LOGIC_GATES.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BUILD THE LOGIC GATES WITH NEURAL NETWORK\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this Notebook, we are trying to build the Logic Gates Known as OR GATE, XOR GATE and AND GATE. To build theses gates, we need two entries and one output. The entries are the combination of 0 and 1 and the Output can only be 0 or 1. We will use Numpy and sklearn librairies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OR GATE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table>\n",
    "    <tr>\n",
    "        <td>\n",
    "            x1\n",
    "        </td>\n",
    "        <td>\n",
    "            x2\n",
    "        </td>\n",
    "        <td>\n",
    "            output\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            0\n",
    "        </td>\n",
    "        <td>\n",
    "            0\n",
    "        </td>\n",
    "        <td>\n",
    "            0\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            0\n",
    "        </td>\n",
    "        <td>\n",
    "            1\n",
    "        </td>\n",
    "        <td>\n",
    "            1\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            1\n",
    "        </td>\n",
    "        <td>\n",
    "            0\n",
    "        </td>\n",
    "        <td>\n",
    "            1\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            1\n",
    "        </td>\n",
    "        <td>\n",
    "            1\n",
    "        </td>\n",
    "        <td>\n",
    "            1\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X is [[0 0]\n",
      " [0 1]\n",
      " [1 0]\n",
      " [1 1]]\n",
      "Y is [0 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "#create the entries and the output\n",
    "# the entries can be represented by an array of dimension 2 (4,2)\n",
    "#the output can be represented by a vector (4,1)\n",
    "\n",
    "X_OR= np.array([[0,0],[0,1],[1,0],[1,1]])\n",
    "Y_OR= np.array([0,1,1,1])\n",
    "\n",
    "#visualisation\n",
    "print(\"X is\", X_OR)\n",
    "print(\"Y is\", Y_OR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.72902212\n",
      "Iteration 2, loss = 0.72079697\n",
      "Iteration 3, loss = 0.71278751\n",
      "Iteration 4, loss = 0.70499718\n",
      "Iteration 5, loss = 0.69742895\n",
      "Iteration 6, loss = 0.69008550\n",
      "Iteration 7, loss = 0.68296919\n",
      "Iteration 8, loss = 0.67608191\n",
      "Iteration 9, loss = 0.66942511\n",
      "Iteration 10, loss = 0.66299969\n",
      "Iteration 11, loss = 0.65680606\n",
      "Iteration 12, loss = 0.65084412\n",
      "Iteration 13, loss = 0.64511322\n",
      "Iteration 14, loss = 0.63961221\n",
      "Iteration 15, loss = 0.63433941\n",
      "Iteration 16, loss = 0.62929264\n",
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      "Iteration 44, loss = 0.55991314\n",
      "Iteration 45, loss = 0.55911887\n",
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      "Iteration 49, loss = 0.55646703\n",
      "Iteration 50, loss = 0.55591399\n",
      "Iteration 51, loss = 0.55539719\n",
      "Iteration 52, loss = 0.55491318\n",
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      "Iteration 67, loss = 0.54987031\n",
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      "Iteration 69, loss = 0.54933896\n",
      "Iteration 70, loss = 0.54907639\n",
      "Iteration 71, loss = 0.54881516\n",
