loss: 0.0787\n",
      "Epoch 26/200\n",
      "2687/2687 [==============================] - 1s 187us/step - loss: 0.0618 - val_loss: 0.0770\n",
      "Epoch 27/200\n",
      "2687/2687 [==============================] - 1s 194us/step - loss: 0.0601 - val_loss: 0.0753\n",
      "Epoch 28/200\n",
      "2687/2687 [==============================] - 1s 188us/step - loss: 0.0585 - val_loss: 0.0737\n",
      "Epoch 29/200\n",
      "2687/2687 [==============================] - 1s 218us/step - loss: 0.0571 - val_loss: 0.0720\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 78/200\n",
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      "Epoch 154/200\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "2687/2687 [==============================] - 1s 196us/step - loss: 0.0145 - val_loss: 0.0293\n",
      "Epoch 155/200\n",
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      "2687/2687 [==============================] - 0s 176us/step - loss: 0.0131 - val_loss: 0.0291\n",
      "Epoch 191/200\n",
      "2687/2687 [==============================] - 1s 186us/step - loss: 0.0131 - val_loss: 0.0291\n",
      "Epoch 192/200\n",
      "2687/2687 [==============================] - 1s 189us/step - loss: 0.0131 - val_loss: 0.0291\n",
      "Epoch 193/200\n",
      "2687/2687 [==============================] - 0s 182us/step - loss: 0.0131 - val_loss: 0.0291\n",
      "Epoch 194/200\n",
      "2687/2687 [==============================] - 1s 187us/step - loss: 0.0130 - val_loss: 0.0291\n",
      "Epoch 195/200\n",
      "2687/2687 [==============================] - 1s 187us/step - loss: 0.0130 - val_loss: 0.0290\n",
      "Epoch 196/200\n",
      "2687/2687 [==============================] - 1s 189us/step - loss: 0.0130 - val_loss: 0.0289\n",
      "Epoch 197/200\n",
      "2687/2687 [==============================] - 0s 184us/step - loss: 0.0129 - val_loss: 0.0289\n",
      "Epoch 198/200\n",
      "2687/2687 [==============================] - 1s 190us/step - loss: 0.0129 - val_loss: 0.0288\n",
      "Epoch 199/200\n",
      "2687/2687 [==============================] - 0s 181us/step - loss: 0.0129 - val_loss: 0.0287\n",
      "Epoch 200/200\n",
      "2687/2687 [==============================] - 0s 184us/step - loss: 0.0128 - val_loss: 0.0286\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x140c266a0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Neural network with hidden layer size = 3\n",
    "np.random.seed(seed)\n",
    "random.set_seed(seed)\n",
    "model_2 = Sequential() \n",
    "model_2.add(Dense(3, activation='relu', input_dim=2))\n",
    "model_2.add(Dense(1, activation='sigmoid'))\n",
    "model_2.compile(optimizer='sgd', loss='binary_crossentropy') \n",
    "# train the model for 200 epoches and batch size 5\n",
    "model_2.fit(feats, target, batch_size=5, epochs=200, verbose=1, validation_split=0.2, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the decision boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Decision Boundary for Neural Network with hidden layer size 3')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)\n",
    "plot_decision_boundary(lambda x: model_2.predict(x), feats, target) \n",
    "plt.title(\"Decision Boundary for Neural Network with hidden layer size 3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a neural network with hidden layer of size 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 2687 samples, validate on 672 samples\n",
      "Epoch 1/400\n",
      "2687/2687 [==============================] - 1s 214us/step - loss: 0.4972 - val_loss: 0.3903\n",
      "Epoch 2/400\n",
      "2687/2687 [==============================] - 1s 187us/step - loss: 0.3003 - val_loss: 0.2724\n",
      "Epoch 3/400\n",
      "2687/2687 [==============================] - 1s 189us/step - loss: 0.2293 - val_loss: 0.2229\n",
      "Epoch 4/400\n",
      "2687/2687 [==============================] - 1s 189us/step - loss: 0.1907 - val_loss: 0.1910\n",
      "Epoch 5/400\n",
      "2687/2687 [==============================] - 1s 187us/step - loss: 0.1625 - val_loss: 0.1667\n",
      "Epoch 6/400\n",
      "2687/2687 [==============================] - 1s 191us/step - loss: 0.1395 - val_loss: 0.1462\n",
      "Epoch 7/400\n",
