Perceptron_visualization.ipynb

perceptron_visualization.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Perceptron_visualization.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPzNevvZGioO3jzN0FSeSjG",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/bridgesign/3037e40ef83918384acc892ddf5ff6a6/perceptron_visualization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mPT0hmavPClP"
      },
      "source": [
        "# Perceptron Learning Visualization\n",
        "\n",
        "### Imports and Environment Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XvPgpUmKPBr6"
      },
      "source": [
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NLzRGILUOz9B"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from matplotlib import animation, rc\n",
        "from IPython.display import HTML"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oHTNAjETPfd6"
      },
      "source": [
        "### Parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ak0tE4ZtPjV5"
      },
      "source": [
        "num_samples = 50\n",
        "num_iterations = 35\n",
        "# Set the seed\n",
        "np.random.seed(17110125)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C8wENjUsPcYM"
      },
      "source": [
        "### Dummy Data Creation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ZJb6adUPRlM"
      },
      "source": [
        "# Random Samples\n",
        "samples = np.random.randn(num_samples, 2)\n",
        "\n",
        "# Random Selected separating line\n",
        "w_actual = np.random.randn(2)\n",
        "w_actual = w_actual/np.linalg.norm(w_actual)\n",
        "b_actual = np.random.randn()\n",
        "\n",
        "# Generating labels based on the selected separation\n",
        "labels = np.array([np.sign(np.dot(w_actual, sample)+b_actual) for sample in samples])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sRyWsSoAUInn"
      },
      "source": [
        "### Initiazing Perceptron"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bQvKqLx1UHjP"
      },
      "source": [
        "w_perceptron = np.zeros(2)\n",
        "b_perceptron = 0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5C1upQDSTkKv"
      },
      "source": [
        "### Plotting the Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kqrDSK-JSQyB",
        "outputId": "5320b550-8d54-4451-9937-7ceda9d7c309",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        }
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(9, 7))\n",
        "\n",
        "# Setting Plot Limits\n",
        "xmin = np.min(samples[:,0])\n",
        "xmin = xmin*1.3 if xmin < 0 else xmin*0.77\n",
        "xmax = np.max(samples[:,0])\n",
        "xmax = xmax*1.3 if xmax > 0 else xmax*0.77\n",
        "\n",
        "ymin = np.min(samples[:,1])\n",
        "ymin = ymin*1.3 if ymin < 0 else ymin*0.77\n",
        "ymax = np.max(samples[:,1])\n",
        "ymax = ymax*1.3 if ymax > 0 else ymax*0.77\n",
        "\n",
        "\n",
        "ax.set_xlim(xmin, xmax)\n",
        "ax.set_ylim(ymin, ymax)\n",
        "\n",
        "# Plotting the +1 label points\n",
        "positive = np.array([samples[j] for j in range(num_samples) if labels[j]==1])\n",
        "ax.scatter(positive[:,0], positive[:,1], c='blue', marker='o', label='Postive samples')\n",
        "# Plotting the -1 label points\n",
        "negative = np.array([samples[j] for j in range(num_samples) if labels[j]==-1])\n",
        "ax.scatter(negative[:,0], negative[:,1], c='red', marker='x', label='Negative samples')\n",
        "\n",
        "# Plot the actual line\n",
        "ax.plot(\n",
        "    [xmin, xmax],\n",
        "    [-(b_actual+xmin*w_actual[0])/w_actual[1], -(b_actual+xmax*w_actual[0])/w_actual[1]],\n",
        "    '--',c='black', label='Actual Line'\n",
        "    )\n",
        "\n",
        "# Perceptron Line\n",
        "line, = ax.plot([xmin, xmax], [0, 0], lw=1.5,c='green' ,markersize=0, label=\"Perceptron Line\")\n",
        "\n",
        "ax.legend(loc=\"upper right\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f0dbbf57940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CLRyFvp7VTT9"
      },
      "source": [
        "def perceptron_single_train(j):\n",
        "  global w_perceptron\n",
        "  global b_perceptron\n",
        "  global frame_count\n",
        "  list_i = [i for i in range(num_samples) if labels[i]*(b_perceptron+np.dot(samples[i], w_perceptron)) <= 0]\n",
        "  # Random choice\n",
        "  ll = len(list_i)\n",
        "  if ll:\n",
        "    i = np.random.choice(len(list_i))\n",
        "  else:\n",
        "    return (line,)\n",
        "  w_perceptron = w_perceptron + labels[i]*samples[i]\n",
        "  b_perceptron = b_perceptron + labels[i]\n",
        "\n",
        "  # Set Line data for visualization\n",
        "  y_xmin = -(b_perceptron+w_perceptron[0]*xmin)/w_perceptron[1]\n",
        "  y_xmax = -(b_perceptron+w_perceptron[0]*xmax)/w_perceptron[1]\n",
        "  line.set_data([xmin, xmax], [y_xmin, y_xmax])\n",
        "  return (line,)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mz_W2PoteItM"
      },
      "source": [
        "# Define the animator\n",
        "anim = animation.FuncAnimation(fig, perceptron_single_train,\n",
        "                               frames=num_iterations, interval=1300, blit=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xxLXGEIWeWnH",
        "outputId": "7acf19a6-57e3-4387-b583-8933a5d53ce3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        }
      },
      "source": [
        "HTML(anim.to_html5_video())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\">\n",
              "  Your browser does not support the video tag.\n",
              "</video>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0LWssStPCK7X"
      },
      "source": [
        "# References\n",
        "Lecture Slides of CS691: Advanced ML course at IITGN"
      ]
    }
  ]
}