      "Iteration 72, loss = 0.54855483\n",
      "Iteration 73, loss = 0.54829501\n",
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      "Iteration 75, loss = 0.54777559\n",
      "Iteration 76, loss = 0.54751548\n",
      "Iteration 77, loss = 0.54725483\n",
      "Iteration 78, loss = 0.54699350\n",
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      "Iteration 803, loss = 0.09553333\n",
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      "Iteration 805, loss = 0.09487674\n",
      "Iteration 806, loss = 0.09455052\n",
      "Iteration 807, loss = 0.09422568\n",
      "Iteration 808, loss = 0.09390221\n",
      "Iteration 809, loss = 0.09358011\n",
      "Iteration 810, loss = 0.09325937\n",
      "Iteration 811, loss = 0.09293998\n",
      "Iteration 812, loss = 0.09262195\n",
      "Iteration 813, loss = 0.09230526\n",
      "Iteration 814, loss = 0.09198991\n",
      "Iteration 815, loss = 0.09167589\n",
      "Iteration 816, loss = 0.09136320\n",
      "Iteration 817, loss = 0.09105183\n",
      "Iteration 818, loss = 0.09074178\n",
      "Iteration 819, loss = 0.09043304\n",
      "Iteration 820, loss = 0.09012561\n",
      "Iteration 821, loss = 0.08981947\n",
      "Iteration 822, loss = 0.08951463\n",
      "Iteration 823, loss = 0.08921108\n",
      "Iteration 824, loss = 0.08890882\n",
      "Iteration 825, loss = 0.08860783\n",
      "Iteration 826, loss = 0.08830812\n",
      "Iteration 827, loss = 0.08800967\n",
      "Iteration 828, loss = 0.08771249\n",
      "Iteration 829, loss = 0.08741656\n",
      "Iteration 830, loss = 0.08712189\n",
      "Iteration 831, loss = 0.08682846\n",
      "Iteration 832, loss = 0.08653627\n",
      "Iteration 833, loss = 0.08624532\n",
      "Iteration 834, loss = 0.08595560\n",
      "Iteration 835, loss = 0.08566710\n",
      "Iteration 836, loss = 0.08537983\n",
      "Iteration 837, loss = 0.08509377\n",
      "Iteration 838, loss = 0.08480892\n",
      "Iteration 839, loss = 0.08452527\n",
      "Iteration 840, loss = 0.08424282\n",
      "Iteration 841, loss = 0.08396157\n",
      "Iteration 842, loss = 0.08368151\n",
      "Iteration 843, loss = 0.08340263\n",
      "Iteration 844, loss = 0.08312492\n",
      "Iteration 845, loss = 0.08284840\n",
      "Iteration 846, loss = 0.08257304\n",
      "Iteration 847, loss = 0.08229884\n",
      "Iteration 848, loss = 0.08202580\n",
      "Iteration 849, loss = 0.08175392\n",
      "Iteration 850, loss = 0.08148318\n",
      "Iteration 851, loss = 0.08121359\n",
      "Iteration 852, loss = 0.08094513\n",
      "Iteration 853, loss = 0.08067781\n",
      "Iteration 854, loss = 0.08041162\n",
      "Iteration 855, loss = 0.08014654\n",
      "Iteration 856, loss = 0.07988259\n",
      "Iteration 857, loss = 0.07961975\n",
      "Iteration 858, loss = 0.07935802\n",
      "Iteration 859, loss = 0.07909740\n",
      "Iteration 860, loss = 0.07883787\n",
      "Iteration 861, loss = 0.07857944\n",
      "Iteration 862, loss = 0.07832209\n",
      "Iteration 863, loss = 0.07806583\n",
      "Iteration 864, loss = 0.07781065\n",
      "Iteration 865, loss = 0.07755654\n",
      "Iteration 866, loss = 0.07730351\n",
      "Iteration 867, loss = 0.07705154\n",
      "Iteration 868, loss = 0.07680063\n",
      "Iteration 869, loss = 0.07655077\n",
      "Iteration 870, loss = 0.07630197\n",
      "Iteration 871, loss = 0.07605421\n",
      "Iteration 872, loss = 0.07580749\n",
      "Iteration 873, loss = 0.07556182\n",