      "2687/2687 [==============================] - 0s 183us/step - loss: 0.1199 - val_loss: 0.1275\n",
      "Epoch 8/400\n",
      "2687/2687 [==============================] - 1s 190us/step - loss: 0.1031 - val_loss: 0.1110\n",
      "Epoch 9/400\n",
      "2687/2687 [==============================] - 0s 184us/step - loss: 0.0890 - val_loss: 0.0973\n",
      "Epoch 10/400\n",
      "2687/2687 [==============================] - 1s 187us/step - loss: 0.0774 - val_loss: 0.0862\n",
      "Epoch 11/400\n",
      "2687/2687 [==============================] - 0s 182us/step - loss: 0.0679 - val_loss: 0.0772\n",
      "Epoch 12/400\n",
      "2687/2687 [==============================] - 0s 177us/step - loss: 0.0605 - val_loss: 0.0704\n",
      "Epoch 13/400\n",
      "2687/2687 [==============================] - 0s 182us/step - loss: 0.0547 - val_loss: 0.0649\n",
      "Epoch 14/400\n",
      "2687/2687 [==============================] - 0s 178us/step - loss: 0.0501 - val_loss: 0.0606\n",
      "Epoch 15/400\n",
      "2687/2687 [==============================] - 0s 175us/step - loss: 0.0463 - val_loss: 0.0571\n",
      "Epoch 16/400\n",
      "2687/2687 [==============================] - 0s 180us/step - loss: 0.0432 - val_loss: 0.0541\n",
      "Epoch 17/400\n",
      "2687/2687 [==============================] - 0s 178us/step - loss: 0.0406 - val_loss: 0.0517\n",
      "Epoch 18/400\n",
      "2687/2687 [==============================] - 0s 176us/step - loss: 0.0383 - val_loss: 0.0496\n",
      "Epoch 19/400\n",
      "2687/2687 [==============================] - 0s 178us/step - loss: 0.0364 - val_loss: 0.0477\n",
      "Epoch 20/400\n",
      "2687/2687 [==============================] - 0s 179us/step - loss: 0.0347 - val_loss: 0.0461\n",
      "Epoch 21/400\n",
      "2687/2687 [==============================] - 0s 175us/step - loss: 0.0332 - val_loss: 0.0446\n",
      "Epoch 22/400\n",
      "2687/2687 [==============================] - 0s 184us/step - loss: 0.0318 - val_loss: 0.0433\n",
      "Epoch 23/400\n",
      "2687/2687 [==============================] - 0s 171us/step - loss: 0.0306 - val_loss: 0.0422\n",
      "Epoch 24/400\n",
      "2687/2687 [==============================] - 0s 178us/step - loss: 0.0295 - val_loss: 0.0411\n",
      "Epoch 25/400\n",
      "2687/2687 [==============================] - 0s 173us/step - loss: 0.0285 - val_loss: 0.0401\n",
      "Epoch 26/400\n",
      "2687/2687 [==============================] - 0s 180us/step - loss: 0.0276 - val_loss: 0.0393\n",
      "Epoch 27/400\n",
      "2687/2687 [==============================] - 0s 178us/step - loss: 0.0268 - val_loss: 0.0385\n",
      "Epoch 28/400\n",
      "2687/2687 [==============================] - 0s 177us/step - loss: 0.0260 - val_loss: 0.0377\n",
      "Epoch 29/400\n",
      "2687/2687 [==============================] - 0s 177us/step - loss: 0.0253 - val_loss: 0.0370\n",
      "Epoch 30/400\n",
      "2687/2687 [==============================] - 0s 172us/step - loss: 0.0246 - val_loss: 0.0364\n",
      "Epoch 31/400\n",
      "2687/2687 [==============================] - 0s 177us/step - loss: 0.0240 - val_loss: 0.0358\n",
      "Epoch 32/400\n",
      "2687/2687 [==============================] - 0s 176us/step - loss: 0.0235 - val_loss: 0.0353\n",
      "Epoch 33/400\n",
      "2687/2687 [==============================] - 0s 178us/step - loss: 0.0229 - val_loss: 0.0348\n",
      "Epoch 34/400\n",
      "2687/2687 [==============================] - 0s 185us/step - loss: 0.0224 - val_loss: 0.0343\n",
      "Epoch 35/400\n",
      "2687/2687 [==============================] - 0s 175us/step - loss: 0.0220 - val_loss: 0.0338\n",
      "Epoch 36/400\n",
      "2687/2687 [==============================] - 0s 170us/step - loss: 0.0215 - val_loss: 0.0334\n",
      "Epoch 37/400\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 78/400\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "2687/2687 [==============================] - 1s 204us/step - loss: 0.0102 - val_loss: 0.0240\n",
      "Epoch 155/400\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
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     "text": [
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      "2687/2687 [==============================] - 1s 189us/step - loss: 0.0079 - val_loss: 0.0242\n",
