perceptron.ipynb

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  "metadata": {
    "colab": {
      "name": "perceptron.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPZfhJXqRf8FF6g7TKhYGly",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/sridevibonthu/Deep-Learning-Basics/blob/master/perceptron.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V_QsWIvbubnC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "# Setting the random seed, feel free to change it and see different solutions.\n",
        "np.random.seed(42)\n",
        "\n",
        "def stepFunction(t):\n",
        "    if t >= 0:\n",
        "        return 1\n",
        "    return 0\n",
        "\n",
        "def prediction(X, W, b):\n",
        "    return stepFunction((np.matmul(X,W)+b)[0])\n",
        "\n",
        "# TODO: Fill in the code below to implement the perceptron trick.\n",
        "# The function should receive as inputs the data X, the labels y,\n",
        "# the weights W (as an array), and the bias b,\n",
        "# update the weights and bias W, b, according to the perceptron algorithm,\n",
        "# and return W and b.\n",
        "def perceptronStep(X, y, W, b, learn_rate = 0.01):\n",
        "    \n",
        "    for i in range(len(X)):\n",
        "        yhat = prediction(X[i], W, b)\n",
        "        if y[i]-yhat == 1:\n",
        "            W[0] += X[i][0]*learn_rate\n",
        "            W[1] += X[i][1]*learn_rate\n",
        "            b += learn_rate\n",
        "        elif y[i]-yhat == -1:\n",
        "            W[0] -= X[i][0]*learn_rate\n",
        "            W[1] -= X[i][1]*learn_rate\n",
        "            b -= learn_rate\n",
        "    return W, b\n",
        "    \n",
        "# This function runs the perceptron algorithm repeatedly on the dataset,\n",
        "# and returns a few of the boundary lines obtained in the iterations,\n",
        "# for plotting purposes.\n",
        "# Feel free to play with the learning rate and the num_epochs,\n",
        "# and see your results plotted below.\n",
        "def trainPerceptronAlgorithm(X, y, learn_rate = 0.01, num_epochs = 25):\n",
        "    x_min, x_max = min(X.T[0]), max(X.T[0])\n",
        "    y_min, y_max = min(X.T[1]), max(X.T[1])\n",
        "    W = np.array(np.random.rand(2,1))\n",
        "    b = np.random.rand(1)[0] + x_max\n",
        "    # These are the solution lines that get plotted below.\n",
        "    boundary_lines = []\n",
        "    for i in range(num_epochs):\n",
        "        # In each epoch, we apply the perceptron step.\n",
        "        W, b = perceptronStep(X, y, W, b, learn_rate)\n",
        "        boundary_lines.append((-W[0]/W[1], -b/W[1]))\n",
        "    return boundary_lines\n"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}