      "Iteration 874, loss = 0.07531717\n",
      "Iteration 875, loss = 0.07507355\n",
      "Iteration 876, loss = 0.07483096\n",
      "Iteration 877, loss = 0.07458938\n",
      "Iteration 878, loss = 0.07434882\n",
      "Iteration 879, loss = 0.07410927\n",
      "Iteration 880, loss = 0.07387072\n",
      "Iteration 881, loss = 0.07363317\n",
      "Iteration 882, loss = 0.07339662\n",
      "Iteration 883, loss = 0.07316106\n",
      "Iteration 884, loss = 0.07292649\n",
      "Iteration 885, loss = 0.07269290\n",
      "Iteration 886, loss = 0.07246028\n",
      "Iteration 887, loss = 0.07222865\n",
      "Iteration 888, loss = 0.07199798\n",
      "Iteration 889, loss = 0.07176827\n",
      "Iteration 890, loss = 0.07153953\n",
      "Iteration 891, loss = 0.07131174\n",
      "Iteration 892, loss = 0.07108491\n",
      "Iteration 893, loss = 0.07085902\n",
      "Iteration 894, loss = 0.07063408\n",
      "Iteration 895, loss = 0.07041007\n",
      "Iteration 896, loss = 0.07018700\n",
      "Iteration 897, loss = 0.06996486\n",
      "Iteration 898, loss = 0.06974365\n",
      "Iteration 899, loss = 0.06952336\n",
      "Iteration 900, loss = 0.06930398\n",
      "Iteration 901, loss = 0.06908552\n",
      "Iteration 902, loss = 0.06886797\n",
      "Iteration 903, loss = 0.06865133\n",
      "Iteration 904, loss = 0.06843559\n",
      "Iteration 905, loss = 0.06822074\n",
      "Iteration 906, loss = 0.06800679\n",
      "Iteration 907, loss = 0.06779372\n",
      "Iteration 908, loss = 0.06758154\n",
      "Iteration 909, loss = 0.06737025\n",
      "Iteration 910, loss = 0.06715983\n",
      "Iteration 911, loss = 0.06695028\n",
      "Iteration 912, loss = 0.06674160\n",
      "Iteration 913, loss = 0.06653378\n",
      "Iteration 914, loss = 0.06632683\n",
      "Iteration 915, loss = 0.06612074\n",
      "Iteration 916, loss = 0.06591549\n",
      "Iteration 917, loss = 0.06571110\n",
      "Iteration 918, loss = 0.06550755\n",
      "Iteration 919, loss = 0.06530484\n",
      "Iteration 920, loss = 0.06510297\n",
      "Iteration 921, loss = 0.06490194\n",
      "Iteration 922, loss = 0.06470173\n",
      "Iteration 923, loss = 0.06450235\n",
      "Iteration 924, loss = 0.06430380\n",
      "Iteration 925, loss = 0.06410606\n",
      "Iteration 926, loss = 0.06390914\n",
      "Iteration 927, loss = 0.06371302\n",
      "Iteration 928, loss = 0.06351772\n",
      "Iteration 929, loss = 0.06332322\n",
      "Iteration 930, loss = 0.06312951\n",
      "Iteration 931, loss = 0.06293661\n",
      "Iteration 932, loss = 0.06274450\n",
      "Iteration 933, loss = 0.06255317\n",
      "Iteration 934, loss = 0.06236263\n",
      "Iteration 935, loss = 0.06217288\n",
      "Iteration 936, loss = 0.06198390\n",
      "Iteration 937, loss = 0.06179569\n",
      "Iteration 938, loss = 0.06160826\n",
      "Iteration 939, loss = 0.06142159\n",
      "Iteration 940, loss = 0.06123569\n",
      "Iteration 941, loss = 0.06105055\n",
      "Iteration 942, loss = 0.06086616\n",
      "Iteration 943, loss = 0.06068253\n",
      "Iteration 944, loss = 0.06049965\n",
      "Iteration 945, loss = 0.06031751\n",
      "Iteration 946, loss = 0.06013612\n",
      "Iteration 947, loss = 0.05995546\n",
      "Iteration 948, loss = 0.05977555\n",
      "Iteration 949, loss = 0.05959636\n",
      "Iteration 950, loss = 0.05941791\n",
      "Iteration 951, loss = 0.05924018\n",
      "Iteration 952, loss = 0.05906317\n",