      "Epoch 359/400\n",
      "2687/2687 [==============================] - 0s 183us/step - loss: 0.0079 - val_loss: 0.0242\n",
      "Epoch 360/400\n",
      "2687/2687 [==============================] - 0s 182us/step - loss: 0.0079 - val_loss: 0.0242\n",
      "Epoch 361/400\n",
      "2687/2687 [==============================] - 0s 182us/step - loss: 0.0079 - val_loss: 0.0242\n",
      "Epoch 362/400\n",
      "2687/2687 [==============================] - 0s 185us/step - loss: 0.0079 - val_loss: 0.0242\n",
      "Epoch 363/400\n",
      "2687/2687 [==============================] - 0s 181us/step - loss: 0.0079 - val_loss: 0.0242\n",
      "Epoch 364/400\n",
      "2687/2687 [==============================] - 0s 183us/step - loss: 0.0079 - val_loss: 0.0242\n",
      "Epoch 365/400\n",
      "2687/2687 [==============================] - 0s 181us/step - loss: 0.0079 - val_loss: 0.0242\n",
      "Epoch 366/400\n",
      "2687/2687 [==============================] - 1s 190us/step - loss: 0.0079 - val_loss: 0.0242\n",
      "Epoch 367/400\n",
      "2687/2687 [==============================] - 1s 190us/step - loss: 0.0079 - val_loss: 0.0242\n",
      "Epoch 368/400\n",
      "2687/2687 [==============================] - 0s 182us/step - loss: 0.0079 - val_loss: 0.0242\n",
      "Epoch 369/400\n",
      "2687/2687 [==============================] - 0s 181us/step - loss: 0.0078 - val_loss: 0.0242\n",
      "Epoch 370/400\n",
      "2687/2687 [==============================] - 0s 180us/step - loss: 0.0078 - val_loss: 0.0242\n",
      "Epoch 371/400\n",
      "2687/2687 [==============================] - 0s 181us/step - loss: 0.0078 - val_loss: 0.0242\n",
      "Epoch 372/400\n",
      "2687/2687 [==============================] - 0s 183us/step - loss: 0.0078 - val_loss: 0.0242\n",
      "Epoch 373/400\n",
      "2687/2687 [==============================] - 0s 180us/step - loss: 0.0078 - val_loss: 0.0242\n",
      "Epoch 374/400\n",
      "2687/2687 [==============================] - 0s 182us/step - loss: 0.0078 - val_loss: 0.0242\n",
      "Epoch 375/400\n",
      "2687/2687 [==============================] - 0s 182us/step - loss: 0.0078 - val_loss: 0.0242\n",
      "Epoch 376/400\n",
      "2687/2687 [==============================] - 1s 187us/step - loss: 0.0078 - val_loss: 0.0242\n",
      "Epoch 377/400\n",
      "2687/2687 [==============================] - 0s 182us/step - loss: 0.0078 - val_loss: 0.0242\n",
      "Epoch 378/400\n",
      "2687/2687 [==============================] - 0s 185us/step - loss: 0.0078 - val_loss: 0.0242\n",
      "Epoch 379/400\n",
      "2687/2687 [==============================] - 0s 181us/step - loss: 0.0078 - val_loss: 0.0242\n",
      "Epoch 380/400\n",
      "2687/2687 [==============================] - 0s 184us/step - loss: 0.0078 - val_loss: 0.0242\n",
      "Epoch 381/400\n",
      "2687/2687 [==============================] - 0s 183us/step - loss: 0.0078 - val_loss: 0.0242\n",
      "Epoch 382/400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2687/2687 [==============================] - 1s 186us/step - loss: 0.0078 - val_loss: 0.0242\n",
      "Epoch 383/400\n",
      "2687/2687 [==============================] - 0s 181us/step - loss: 0.0078 - val_loss: 0.0242\n",
      "Epoch 384/400\n",
      "2687/2687 [==============================] - 0s 184us/step - loss: 0.0078 - val_loss: 0.0242\n",
      "Epoch 385/400\n",
      "2687/2687 [==============================] - 0s 182us/step - loss: 0.0078 - val_loss: 0.0242\n",
      "Epoch 386/400\n",
      "2687/2687 [==============================] - 0s 182us/step - loss: 0.0078 - val_loss: 0.0243\n",
      "Epoch 387/400\n",
      "2687/2687 [==============================] - 0s 181us/step - loss: 0.0078 - val_loss: 0.0243\n",
      "Epoch 388/400\n",
      "2687/2687 [==============================] - 0s 183us/step - loss: 0.0077 - val_loss: 0.0243\n",
      "Epoch 389/400\n",
      "2687/2687 [==============================] - 0s 180us/step - loss: 0.0077 - val_loss: 0.0243\n",
      "Epoch 390/400\n",
      "2687/2687 [==============================] - 0s 179us/step - loss: 0.0077 - val_loss: 0.0243\n",
      "Epoch 391/400\n",
      "2687/2687 [==============================] - 1s 205us/step - loss: 0.0077 - val_loss: 0.0243\n",
