NN with visualize.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random, os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_data(inputs,targets,weights):\n",
    "    # fig config\n",
    "    plt.figure(figsize=(10,6))\n",
    "    plt.grid(True)\n",
    "\n",
    "    #plot input samples(2D data points) and i have two classes. \n",
    "    #one is +1 and second one is -1, so it red color for +1 and blue color for -1\n",
    "    for input,target in zip(inputs,targets):\n",
    "        plt.plot(input[0],input[1],'ro' if (target == 1.0) else 'bo')\n",
    "\n",
    "    # Here i am calculating slope and intercept with given three weights\n",
    "    for i in np.linspace(np.amin(inputs[:,:1]),np.amax(inputs[:,:1])):\n",
    "        #slope = -(weights[0]/weights[2])/(weights[0]/weights[1])  \n",
    "        #intercept = -weights[0]/weights[2]\n",
    "        slope = -(weights[2]/weights[0])/(weights[2]/weights[1])  \n",
    "        intercept = -weights[2]/weights[0]\n",
    "\n",
    "        #y =mx+c, m is slope and c is intercept\n",
    "        y = (slope*i) + intercept\n",
    "        plt.plot(i, y,'ko')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20, 2)\n",
      "(40, 2)\n",
      "(40,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = 1 #learning rate\n",
    "bias = 1 #value of bias\n",
    "weights = [random.random(),random.random(),random.random()] #weights generated in a list (3 weights in total for 2 neurons and the bias)\n",
    "\n",
    "def Perceptron(input1, input2, output) :\n",
    "    outputP = input1*weights[0]+input2*weights[1]+bias*weights[2]\n",
    "    if outputP > 0 : #activation function (here Heaviside)\n",
    "        outputP = 1\n",
    "    else :\n",
    "        outputP = 0\n",
    "    error = output-outputP\n",
    "    weights[0] += error * input1 * lr\n",
    "    weights[1] += error * input2 * lr\n",
    "    weights[2] += error * bias * lr\n",
    "\n",
    "\n",
    "cov  = [[1,0],[0,1]]\n",
    "N = 20\n",
    "\n",
    "mean = [5,5]\n",
    "dot_0 = np.random.multivariate_normal(mean, cov, N)\n",
    "print(dot_0.shape)\n",
    "\n",
    "mean = [0,0]\n",
    "dot_1 = np.random.multivariate_normal(mean, cov, N)\n",
    "\n",
    "data  = np.concatenate((dot_0, dot_1), axis=0)\n",
    "label = np.concatenate((np.full(20,0), np.full(20,1)), axis=0)\n",
    "print(data.shape)\n",
    "print(label.shape)\n",
    "\n",
    "for i in range(30) :\n",
    "    for index in range(0,40):\n",
    "        Perceptron(data[index, 0],data[index, 1],label[index])\n",
    "    if i % 5 == 0:\n",
    "        plot_data(data, label, np.array(weights))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import jovian\n",
    "jovian.commit()"
   ]
  }
 ],
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