      "Iteration 953, loss = 0.05888688\n",
      "Iteration 954, loss = 0.05871131\n",
      "Iteration 955, loss = 0.05853645\n",
      "Iteration 956, loss = 0.05836230\n",
      "Iteration 957, loss = 0.05818886\n",
      "Iteration 958, loss = 0.05801612\n",
      "Iteration 959, loss = 0.05784408\n",
      "Iteration 960, loss = 0.05767273\n",
      "Iteration 961, loss = 0.05750208\n",
      "Iteration 962, loss = 0.05733212\n",
      "Iteration 963, loss = 0.05716284\n",
      "Iteration 964, loss = 0.05699425\n",
      "Iteration 965, loss = 0.05682634\n",
      "Iteration 966, loss = 0.05665910\n",
      "Iteration 967, loss = 0.05649254\n",
      "Iteration 968, loss = 0.05632665\n",
      "Iteration 969, loss = 0.05616143\n",
      "Iteration 970, loss = 0.05599687\n",
      "Iteration 971, loss = 0.05583298\n",
      "Iteration 972, loss = 0.05566974\n",
      "Iteration 973, loss = 0.05550716\n",
      "Iteration 974, loss = 0.05534523\n",
      "Iteration 975, loss = 0.05518395\n",
      "Iteration 976, loss = 0.05502332\n",
      "Iteration 977, loss = 0.05486333\n",
      "Iteration 978, loss = 0.05470399\n",
      "Iteration 979, loss = 0.05454528\n",
      "Iteration 980, loss = 0.05438720\n",
      "Iteration 981, loss = 0.05422976\n",
      "Iteration 982, loss = 0.05407294\n",
      "Iteration 983, loss = 0.05391675\n",
      "Iteration 984, loss = 0.05376119\n",
      "Iteration 985, loss = 0.05360624\n",
      "Iteration 986, loss = 0.05345191\n",
      "Iteration 987, loss = 0.05329820\n",
      "Iteration 988, loss = 0.05314509\n",
      "Iteration 989, loss = 0.05299260\n",
      "Iteration 990, loss = 0.05284071\n",
      "Iteration 991, loss = 0.05268943\n",
      "Iteration 992, loss = 0.05253874\n",
      "Iteration 993, loss = 0.05238866\n",
      "Iteration 994, loss = 0.05223917\n",
      "Iteration 995, loss = 0.05209027\n",
      "Iteration 996, loss = 0.05194196\n",
      "Iteration 997, loss = 0.05179423\n",
      "Iteration 998, loss = 0.05164709\n",
      "Iteration 999, loss = 0.05150053\n",
      "Iteration 1000, loss = 0.05135456\n",
      "The Prediction is given by [0 1 1 1]\n",
      "The score is 1.0\n"
     ]
    }
   ],
   "source": [
    "#create the model\n",
    "modelOR= MLPClassifier(hidden_layer_sizes=(50,), activation='logistic', solver='adam', max_iter=1000, verbose=10)\n",
    "\n",
    "#train the model\n",
    "modelOR.fit(X_OR,Y_OR)\n",
    "\n",
    "#make a prediction\n",
    "Y_OR_hate= modelOR.predict(X_OR)\n",
    "print(\"The Prediction is given by\",Y_OR_hate)\n",
    "\n",
    "#compute the score of our model\n",
    "score = modelOR.score(X_OR,Y_OR)\n",
    "print(\"The score is\",score)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As we can noticed, Y_OR_hate is the same as Y_OR. It means that the model suceed to predict the OR GATE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XOR GATE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table>\n",
    "    <tr>\n",
    "        <td>\n",
    "            x1\n",
    "        </td>\n",
    "        <td>\n",
    "            x2\n",
    "        </td>\n",
    "        <td>\n",
    "            output\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            0\n",
    "        </td>\n",
    "        <td>\n",
    "            0\n",
    "        </td>\n",
    "        <td>\n",
    "            0\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            0\n",