      "Epoch 392/400\n",
      "2687/2687 [==============================] - 1s 194us/step - loss: 0.0077 - val_loss: 0.0243\n",
      "Epoch 393/400\n",
      "2687/2687 [==============================] - 0s 182us/step - loss: 0.0077 - val_loss: 0.0243\n",
      "Epoch 394/400\n",
      "2687/2687 [==============================] - 1s 203us/step - loss: 0.0077 - val_loss: 0.0243\n",
      "Epoch 395/400\n",
      "2687/2687 [==============================] - 1s 199us/step - loss: 0.0077 - val_loss: 0.0243\n",
      "Epoch 396/400\n",
      "2687/2687 [==============================] - 1s 207us/step - loss: 0.0077 - val_loss: 0.0243\n",
      "Epoch 397/400\n",
      "2687/2687 [==============================] - 1s 289us/step - loss: 0.0077 - val_loss: 0.0243\n",
      "Epoch 398/400\n",
      "2687/2687 [==============================] - 1s 244us/step - loss: 0.0077 - val_loss: 0.0243\n",
      "Epoch 399/400\n",
      "2687/2687 [==============================] - 1s 261us/step - loss: 0.0077 - val_loss: 0.0243\n",
      "Epoch 400/400\n",
      "2687/2687 [==============================] - 1s 313us/step - loss: 0.0077 - val_loss: 0.0243\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x144a08128>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(seed)\n",
    "random.set_seed(seed)\n",
    "model_3 = Sequential() \n",
    "model_3.add(Dense(6, activation='relu', input_dim=2))\n",
    "model_3.add(Dense(1, activation='sigmoid'))\n",
    "model_3.compile(optimizer='sgd', loss='binary_crossentropy') \n",
    "# train the model for 400 epoches\n",
    "model_3.fit(feats, target, batch_size=5, epochs=400, verbose=1, validation_split=0.2, shuffle=False) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the decision boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Decision Boundary for Neural Network with hidden layer size 6')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)\n",
    "plot_decision_boundary(lambda x: model_3.predict(x), feats, target) \n",
    "plt.title(\"Decision Boundary for Neural Network with hidden layer size 6\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a neural network with hidden layer of size 3 and tanh activation function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 2687 samples, validate on 672 samples\n",
      "Epoch 1/200\n",
      "2687/2687 [==============================] - 1s 292us/step - loss: 0.4163 - val_loss: 0.3552\n",
      "Epoch 2/200\n",
      "2687/2687 [==============================] - 1s 218us/step - loss: 0.3127 - val_loss: 0.3385\n",
      "Epoch 3/200\n",
      "2687/2687 [==============================] - 1s 200us/step - loss: 0.3005 - val_loss: 0.3292\n",
      "Epoch 4/200\n",
      "2687/2687 [==============================] - 1s 204us/step - loss: 0.2925 - val_loss: 0.3203\n",
      "Epoch 5/200\n",
      "2687/2687 [==============================] - 1s 208us/step - loss: 0.2853 - val_loss: 0.3109\n",
      "Epoch 6/200\n",
      "2687/2687 [==============================] - 1s 241us/step - loss: 0.2782 - val_loss: 0.3007\n",
      "Epoch 7/200\n",
      "2687/2687 [==============================] - 1s 266us/step - loss: 0.2706 - val_loss: 0.2898\n",
      "Epoch 8/200\n",
      "2687/2687 [==============================] - 1s 205us/step - loss: 0.2623 - val_loss: 0.2784\n",
      "Epoch 9/200\n",
      "2687/2687 [==============================] - 1s 195us/step - loss: 0.2533 - val_loss: 0.2666\n",
      "Epoch 10/200\n",
      "2687/2687 [==============================] - 1s 216us/step - loss: 0.2436 - val_loss: 0.2548\n",
      "Epoch 11/200\n",
      "2687/2687 [==============================] - 1s 238us/step - loss: 0.2337 - val_loss: 0.2437\n",
      "Epoch 12/200\n",
      "2687/2687 [==============================] - 1s 384us/step - loss: 0.2246 - val_loss: 0.2337\n",
      "Epoch 13/200\n",
      "2687/2687 [==============================] - 1s 363us/step - loss: 0.2165 - val_loss: 0.2248\n",
      "Epoch 14/200\n",
      "2687/2687 [==============================] - 1s 392us/step - loss: 0.2094 - val_loss: 0.2168\n",
      "Epoch 15/200\n",
      "2687/2687 [==============================] - 1s 249us/step - loss: 0.2029 - val_loss: 0.2095\n",
      "Epoch 16/200\n",
      "2687/2687 [==============================] - 1s 236us/step - loss: 0.1969 - val_loss: 0.2028\n",
