perceptron_logic_gates.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Perceptron Implementation in Logic Gates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, I've used building blocks of *Neural Network: perceptrons* to model the fundamental building blocks of computers — logic gates.\n",
    "\n",
    "1. AND gate - The table below shows the results of an AND gate. Given two inputs, an AND gate will output a 1 only if both inputs are a 1.\n",
    "2. XOR gate — a gate that outputs a 1 only if one of the inputs is a 1.\n",
    "\n",
    "Goals:\n",
    "\n",
    "- AND gate can be thought of as **linearly separable** data and *Perceptron* can be trained to perform AND.\n",
    "- XOR gate isn’t **linearly separable** and a *Perceptron* fails to learn XOR.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img align=\"center\" alt=\"Profile\" width=\"502px\" src=\"logic.PNG\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Perceptron\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from itertools import product"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creation of AND Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = [[0, 0], \n",
    "         [0, 1], \n",
    "         [1, 0], \n",
    "         [1, 1]]\n",
    "\n",
    "\n",
    "labels1 = [0, \n",
    "           0, \n",
    "           0, \n",
    "           1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization of AND Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fKel/gTcCc4F7gBUta9YA36HxDavPBv5ntufu8X7fDhzf/Hp1v+63yl4nrfsBjW+08v7ZnrvHP7cLgQeAk5rHr5vtuXu8308Cn29+PQQ8Dcyd7dmPcL/nAWcA9x/mek871S/P0FcCw5m5MzPHgE3A2pY1a4Fbs2ErsDAiTpjpQbuk434z867MfKZ5uJXGN+buR1V+bgE+DnwdeGImh+uBKvu9HLgtMx8FyMx+3nOV/SZwXPNbWR5LI+jjMztmd2TmnTTmP5yedqpfgr4Y2DXpeKR5brpr+sV09/IRGr/r96OOe42IxcAlwHr6X5Wf2zcBx0fEDyNie0RcMWPTdV+V/d4AnALsBu4DrsnMiZkZb8b1tFO9+dbT3RdtzrW+37LKmn5ReS8RcQGNoL+jpxP1TpW9fgm4NjMPNp7E9bUq+x0E3ga8C1gA/HdEbM3MX/R6uB6ost+LgbuBC4HfAf4zIn6cmb/u9XCzoKed6pegjwAnTjpeQuN38+mu6ReV9hIRpwI3A6sz86kZmq3bquy1BmxqxnwRsCYixjPzmzMzYldV/bX8ZGbuAfZExJ3AaUA/Br3Kfq8EPpeNF5mHI+Jh4GTgpzMz4ozqaaf65SWXbcDyiFgWEXOBS4HNLWs2A1c0/xT5bOC5zHxspgftko77jYiTgNuAD/XpM7ff6LjXzFyWmUszcynwNeDP+jTmUO3X8reAcyNiMCKOBs4CHpzhObulyn4fpfF/I0TE64E3AztndMqZ09NO9cUz9Mwcj4irgS00/tR8Y2buiIirmtfX03j3wxpgGHiBxu/6fanifj8NvBa4qfnMdTz78G+uq7jXYlTZb2Y+GBHfBe4FJoCbM7Pt2+Be6Sr+/F4P3BIR99F4SeLazOzLv1Y3Ir4KnA8siogR4DPAUTAznfKj/5JUiH55yUWS1IFBl6RCGHRJKoRBl6RCGHRJKoRBl6RCGHRJKsT/AyKxrQE843vJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x = [point[0] for point in data1], \n",
    "            y = [point[1] for point in data1], \n",
    "            c = labels1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This data is Linearly Separable indeed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building Perceptron\n",
    "\n",
    "Now that I have set **AND** data, I've build a perceptron to learn AND. The default iteration is 1000. I've cut the training pretty short to see if the algorithm learns **AND** even with very little training."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "classifier1 = Perceptron(max_iter = 40)\n",
    "classifier1.fit(data1, labels1)\n",
    "\n",
    "print(classifier1.score(data1, labels1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Perfectly Scored 100% !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the Perceptron"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The perceptron has been trained correctly with AND gate, but let’s try to visualize what decision boundary it is making."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-2.  2.  0.]\n"
     ]
    }
   ],
   "source": [
    "print(classifier1.decision_function([[0,0], \n",
    "                                     [1,1], \n",
    "                                     [0.5, 0.5]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_values = np.linspace(0, 1, 100)\n",
    "y_values = np.linspace(0, 1, 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2.         1.97979798 1.95959596 ... 0.04040404 0.02020202 0.        ]\n",
      " [1.97979798 1.95959596 1.93939394 ... 0.02020202 0.         0.02020202]\n",
      " [1.95959596 1.93939394 1.91919192 ... 0.         0.02020202 0.04040404]\n",
      " ...\n",
      " [0.04040404 0.02020202 0.         ... 1.91919192 1.93939394 1.95959596]\n",
      " [0.02020202 0.         0.02020202 ... 1.93939394 1.95959596 1.97979798]\n",