    "        </td>\n",
    "        <td>\n",
    "            1\n",
    "        </td>\n",
    "        <td>\n",
    "            1\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            1\n",
    "        </td>\n",
    "        <td>\n",
    "            0\n",
    "        </td>\n",
    "        <td>\n",
    "            1\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            1\n",
    "        </td>\n",
    "        <td>\n",
    "            1\n",
    "        </td>\n",
    "        <td>\n",
    "            0\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X is [[0 0]\n",
      " [0 1]\n",
      " [1 0]\n",
      " [1 1]]\n",
      "Y is [0 1 1 0]\n"
     ]
    }
   ],
   "source": [
    "#create the entries and the output\n",
    "# the entries can be represented by an array of dimension 2 (4,2)\n",
    "#the output can be represented by a vector (4,1)\n",
    "\n",
    "X_XOR= np.array([[0,0],[0,1],[1,0],[1,1]])\n",
    "Y_XOR= np.array([0,1,1,0])\n",
    "\n",
    "#visualisation\n",
    "print(\"X is\", X_XOR)\n",
    "print(\"Y is\", Y_XOR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.69509008\n",
      "Iteration 2, loss = 0.69435593\n",
      "Iteration 3, loss = 0.69383538\n",
      "Iteration 4, loss = 0.69352292\n",
      "Iteration 5, loss = 0.69339737\n",
      "Iteration 6, loss = 0.69341545\n",
      "Iteration 7, loss = 0.69351336\n",
      "Iteration 8, loss = 0.69362329\n",
      "Training loss did not improve more than tol=0.000100 for two consecutive epochs. Stopping.\n",
      "The Prediction is given by [1 0 1 1]\n",
      "The score is 0.25\n"
     ]
    }
   ],
   "source": [
    "#create the model\n",
    "modelXOR= MLPClassifier(hidden_layer_sizes=(50,), activation='logistic', solver='adam', max_iter=20, verbose=10)\n",
    "\n",
    "#train the model\n",
    "modelXOR.fit(X_XOR,Y_XOR)\n",
    "\n",
    "#make a prediction\n",
    "Y_XOR_hate= modelXOR.predict(X_XOR)\n",
    "print(\"The Prediction is given by\",Y_XOR_hate)\n",
    "\n",
    "#compute the score of our model\n",
    "score = modelXOR.score(X_XOR,Y_XOR)\n",
    "print(\"The score is\",score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AND GATE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table>\n",
    "    <tr>\n",
    "        <td>\n",
    "            x1\n",
    "        </td>\n",
    "        <td>\n",
    "            x2\n",
    "        </td>\n",
    "        <td>\n",
    "            output\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            0\n",
    "        </td>\n",
    "        <td>\n",
    "            0\n",
    "        </td>\n",
    "        <td>\n",
    "            0\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            0\n",
    "        </td>\n",
    "        <td>\n",
    "            1\n",
    "        </td>\n",
    "        <td>\n",
    "            0\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            1\n",
    "        </td>\n",
    "        <td>\n",
    "            0\n",
    "        </td>\n",
    "        <td>\n",
    "            0\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            1\n",
    "        </td>\n",
    "        <td>\n",
    "            1\n",
    "        </td>\n",
    "        <td>\n",
    "            1\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X is [[0 0]\n",
      " [0 1]\n",
      " [1 0]\n",
      " [1 1]]\n",
      "Y is [0 0 0 1]\n"
     ]
    }
   ],
   "source": [