      "Epoch 17/200\n",
      "2687/2687 [==============================] - 1s 211us/step - loss: 0.1913 - val_loss: 0.1966\n",
      "Epoch 18/200\n",
      "2687/2687 [==============================] - 1s 245us/step - loss: 0.1859 - val_loss: 0.1907\n",
      "Epoch 19/200\n",
      "2687/2687 [==============================] - 1s 266us/step - loss: 0.1807 - val_loss: 0.1849\n",
      "Epoch 20/200\n",
      "2687/2687 [==============================] - 1s 216us/step - loss: 0.1755 - val_loss: 0.1793\n",
      "Epoch 21/200\n",
      "2687/2687 [==============================] - 1s 210us/step - loss: 0.1700 - val_loss: 0.1737\n",
      "Epoch 22/200\n",
      "2687/2687 [==============================] - 1s 230us/step - loss: 0.1644 - val_loss: 0.1683\n",
      "Epoch 23/200\n",
      "2687/2687 [==============================] - 1s 212us/step - loss: 0.1585 - val_loss: 0.1632\n",
      "Epoch 24/200\n",
      "2687/2687 [==============================] - 1s 214us/step - loss: 0.1523 - val_loss: 0.1586\n",
      "Epoch 25/200\n",
      "2687/2687 [==============================] - 1s 241us/step - loss: 0.1462 - val_loss: 0.1543\n",
      "Epoch 26/200\n",
      "2687/2687 [==============================] - 1s 246us/step - loss: 0.1403 - val_loss: 0.1502\n",
      "Epoch 27/200\n",
      "2687/2687 [==============================] - 1s 235us/step - loss: 0.1348 - val_loss: 0.1462\n",
      "Epoch 28/200\n",
      "2687/2687 [==============================] - 1s 303us/step - loss: 0.1297 - val_loss: 0.1421\n",
      "Epoch 29/200\n",
      "2687/2687 [==============================] - 1s 238us/step - loss: 0.1250 - val_loss: 0.1381\n",
      "Epoch 30/200\n",
      "2687/2687 [==============================] - 1s 244us/step - loss: 0.1206 - val_loss: 0.1342\n",
      "Epoch 31/200\n",
      "2687/2687 [==============================] - 1s 248us/step - loss: 0.1166 - val_loss: 0.1303\n",
      "Epoch 32/200\n",
      "2687/2687 [==============================] - 1s 232us/step - loss: 0.1128 - val_loss: 0.1267\n",
      "Epoch 33/200\n",
      "2687/2687 [==============================] - 1s 236us/step - loss: 0.1092 - val_loss: 0.1231\n",
      "Epoch 34/200\n",
      "2687/2687 [==============================] - 1s 250us/step - loss: 0.1058 - val_loss: 0.1196\n",
      "Epoch 35/200\n",
      "2687/2687 [==============================] - 1s 203us/step - loss: 0.1026 - val_loss: 0.1163\n",
      "Epoch 36/200\n",
      "2687/2687 [==============================] - 1s 223us/step - loss: 0.0995 - val_loss: 0.1131\n",
      "Epoch 37/200\n",
      "2687/2687 [==============================] - 1s 296us/step - loss: 0.0966 - val_loss: 0.1101\n",
      "Epoch 38/200\n",
      "2687/2687 [==============================] - 1s 269us/step - loss: 0.0939 - val_loss: 0.1072\n",
      "Epoch 39/200\n",
      "2687/2687 [==============================] - 1s 253us/step - loss: 0.0913 - val_loss: 0.1044\n",
      "Epoch 40/200\n",
      "2687/2687 [==============================] - 1s 223us/step - loss: 0.0888 - val_loss: 0.1018\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 78/200\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 155/200\n",
      "2687/2687 [==============================] - 1s 186us/step - loss: 0.0301 - val_loss: 0.0450\n",
      "Epoch 156/200\n",
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      "Epoch 160/200\n",
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      "Epoch 161/200\n",
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      "Epoch 162/200\n",
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      "Epoch 163/200\n",
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      "Epoch 164/200\n",
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      "Epoch 165/200\n",
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      "Epoch 190/200\n",
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      "Epoch 196/200\n",
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      "Epoch 197/200\n",
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      "Epoch 198/200\n",
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      "Epoch 199/200\n",