      " [0.         0.02020202 0.04040404 ... 1.95959596 1.97979798 2.        ]]\n"
     ]
    }
   ],
   "source": [
    "# Finding Every possible combination of (x, y)\n",
    "\n",
    "point_grid = list(product(x_values, y_values))\n",
    "\n",
    "\n",
    "# Distance of those points from Decision boundary\n",
    "distances = classifier1.decision_function(point_grid)\n",
    "abs_distance = [abs(dist) for dist in distances]\n",
    "\n",
    "\n",
    "# Reshaping the Distances as scaler 2D array\n",
    "distance_matrix = np.reshape(abs_distance, (100,100))\n",
    "print(distance_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "heatmap = plt.pcolormesh(x_values, \n",
    "                         y_values, \n",
    "                         distance_matrix)\n",
    "\n",
    "plt.colorbar(heatmap)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A great visualization of what the *Perceptron* is doing. The purple line where the distances are 0, that’s the decision boundary!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creation of XOR Data Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5\n"
     ]
    }
   ],
   "source": [
    "data = [[0, 0], \n",
    "        [0, 1], \n",
    "        [1, 0], \n",
    "        [1, 1]]\n",
    "\n",
    "labels = [0, \n",
    "          1, \n",
    "          1, \n",
    "          0]\n",
    "\n",
    "\n",
    "\n",
    "classifier = Perceptron(max_iter = 40)\n",
    "classifier.fit(data, labels)\n",
    "print(classifier.score(data, labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Only Scored 50% !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization of XOR Decision Boundary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "print(classifier.decision_function([[0,0], \n",
    "                                    [1,1], \n",
    "                                    [0.5, 0.5]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_values = np.linspace(0, 1, 100)\n",
    "y_values = np.linspace(0, 1, 100)\n",
    "\n",
    "\n",
    "point_grid = list(product(x_values, y_values))\n",
    "\n",
    "distances = classifier.decision_function(point_grid)\n",
    "abs_distance = [abs(dist) for dist in distances]\n",
    "\n",
    "\n",
    "\n",
    "distance_matrix = np.reshape(abs_distance, (100,100))\n",
    "\n",
    "\n",
    "heatmap = plt.pcolormesh(x_values, \n",
    "                         y_values, \n",
    "                         distance_matrix)\n",
    "\n",
    "plt.colorbar(heatmap)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Perceptrons can’t solve problems that aren’t linearly separable. There is no visible Decsion Boundary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creation of OR Data Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "data = [[0, 0], \n",
    "        [0, 1], \n",
    "        [1, 0], \n",
    "        [1, 1]]\n",
    "\n",
    "labels = [0, \n",
    "          1, \n",
    "          1, \n",
    "          1]\n",
    "\n",
    "\n",
    "\n",
    "classifier = Perceptron(max_iter = 40)\n",
    "classifier.fit(data, labels)\n",
    "\n",
    "print(classifier.score(data, labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Perfectly Scored 100% !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization of OR Decision Boundary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.  3.  1.]\n"
     ]
    }
   ],
   "source": [
    "print(classifier.decision_function([[0,0], \n",
    "                                    [1,1], \n",
    "                                    [0.5, 0.5]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_values = np.linspace(0, 1, 100)\n",
    "y_values = np.linspace(0, 1, 100)\n",
    "\n",
    "\n",
    "point_grid = list(product(x_values, y_values))\n",
    "\n",
    "distances = classifier.decision_function(point_grid)\n",
    "abs_distance = [abs(dist) for dist in distances]\n",
    "\n",
    "\n",
    "\n",
    "distance_matrix = np.reshape(abs_distance, (100,100))\n",
    "\n",
    "\n",
    "heatmap = plt.pcolormesh(x_values, \n",
    "                         y_values, \n",
    "                         distance_matrix)\n",
    "\n",
    "plt.colorbar(heatmap)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A great visualization of what the *Perceptron* is doing. The purple line where the distances are 0, that’s the decision boundary for OR gate! "
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}