    "#create the entries and the output\n",
    "# the entries can be represented by an array of dimension 2 (4,2)\n",
    "#the output can be represented by a vector (4,1)\n",
    "\n",
    "X_AND= np.array([[0,0],[0,1],[1,0],[1,1]])\n",
    "Y_AND= np.array([0,0,0,1])\n",
    "\n",
    "#visualisation\n",
    "print(\"X is\", X_AND)\n",
    "print(\"Y is\", Y_AND)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.78724989\n",
      "Iteration 2, loss = 0.77827713\n",
      "Iteration 3, loss = 0.76950945\n",
      "Iteration 4, loss = 0.76095101\n",
      "Iteration 5, loss = 0.75260554\n",
      "Iteration 6, loss = 0.74447640\n",
      "Iteration 7, loss = 0.73656645\n",
      "Iteration 8, loss = 0.72887808\n",
      "Iteration 9, loss = 0.72141308\n",
      "Iteration 10, loss = 0.71417264\n",
      "Iteration 11, loss = 0.70715727\n",
      "Iteration 12, loss = 0.70036671\n",
      "Iteration 13, loss = 0.69379994\n",
      "Iteration 14, loss = 0.68745508\n",
      "Iteration 15, loss = 0.68132944\n",
      "Iteration 16, loss = 0.67541951\n",
      "Iteration 17, loss = 0.66972112\n",
      "Iteration 18, loss = 0.66422956\n",
      "Iteration 19, loss = 0.65893978\n",
      "Iteration 20, loss = 0.65384659\n",
      "Iteration 21, loss = 0.64894480\n",
      "Iteration 22, loss = 0.64422932\n",
      "Iteration 23, loss = 0.63969517\n",
      "Iteration 24, loss = 0.63533754\n",
      "Iteration 25, loss = 0.63115172\n",
      "Iteration 26, loss = 0.62713306\n",
      "Iteration 27, loss = 0.62327699\n",
      "Iteration 28, loss = 0.61957893\n",
      "Iteration 29, loss = 0.61603430\n",
      "Iteration 30, loss = 0.61263852\n",
      "Iteration 31, loss = 0.60938696\n",
      "Iteration 32, loss = 0.60627501\n",
      "Iteration 33, loss = 0.60329800\n",
      "Iteration 34, loss = 0.60045128\n",
      "Iteration 35, loss = 0.59773019\n",
      "Iteration 36, loss = 0.59513008\n",
      "Iteration 37, loss = 0.59264631\n",
      "Iteration 38, loss = 0.59027430\n",
      "Iteration 39, loss = 0.58800948\n",
      "Iteration 40, loss = 0.58584735\n",
      "Iteration 41, loss = 0.58378348\n",
      "Iteration 42, loss = 0.58181351\n",
      "Iteration 43, loss = 0.57993315\n",
      "Iteration 44, loss = 0.57813822\n",
      "Iteration 45, loss = 0.57642462\n",
      "Iteration 46, loss = 0.57478837\n",
      "Iteration 47, loss = 0.57322560\n",
      "Iteration 48, loss = 0.57173254\n",
      "Iteration 49, loss = 0.57030555\n",
      "Iteration 50, loss = 0.56894111\n",
      "Iteration 51, loss = 0.56763582\n",
      "Iteration 52, loss = 0.56638642\n",
      "Iteration 53, loss = 0.56518975\n",
      "Iteration 54, loss = 0.56404281\n",
      "Iteration 55, loss = 0.56294270\n",
      "Iteration 56, loss = 0.56188665\n",
      "Iteration 57, loss = 0.56087203\n",
      "Iteration 58, loss = 0.55989631\n",
      "Iteration 59, loss = 0.55895710\n",
      "Iteration 60, loss = 0.55805213\n",
      "Iteration 61, loss = 0.55717923\n",
      "Iteration 62, loss = 0.55633634\n",
      "Iteration 63, loss = 0.55552153\n",
      "Iteration 64, loss = 0.55473297\n",
      "Iteration 65, loss = 0.55396891\n",
      "Iteration 66, loss = 0.55322772\n",
      "Iteration 67, loss = 0.55250788\n",
      "Iteration 68, loss = 0.55180791\n",
      "Iteration 69, loss = 0.55112648\n",
      "Iteration 70, loss = 0.55046229\n",
      "Iteration 71, loss = 0.54981415\n",
      "Iteration 72, loss = 0.54918094\n",