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      "Epoch 200/200\n",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x144ee5470>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Neural network with hidden layer size = 3 with tanh activation function\n",
    "np.random.seed(seed)\n",
    "random.set_seed(seed)\n",
    "model_4 = Sequential() \n",
    "model_4.add(Dense(3, activation='tanh', input_dim=2))\n",
    "model_4.add(Dense(1, activation='sigmoid'))\n",
    "model_4.compile(optimizer='sgd', loss='binary_crossentropy') \n",
    "# train the model for 200 epoches and batch size 5\n",
    "model_4.fit(feats, target, batch_size=5, epochs=200, verbose=1, validation_split=0.2, shuffle=False) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the decision boundary on the training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Decision Boundary for Neural Network with hidden layer size 3')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)\n",
    "plot_decision_boundary(lambda x: model_4.predict(x), feats, target) \n",
    "plt.title(\"Decision Boundary for Neural Network with hidden layer size 3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Neural network with hidden layer size = 6 with tanh activation function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 2687 samples, validate on 672 samples\n",
      "Epoch 1/400\n",
      "2687/2687 [==============================] - 1s 234us/step - loss: 0.3822 - val_loss: 0.3031\n",
      "Epoch 2/400\n",
      "2687/2687 [==============================] - 1s 211us/step - loss: 0.2594 - val_loss: 0.2736\n",
      "Epoch 3/400\n",
      "2687/2687 [==============================] - 1s 194us/step - loss: 0.2388 - val_loss: 0.2546\n",
      "Epoch 4/400\n",
      "2687/2687 [==============================] - 1s 202us/step - loss: 0.2239 - val_loss: 0.2387\n",
      "Epoch 5/400\n",
      "2687/2687 [==============================] - 1s 229us/step - loss: 0.2112 - val_loss: 0.2250\n",
      "Epoch 6/400\n",
      "2687/2687 [==============================] - 1s 214us/step - loss: 0.1998 - val_loss: 0.2130\n",
      "Epoch 7/400\n",
      "2687/2687 [==============================] - 1s 204us/step - loss: 0.1895 - val_loss: 0.2021\n",
      "Epoch 8/400\n",
      "2687/2687 [==============================] - 1s 203us/step - loss: 0.1800 - val_loss: 0.1922\n",
      "Epoch 9/400\n",
      "2687/2687 [==============================] - 1s 194us/step - loss: 0.1712 - val_loss: 0.1831\n",
      "Epoch 10/400\n",
      "2687/2687 [==============================] - 1s 201us/step - loss: 0.1631 - val_loss: 0.1747\n",
      "Epoch 11/400\n",
      "2687/2687 [==============================] - 1s 212us/step - loss: 0.1555 - val_loss: 0.1670\n",
      "Epoch 12/400\n",
      "2687/2687 [==============================] - 1s 213us/step - loss: 0.1485 - val_loss: 0.1598\n",
      "Epoch 13/400\n",
      "2687/2687 [==============================] - 1s 242us/step - loss: 0.1420 - val_loss: 0.1531\n",
      "Epoch 14/400\n",
      "2687/2687 [==============================] - 1s 241us/step - loss: 0.1358 - val_loss: 0.1469\n",
      "Epoch 15/400\n",
      "2687/2687 [==============================] - 1s 204us/step - loss: 0.1301 - val_loss: 0.1411\n",
      "Epoch 16/400\n",
      "2687/2687 [==============================] - 1s 204us/step - loss: 0.1247 - val_loss: 0.1356\n",
      "Epoch 17/400\n",
      "2687/2687 [==============================] - 1s 200us/step - loss: 0.1197 - val_loss: 0.1305\n",
      "Epoch 18/400\n",
      "2687/2687 [==============================] - 1s 203us/step - loss: 0.1149 - val_loss: 0.1257\n",
      "Epoch 19/400\n",
      "2687/2687 [==============================] - 1s 195us/step - loss: 0.1104 - val_loss: 0.1211\n",
      "Epoch 20/400\n",
      "2687/2687 [==============================] - 1s 202us/step - loss: 0.1061 - val_loss: 0.1168\n",
      "Epoch 21/400\n",
      "2687/2687 [==============================] - 1s 195us/step - loss: 0.1020 - val_loss: 0.1128\n",
      "Epoch 22/400\n",
      "2687/2687 [==============================] - 1s 201us/step - loss: 0.0982 - val_loss: 0.1089\n",
      "Epoch 23/400\n",
      "2687/2687 [==============================] - 1s 194us/step - loss: 0.0946 - val_loss: 0.1053\n",
      "Epoch 24/400\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 78/400\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 155/400\n",
      "2687/2687 [==============================] - 1s 336us/step - loss: 0.0238 - val_loss: 0.0333\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "text": [
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      "2687/2687 [==============================] - 0s 181us/step - loss: 0.0172 - val_loss: 0.0272\n",
      "Epoch 346/400\n",
      "2687/2687 [==============================] - 0s 181us/step - loss: 0.0172 - val_loss: 0.0272\n",
      "Epoch 347/400\n",
      "2687/2687 [==============================] - 1s 190us/step - loss: 0.0171 - val_loss: 0.0272\n",
      "Epoch 348/400\n",
      "2687/2687 [==============================] - 0s 181us/step - loss: 0.0171 - val_loss: 0.0272\n",
      "Epoch 349/400\n",
      "2687/2687 [==============================] - 0s 180us/step - loss: 0.0171 - val_loss: 0.0271\n",
      "Epoch 350/400\n",
      "2687/2687 [==============================] - 0s 181us/step - loss: 0.0171 - val_loss: 0.0271\n",
      "Epoch 351/400\n",
      "2687/2687 [==============================] - 0s 181us/step - loss: 0.0171 - val_loss: 0.0271\n",
      "Epoch 352/400\n",
      "2687/2687 [==============================] - 0s 181us/step - loss: 0.0170 - val_loss: 0.0271\n",
      "Epoch 353/400\n",
      "2687/2687 [==============================] - 0s 181us/step - loss: 0.0170 - val_loss: 0.0271\n",
      "Epoch 354/400\n",
      "2687/2687 [==============================] - 0s 180us/step - loss: 0.0170 - val_loss: 0.0271\n",
      "Epoch 355/400\n",
      "2687/2687 [==============================] - 0s 179us/step - loss: 0.0170 - val_loss: 0.0270\n",
      "Epoch 356/400\n",
      "2687/2687 [==============================] - 0s 184us/step - loss: 0.0170 - val_loss: 0.0270\n",
      "Epoch 357/400\n",
      "2687/2687 [==============================] - 0s 179us/step - loss: 0.0170 - val_loss: 0.0270\n",
      "Epoch 358/400\n",
      "2687/2687 [==============================] - 0s 178us/step - loss: 0.0169 - val_loss: 0.0270\n",
      "Epoch 359/400\n",
      "2687/2687 [==============================] - 0s 181us/step - loss: 0.0169 - val_loss: 0.0270\n",
      "Epoch 360/400\n",
      "2687/2687 [==============================] - 0s 176us/step - loss: 0.0169 - val_loss: 0.0270\n",
      "Epoch 361/400\n",
      "2687/2687 [==============================] - 0s 182us/step - loss: 0.0169 - val_loss: 0.0270\n",
      "Epoch 362/400\n",
      "2687/2687 [==============================] - 0s 184us/step - loss: 0.0169 - val_loss: 0.0269\n",
      "Epoch 363/400\n",
      "2687/2687 [==============================] - 0s 182us/step - loss: 0.0169 - val_loss: 0.0269\n",
      "Epoch 364/400\n",
      "2687/2687 [==============================] - 0s 180us/step - loss: 0.0168 - val_loss: 0.0269\n",
      "Epoch 365/400\n",
      "2687/2687 [==============================] - 0s 184us/step - loss: 0.0168 - val_loss: 0.0269\n",
      "Epoch 366/400\n",
      "2687/2687 [==============================] - 0s 179us/step - loss: 0.0168 - val_loss: 0.0269\n",
      "Epoch 367/400\n",
      "2687/2687 [==============================] - 0s 185us/step - loss: 0.0168 - val_loss: 0.0269\n",
      "Epoch 368/400\n",
      "2687/2687 [==============================] - 0s 179us/step - loss: 0.0168 - val_loss: 0.0268\n",
      "Epoch 369/400\n",
      "2687/2687 [==============================] - 0s 178us/step - loss: 0.0168 - val_loss: 0.0268\n",
      "Epoch 370/400\n",
      "2687/2687 [==============================] - 0s 179us/step - loss: 0.0167 - val_loss: 0.0268\n",
      "Epoch 371/400\n",
      "2687/2687 [==============================] - 0s 181us/step - loss: 0.0167 - val_loss: 0.0268\n",
      "Epoch 372/400\n",
      "2687/2687 [==============================] - 1s 195us/step - loss: 0.0167 - val_loss: 0.0268\n",
      "Epoch 373/400\n",
      "2687/2687 [==============================] - 1s 222us/step - loss: 0.0167 - val_loss: 0.0268\n",
      "Epoch 374/400\n",
      "2687/2687 [==============================] - 1s 207us/step - loss: 0.0167 - val_loss: 0.0268\n",
      "Epoch 375/400\n",
      "2687/2687 [==============================] - 1s 196us/step - loss: 0.0167 - val_loss: 0.0267\n",
      "Epoch 376/400\n",