      "Iteration 73, loss = 0.54856161\n",
      "Iteration 74, loss = 0.54795518\n",
      "Iteration 75, loss = 0.54736073\n",
      "Iteration 76, loss = 0.54677741\n",
      "Iteration 77, loss = 0.54620443\n",
      "Iteration 78, loss = 0.54564103\n",
      "Iteration 79, loss = 0.54508654\n",
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      "Iteration 946, loss = 0.08447464\n",
      "Iteration 947, loss = 0.08425332\n",
      "Iteration 948, loss = 0.08403270\n",
      "Iteration 949, loss = 0.08381278\n",
      "Iteration 950, loss = 0.08359356\n",
      "Iteration 951, loss = 0.08337503\n",
      "Iteration 952, loss = 0.08315719\n",
      "Iteration 953, loss = 0.08294005\n",
      "Iteration 954, loss = 0.08272359\n",
      "Iteration 955, loss = 0.08250782\n",
      "Iteration 956, loss = 0.08229274\n",
      "Iteration 957, loss = 0.08207833\n",
      "Iteration 958, loss = 0.08186461\n",
      "Iteration 959, loss = 0.08165156\n",
      "Iteration 960, loss = 0.08143919\n",
      "Iteration 961, loss = 0.08122749\n",
      "Iteration 962, loss = 0.08101647\n",
      "Iteration 963, loss = 0.08080611\n",
      "Iteration 964, loss = 0.08059642\n",
      "Iteration 965, loss = 0.08038740\n",
      "Iteration 966, loss = 0.08017904\n",
      "Iteration 967, loss = 0.07997134\n",
      "Iteration 968, loss = 0.07976430\n",
      "Iteration 969, loss = 0.07955792\n",
      "Iteration 970, loss = 0.07935219\n",
      "Iteration 971, loss = 0.07914712\n",
      "Iteration 972, loss = 0.07894270\n",
      "Iteration 973, loss = 0.07873892\n",
      "Iteration 974, loss = 0.07853580\n",
      "Iteration 975, loss = 0.07833331\n",
      "Iteration 976, loss = 0.07813147\n",
      "Iteration 977, loss = 0.07793028\n",
      "Iteration 978, loss = 0.07772972\n",
      "Iteration 979, loss = 0.07752979\n",
      "Iteration 980, loss = 0.07733050\n",
      "Iteration 981, loss = 0.07713185\n",
      "Iteration 982, loss = 0.07693382\n",
      "Iteration 983, loss = 0.07673643\n",
      "Iteration 984, loss = 0.07653966\n",
      "Iteration 985, loss = 0.07634351\n",
      "Iteration 986, loss = 0.07614799\n",
      "Iteration 987, loss = 0.07595309\n",
      "Iteration 988, loss = 0.07575881\n",
      "Iteration 989, loss = 0.07556514\n",
      "Iteration 990, loss = 0.07537209\n",
      "Iteration 991, loss = 0.07517966\n",
      "Iteration 992, loss = 0.07498783\n",
      "Iteration 993, loss = 0.07479661\n",
      "Iteration 994, loss = 0.07460600\n",
      "Iteration 995, loss = 0.07441600\n",
      "Iteration 996, loss = 0.07422660\n",
      "Iteration 997, loss = 0.07403779\n",
      "Iteration 998, loss = 0.07384959\n",
      "Iteration 999, loss = 0.07366199\n",
      "Iteration 1000, loss = 0.07347498\n",
      "The Prediction is given by [0 0 0 1]\n",
      "The score is 1.0\n"
     ]
    }
   ],
   "source": [
    "#create the model\n",
    "modelAND= MLPClassifier(hidden_layer_sizes=(50,), activation='logistic', solver='adam', max_iter=1000, verbose=10)\n",
    "\n",
    "#train the model\n",
    "modelAND.fit(X_AND,Y_AND)\n",
    "\n",
    "#make a prediction\n",
    "Y_AND_hate= modelAND.predict(X_AND)\n",
    "print(\"The Prediction is given by\",Y_AND_hate)\n",
    "\n",
    "#compute the score of our model\n",
    "score = modelAND.score(X_AND,Y_AND)\n",
    "print(\"The score is\",score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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