      "2687/2687 [==============================] - 1s 226us/step - loss: 0.0166 - val_loss: 0.0267\n",
      "Epoch 377/400\n",
      "2687/2687 [==============================] - 1s 225us/step - loss: 0.0166 - val_loss: 0.0267\n",
      "Epoch 378/400\n",
      "2687/2687 [==============================] - 1s 217us/step - loss: 0.0166 - val_loss: 0.0267\n",
      "Epoch 379/400\n",
      "2687/2687 [==============================] - 1s 200us/step - loss: 0.0166 - val_loss: 0.0267\n",
      "Epoch 380/400\n",
      "2687/2687 [==============================] - 1s 193us/step - loss: 0.0166 - val_loss: 0.0267\n",
      "Epoch 381/400\n",
      "2687/2687 [==============================] - 1s 200us/step - loss: 0.0166 - val_loss: 0.0267\n",
      "Epoch 382/400\n",
      "2687/2687 [==============================] - 1s 205us/step - loss: 0.0165 - val_loss: 0.0266\n",
      "Epoch 383/400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2687/2687 [==============================] - 1s 198us/step - loss: 0.0165 - val_loss: 0.0266\n",
      "Epoch 384/400\n",
      "2687/2687 [==============================] - 1s 190us/step - loss: 0.0165 - val_loss: 0.0266\n",
      "Epoch 385/400\n",
      "2687/2687 [==============================] - 1s 193us/step - loss: 0.0165 - val_loss: 0.0266\n",
      "Epoch 386/400\n",
      "2687/2687 [==============================] - 1s 189us/step - loss: 0.0165 - val_loss: 0.0266\n",
      "Epoch 387/400\n",
      "2687/2687 [==============================] - 1s 197us/step - loss: 0.0165 - val_loss: 0.0266\n",
      "Epoch 388/400\n",
      "2687/2687 [==============================] - 1s 197us/step - loss: 0.0164 - val_loss: 0.0266\n",
      "Epoch 389/400\n",
      "2687/2687 [==============================] - 1s 208us/step - loss: 0.0164 - val_loss: 0.0265\n",
      "Epoch 390/400\n",
      "2687/2687 [==============================] - 1s 225us/step - loss: 0.0164 - val_loss: 0.0265\n",
      "Epoch 391/400\n",
      "2687/2687 [==============================] - 1s 263us/step - loss: 0.0164 - val_loss: 0.0265\n",
      "Epoch 392/400\n",
      "2687/2687 [==============================] - 1s 261us/step - loss: 0.0164 - val_loss: 0.0265\n",
      "Epoch 393/400\n",
      "2687/2687 [==============================] - 1s 192us/step - loss: 0.0164 - val_loss: 0.0265\n",
      "Epoch 394/400\n",
      "2687/2687 [==============================] - 1s 232us/step - loss: 0.0164 - val_loss: 0.0265\n",
      "Epoch 395/400\n",
      "2687/2687 [==============================] - 1s 202us/step - loss: 0.0163 - val_loss: 0.0265\n",
      "Epoch 396/400\n",
      "2687/2687 [==============================] - 1s 193us/step - loss: 0.0163 - val_loss: 0.0265\n",
      "Epoch 397/400\n",
      "2687/2687 [==============================] - 0s 179us/step - loss: 0.0163 - val_loss: 0.0264\n",
      "Epoch 398/400\n",
      "2687/2687 [==============================] - 1s 186us/step - loss: 0.0163 - val_loss: 0.0264\n",
      "Epoch 399/400\n",
      "2687/2687 [==============================] - 1s 210us/step - loss: 0.0163 - val_loss: 0.0264\n",
      "Epoch 400/400\n",
      "2687/2687 [==============================] - 1s 212us/step - loss: 0.0163 - val_loss: 0.0264\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x1453b5198>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(seed)\n",
    "random.set_seed(seed)\n",
    "model_5 = Sequential() \n",
    "model_5.add(Dense(6, activation='tanh', input_dim=2))\n",
    "model_5.add(Dense(1, activation='sigmoid'))\n",
    "model_5.compile(optimizer='sgd', loss='binary_crossentropy') \n",
    "# train the model for 400 epoches\n",
    "model_5.fit(feats, target, batch_size=5, epochs=400, verbose=1, validation_split=0.2, shuffle=False) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the decision boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Decision Boundary for Neural Network with hidden layer size 6')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)\n",
    "plot_decision_boundary(lambda x: model_5.predict(x), feats, target) \n",
    "plt.title(\"Decision Boundary for Neural Network with hidden layer size 6\") "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py3.7",
   "language": "python",
   "name": "py3.7"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}